SpatialDDS: A Protocol for Real-World Spatial Computing

An open invitation to build a shared bus for spatial data, AI world models, and digital twins.

Version: 1.6 (Draft)

Date: 2025-XX-XX

Author: James Jackson [Open AR Cloud] – james.jackson [at] openarcloud [dot] org

Contents

Part I – Overview

Get oriented with the motivation, core building blocks, practical scenarios, and forward-looking roadmap before diving into the normative material.

  1. Introduction
  2. Conventions (Normative)
  3. IDL Profiles 3.3.1 Topic Naming (Normative) 3.3.4 Coverage Model (Normative)
  4. Operational Scenarios
  5. Conclusion
  6. Future Directions

Part II – Reference

Specifications, identifiers, supporting glossaries, and appendices that implementers can consult while building SpatialDDS solutions.

  1. SpatialDDS URIs
  2. Example Manifests
  3. Glossary of Acronyms
  4. References
  5. Appendices

1. Introduction

SpatialDDS is a lightweight, standards-based protocol, built on OMG DDS, for real-time exchange of spatial world models. It is designed as a shared data bus that allows devices, services, and AI agents to publish and subscribe to structured representations of the physical world — from pose graphs and 3D geometry to anchors, semantic detections, and service discovery. By providing a common substrate, SpatialDDS enables applications in robotics, AR/XR, digital twins, and smart cities to interoperate while also supporting new AI-driven use cases such as perception services, neural maps, and planning agents.

At its core, SpatialDDS is defined through IDL profiles that partition functionality into clean modules:

  • Core: pose graphs, geometry tiles, anchors, transforms, and blobs.
  • Discovery: lightweight announce messages and manifests for services, coverage, anchors, and content.
  • Anchors: durable anchors and registry updates for persistent world-locked reference points.
  • Extensions: optional domain-specific profiles including the shared Sensing Common base types plus VIO sensors, vision streams, SLAM frontend features, semantic detections, radar detections/tensors, lidar streams, and AR+Geo.

This profile-based design keeps the protocol lean and interoperable, while letting communities adopt only the pieces they need.

1.1 Conceptual Overview (Informative)

This section explains the core ideas behind SpatialDDS without reference to specific IDL types or field names. Readers who understand these six concepts can navigate the rest of the specification efficiently. Everything below is informative — normative rules appear in §2 onward.

The Bus

SpatialDDS is a shared data bus. Devices, services, and AI agents publish and subscribe to typed messages describing the physical world — poses, geometry, anchors, detections, sensor streams. The bus is peer-to-peer (no central broker) and built on OMG DDS, which provides automatic discovery, schema enforcement, and fine-grained quality-of-service control. If you've used ROS 2 topics or MQTT with schemas, the publish/subscribe model is familiar. The difference is that SpatialDDS defines what the messages mean spatially, not just how they're delivered.

Profiles

SpatialDDS is modular. Functionality is organized into profiles — self-contained groups of message types that can be adopted independently:

  • Core defines the universal building blocks: pose graphs, 3D geometry tiles, blob transport, and geo-anchoring primitives.
  • Discovery lets participants find each other, advertise what they publish, and negotiate compatible versions.
  • Anchors adds durable, world-locked reference points that persist across sessions.
  • Extensions add domain-specific capabilities. A shared Sensing Common base provides frame metadata, calibration, ROI negotiation, and codec descriptors. Radar, lidar, and vision profiles build on that base. Additional extensions cover VIO, SLAM frontends, semantics, and AR+Geo alignment.

An implementation includes only the profiles it needs. An AR headset might use Core + Discovery + Anchors. A radar truck adds the radar extension. A digital twin backend subscribes to everything. Profile negotiation happens automatically — participants advertise what they support and the system converges on compatible versions.

Frames and Anchors

Every spatial message exists in a reference frame — a coordinate system identified by a UUID and a human-readable fully qualified name. Frames form a directed acyclic graph: a device has a body frame, sensors have frames relative to the body, and the body frame is related to a map frame by a transform. Anchors are special frames that are durable and globally positioned — a surveyed point on a building corner, a VPS-derived fix at a street intersection. They bridge the gap between local device coordinates and the real world, allowing multiple devices to share a common spatial context.

Discovery: Two Layers

Finding things happens in two stages:

  1. Network bootstrap answers "where is the DDS domain?" A device arriving at a venue, connecting to a network, or scanning a QR code obtains a small bootstrap manifest containing a domain ID and initial peer addresses. On-premises mechanisms include mDNS-based DNS-SD, a well-known HTTPS path, QR codes, and BLE beacons. For Internet-scale discovery, a geospatial DNS-SD binding allows clients with only a GPS fix to locate services by encoding their position as a geohash subdomain. (See §3.3.0 for the full bootstrap specification.)
  2. Service discovery answers "what's available near me?" For Internet-scale deployments, an HTTP search endpoint (/.well-known/spatialdds/search) accepts spatial queries and returns service manifests with DDS connection hints — no bus membership required. For on-premises deployments, the device subscribes to well-known DDS discovery topics and receives announcements directly. Both paths expose the same spatial coverage model.

URIs and Manifests

Every significant resource — an anchor, a service, a content bundle, a tileset — has a stable SpatialDDS URI (e.g., spatialdds://museum.example.org/hall1/anchor/main-entrance). URIs are lightweight handles passed around in discovery messages, QR codes, and application logic. When a client needs the full details, it resolves the URI to a manifest — a small JSON document describing the resource's capabilities, spatial coverage, and assets. Resolution follows a defined chain: check cache, try an advertised resolver, fall back to HTTPS. (See §7 for URI syntax and resolution rules; §8 for manifest structure.)

The Wire Stays Light

SpatialDDS messages are small and typed. Heavy content — meshes, point clouds, video frames, neural network weights — is never inlined in messages. Instead, messages carry blob references (IDs + checksums), and the actual bytes are transferred as blob chunks or fetched out-of-band via asset URIs. This keeps the bus fast and predictable even when the data behind it is large.

The specification is organized in two parts, as shown in the table of contents:

  • Part I (§1–§6) provides motivation, conventions, profile descriptions, operational scenarios, and forward-looking discussion.
  • Part II (§7–Appendices) contains the reference material: URI scheme and resolution, manifest examples, glossary, and the authoritative IDL appendices (A through E).

Most of Part I is informative context. Three sections within it contain normative rules and are labeled accordingly in their headings:

  • §2 Conventions (Normative) — global rules for optional fields, numeric validity, quaternion order, ordering, IDL structure, and security.
  • §3.3.1 Topic Naming (Normative) — how topic names are structured and what fields are required.
  • §3.3.4 Coverage Model (Normative) — how spatial coverage is declared and evaluated.

In the appendices, IDL definitions (Appendices A–D) are always normative. Appendix E contains provisional extension examples and is explicitly informative. Appendix F defines the URI ABNF (normative). Appendix F.X (query expression grammar) is informative. Appendix G (frame identifiers) is an informative reference. Appendix H (operational scenarios) is informative.

When in doubt about whether something is normative: if it uses RFC 2119 keywords (MUST, SHALL, SHOULD, MAY), it's normative regardless of where it appears.

For role-specific guidance on which sections to read first, see the Reading Guide below.

Reading Guide (Informative)

  • Architects & product planners — Start with §1 and §2 to internalize the motivation, shared conventions, and global rules before drilling into profiles.
  • Implementers & SDK authors — Focus on Part II plus Appendix A (core IDLs), Appendix B (discovery), Appendix C (anchors), and Appendix D (extensions).
  • Routing, filtering, and coverage developers — Read §3.3 (Discovery), §3.3.4 (Coverage Model), and Appendix B/F.X for the binding grammars.

Why DDS?

SpatialDDS builds directly on the OMG Data Distribution Service (DDS), a proven standard for real-time distributed systems. DDS provides:

  • Peer-to-peer publish/subscribe with automatic discovery, avoiding centralized brokers.
  • Typed data with schema enforcement, versioning, and language bindings.
  • Fine-grained QoS for reliability, liveliness, durability, and latency control.
  • Scalability across edge devices, vehicles, and cloud backends.

This foundation ensures that SpatialDDS is not just a message format, but a full-fledged, high-performance middleware for spatial computing.

Benefits across domains

  • Robotics & Autonomous Vehicles: Share pose graphs, maps, and detections across robots, fleets, and control centers.
  • Augmented & Mixed Reality: Fuse VPS results and anchors into persistent, shared spatial contexts; stream geometry and semantics to clients.
  • Digital Twins & Smart Cities: Ingest real-time streams of geometry, anchors, and semantics into twin backends, and republish predictive overlays.
  • IoT & Edge AI: Integrate lightweight perception services, sensors, and planners that consume and enrich the shared world model.
  • AI World Models & Agents: Provide foundation models and AI agents with a structured, typed view of the physical world for perception, reasoning, and planning.

Design Principles

  • Keep the wire light SpatialDDS defines compact, typed messages via IDL. Heavy or variable content (meshes, splats, masks, assets) is carried as blobs, referenced by stable IDs. This avoids bloating the bus while keeping payloads flexible.
  • Profiles, not monoliths SpatialDDS is organized into modular profiles. Core, Discovery, and Anchors form the foundation; Extension Profiles add domain-specific capabilities. Implementations include only what they need while maintaining interoperability.
  • AI-ready, domain-neutral While motivated by SLAM, AR, robotics, and digital twins, the schema is deliberately generic. Agents, foundation models, and AI services can publish and subscribe alongside devices without special treatment.
  • Anchors as first-class citizens Anchors provide durable, shared reference points that bridge positioning, mapping, and content attachment. The Anchor Registry makes them discoverable and persistent across sessions.
  • Discovery without heaviness Lightweight announce messages plus JSON manifests allow services (like VPS, mapping, or anchor registries) and content/experiences to be discovered at runtime without centralized registries.
  • Interoperability with existing standards SpatialDDS is designed to align with and complement related standards such as OGC GeoPose, CityGML/3D Tiles, and Khronos OpenXR. This ensures it can plug into existing ecosystems rather than reinvent them.

Specification Layers (Informative)

Layer Purpose Core Artifacts
Core Transport Pub/Sub framing, QoS, reliability core, discovery IDLs
Spatial Semantics Anchors, poses, transforms, manifests anchors, geo, manifests
Sensing Extensions Radar, LiDAR, Vision modules sensing.* profiles

Architecture Overview & Data Flow

Before diving into identifiers and manifests, it helps to see how SpatialDDS components interlock when a client joins the bus. The typical flow looks like:

High-level layering

SpatialDDS follows the same four-layer model shown in the architecture diagrams:

Applications
    ↓ use
SpatialDDS Profiles
    ↓ define
DDS Topics (typed + QoS)
    ↓ are described by
Discovery & Manifests
    ↓ reference
spatial:// URIs
  • Applications (AR, robotics, digital twins, telco sensing, AI runtimes) use SpatialDDS profiles instead of raw DDS topics.
  • Profiles define the shared types, semantics, and QoS groupings.
  • DDS topics carry typed streams with well-known QoS names.
  • Discovery and manifests describe the available streams and their spatial coverage.
  • URIs provide stable identifiers for anchors, maps, content, and services.

This textual view matches the layered diagrams used in the presentation.

SpatialDDS URI ──▶ Manifest Resolver ──▶ Discovery Topic ──▶ DDS/Data Streams ──▶ Shared State & Anchors
        │                 │                      │                   │                      │
   (§7)             (§8)                (§3.3)                   (§3)                   (§5 & Appendix C)
  1. URI → Manifest lookup – Durable SpatialDDS URIs point to JSON manifests that describe services, anchor sets, or content. Clients resolve the URI via HTTPS/TLS or a validated local cache per the SpatialURI Resolution rules (§7.5.5) to fetch capabilities, QoS hints, and connection parameters.
  2. Discovery → selecting a service – Guided by the manifest and Discovery profile messages, participants determine which SpatialDDS services are available in their vicinity, their coverage areas, and how to engage them.
  3. Transport → messages on stream or DDS – With a target service selected, the client joins the appropriate DDS domain/partition or auxiliary transport identified in the manifest and begins exchanging typed IDL messages for pose graphs, geometry, or perception streams.
  4. State updates / anchor resolution – As data flows, participants publish and subscribe to state changes. Anchor registries and anchor delta messages keep spatial references aligned so downstream applications can resolve world-locked content with shared context.

This loop repeats as participants encounter new SpatialDDS URIs—keeping discovery, transport, and shared state synchronized.

SpatialDDS URIs

SpatialDDS URIs give every anchor, service, and content bundle a stable handle that can be shared across devices and transports while still resolving to rich manifest metadata. They are the glue between lightweight on-bus messages and descriptive out-of-band manifests, ensuring that discovery pointers stay durable even as infrastructure moves. Section 6 (SpatialDDS URIs) defines the precise syntax, allowed types, and resolver requirements for these identifiers.

// SPDX-License-Identifier: MIT // SpatialDDS Specification 1.6 (© Open AR Cloud Initiative)

2. Conventions (Normative)

This section centralizes the rules that apply across every SpatialDDS profile. Individual sections reference these shared requirements instead of repeating them. See Appendix A (core), Appendix B (discovery), Appendix C (anchors), and Appendix D (extensions) for the canonical IDL definitions that implement these conventions.

2.1 Orientation & Frame References

  • All quaternion fields, manifests, and IDLs SHALL use the (x, y, z, w) order that aligns with OGC GeoPose.
  • Frames are represented exclusively with FrameRef { uuid, fqn }. The UUID is authoritative; the fully qualified name is a human-readable alias. Appendix G defines the authoritative frame model.
  • Example JSON shape: 
    "frame_ref": { "uuid": "00000000-0000-4000-8000-000000000000", "fqn": "earth-fixed/map/device" }

Quaternion Convention Reference (Informative)

SpatialDDS uses (x, y, z, w) component order for all quaternion fields, aligning with OGC GeoPose. Adjacent ecosystems use different conventions; implementers ingesting external data MUST reorder components before publishing to the bus.

Source Order Conversion to SpatialDDS
OGC GeoPose (x, y, z, w) None
ROS 2 (geometry_msgs/Quaternion) (x, y, z, w) None
nuScenes / pyquaternion (w, x, y, z) (q[1], q[2], q[3], q[0])
Eigen (default) (w, x, y, z) (q.x(), q.y(), q.z(), q.w())
Unity (x, y, z, w) None (left-handed)
Unreal Engine (x, y, z, w) None (left-handed)
OpenXR (x, y, z, w) None
glTF (x, y, z, w) None

Handedness note (Informative): SpatialDDS does not prescribe handedness. Frame semantics are defined by FrameRef and transform chains, not by a global axis convention. Producers from left-handed engines (Unity, Unreal) must ensure the transform chain is consistent, not merely that the quaternion component order matches.

2.2 Optional Fields & Discriminated Unions

  • Optional scalars, structs, and arrays MUST be guarded by an explicit has_* boolean immediately preceding the field.
  • Mutually exclusive payloads SHALL be modeled as discriminated unions; do not overload presence flags to signal exclusivity.
  • Schema evolution leverages @extensibility(APPENDABLE); omit fields only when the IDL version removes them, never as an on-wire sentinel.
  • See CovMatrix in Appendix A for the reference discriminated union pattern used for covariance.
  • See FramedPose in Appendix A for the reference bundled-pose pattern. Prefer FramedPose over scattering PoseSE3 + FrameRef + CovMatrix + Time as sibling fields on a struct.

2.3 Numeric Validity & NaN Deprecation

  • NaN, Inf, or other sentinels SHALL NOT signal absence or "unbounded" values; explicit presence flags govern validity.
  • Fields guarded by has_* flags are meaningful only when the flag is true. When the flag is false, consumers MUST ignore the payload regardless of its contents.
  • When a has_* flag is true, non-finite numbers MUST be rejected wherever geographic coordinates, quaternions, coverage bounds, or similar numeric payloads appear.
  • Producers SHOULD avoid emitting non-finite numbers; consumers MAY treat such samples as malformed and drop them.

2.4 Conventions Quick Table (Informative)

Pattern Rule
Optional fields All optional values use a has_* flag.
NaN/Inf Never valid; treated as malformed input.
Quaternion order Always (x, y, z, w) GeoPose order.
Frames FrameRef.uuid is authoritative.
Ordering (source_id, seq) is canonical.

2.5 Canonical Ordering & Identity

These rules apply to any message that carries the trio { stamp, source_id, seq }.

Field semantics

  • stamp — Event time chosen by the producer.
  • source_id — Stable writer identity within a deployment.
  • seq — Per-source_id strictly monotonic unsigned 64-bit counter.

Identity & idempotency

  • The canonical identity of a sample is the tuple (source_id, seq).
  • Consumers MUST treat duplicate tuples as the same logical sample.
  • If seq wraps or resets, the producer MUST change source_id (or use a profile with an explicit writer epoch).

Ordering rules

  1. Intra-source — Order solely by seq. Missing values under RELIABLE QoS indicate loss.
  2. Inter-source merge — Order by (stamp, source_id, seq) within a bounded window selected by the consumer.

Synthesizing (source_id, seq) from External Data (Informative)
Datasets and replay tools that lack native per-writer sequence counters SHOULD synthesize them as follows: 1. Set source_id to a stable identifier for the data source (e.g., dataset name + sensor channel). 2. Assign seq by sorting samples by timestamp within each source_id and numbering from 0. 3. If the dataset contains gaps or non-monotonic timestamps, sort by the dataset's native ordering key and number from 0.

This produces a valid (source_id, seq) tuple without requiring the original system to have had one.

2.6 DDS / IDL Structure

  • All SpatialDDS modules conform to OMG IDL 4.2 and DDS-XTypes 1.3.
  • Extensibility SHALL be declared via @extensibility(APPENDABLE).
  • Consumers MUST ignore unknown appended fields in APPENDABLE types.
  • Compound identity SHALL be declared with multiple @key annotations.
  • Field initialization remains a runtime concern and SHALL NOT be encoded in IDL.
  • Abridged snippets within the main body are informative; the appendices contain the authoritative IDLs listed above.

2.7 Security Model (Normative)

2.7.1 Threat model (informative background)

SpatialDDS deployments may involve untrusted or partially trusted networks and intermediaries. Threats include: - Spoofing: malicious participants advertising fake services or content. - Tampering: modification of messages, manifests, or blob payloads in transit. - Replay: re-sending previously valid messages (e.g., ANNOUNCE, responses) outside their intended validity window. - Unauthorized access: clients subscribing to sensitive streams or publishing unauthorized updates. - Privacy leakage: exposure of user location, sensor frames, or inferred trajectories.

2.7.2 Trust boundaries

SpatialDDS distinguishes among: - Local transport fabric (e.g., DDS domain): participants may be on a shared L2/L3 network, but not necessarily trusted. - Resolution channels (e.g., HTTPS retrieval or local cache): used to fetch manifests and referenced resources. - Device/app policy: the client’s local trust store and decision logic.

2.7.3 Normative requirements

  1. Service authenticity. A client MUST authenticate the authority of a spatialdds:// URI (or the service/entity that advertises it) before trusting any security-sensitive content derived from it (e.g., localization results, transforms, anchors, content attachments).
  2. Integrity. When security is enabled by deployment policy or indicated via auth_hint, clients MUST reject data that fails integrity verification.
  3. Authorization. When security is enabled, services MUST enforce authorization for publish/subscribe operations that expose or modify sensitive spatial state (e.g., anchors, transforms, localization results, raw sensor frames).
  4. Confidentiality. Services SHOULD protect confidentiality for user-associated location/sensor payloads when transmitted beyond a physically trusted local network.
  5. Discovery trust. Clients MUST NOT treat Discovery/ANNOUNCE messages as sufficient proof of service authenticity on their own. ANNOUNCE may be used for bootstrapping only when accompanied by one of: (a) transport-level security that authenticates the publisher (e.g., DDS Security), or (b) authenticated retrieval and verification of an authority-controlled artifact (e.g., a manifest fetched over HTTPS/TLS, or a signed manifest) that binds the service identity to the advertised topics/URIs.

2.7.4 Validity and replay considerations

Implementations SHOULD enforce TTL and timestamps to mitigate replay. Where TTL exists (e.g., in Discovery messages), recipients SHOULD discard messages outside the declared validity interval.

2.7.5 DDS Security Binding (Normative)

SpatialDDS deployments that require authentication, authorization, integrity, or confidentiality over DDS MUST use OMG DDS Security as the minimum on-bus security contract. This includes:

  • Authentication: PKI-based authentication as defined by DDS Security.
  • Access control: governance and permissions documents configured per DDS Security.
  • Cryptographic protection: when confidentiality or integrity is required by policy, endpoints MUST enable DDS Security cryptographic plugins.

Cloud and enterprise authorization mechanisms (OAuth 2.0/OIDC, SPIFFE/SPIRE, mutual TLS) MAY be layered via the auth_hint field. auth_hint extends the authorization model to HTTP-resolved resources (manifests, blob stores, service APIs) without replacing the on-bus DDS Security contract.

Operational mapping (non-exhaustive): - Participants join a DDS Domain; security configuration applies to DomainParticipants and topics as governed by DDS Security governance rules. - Discovery/ANNOUNCE messages that convey service identifiers, manifest URIs, or access hints SHOULD be protected when operating on untrusted networks.

Interoperability note (informative): This specification does not redefine DDS Security. Implementations should use vendor-compatible DDS Security configuration mechanisms.

2.7.6 Spatial Privacy (Normative Guidance)

SpatialDDS streams carrying GeoPose, FramedPose, or ego-pose trajectories constitute personal location data when they describe individual users or devices. Deployments operating under privacy regulations (GDPR, CCPA, or equivalent) SHOULD apply the following mitigations:

  • Pose quantization. Reduce pose precision to the minimum required by the application (e.g., 1 m position, 5° orientation for building-level occupancy; full precision for SLAM).
  • Trajectory truncation. Limit the temporal extent of published pose histories. Fixed-lag smoothing windows (sensing profiles) naturally bound trajectory length; persistent storage of full trajectories requires explicit consent.
  • Pseudonymization. Use rotating source_id values that cannot be linked across sessions without a key held by the data controller.
  • Consent and purpose limitation. Operators publishing ego-pose streams to shared SpatialDDS domains MUST ensure that participants have consented to the spatial data sharing arrangement and that the data is used only for the stated purpose (e.g., collaborative SLAM, fleet coordination).

These mitigations are normative guidance (SHOULD), not normative requirements (MUST), because privacy requirements vary by jurisdiction, deployment context, and application domain. Implementers are responsible for compliance with applicable privacy regulations.

2.8 Enum Serialization (Normative)

When SpatialDDS types are serialized to JSON (manifests, HTTP payloads, diagnostic logs), enum values MUST be emitted as their IDL identifier string (e.g., "GAUSSIAN_SPLAT", not 1). Decoders MUST accept both the string identifier and the integer @value form. Unknown string identifiers MUST be rejected; unknown integer values MUST be treated as the enum's highest-numbered fallback value (e.g., OTHER_RADIO, CUSTOM) if one exists, or rejected otherwise.

On the DDS wire (CDR encoding), enum values use their integer @value(N) per OMG IDL specification. This rule applies only to JSON serialization contexts.

2.9 Time Semantics (Normative)

All Time values in SpatialDDS MUST represent UTC seconds since the Unix epoch (1970-01-01T00:00:00Z), excluding leap seconds (i.e., POSIX time / clock_gettime(CLOCK_REALTIME)). nanosec MUST be in the range [0, 999999999].

Producers operating in environments with hardware time synchronization SHOULD document their clock source via a MetaKV entry on the associated meta type with namespace = "time" and the following keys:

Key Values Example
clock_source ptp, pps, gnss, ntp, system "ptp"
clock_accuracy_ns estimated accuracy in nanoseconds "1000"
leap_second_mode posix (default), tai, utc_with_leap "posix"

Consumers performing cross-device temporal association (e.g., multi-robot loop closure, multi-operator fusion) SHOULD verify that all sources share a common clock domain before assuming sub-millisecond time alignment. When clock domains differ, consumers MUST estimate and compensate clock offsets before temporal association.

Default assumption: If no time metadata is present, consumers MUST assume clock_source = "system" with no accuracy guarantee.

2.10 Bounding Box Ordering (Normative)

  • Geographic CRS (WGS84): bbox arrays MUST use GeoJSON ordering: [lon_min, lat_min, lon_max, lat_max] (2D) or [lon_min, lat_min, alt_min, lon_max, lat_max, alt_max] (3D).
  • Local / ENU CRS: Aabb3 uses {min_xyz, max_xyz} where each is a Vec3 in the local coordinate frame.

JSON examples throughout this specification MUST follow these conventions. Where a CoverageElement uses crs = "EPSG:4326", the bbox array uses GeoJSON ordering. Where crs is absent or local, the aabb field uses Aabb3 semantics.

2.11 Schema Stability Signaling (Normative)

The schema_version string present on all Meta and Frame types (e.g., "spatial.sensing.vision/1.5") implicitly indicates stability: profiles listed in Appendices A–D are stable; profiles in Appendix E are provisional or informative.

For runtime discrimination, producers of provisional types SHOULD include a MetaKV entry with namespace = "schema" and key stability set to "provisional". Consumers in production deployments MAY use this flag to filter or warn on provisional data.

Example:

{
  "namespace": "schema",
  "json": "{\"stability\": \"provisional\"}"
}

Additionally, the caps.features field in Announce MAY carry feature flags prefixed with provisional. (e.g., "provisional.rf_beam", "provisional.radio"). Consumers MAY filter Announce messages to exclude provisional features in production deployments.

// SPDX-License-Identifier: MIT // SpatialDDS Specification 1.6 (© Open AR Cloud Initiative)

3. IDL Profiles

The SpatialDDS IDL bundle defines the schemas used to exchange real-world spatial data over DDS. It is organized into complementary profiles: Core, which provides the backbone for pose graphs, geometry, and geo-anchoring; Discovery, which enables lightweight announcements of services, coverage, anchors, and content; and Anchors, which adds support for publishing and updating sets of durable world-locked anchors. Together, these profiles give devices, services, and applications a common language for building, sharing, and aligning live world models—while staying codec-agnostic, forward-compatible, and simple enough to extend for domains such as robotics, AR/XR, IoT, and smart cities.

See §2 Conventions for global normative rules.

3.1 IDL Profile Versioning & Negotiation (Normative)

SpatialDDS uses semantic versioning tokens of the form name@MAJOR.MINOR.

  • MAJOR increments for breaking schema or wire changes.
  • MINOR increments for additive, compatible changes.

Identifier conventions: Profile tokens use name@MAJOR.MINOR (e.g., core@1.6). Module identifiers use spatial.<profile>/MAJOR.MINOR (e.g., spatial.core/1.6). These are canonically related: core@1.6 ⇔ spatial.core/1.6.

Participants advertise supported ranges via caps.supported_profiles (discovery) and manifest capabilities blocks. Consumers select the highest compatible minor within any shared major. Backward-compatibility clauses from 1.3 are retired; implementations only negotiate within their common majors. SpatialDDS 1.6 uses a single canonical quaternion order (x, y, z, w) across manifests, discovery payloads, and IDL messages.

3.2 Core SpatialDDS

The Core profile defines the essential building blocks for representing and sharing a live world model over DDS. It focuses on a small, stable set of concepts: pose graphs, 3D geometry tiles, blob transport for large payloads, and geo-anchoring primitives such as anchors, transforms, and simple GeoPoses. The design is deliberately lightweight and codec-agnostic: tiles reference payloads but do not dictate mesh formats, and anchors define stable points without tying clients to a specific localization method. All quaternion fields follow the OGC GeoPose component order (x, y, z, w) so orientation data can flow between GeoPose-aware systems without reordering. By centering on graph + geometry + anchoring, the Core profile provides a neutral foundation that can support diverse pipelines across robotics, AR, IoT, and smart city contexts.

GNSS diagnostics (Normative): NavSatStatus is a companion to GeoPose that carries GNSS receiver diagnostics (fix type, DOP, satellite count, ground velocity) on a parallel topic. It is published alongside GNSS-derived GeoPoses and MUST NOT be used to annotate non-GNSS localization outputs.

NavSatStatus Topic (Normative): NavSatStatus SHOULD be published on the topic spatialdds/geo/<gnss_id>/navsat_status/v1, where <gnss_id> matches the @key gnss_id in the struct. NavSatStatus SHOULD use the same QoS profile as the associated GeoPose stream.

NavSatStatus is registered as type navsat_status in the registered types table (§3.3.2). Producers publishing GNSS-derived GeoPoses SHOULD include a TopicMeta entry for NavSatStatus in their Announce.topics[]. Consumers MAY discover NavSatStatus topics through standard discovery mechanisms.

Planned Trajectory (Normative): PlannedTrajectory represents an agent's intended future path. It is published at the agent's replan rate (typically 1–10 Hz) and superseded by each new plan revision. Consumers MUST use the most recent plan_revision for a given agent_id and discard older revisions.

Waypoint timestamps represent planned arrival times in the future. Consumers SHOULD treat these as estimates subject to replanning. The position_uncertainty_m field, when present, indicates the planner's confidence in the waypoint position and typically grows with distance from the current state.

PlannedTrajectory is registered as type planned_trajectory in §3.3.2 and SHOULD be advertised on the topic spatialdds/<scene>/plan/<agent_id>/trajectory/v1 using the EVENT_RT QoS profile.

Entity Binding (Normative): EntityBinding provides cross-topic correlation without imposing a scene graph hierarchy. Multiple publishers MAY publish bindings for the same entity_id; consumers MUST merge component lists and resolve conflicts (e.g., by preferring the binding with the most recent stamp or the highest-confidence source).

EntityBinding is intentionally flat — it does not express parent-child relationships, ownership, or transform inheritance. Consumers requiring hierarchical scene graph semantics SHOULD build their own entity hierarchy from the bindings received.

EntityBinding is registered as type entity_binding in §3.3.2 and SHOULD be advertised on the topic spatialdds/<scene>/entity/binding/v1. Publishers SHOULD use RELIABLE + TRANSIENT_LOCAL QoS so that late-joining consumers receive the current set of entity correlations.

Blob Reassembly (Normative)

Blob payloads are transported as BlobChunk sequences. Consumers MUST be prepared for partial delivery and SHOULD apply a per-blob timeout window based on expected rate and total_chunks.

  • Timeout guidance: Consumers SHOULD apply a per-blob timeout of at least 2 × (total_chunks / expected_rate) seconds when an expected rate is known.
  • Failure handling: If all chunks have not arrived within this window under RELIABLE QoS, the consumer SHOULD discard the partial blob and MAY re-request it via SnapshotRequest.
  • BEST_EFFORT behavior: Under BEST_EFFORT QoS, consumers MUST NOT assume complete delivery and SHOULD treat blobs as opportunistic.
  • Memory pressure: Consumers MAY discard partial blobs early under memory pressure, but MUST NOT treat them as valid payloads.

Frame Identifiers (Reference)

SpatialDDS uses structured frame references via the FrameRef { uuid, fqn } type. See Appendix G Frame Identifiers (Informative Reference) for the complete definition and naming rules.

Each Transform expresses a pose that maps coordinates from the from frame into the to frame (parent → child).

3.3 Discovery

Discovery is how SpatialDDS peers find each other, advertise what they publish, and select compatible streams. Deployments can expose discovery using a DDS binding (query/announce on well-known topics), an HTTP binding (a REST endpoint that accepts spatial queries and returns service manifests), or both. HTTP resolvers may act as gateways to a DDS bus without changing the client-facing contract.

How it works (at a glance)

  1. Announce — each node periodically publishes an announcement with capabilities and topics (DDS), or registers its manifest with an HTTP discovery service.
  2. Query — clients publish spatial filters on the DDS bus (CoverageQuery), or issue an HTTP search request to /.well-known/spatialdds/search.
  3. Select — clients subscribe to chosen topics; negotiation picks the highest compatible minor per profile.

3.3.0 Discovery Layers & Bootstrap (Normative)

SpatialDDS distinguishes three discovery layers:

  • Layer 1 — Network Bootstrap: how a device discovers that a SpatialDDS deployment exists and obtains initial connection parameters. This is transport and access-network dependent (mDNS, Geospatial DNS-SD, QR codes, HTTPS well-known path).
  • Layer 1.5 — HTTP Discovery (optional): how a device, without joining a DDS domain, queries for services by spatial region via an HTTP endpoint. This is the bridge between bootstrap and on-bus discovery for Internet-scale deployments where the client and service may be on different networks.
  • Layer 2 — On-Bus Discovery: how a device, once connected to a DDS domain, discovers services, coverage, and streams via DDS topics. This is what the Discovery profile's IDL types define.

Layer 1 mechanisms deliver a Bootstrap Manifest that provides the parameters needed to transition to Layer 1.5 or Layer 2. Layer 1.5 delivers Service Manifests (§8.2.3) that provide the DDS connection parameters needed to transition to Layer 2. Clients MAY skip Layer 1.5 if Layer 1 already provides sufficient connection information (e.g., local mDNS bootstrap on the venue LAN).

Bootstrap Manifest (Normative)

A bootstrap manifest is a small JSON document resolved by Layer 1 mechanisms:

{
  "spatialdds_bootstrap": "1.6",
  "domain_id": 42,
  "initial_peers": [
    "udpv4://192.168.1.100:7400",
    "udpv4://10.0.0.50:7400"
  ],
  "partitions": ["venue/museum-west"],
  "discovery_topic": "spatialdds/discovery/announce/v1",
  "manifest_uri": "spatialdds://museum.example.org/west/service/discovery",
  "auth": {
    "method": "none"
  }
}

Field definitions

Field Required Description
spatialdds_bootstrap REQUIRED Bootstrap schema version (e.g., "1.6")
domain_id REQUIRED DDS domain ID to join
initial_peers REQUIRED One or more DDS peer locators for initial discovery
partitions OPTIONAL DDS partition(s) to join. Empty or absent means default partition.
discovery_topic OPTIONAL Override for the well-known announce topic. Defaults to spatialdds/discovery/announce/v1.
manifest_uri OPTIONAL A spatialdds:// URI for the deployment's root manifest.
auth OPTIONAL Authentication hint. method is one of "none", "dds-security", "token".

Normative rules

  • domain_id MUST be a valid DDS domain ID (0–232 per the RTPS specification; higher values may require non-standard configuration).
  • initial_peers MUST contain at least one locator. Locator format follows the DDS implementation's peer descriptor syntax.
  • Consumers SHOULD attempt all listed peers and use the first that responds.
  • The bootstrap manifest is a discovery aid, not a security boundary. Deployments requiring authentication MUST use DDS Security or an equivalent transport-level mechanism.
Well-Known HTTPS Path (Normative)

Clients MAY fetch the bootstrap manifest from:

https://{authority}/.well-known/spatialdds

The response MUST be application/json using the bootstrap manifest schema. Servers SHOULD set Cache-Control headers appropriate to their deployment (e.g., max-age=300).

Note: Three well-known paths are defined under the /.well-known/spatialdds namespace. The bootstrap path (/.well-known/spatialdds) returns a Bootstrap Manifest. The resolver metadata path (/.well-known/spatialdds-resolver) returns resolver metadata for URI resolution (§7.5.2). The search path (/.well-known/spatialdds/search) accepts spatial discovery queries and returns matching service manifests. All three serve distinct functions and MAY coexist on the same authority.

HTTP Discovery Search Binding (Normative)

The HTTP discovery search binding allows clients to query for SpatialDDS services by spatial region without joining a DDS domain. It mirrors the on-bus CoverageQuery / CoverageResponse pattern over HTTP, using the same coverage semantics (§3.3.4) and returning standard service manifests (§8.2.3).

Endpoint:

POST https://{authority}/.well-known/spatialdds/search
Content-Type: application/json

Request body:

Field Type Required Description
coverage array of CoverageElement REQUIRED One or more spatial regions of interest. Uses the same CoverageElement schema as CoverageQuery.coverage — type, bbox, aabb, crs, frame_ref, global.
filter CoverageFilter OPTIONAL Structured filter matching CoverageFilter — type_in, qos_profile_in, module_id_in. Empty arrays mean "match all."
kind array of string OPTIONAL Filter by service kind: "VPS", "MAPPING", "RELOCAL", "SEMANTICS", "STORAGE", "CONTENT", "ANCHOR_REGISTRY", "OTHER". Empty or absent means all kinds.
geohash string OPTIONAL Geohash string (3–7 characters). Shorthand for an earth-fixed bbox query. When present, the server expands the geohash to its bounding box and treats it as an additional coverage element.
max_results integer OPTIONAL Maximum number of results to return (default: server-defined, recommended ≤100).
page_token string OPTIONAL Opaque token from a previous response for pagination.

Minimal example — query by geohash:

POST /.well-known/spatialdds/search
Content-Type: application/json

{
  "geohash": "9q8yy"
}

Full example — query by bbox with service kind filter:

POST /.well-known/spatialdds/search
Content-Type: application/json

{
  "coverage": [
    {
      "type": "bbox",
      "crs": "EPSG:4979",
      "bbox": [-122.420, 37.785, -122.405, 37.800]
    }
  ],
  "kind": ["VPS"],
  "filter": {
    "type_in": ["geopose"],
    "qos_profile_in": [],
    "module_id_in": []
  },
  "max_results": 10
}

Response body:

On success, the server MUST return HTTP 200 OK with Content-Type: application/json. The body is a JSON object:

Field Type Required Description
results array of Manifest REQUIRED Array of service manifests (§8.2.3 schema). Empty array if no services match.
next_page_token string OPTIONAL Opaque token for fetching the next page. Absent or empty string means no more results. 
{
  "results": [
    {
      "id": "spatialdds://acme-vps.example/sf-downtown/service/vps-main",
      "profile": "spatial.manifest@1.6",
      "rtype": "service",
      "service": {
        "service_id": "vps-main",
        "kind": "VPS",
        "name": "SF Downtown Visual Positioning",
        "org": "acme-vps.example",
        "version": "2025-q4",
        "connection": {
          "domain_id": 100,
          "initial_peers": ["tcpv4://vps.acme-vps.example:7400"],
          "partitions": ["sf/downtown"]
        },
        "topics": [
          { "name": "spatialdds/vps/query/v1", "type": "vps_query", "version": "v1", "qos_profile": "VPS_REQ" },
          { "name": "spatialdds/vps/result/v1", "type": "geopose", "version": "v1", "qos_profile": "VPS_RESP" }
        ]
      },
      "coverage": {
        "frame_ref": { "uuid": "ae6f0a3e-7a3e-4b1e-9b1f-0e9f1b7c1a10", "fqn": "earth-fixed" },
        "has_bbox": true,
        "bbox": [-122.420, 37.785, -122.405, 37.800],
        "global": false
      },
      "stamp": { "sec": 1735689600, "nanosec": 0 },
      "ttl_sec": 3600
    }
  ],
  "next_page_token": ""
}

GET convenience form:

For simple geohash-based queries (e.g., from a Geospatial DNS-SD muri), servers MUST also support:

GET https://{authority}/.well-known/spatialdds/search?geohash={geohash}
GET https://{authority}/.well-known/spatialdds/search?geohash={geohash}&kind={kind}

The GET form is equivalent to a POST with {"geohash": "{geohash}"} (and optional kind filter). The response format is identical.

Spatial matching semantics:

The server evaluates spatial overlap using the same intersects predicate as the on-bus CoverageQuery: a service matches if its coverage region intersects any of the requested coverage elements. When geohash is provided, the server expands it to its bounding box and applies the same intersection test. Services with coverage.global == true match all queries.

Error handling:

Status Meaning
200 Success. Body contains results (may be empty).
400 Malformed request (invalid geohash, missing coverage, bad JSON).
401 / 403 Authentication required or insufficient.
404 The /.well-known/spatialdds/search endpoint is not supported by this authority.
429 Rate limited. Client SHOULD retry with exponential backoff.
5xx Server error.

Normative rules:

  • Servers implementing the HTTP discovery search binding MUST support the POST form. The GET convenience form is also REQUIRED for interoperability with the Geospatial DNS-SD binding.
  • The response MUST use the §8.2.3 service manifest schema for each result. Clients MUST be able to extract service.connection from any result and use it to join the service's DDS domain.
  • Servers MUST respect the Coverage Model (§3.3.4) when evaluating spatial overlap: coverage_frame_ref, bbox, aabb, and global flags all apply.
  • Servers SHOULD set Cache-Control headers appropriate to the deployment. Responses to geohash queries at precision 5 (city-district scale) MAY be cached for 60–300 seconds.
  • Pagination follows the same contract as on-bus CoverageResponse: tokens are opaque, results are best-effort, and an empty next_page_token means no further pages.
  • Servers MAY return results for all resource types (services, content, anchor sets) or restrict to services only. When kind is absent, servers SHOULD return services only unless the client explicitly requests other types via the filter field.
  • The HTTP search endpoint and the on-bus CoverageQuery are independent mechanisms. Servers MAY implement one or both. Servers that implement both SHOULD return consistent results for equivalent queries.
  • HTTPS with TLS is REQUIRED. Authentication follows the same rules as §7.5.4.

Relationship to other well-known paths:

Path Function Returns
/.well-known/spatialdds Bootstrap manifest Bootstrap Manifest (domain_id, peers, partitions)
/.well-known/spatialdds-resolver Resolver metadata Resolver metadata (https_base, cache_ttl)
/.well-known/spatialdds/search Spatial discovery query Array of service manifests

All three paths MAY coexist on the same authority. They serve distinct functions and do not conflict.

Relationship to Geospatial DNS-SD:

The Geospatial DNS-SD binding's muri TXT record value SHOULD point to the search endpoint's GET convenience form:

muri=https://discovery.example.org/.well-known/spatialdds/search?geohash=9q8yy

This directly connects the DNS bootstrap (Layer 1) to HTTP discovery (Layer 1.5) without requiring any intermediate resolution step.

DNS-SD Binding (Normative)

DNS-SD is the recommended first binding for local bootstrap.

Service type: _spatialdds._udp

TXT record keys

Key Maps to Example
ver spatialdds_bootstrap 1.6
did domain_id 42
part partitions (comma-separated) venue/museum-west
muri manifest_uri spatialdds://museum.example.org/west/service/discovery

Resolution flow

  1. Device queries for _spatialdds._udp.local (mDNS) or _spatialdds._udp.<domain> (wide-area DNS-SD).
  2. SRV record provides host and port for the initial DDS peer.
  3. TXT record provides domain ID, partitions, and optional manifest URI.
  4. Device constructs a bootstrap manifest from the SRV + TXT data and joins the DDS domain.
  5. On-bus Discovery (Layer 2) takes over.

Normative rules

  • did is REQUIRED in the TXT record.
  • The SRV target and port MUST resolve to a reachable DDS peer locator.
  • If muri is present, clients SHOULD resolve it after joining the domain to obtain full deployment metadata.
Geospatial DNS-SD Binding (Normative)

The geospatial DNS-SD binding allows a client with a GPS fix to discover SpatialDDS services by encoding its location as a geohash subdomain. This binding targets Internet-scale deployments where clients and services are on different networks.

Subdomain pattern:

_spatialdds._udp.<geohash>.geo.<authority>

where <geohash> is a standard base32 geohash [8] of the client's position and <authority> is the DNS zone hosting the discovery registry.

Geohash precision levels

Characters Cell size (approx.) Typical use
3 ~156 km × 156 km Metro region / country subdivision
4 ~39 km × 20 km City
5 ~5 km × 5 km District / neighborhood
6 ~1.2 km × 0.6 km Block / venue cluster
7 ~153 m × 153 m Single venue

Clients SHOULD query at precision 5 (neighborhood scale) by default. Finer precision (6–7) is appropriate when the client has high-accuracy GNSS (RTK or similar).

TXT record keys

The TXT record uses the same key set as the local DNS-SD binding, with one addition:

Key Required Description
ver REQUIRED Bootstrap schema version (e.g., 1.6)
did OPTIONAL DDS domain ID. OPTIONAL because the geospatial binding's primary role is to hand off to an HTTP discovery service via muri, not to provide direct DDS connection.
muri REQUIRED HTTPS URL or spatialdds:// URI for the discovery service, with the geohash passed as a query parameter or path segment.
part OPTIONAL DDS partition hint (comma-separated).

Resolution flow

  1. Client obtains its position (GPS, network location, or manual entry).
  2. Client computes the base32 geohash at precision 5 (e.g., 37.7749°N, 122.4194°W → 9q8yy).
  3. Client issues a DNS TXT query for _spatialdds._udp.9q8yy.geo.<authority>.
  4. If the query returns NXDOMAIN, the client truncates to precision 4 (9q8y) and retries. This continues down to precision 3. If precision 3 also returns NXDOMAIN, bootstrap fails for this authority.
  5. On success, the client extracts muri from the TXT record.
  6. Client issues an HTTPS GET to the muri URL, which returns one or more SpatialDDS service manifests (§8.2.3) for services covering that geohash cell.
  7. Client selects a service and connects using the connection hints in the manifest.

Example DNS records (Route 53 / authoritative DNS)

;; San Francisco downtown (~5 km² cell)
_spatialdds._udp.9q8yy.geo.spatialdds.example.org.  TXT  "ver=1.6" "muri=https://discovery.spatialdds.example.org/v1/services?geohash=9q8yy"

;; San Francisco marina district
_spatialdds._udp.9q8yk.geo.spatialdds.example.org.  TXT  "ver=1.6" "muri=https://discovery.spatialdds.example.org/v1/services?geohash=9q8yk"

;; London Soho
_spatialdds._udp.gcpvj.geo.spatialdds.example.org.  TXT  "ver=1.6" "muri=https://discovery.spatialdds.example.org/v1/services?geohash=gcpvj"

Example HTTPS response (from the muri endpoint)

The discovery service returns an array of standard SpatialDDS service manifests (§8.2.3):

[
  {
    "id": "spatialdds://provider-a.example/sf-downtown/service/vps-main",
    "profile": "spatial.manifest@1.6",
    "rtype": "service",
    "service": {
      "service_id": "vps-main",
      "kind": "VPS",
      "name": "SF Downtown Visual Positioning",
      "org": "provider-a.example",
      "connection": {
        "domain_id": 100,
        "initial_peers": ["tcpv4://vps.provider-a.example:7400"]
      },
      "topics": [
        { "name": "spatialdds/vps/pose/v1", "type": "geopose", "version": "v1", "qos_profile": "POSE_RT" }
      ]
    },
    "coverage": {
      "frame_ref": { "uuid": "ae6f0a3e-7a3e-4b1e-9b1f-0e9f1b7c1a10", "fqn": "earth-fixed" },
      "has_bbox": true,
      "bbox": [-122.420, 37.785, -122.405, 37.800],
      "global": false
    },
    "stamp": { "sec": 1714070400, "nanosec": 0 },
    "ttl_sec": 3600
  }
]

DNS zone delegation for federated operation

Operators MAY delegate geohash-prefixed subdomains to independent authorities, enabling federated discovery where different organizations manage different geographic regions:

;; Top-level authority delegates San Francisco (geohash prefix "9q8") to provider A
9q8.geo.spatialdds.example.org.   NS  ns1.provider-a.example.

;; Top-level authority delegates London (geohash prefix "gcpv") to provider B
gcpv.geo.spatialdds.example.org.  NS  ns1.provider-b.example.

Each delegate manages all geohash cells under its prefix using standard DNS zone management. This mirrors the hierarchical structure of the DNS itself.

Normative rules

  • muri is REQUIRED in the TXT record for geospatial bindings. The geospatial binding's purpose is to locate an HTTP discovery endpoint; direct DDS connection via did + SRV alone is NOT sufficient because the client's network path to the DDS domain is not implied by geographic proximity.
  • ver is REQUIRED and MUST match the local DNS-SD binding's version key.
  • The geohash MUST be a valid base32 geohash [8] of 3–7 characters. Clients MUST reject TXT records found under geohash subdomains shorter than 3 characters or longer than 7 characters.
  • The fallback-to-shorter-prefix algorithm MUST NOT retry below precision 3 to avoid excessive DNS queries.
  • The muri endpoint MUST return application/json containing either a single service manifest (§8.2.3) or a JSON array of service manifests. An empty array indicates no services in the requested cell.
  • DNS operators SHOULD populate records at precision 5 for general use. Finer precision (6–7) MAY be added for dense urban areas with multiple providers per neighborhood.
  • Clients MUST validate the coverage field in returned manifests against their actual position. A geohash cell is an approximation; the manifest's bbox or coverage elements are authoritative for determining whether a service actually covers the client's location.
  • DNS TTLs SHOULD be set appropriately for the deployment's dynamism. Static deployments (fixed VPS infrastructure) MAY use TTLs of 3600 seconds or more. Dynamic deployments (pop-up events, temporary coverage) SHOULD use shorter TTLs (60–300 seconds).

Relationship to local DNS-SD

The geospatial and local DNS-SD bindings serve different deployment scales and MAY coexist:

Binding Network scope Client prerequisite Primary output
Local DNS-SD (mDNS) Same LAN WiFi connection DDS domain_id + peer locator
Local DNS-SD (wide-area) Known authority Domain name (from QR, app config) DDS domain_id + peer locator
Geospatial DNS-SD Internet GPS fix HTTP discovery URL → service manifests

A client arriving at a venue MAY try local mDNS first (fastest, no Internet dependency), fall back to geospatial DNS if mDNS yields no results (works over cellular, finds services across networks), and finally fall back to the HTTPS well-known path if a venue domain is available.

Other Bootstrap Mechanisms (Informative)
  • DHCP: vendor-specific option carrying a URL to the bootstrap manifest.
  • QR / NFC / BLE beacons: encode a spatialdds:// URI or direct URL to the bootstrap manifest.
  • Mobile / MEC: edge discovery APIs provide a URL to the bootstrap manifest.
Complete Bootstrap Chain (Informative)

Path A — Local bootstrap (same LAN)

Access Network           Bootstrap              DDS Domain            On-Bus Discovery
     │                      │                       │                       │
     │  WiFi/5G/BLE/QR      │                       │                       │
     ├─────────────────────► │                       │                       │
     │                       │  DNS-SD (mDNS) /      │                       │
     │                       │  .well-known / QR      │                       │
     │                       ├─────────────────────► │                       │
     │                       │  Bootstrap Manifest   │                       │
     │                       │  (domain_id, peers,   │                       │
     │                       │   partitions)         │                       │
     │                       │ ◄─────────────────────┤                       │
     │                       │                       │  Join DDS domain      │
     │                       │                       ├─────────────────────► │
     │                       │                       │  Subscribe to         │
     │                       │                       │  .../announce/v1      │
     │                       │                       │  Receive Announce     │
     │                       │                       │  Issue CoverageQuery  │
     │                       │                       │  Select streams       │
     │                       │                       │  Begin operation      │

Path B — Internet bootstrap (cross-network, geospatial)

GPS Fix               Geo DNS-SD            HTTP Discovery         DDS Domain
  │                      │                       │                       │
  │  Compute geohash     │                       │                       │
  ├─────────────────────►│                       │                       │
  │                      │  TXT query:           │                       │
  │                      │  _spatialdds._udp     │                       │
  │                      │  .<geohash>.geo.<auth> │                       │
  │                      ├──────────────────────►│                       │
  │                      │  TXT: muri=https://…  │                       │
  │                      │◄──────────────────────┤                       │
  │                      │                       │                       │
  │  HTTPS GET muri      │                       │                       │
  ├──────────────────────────────────────────────►│                       │
  │                      │                       │  Service manifest(s)  │
  │                      │                       │  (domain_id, peers,   │
  │                      │                       │   topics, coverage)   │
  │◄──────────────────────────────────────────────┤                       │
  │                      │                       │                       │
  │  Select service, connect via TCP/TLS         │                       │
  ├──────────────────────────────────────────────────────────────────────►│
  │                      │                       │  Begin operation      │

Key messages (abridged IDL)

(Abridged IDL — see Appendix B for full definitions.) 

// ABRIDGED — see Appendix B for normative definitions
// Message shapes shown for orientation only
@extensibility(APPENDABLE) struct ProfileSupport { string name; uint32 major; uint32 min_minor; uint32 max_minor; boolean preferred; }
@extensibility(APPENDABLE) struct Capabilities   { sequence<ProfileSupport,64> supported_profiles; sequence<string,32> preferred_profiles; sequence<FeatureFlag,64> features; }
@extensibility(APPENDABLE) struct TopicMeta      { string name; string type; string version; string qos_profile; float32 target_rate_hz; uint32 max_chunk_bytes; }

@extensibility(APPENDABLE) struct Announce {
  // ... node identity, endpoints ...
  Capabilities caps;                  // profiles, preferences, features
  sequence<TopicMeta,128> topics;     // typed topics offered by this node
}

@extensibility(APPENDABLE) struct CoverageFilter {
  sequence<string,16> type_in;
  sequence<string,16> qos_profile_in;
  sequence<string,16> module_id_in;
}

@extensibility(APPENDABLE) struct CoverageQuery {
  // minimal illustrative fields
  boolean has_filter;
  CoverageFilter filter; // preferred in 1.5
  string expr;           // deprecated in 1.5; Appendix F.X grammar
  string reply_topic;    // topic to receive results
  string query_id;       // correlate request/response
}

The expression syntax is retained for legacy deployments and defined in Appendix F.X; `expr` is deprecated in 1.5 in favor of `filter`.

@extensibility(APPENDABLE) struct CoverageResponse {
  string query_id;
  sequence<Announce,256> results;
  string next_page_token;
}

Minimal examples (JSON)

Announce (capabilities + topics) 

{
  "caps": {
    "supported_profiles": [
      { "name": "core",           "major": 1, "min_minor": 0, "max_minor": 3 },
      { "name": "discovery",      "major": 1, "min_minor": 1, "max_minor": 2 }
    ],
    "preferred_profiles": ["discovery@1.2"],
    "features": ["blob.crc32"]
  },
  "topics": [
    { "name": "spatialdds/perception/cam_front/video_frame/v1", "type": "video_frame", "version": "v1", "qos_profile": "VIDEO_LIVE" },
    { "name": "spatialdds/perception/radar_1/radar_detection/v1",  "type": "radar_detection", "version": "v1", "qos_profile": "RADAR_RT"   },
    { "name": "spatialdds/perception/radar_1/radar_tensor/v1",     "type": "radar_tensor", "version": "v1", "qos_profile": "RADAR_RT"      }
  ]
}

Query + Response 

{
  "query_id": "q1",
  "has_filter": true,
  "filter": {
    "type_in": ["radar_detection", "radar_tensor"],
    "qos_profile_in": [],
    "module_id_in": ["spatial.discovery/1.4", "spatial.discovery/1.6"]
  },
  "expr": "",
  "reply_topic": "spatialdds/discovery/response/q1",
  "stamp": { "sec": 1714070400, "nanosec": 0 },
  "ttl_sec": 30
}
{ "query_id": "q1", "results": [ { "caps": { "supported_profiles": [ { "name": "discovery", "major": 1, "min_minor": 1, "max_minor": 2 } ] }, "topics": [ { "name": "spatialdds/perception/radar_1/radar_detection/v1", "type": "radar_detection", "version": "v1", "qos_profile": "RADAR_RT" }, { "name": "spatialdds/perception/radar_1/radar_tensor/v1", "type": "radar_tensor", "version": "v1", "qos_profile": "RADAR_RT" } ] } ], "next_page_token": "" }

Norms & filters

  • Announces MUST include caps.supported_profiles; peers choose the highest compatible minor within a shared major.
  • Each advertised topic MUST declare name, type, version, and qos_profile per Topic Identity (§3.3.1); optional throughput hints (target_rate_hz, max_chunk_bytes) are additive.
  • Discovery topics SHALL restrict type to {geometry_tile, video_frame, radar_detection, radar_tensor, seg_mask, desc_array, rf_beam, radio_scan}, version to v1, and qos_profile to {GEOM_TILE, VIDEO_LIVE, RADAR_RT, SEG_MASK_RT, DESC_BATCH, RF_BEAM_RT, RADIO_SCAN_RT}.
  • caps.preferred_profiles is an optional tie-breaker within the same major.
  • caps.features carries namespaced feature flags; unknown flags MUST be ignored.
  • FeatureFlag is a struct (not a raw string) to allow future appended fields (e.g., version or parameters) without breaking wire compatibility.
  • CoverageQuery.filter provides structured matching for type, qos_profile, and module_id.
  • Empty sequences in CoverageFilter mean “match all” for that field.
  • When multiple filter fields are populated, they are ANDed; a result MUST match at least one value in every non-empty sequence.
  • Version range matching stays in profile negotiation (supported_profiles with min_minor/max_minor), not in coverage queries.
  • CoverageQuery.expr is deprecated in 1.5 and will be removed in 2.0. If has_filter is true, responders MUST ignore expr. New implementations MUST NOT generate expr; they MUST use filter exclusively. Implementations supporting expr for backward compatibility SHOULD log a deprecation warning.
  • Responders page large result sets via next_page_token; every response MUST echo the caller’s query_id.

Pagination Contract (Normative)

  1. Opacity. Page tokens are opaque strings produced by the responder. Consumers MUST NOT parse, construct, or modify them.
  2. Consistency. Results are best-effort. Pages may include duplicates or miss nodes that arrived/departed between pages. Consumers SHOULD deduplicate by service_id.
  3. Expiry. Responders SHOULD honor page tokens for at least ttl_sec seconds from the originating query’s stamp. After expiry, responders MAY return an empty result set rather than an error.
  4. Termination. An empty string in next_page_token means no further pages remain.
  5. Page size. Responders choose page size. Consumers MUST accept any non-zero page size.

Announce Lifecycle (Normative)

  • Departure: A node that leaves the bus gracefully SHOULD publish a Depart message. Consumers MUST remove the corresponding service_id from their local directory upon receiving Depart. Depart does not replace TTL-based expiry.
  • Staleness: Consumers SHOULD discard Announce samples where now - stamp > 2 * ttl_sec.
  • Re-announce cadence: Producers SHOULD re-announce at intervals no greater than ttl_sec / 2 to prevent premature expiry.
  • Rate limiting: Producers SHOULD NOT re-announce more frequently than once per second unless capabilities, coverage, or topics have changed. Consumers MAY rate-limit processing per service_id.

Well-Known Discovery Topics (Normative)

Message Type Topic Name
Announce spatialdds/discovery/announce/v1
Depart spatialdds/discovery/depart/v1
CoverageQuery spatialdds/discovery/query/v1
CoverageHint spatialdds/discovery/coverage_hint/v1
ContentAnnounce spatialdds/discovery/content/v1

CoverageResponse uses the reply_topic specified in the originating CoverageQuery.

QoS defaults for discovery topics

Topic Reliability Durability History
announce RELIABLE TRANSIENT_LOCAL KEEP_LAST(1) per key
depart RELIABLE VOLATILE KEEP_LAST(1) per key
query RELIABLE VOLATILE KEEP_ALL
coverage_hint BEST_EFFORT VOLATILE KEEP_LAST(1) per key
content RELIABLE TRANSIENT_LOCAL KEEP_LAST(1) per key

CoverageResponse reply topic QoS (Normative)
The writer for reply_topic SHOULD use RELIABLE, VOLATILE, KEEP_ALL.
The querier SHOULD create a matching reader before publishing the CoverageQuery.

Discovery trust (Normative)

ANNOUNCE messages provide discovery convenience and are not, by themselves, authoritative. Clients MUST apply the Security Model requirements in §2.7 before trusting advertised URIs, topics, or services.

Asset references

Discovery announcements and manifests share a single AssetRef structure composed of URI, media type, integrity hash, and optional MetaKV metadata bags. AssetRef and MetaKV are normative types for asset referencing in the Discovery profile.

auth_hint (Normative)

auth_hint provides a machine-readable hint describing how clients can authenticate and authorize access to the service or resolve associated resources. auth_hint does not replace deployment policy; clients may enforce stricter requirements than indicated.

  • If auth_hint is empty or omitted, it means “no authentication hint provided.” Clients MUST fall back to deployment policy (e.g., DDS Security configuration, trusted network assumptions, or authenticated manifest retrieval).
  • If auth_hint is present, it MUST be interpreted as one or more auth URIs encoded as a comma-separated list.

Grammar (normative):
auth_hint := auth-uri ("," auth-uri)*
auth-uri := scheme ":" scheme-specific

Required schemes (minimum set): - ddssec: indicates that the DDS transport uses OMG DDS Security (governance/permissions) for authentication and access control. - Example: ddssec:profile=default - Example: ddssec:governance=spatialdds://auth.example/…/governance.xml;permissions=spatialdds://auth.example/…/permissions.xml - oauth2: indicates OAuth2-based access for HTTP(S) resolution or service APIs. - Example: oauth2:issuer=https://auth.example.com;aud=spatialdds;scope=vps.localize - mtls: indicates mutual TLS for HTTP(S) resolution endpoints. - Example: mtls:https://resolver.example.com

Client behavior (normative): - A client MUST treat auth_hint as advisory configuration and MUST still validate the authenticity of the service/authority via a trusted mechanism (DDS Security identity or authenticated artifact retrieval). - If the client does not support any scheme listed in auth_hint, it MUST fail gracefully and report “unsupported authentication scheme.”

Examples (informative): - auth_hint="ddssec:profile=city-austin" - auth_hint="ddssec:governance=spatialdds://city.example/…/gov.xml,oauth2:issuer=https://auth.city.example;aud=spatialdds;scope=catalog.read"

What fields mean (quick reference)

Field Use
caps.supported_profiles Version ranges per profile. Peers select the highest compatible minor within a shared major.
caps.preferred_profiles Optional tie-breaker hint (only within a major).
caps.features Optional feature flags (namespaced strings). Unknown flags can be ignored.
topics[].type / version / qos_profile Topic Identity keys used to filter and match streams; see the allowed sets above.
reply_topic, query_id Allows asynchronous, paged responses and correlation.

Practical notes

  • Announce messages stay small and periodic; re-announce whenever capabilities, coverage, or topics change.
  • Queries are stateless filters. Responders may page through results; clients track next_page_token until empty.
  • Topic names follow spatialdds/<domain>/<stream>/<type>/<version> per §3.3.1; filter by type and qos_profile instead of parsing payloads.
  • Negotiation is automatic once peers see each other’s supported_profiles; emit diagnostics like NO_COMMON_MAJOR(name) when selection fails.

Summary

Discovery keeps the wire simple: nodes publish what they have, clients filter for what they need, and the system converges on compatible versions. Use typed topic metadata to choose streams, rely on capabilities to negotiate versions without additional application-level handshakes, and treat discovery traffic as the lightweight directory for every SpatialDDS deployment.

3.3.1 Topic Naming (Normative)

SpatialDDS topics are identified by a structured name, a type, a version, and a declared Quality-of-Service (QoS) profile. Together these define both what a stream carries and how it behaves on the wire.

Each topic follows this pattern: 

spatialdds/<domain>/<stream>/<type>/<version>
| Segment | Meaning | Example | |----------|----------|----------| | <domain> | Logical app domain | perception | | <stream> | Sensor or stream ID | cam_front | | <type> | Registered data type | video_frame | | <version> | Schema or message version | v1 |

Example
{
  "name": "spatialdds/perception/radar_1/radar_detection/v1",
  "type": "radar_detection",
  "version": "v1",
  "qos_profile": "RADAR_RT"
}
Topic Version Stability (Normative)

The version segment in topic names (e.g., /v1) corresponds to the profile MAJOR version, not the MINOR version. Topic names change only when a profile increments its MAJOR version number. Concretely:

  • spatial.sensing.vision/1.5 → spatial.sensing.vision/1.6: topic names remain /v1 (same MAJOR).
  • spatial.sensing.vision/1.5 → spatial.sensing.vision/2.0: topic names change to /v2 (MAJOR incremented).

Profile MINOR bumps (@extensibility(APPENDABLE) additions) MUST NOT change topic names. This guarantees that consumers subscribing to /v1 topics continue to receive messages after MINOR-version updates without resubscribing.

3.3.2 Typed Topics Registry

Type Typical Payload Notes
geometry_tile 3D tile data (GLB, 3D Tiles) Large, reliable transfers
video_frame Encoded video/image Real-time camera streams
radar_detection Per-frame detection set Structured radar detections
radar_tensor N-D float/int tensor Raw/processed radar data cube
rf_beam Beam sweep power vectors Phased-array beam power measurements
radio_scan Per-scan radio observations WiFi/BLE/UWB/cellular fingerprint observations
seg_mask Binary or PNG mask Frame-aligned segmentation
desc_array Feature descriptor sets Vector or embedding batches
map_meta Map lifecycle descriptor Latched; TRANSIENT_LOCAL
map_alignment Inter-map transform Latched; TRANSIENT_LOCAL
map_event Map lifecycle event Lightweight notifications
spatial_zone Named zone definition Latched; TRANSIENT_LOCAL
spatial_event Spatially-scoped event Typed alerts and anomalies
zone_state Zone occupancy snapshot Periodic dashboard feed
agent_status Agent availability advertisement Latched; TRANSIENT_LOCAL (provisional)
task_offer Agent bid on a task Volatile offer with TTL (provisional)
task_assignment Coordinator task binding Latched; TRANSIENT_LOCAL (provisional)
navsat_status GNSS receiver diagnostics Companion to GeoPose
planned_trajectory Future agent trajectory with waypoints Intent sharing, cooperative planning
entity_binding Cross-topic entity correlation Scene graph construction, digital twins

These registered types ensure consistent topic semantics without altering wire framing. New types can be registered additively through this table or extensions.

Implementations defining custom type and qos_profile values SHOULD follow the naming pattern (myorg.depth_frame, DEPTH_LIVE) and document their DDS QoS mapping.

3.3.3 QoS Profiles

QoS profiles define delivery guarantees and timing expectations for each topic type.

Profile Reliability Ordering Typical Deadline Use Case
GEOM_TILE Reliable Ordered 200 ms 3D geometry, large tile data
VIDEO_LIVE Best-effort Ordered 33 ms Live video feeds
VIDEO_ARCHIVE Reliable Ordered 200 ms Replay or stored media
RADAR_RT Partial Ordered 20 ms Real-time radar data (detections or tensors)
RF_BEAM_RT Best-effort Ordered 20 ms Real-time beam sweep data
RADIO_SCAN_RT Best-effort Ordered 500 ms Radio fingerprint scans (WiFi/BLE/UWB)
SEG_MASK_RT Best-effort Ordered 33 ms Live segmentation masks
DESC_BATCH Reliable Ordered 100 ms Descriptor or feature batches
MAP_META Reliable Ordered 1000 ms Map descriptors, alignments, events
ZONE_META Reliable Ordered 1000 ms Zone definitions, zone state
EVENT_RT Reliable Ordered 100 ms Spatial events and alerts
Notes
  • Each topic advertises its qos_profile during discovery.
  • Profiles capture trade-offs between latency, reliability, and throughput.
  • Implementations may tune low-level DDS settings, but the profile name is canonical.
  • Mixing unrelated data (e.g., radar + video) in a single QoS lane is discouraged.
Discovery and Manifest Integration

Every Announce.topics[] entry and manifest topic reference SHALL include: - type — one of the registered type values - version — the schema or message version - qos_profile — one of the standard or extended QoS names

For each advertised topic, type, version, and qos_profile MUST be present and MUST either match a registered value in this specification or a documented deployment-specific extension.

Consumers use these three keys to match and filter streams without inspecting payload bytes. Brokers and routers SHOULD isolate lanes by (topic, stream_id, qos_profile) to avoid head-of-line blocking.

3.3.4 Coverage Model (Normative)

  • coverage_frame_ref is the canonical frame for an announcement. CoverageElement.frame_ref MAY override it, but SHOULD be used sparingly (e.g., mixed local frames). If absent, consumers MUST use coverage_frame_ref.
  • When coverage_eval_time is present, consumers SHALL evaluate any referenced transforms at that instant before interpreting coverage_frame_ref.
  • When CoverageElement.has_coverage_window is true, the coverage geometry is valid only between coverage_window_start and coverage_window_end. Consumers MUST NOT assume coverage outside this window. Producers advertising moving coverage (e.g., patrol routes, fleet trajectories) SHOULD publish updated Announce messages as coverage windows expire.
  • When has_coverage_window is false, the coverage is persistent (time-invariant) and remains valid until the Announce is superseded or the participant leaves the domain.
  • coverage_eval_time and coverage_window serve different purposes: coverage_eval_time specifies when to evaluate time-varying transforms; coverage_window specifies when the coverage itself is valid. Both MAY be present simultaneously.
  • global == true means worldwide coverage regardless of regional hints. Producers MAY omit bbox, geohash, or elements in that case.
  • When global == false, producers MAY supply any combination of regional hints; consumers SHOULD treat the union of all regions as the effective coverage.
  • Manifests MAY provide any combination of bbox, geohash, and elements. Discovery coverage MAY omit geohash and rely solely on bbox and aabb. Consumers SHALL treat all hints consistently according to the Coverage Model.
  • When has_bbox == true, bbox MUST contain finite coordinates; consumers SHALL reject non-finite values. When has_bbox == false, consumers MUST ignore bbox entirely. Same rules apply to has_aabb and aabb.
  • Earth-fixed frames (fqn rooted at earth-fixed) encode WGS84 longitude/latitude/height. Local frames MUST reference anchors or manifests that describe the transform back to an earth-fixed root (Appendix G).
  • Discovery announces and manifests share the same coverage semantics and flags. CoverageQuery responders SHALL apply these rules consistently when filtering or paginating results.
  • See §2 Conventions for global normative rules.

Earth-fixed roots and local frames

For global interoperability, SpatialDDS assumes that earth-fixed frames (e.g., WGS84 longitude/latitude/height) form the root of the coverage hierarchy. Local frames (for devices, vehicles, buildings, or ships) may appear in coverage elements, but if the coverage is intended to be globally meaningful, these local frames must be relatable to an earth-fixed root through declared transforms or manifests.

Implementations are not required to resolve every local frame at runtime, but when they do, the resulting coverage must be interpretable in an earth-fixed reference frame.

Local-Frame Datasets Without GPS (Informative)

Some datasets and deployments operate entirely in a local metric coordinate frame without a known WGS84 origin. In this case:

  1. The coverage_frame_ref SHOULD reference a local frame (e.g., fqn = "map/local"), not earth-fixed.
  2. GeoPose fields (lat_deg, lon_deg, alt_m) MUST NOT be populated with fabricated values. Use local FrameTransform instead.
  3. The Anchors profile can bridge local and earth-fixed frames when a GPS fix or survey becomes available.
  4. coverage.global MUST be false for local-frame-only deployments.

This is the expected path for indoor robotics, warehouse automation, and datasets recorded without RTK-GPS.

Coverage Evaluation Pseudocode (Informative)

if coverage.global:
    regions = WORLD
else:
    regions = union(bbox, geohash, elements[*].aabb)
frame = coverage_frame_ref unless element.frame_ref present
evaluate transforms at coverage_eval_time if present
Implementation Guidance (Non-Normative)
  • No change to on-wire framing — this metadata lives at the discovery layer.
  • Named QoS profiles simplify cross-vendor interoperability and diagnostics.
  • For custom types, follow the same naming pattern and document new QoS presets.
  • All examples and tables herein are additive.
Discovery recipe (tying the examples together)
  1. Announce — the producer sends Announce (see JSON example above) to advertise caps and topics.
  2. CoverageQuery — the consumer issues a CoverageQuery (see query JSON) to filter by profile, topic type, or QoS.
  3. CoverageResponse — the Discovery producer replies with CoverageResponse (see response JSON), returning results plus an optional next_page_token for pagination.

3.4 Anchors

The Anchors profile provides a structured way to share and update collections of durable, world-locked anchors. While Core includes individual GeoAnchor messages, this profile introduces constructs such as AnchorSet for publishing bundles (e.g., a venue’s anchor pack) and AnchorDelta for lightweight updates. This makes it easy for clients to fetch a set of anchors on startup, stay synchronized through incremental changes, and request full snapshots when needed. Anchors complement VPS results by providing the persistent landmarks that make AR content and multi-device alignment stable across sessions and users.

3.5 Profiles Summary

The complete SpatialDDS IDL bundle is organized into the following profiles:

  • Core Profile
    Fundamental building blocks: pose graphs, geometry tiles, anchors, transforms, and blob transport.
  • Discovery Profile Lightweight announce messages plus active query/response bindings for services, coverage areas, anchors, and spatial content or experiences.
  • Anchors Profile
    Durable anchors and the Anchor Registry, enabling persistent world-locked reference points.

Together, Core, Discovery, and Anchors form the foundation of SpatialDDS, providing the minimal set required for interoperability.

  • Extensions
  • Sensing Module Family: sensing.common defines shared frame metadata, calibration, QoS hints, and codec descriptors. Radar, lidar, and vision profiles inherit those types and layer on their minimal deltas—RadSensorMeta/RadDetectionSet/RadTensorMeta/RadTensorFrame for radar, PointCloud/ScanBlock/return_type for lidar, and ImageFrame/SegMask/FeatureArray for vision. The provisional rf_beam extension adds RfBeamMeta/RfBeamFrame/RfBeamArraySet for phased-array beam power measurements, and the provisional radio extension adds RadioSensorMeta/RadioScan for WiFi/BLE/UWB fingerprint transport. Deployments MAY import the specialized profiles independently but SHOULD declare the sensing.common@1.x dependency when they do.
  • VIO Profile: Raw and fused IMU and magnetometer samples for visual-inertial pipelines.
  • SLAM Frontend Profile: Features, descriptors, and keyframes for SLAM and SfM pipelines.
  • Semantics Profile: 2D and 3D detections for AR occlusion, robotics perception, and analytics.
  • AR+Geo Profile: GeoPose, frame transforms, and geo-anchoring structures for global alignment and persistent AR content.
  • Mapping Profile: Map lifecycle descriptors (MapMeta), extended multi-source edge types, inter-map alignment transforms (MapAlignment), and lifecycle events for multi-agent map exchange.
  • Spatial Events Profile: Typed zone definitions (SpatialZone), spatially-scoped events (SpatialEvent), and periodic zone state summaries (ZoneState) for smart infrastructure and safety monitoring.
  • Provisional Extensions (Optional)
  • Neural Profile: Metadata for neural fields (e.g., NeRFs, Gaussian splats) and optional view-synthesis requests.
  • Agent Profile: Generic task and status messages for AI agents and planners.

Together, these profiles give SpatialDDS the flexibility to support robotics, AR/XR, digital twins, IoT, and AI world models—while ensuring that the wire format remains lightweight, codec-agnostic, and forward-compatible.

Profile Matrix (SpatialDDS 1.6)

Profile Version in 1.6 Status 1.6 Change
spatial.core 1.6 Stable Added PlannedTrajectory, EntityBinding
spatial.discovery 1.6 Stable Added CoverageElement.coverage_window
spatial.sensing.common 1.6 Stable Added COV_ROT3, COV_POSE6_TWIST6, Mat12x12
spatial.manifest 1.6 Stable Manifest schema bumped with spec
spatial.anchors 1.5 Stable No change
spatial.argeo 1.5 Stable No change
spatial.sensing.rad 1.5 Stable No change
spatial.sensing.lidar 1.5 Stable No change
spatial.sensing.vision 1.5 Stable No change
spatial.slam_frontend 1.5 Stable No change
spatial.vio 1.5 Stable No change
spatial.semantics 1.5 Stable No change
spatial.mapping 1.5 Stable No change
spatial.events 1.5 Stable No change
spatial.sensing.rf_beam 1.5 Provisional (Appendix E) No change
spatial.sensing.radio 1.5 Provisional (Appendix E) No change
spatial.neural 1.5 Informative example (Appendix E) Demoted from Provisional — design reference only
spatial.agent 1.5 Informative example (Appendix E) Demoted from Provisional — design reference only

Profiles whose IDL is unchanged in 1.6 retain their /1.5 schema_version and MODULE_ID strings. Topic names continue to use the /v1 segment per §3.3.1 Topic Version Stability — minor profile bumps do not change topic names.

spatial.manifest/1.6 defines the JSON schema for SpatialDDS manifests, not an IDL module. It does not have a corresponding MODULE_ID declaration in the IDL. Provisional profile definitions and examples are specified in Appendix E.

The Sensing module family keeps sensor data interoperable: sensing.common unifies pose stamps, calibration blobs, ROI negotiation, and quality reporting. Radar, lidar, and vision modules extend that base without redefining shared scaffolding, ensuring multi-sensor deployments can negotiate payload shapes and interpret frame metadata consistently.

4. Operational Scenarios

SpatialDDS targets a wide range of operational scenarios — local SLAM, shared anchors, VPS-driven global localization, digital twin aggregation, multi-agent collaborative mapping, zone-scoped event monitoring, and AI/ML grounding. Each profile in this specification is exercised by at least one such scenario; the conformance appendix (Appendix I) records the public datasets used to validate them.

For an informative discussion of how these capabilities serve as a grounding layer for AI world models, see Appendix H. For a long-form digital-twin narrative, see the Mapping and Spatial Events sections of Appendix D and the dataset walkthroughs (S3E, ScanNet, LaMAR) in Appendix I.

5. Conclusion

SpatialDDS provides a lightweight, standards-based framework for exchanging real-world spatial data over DDS. By organizing schemas into modular profiles — with Core, Discovery, and Anchors as the foundation and Extensions adding domain-specific capabilities — it supports everything from SLAM pipelines and AR clients to digital twins, smart city infrastructure, and AI-driven world models. Core elements such as pose graphs, geometry tiles, anchors, and discovery give devices and services a shared language for building and aligning live models of the world. The Mapping and Spatial Events extensions add multi-agent map exchange and zone-based alerting for fleet robotics and smart infrastructure, while provisional extensions like Neural and Agent point toward richer semantics and autonomous agents. Taken together, SpatialDDS positions itself as a practical foundation for real-time spatial computing—interoperable, codec-agnostic, and ready to serve as the data bus for AI and human experiences grounded in the physical world.

6. Future Directions

While SpatialDDS establishes a practical baseline for real-time spatial computing, several areas invite further exploration:

  • Reference Implementations
    Open-source libraries and bridges to existing ecosystems (e.g., ROS 2, OpenXR, OGC APIs) would make it easier for developers to adopt SpatialDDS in robotics, AR, and twin platforms.
  • Semantic Enrichment
    Extending beyond 2D/3D detections and spatial events, future work could align with ontologies, scene graphs, and complex event processing patterns to enable richer machine-readable semantics for AI world models and analytics.
  • Neural Integration
    Provisional support for neural fields (NeRFs, Gaussian splats) could mature into a stable profile, ensuring consistent ways to stream and query neural representations across devices and services.
  • Agent Interoperability
    The Agent extension's fleet coordination types (AgentStatus, TaskOffer, TaskAssignment, TaskHandoff) provide the typed data layer for multi-agent allocation. Future work could formalize common allocation patterns (auction-based, priority-queue, spatial-nearest) as reference implementations while keeping the protocol algorithm-agnostic.
  • Collaborative Mapping
    The Mapping extension enables multi-agent map discovery, alignment, and lifecycle coordination. Future work could formalize map merge protocols, distributed optimization coordination, and standardized map quality benchmarks for fleet-scale deployments.
  • Standards Alignment
    Ongoing coordination with OGC, Khronos, W3C, and GSMA initiatives will help ensure SpatialDDS complements existing geospatial, XR, and telecom standards rather than duplicating them.

Wire-Level Interop Testing

Appendix I validates schema expressiveness through static conformance checks. Future revisions will add wire-level interoperability tests across at least two DDS implementations (CycloneDDS and Fast DDS minimum) to validate end-to-end publish/subscribe fidelity, QoS enforcement, and CDR encoding compatibility.

Transport-Agnostic Semantic Layer

The spatial semantics defined by SpatialDDS — FrameRef-by-UUID, the Coverage Model, manifests, the URI scheme, and the dataset conformance methodology — are conceptually separable from the DDS transport binding. Future work will explore canonical bindings to additional transports (gRPC, MCAP files, Arrow Flight) while preserving the semantic layer unchanged. This would position SpatialDDS as an open semantic standard for spatial data, with DDS as the primary real-time binding and additional bindings for offline recording, cloud integration, and ML pipeline ingestion.

Factor Graph Interchange

SpatialDDS's pose-graph types (Node, Edge, MapMeta) carry SLAM factor graph results. A dedicated factor graph interchange format — analogous to ONNX for neural networks — would enable portable exchange of arbitrary factor graph structures between solvers. SpatialDDS would reference such graphs via BlobRef, with MapMeta carrying optimization state metadata. This is a complementary effort, not a SpatialDDS extension.

Bridges to AI/ML Ecosystems

Priority bridges for connecting SpatialDDS to ML training and inference pipelines:

  • SpatialDDS ↔ MCAP recorder/replayer
  • SpatialDDS ↔ Gymnasium observation space adapter
  • SpatialDDS ↔ ROS 2 bridge (reference implementation)

These bridges are implementation artifacts, not spec extensions. They will be maintained in the SpatialDDS-demo repository.

Together, these directions point toward a future where SpatialDDS is not just a protocol but a foundation for an open, interoperable ecosystem of real-time world models.

We invite implementers, researchers, and standards bodies to explore SpatialDDS, contribute extensions, and help shape it into a shared backbone for real-time spatial computing and AI world models.

7. SpatialDDS URIs

7.1 Why SpatialDDS URIs matter

SpatialDDS URIs are the shorthand that lets participants talk about anchors, content, and services without exchanging the full manifests up front. They bridge human concepts—"the anchor in Hall 1" or "the localization service for Midtown"—with machine-readable manifests that deliver the precise data, coordinate frames, and capabilities needed later in the flow.

7.2 Key ingredients

Every SpatialDDS URI names four ideas:

  • Authority – who owns the namespace and keeps the identifiers stable.
  • Zone – a slice of that authority’s catalog, such as a venue, fleet, or logical shard.
  • Type – whether the reference points to an anchor, a bundle of anchors, a piece of content, or a service endpoint.
  • Identifier (with optional version) – the specific record the manifest will describe.

The exact tokens and encoding rules are defined by the individual profiles, but at a glance the URIs read like spatialdds://authority/zone/type/id;v=version. Readers only need to recognize which part expresses ownership, scope, semantics, and revision so they can reason about the rest of the system.

Formal syntax is given in Appendix F.

7.3 Working with SpatialDDS URIs

Once a URI is known, clients resolve it according to the SpatialURI Resolution rules (§7.5), including the HTTPS/TLS binding (§7.5.5). The manifest reveals everything the client needs to act: anchor poses, dependency graphs for experiences, or how to reach a service. Because URIs remain lightweight, they are easy to pass around in tickets, QR codes, or discovery topics while deferring the heavier data fetch until runtime.

7.4 Examples

spatialdds://museum.example.org/hall1/anchor/01J8QDFQX3W9X4CEX39M9ZP6TQ
spatialdds://city.example.net/downtown/service/01HA7M6XVBTF6RWCGN3X05S0SM;v=2024-q2
spatialdds://studio.example.com/stage/content/01HCQF7DGKKB3J8F4AR98MJ6EH

In the manifest samples later in this specification, each of these identifiers expands into a full JSON manifest. Reviewing those examples shows how a single URI flows from a discovery payload, through manifest retrieval, to runtime consumption.

Authorities SHOULD use DNS hostnames they control to ensure globally unique, delegatable SpatialDDS URIs.

7.5 SpatialURI Resolution (Normative)

This section defines the required baseline mechanism for resolving SpatialDDS URIs to concrete resources (for example, JSON manifests). It does not change any IDL definitions.

7.5.1 Resolution Order (Normative)

When resolving a spatialdds:// URI, a client MUST perform the following steps in order:

  1. Validate syntax — The URI MUST conform to Appendix F.
  2. Local cache — If a valid, unexpired cache entry exists, the client MUST use it.
  3. Advertised resolver — If discovery metadata supplies a resolver endpoint, the client MUST use it.
  4. HTTPS fallback — The client MUST attempt HTTPS resolution as defined below.
  5. Failure — If unresolved, the client MUST treat the resolution as failed.

7.5.2 HTTPS Resolution (Required Baseline)

All SpatialDDS authorities MUST support HTTPS-based resolution.

Resolver Metadata (Normative)

Each authority MUST expose the resolver metadata at:

https://{authority}/.well-known/spatialdds-resolver

Minimum response body:

{
  "authority": "example.com",
  "https_base": "https://example.com/spatialdds/resolve",
  "cache_ttl_sec": 300
}
Resolve Request (Normative)

Clients resolve a SpatialURI via:

GET {https_base}?uri={urlencoded SpatialURI}

Example:

GET https://example.com/spatialdds/resolve?uri=spatialdds://example.com/zone:austin/manifest:vps
Resolve Response (Normative)

On success, servers MUST return:

  • HTTP 200 OK
  • The resolved resource body
  • A correct Content-Type
  • At least one integrity signal (ETag, Digest, or a checksum field in the body)

7.5.3 Error Handling (Normative)

Servers MUST use standard HTTP status codes:

  • 400 invalid URI
  • 404 not found
  • 401 / 403 unauthorized
  • 5xx server error

Clients MUST treat any non-200 response as resolution failure.

7.5.4 Security (Normative)

  • HTTPS resolution MUST use TLS.
  • Authentication MAY be required when advertised.
  • Clients MAY enforce local trust policies.

7.5.5 HTTPS/TLS Binding for URI Resolution (Normative)

  1. If a spatialdds:// URI is resolved using HTTP(S), the client MUST use HTTPS and MUST validate the server’s TLS identity (WebPKI or pinned keys by deployment policy).
  2. If OAuth2 is used, clients SHOULD present bearer tokens using the standard Authorization: Bearer <token> header.
  3. Implementations MAY use a local cache for resolution, but cached artifacts MUST be bound to an authenticated origin (e.g., obtained over HTTPS/TLS or validated signature) and MUST respect TTL/expiration.

7.5.6 Authority Expiry and Resolution Failure (Normative)

When URI resolution fails (DNS failure, HTTP timeout, 404/410 response), clients MUST apply the following fallback chain:

  1. Local cache. If a cached manifest exists and its cache_ttl has not expired, the client MAY use the cached version.
  2. Content-addressed fallback. If the cached manifest or a previously-received BlobRef includes a checksum field, the client MAY resolve the content by checksum from any available content-addressed store (e.g., a peer cache or IPFS gateway). Content-addressed resolution is valid only when the checksum matches.
  3. Graceful degradation. If neither cache nor content-addressed resolution succeeds, the client MUST treat the resource as unavailable. Clients MUST NOT fabricate or guess resource content from expired or failed URIs.

Anchor registries and content providers SHOULD design URIs with long-lived authorities. When authority transfer occurs (e.g., organization merger, domain sale), the new authority SHOULD maintain HTTP redirects from the old authority for at least 12 months.

8. Manifest Schema (Normative)

The manifest schema is versioned as spatial.manifest@MAJOR.MINOR, consistent with the IDL profile scheme.

The manifest schema is defined as the spatial.manifest profile. It uses the same name@MAJOR.MINOR convention as IDL profiles, and spatial.manifest@1.6 is the canonical identifier for this specification.

Manifests describe what a SpatialDDS node or dataset provides: capabilities, coverage, and assets. They are small JSON documents resolved via §7.5 and referenced by discovery announces.

8.1 Common Envelope (Normative)

Every spatial.manifest@1.6 document MUST include the following top-level fields:

Field Type Required Description
id string REQUIRED Unique manifest identifier. MUST be either a UUID or a valid spatialdds:// URI.
profile string REQUIRED MUST be spatial.manifest@1.6.
rtype string REQUIRED Resource type: anchor, anchor_set, content, tileset, service, or stream. Determines the required type-specific block.
caps object OPTIONAL Capabilities block. When present, MUST follow the same structure as discovery Capabilities.
coverage object OPTIONAL Coverage block. When present, MUST follow the Coverage Model (§3.3.4).
assets array OPTIONAL Array of AssetRef objects. Each entry MUST include uri, media_type, and hash.
stamp object OPTIONAL Publication timestamp { "sec": <int>, "nanosec": <int> }.
ttl_sec integer OPTIONAL Cache lifetime hint in seconds. Clients SHOULD NOT use a cached manifest beyond stamp + ttl_sec.
auth object OPTIONAL Authentication hints, consistent with auth_hint semantics in §3.3.

Validation rules (Normative):

  • Unknown top-level fields MUST be ignored by consumers (forward compatibility).
  • profile MUST match spatial.manifest@1.<minor> where <minor> ≥ 5. Consumers SHOULD accept any minor ≥ 5 within major 1.
  • When coverage is present, it MUST follow all normative rules from §3.3.4, including has_bbox/has_aabb presence flags and finite coordinate requirements.
  • assets[].hash MUST use the format <algorithm>:<hex> (e.g., sha256:3af2...).

Envelope example (Informative) 

{
  "id": "spatialdds://museum.example.org/hall1/anchor/main-entrance",
  "profile": "spatial.manifest@1.6",
  "rtype": "anchor",
  "stamp": { "sec": 1714070400, "nanosec": 0 },
  "ttl_sec": 3600
}

8.2 Type-Specific Blocks (Normative)

Each rtype value requires a corresponding top-level object with type-specific content. The key name matches the rtype value.

8.2.1 anchor — Single Anchor Manifest

Field Type Required Description
anchor.anchor_id string REQUIRED Matches GeoAnchor.anchor_id.
anchor.geopose object REQUIRED GeoPose with lat_deg, lon_deg, alt_m, q (x,y,z,w), frame_kind, frame_ref.
anchor.method string OPTIONAL Localization method (e.g., Surveyed, GNSS, VisualFix).
anchor.confidence number OPTIONAL 0..1.
anchor.frame_ref object REQUIRED FrameRef for the anchor's local frame.
anchor.checksum string OPTIONAL Integrity hash for the anchor data. 
{
  "id": "spatialdds://museum.example.org/hall1/anchor/main-entrance",
  "profile": "spatial.manifest@1.6",
  "rtype": "anchor",
  "anchor": {
    "anchor_id": "main-entrance",
    "geopose": {
      "lat_deg": 37.7934,
      "lon_deg": -122.3941,
      "alt_m": 12.6,
      "q": [0.0, 0.0, 0.0, 1.0],
      "frame_kind": "ENU",
      "frame_ref": {
        "uuid": "fc6a63e0-99f7-445b-9e38-0a3c8a0c1234",
        "fqn": "earth-fixed"
      }
    },
    "method": "Surveyed",
    "confidence": 0.98,
    "frame_ref": {
      "uuid": "6c2333a0-8bfa-4b43-9ad9-7f22ee4b0001",
      "fqn": "museum/hall1/map"
    }
  },
  "coverage": {
    "frame_ref": { "uuid": "ae6f0a3e-7a3e-4b1e-9b1f-0e9f1b7c1a10", "fqn": "earth-fixed" },
    "has_bbox": true,
    "bbox": [-122.395, 37.793, -122.393, 37.794],
    "global": false
  },
  "stamp": { "sec": 1714070400, "nanosec": 0 },
  "ttl_sec": 86400
}

8.2.2 anchor_set — Anchor Bundle Manifest

Field Type Required Description
anchor_set.set_id string REQUIRED Matches AnchorSet.set_id.
anchor_set.title string OPTIONAL Human-readable name.
anchor_set.provider_id string OPTIONAL Publishing organization.
anchor_set.version string OPTIONAL Set version string.
anchor_set.anchors array REQUIRED Array of anchor objects (same schema as anchor block above, without the envelope).
anchor_set.center_lat number OPTIONAL Approximate center latitude.
anchor_set.center_lon number OPTIONAL Approximate center longitude.
anchor_set.radius_m number OPTIONAL Approximate coverage radius in meters.

8.2.3 service — Service Manifest

Field Type Required Description
service.service_id string REQUIRED Matches Announce.service_id.
service.kind string REQUIRED One of VPS, MAPPING, RELOCAL, SEMANTICS, STORAGE, CONTENT, ANCHOR_REGISTRY, OTHER.
service.name string OPTIONAL Human-readable service name.
service.org string OPTIONAL Operating organization.
service.version string OPTIONAL Service version.
service.connection object OPTIONAL DDS connection hints (see below).
service.topics array OPTIONAL Array of TopicMeta-shaped objects describing available topics.

service.connection fields

Field Type Required Description
domain_id integer OPTIONAL DDS domain ID.
partitions array of string OPTIONAL DDS partitions.
initial_peers array of string OPTIONAL DDS peer locators. 
{
  "id": "spatialdds://city.example.net/downtown/service/vps-main;v=2024-q2",
  "profile": "spatial.manifest@1.6",
  "rtype": "service",
  "service": {
    "service_id": "vps-main",
    "kind": "VPS",
    "name": "Downtown Visual Positioning",
    "org": "city.example.net",
    "version": "2024-q2",
    "connection": {
      "domain_id": 42,
      "partitions": ["city/downtown"],
      "initial_peers": ["udpv4://10.0.1.50:7400"]
    },
    "topics": [
      { "name": "spatialdds/vps/cam_front/video_frame/v1", "type": "video_frame", "version": "v1", "qos_profile": "VIDEO_LIVE" }
    ]
  },
  "caps": {
    "supported_profiles": [
      { "name": "core", "major": 1, "min_minor": 0, "max_minor": 5 },
      { "name": "discovery", "major": 1, "min_minor": 0, "max_minor": 5 }
    ],
    "features": ["blob.crc32"]
  },
  "coverage": {
    "frame_ref": { "uuid": "ae6f0a3e-7a3e-4b1e-9b1f-0e9f1b7c1a10", "fqn": "earth-fixed" },
    "has_bbox": true,
    "bbox": [-122.420, 37.790, -122.410, 37.800],
    "global": false
  },
  "stamp": { "sec": 1714070400, "nanosec": 0 },
  "ttl_sec": 3600
}

8.2.4 content — Content / Experience Manifest

Field Type Required Description
content.content_id string REQUIRED Matches ContentAnnounce.content_id.
content.title string OPTIONAL Human-readable title.
content.summary string OPTIONAL Brief description.
content.tags array of string OPTIONAL Searchable tags.
content.class_id string OPTIONAL Content classification.
content.dependencies array of string OPTIONAL Array of spatialdds:// URIs required before use.
content.available_from object OPTIONAL Time object — content is not valid before this.
content.available_until object OPTIONAL Time object — content expires after this.

8.2.5 tileset — Tileset Manifest

Field Type Required Description
tileset.tileset_id string REQUIRED Unique tileset identifier.
tileset.encoding string REQUIRED Tile encoding (e.g., glTF+Draco, 3DTiles, MPEG-PCC).
tileset.frame_ref object REQUIRED FrameRef for the tileset's coordinate frame.
tileset.version string OPTIONAL Tileset version.
tileset.lod_levels integer OPTIONAL Number of LOD levels.
tileset.tile_count integer OPTIONAL Total tile count (informative hint).

8.2.6 stream — Stream Manifest

Field Type Required Description
stream.stream_id string REQUIRED Matches the stream_id used in sensing profiles.
stream.topic object REQUIRED TopicMeta-shaped object.
stream.connection object OPTIONAL Same schema as service.connection.

8.3 JSON Schema (Normative)

An official JSON Schema for spatial.manifest@1.6 is published at:

https://spatialdds.org/schemas/manifest/1.6/spatial-manifest.schema.json

Manifests MAY include a $schema field pointing to this URL for self-description.

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://spatialdds.org/schemas/manifest/1.6/spatial-manifest.schema.json",
  "title": "SpatialDDS Manifest 1.6",
  "type": "object",
  "required": ["id", "profile", "rtype"],
  "properties": {
    "id": { "type": "string" },
    "profile": { "type": "string", "pattern": "^spatial\\.manifest@1\\.[5-9][0-9]*$" },
    "rtype": { "type": "string", "enum": ["anchor", "anchor_set", "content", "tileset", "service", "stream"] },
    "caps": { "$ref": "#/$defs/Capabilities" },
    "coverage": { "$ref": "#/$defs/Coverage" },
    "assets": { "type": "array", "items": { "$ref": "#/$defs/AssetRef" } },
    "stamp": { "$ref": "#/$defs/Time" },
    "ttl_sec": { "type": "integer", "minimum": 0 },
    "auth": { "type": "object" }
  },
  "oneOf": [
    { "properties": { "rtype": { "const": "anchor" } }, "required": ["anchor"] },
    { "properties": { "rtype": { "const": "anchor_set" } }, "required": ["anchor_set"] },
    { "properties": { "rtype": { "const": "service" } }, "required": ["service"] },
    { "properties": { "rtype": { "const": "content" } }, "required": ["content"] },
    { "properties": { "rtype": { "const": "tileset" } }, "required": ["tileset"] },
    { "properties": { "rtype": { "const": "stream" } }, "required": ["stream"] }
  ],
  "additionalProperties": true
}

8.4 Field Notes (Normative)

  • Capabilities (caps) — declares supported profiles and feature flags. Peers use this to negotiate versions.
  • Coverage (coverage) — See §3.3.4 Coverage Model (Normative). Coverage blocks in manifests and discovery announces share the same semantics. See §2 Conventions for global normative rules.
  • Frame identity. The uuid field is authoritative; fqn is a human-readable alias. Consumers SHOULD match frames by UUID and MAY show fqn in logs or UIs. See Appendix G for the full FrameRef model.
  • Assets (assets) — URIs referencing external content. Each has a uri, media_type, and hash.
  • All orientation fields follow the quaternion order defined in §2.1.

8.5 Practical Guidance (Informative)

  • Keep manifests small and cacheable; they are for discovery, not bulk metadata.
  • When multiple frames exist, use one manifest per frame for clarity.
  • Use HTTPS, DDS, or file URIs interchangeably — the uri scheme is transport-agnostic.
  • Assets should prefer registered media types for interoperability.

8.6 Summary (Informative)

Manifests give every SpatialDDS resource a compact, self-describing identity. They express what exists, where it is, and how to reach it.

9. Glossary of Acronyms

AI – Artificial Intelligence

AR – Augmented Reality

DDS – Data Distribution Service (OMG standard middleware)

DNS-SD – DNS-Based Service Discovery (IETF RFC 6763)

GSMA – GSM Association (global mobile industry group)

IMU – Inertial Measurement Unit

IoT – Internet of Things

MR – Mixed Reality

MSF – Metaverse Standards Forum

NeRF – Neural Radiance Field (neural representation of 3D scenes)

OGC – Open Geospatial Consortium

OMG – Object Management Group (standards body for DDS)

ROS – Robot Operating System

SfM – Structure from Motion

SLAM – Simultaneous Localization and Mapping

VIO – Visual-Inertial Odometry

VLM – Vision-Language Model

VPS – Visual Positioning Service

VR – Virtual Reality

W3C – World Wide Web Consortium

XR – Extended Reality (umbrella term including AR, VR, MR)

10. References

DDS & Middleware

[1] Object Management Group. Data Distribution Service (DDS) for Real-Time Systems. OMG Standard. Available: https://www.omg.org/spec/DDS

[2] Object Management Group. DDS for eXtremely Resource Constrained Environments (DDS-XRCE). OMG Standard. Available: https://www.omg.org/spec/DDS-XRCE

[3] eProsima. Fast DDS Documentation. Available: https://fast-dds.docs.eprosima.com

[4] Eclipse Foundation. Cyclone DDS. Available: https://projects.eclipse.org/projects/iot.cyclonedds

XR & Spatial Computing

[5] Khronos Group. OpenXR Specification. Available: https://www.khronos.org/openxr

[6] Open Geospatial Consortium. OGC GeoPose 1.0 Data Exchange Standard. Available: https://www.ogc.org/standards/geopose

Geospatial Standards

[7] Open Geospatial Consortium. CityGML Standard. Available: https://www.ogc.org/standards/citygml

[8] Geohash. Wikipedia Entry. Available: https://en.wikipedia.org/wiki/Geohash

[8a] Cheshire, S. & Krochmal, M. DNS-Based Service Discovery. IETF RFC 6763. Available: https://www.rfc-editor.org/rfc/rfc6763

[8b] Gulbrandsen, A., Vixie, P., & Esibov, L. A DNS RR for specifying the location of services (DNS SRV). IETF RFC 2782. Available: https://www.rfc-editor.org/rfc/rfc2782

SLAM, SfM & AI World Models

[9] Mur-Artal, R., Montiel, J. M. M., & Tardós, J. D. (2015). ORB-SLAM: A Versatile and Accurate Monocular SLAM System. IEEE Transactions on Robotics, 31(5), 1147–1163.

[10] Schönberger, J. L., & Frahm, J.-M. (2016). Structure-from-Motion Revisited. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4104–4113.

[11] Sarlin, P.-E., Unagar, A., Larsson, M., et al. (2020). From Coarse to Fine: Robust Hierarchical Localization at Large Scale. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 12716–12725.

[12] Google Research. ARCore Geospatial API & Visual Positioning Service. Developer Documentation. Available: https://developers.google.com/ar

Appendix A: Core Profile

The Core profile defines the fundamental data structures for SpatialDDS. It includes pose graphs, 3D geometry tiles, anchors, transforms, and generic blob transport. This is the minimal interoperable baseline for exchanging world models across devices and services.

Common Type Aliases (Normative)

// SPDX-License-Identifier: MIT
// SpatialDDS Common Type Aliases 1.6

#ifndef SPATIAL_COMMON_TYPES_INCLUDED
#define SPATIAL_COMMON_TYPES_INCLUDED

module builtin {
  @extensibility(APPENDABLE) struct Time {
    int32 sec;      // seconds since UNIX epoch (UTC)
    uint32 nanosec; // nanoseconds [0, 1e9)
  };
};

module spatial {
  module common {
    typedef double BBox2D[4];
    typedef double Aabb3D[6];
    typedef double Vec3[3];
    typedef double Mat3x3[9];
    typedef double Mat6x6[36];
    typedef double Mat12x12[144];
    typedef double QuaternionXYZW[4];  // GeoPose order (x, y, z, w)

    enum CovarianceType {
      @value(0) COV_NONE,
      @value(3) COV_POS3,           // 3x3 position covariance
      @value(6) COV_POSE6,          // 6x6 pose covariance (pos + rot)
      @value(9) COV_ROT3,           // 3x3 orientation-only covariance (added in 1.6)
      @value(12) COV_POSE6_TWIST6   // 12x12 pose + velocity covariance (added in 1.6)
    };

    // Stable, typo-proof frame identity shared across all profiles.
    // Equality is by uuid; fqn is a normalized, human-readable alias.
    @extensibility(APPENDABLE) struct FrameRef {
      string uuid;  // REQUIRED: stable identifier for the frame
      string fqn;   // REQUIRED: normalized FQN, e.g., "oarc/rig01/cam_front"
    };

    // Optional namespaced metadata bag for asset descriptors.
    @extensibility(APPENDABLE) struct MetaKV {
      string namespace;  // e.g., "sensing.vision.features"
      string json;       // JSON object string; producer-defined for this namespace
    };

    // Uniform contract for asset references, covering fetch + integrity.
    @extensibility(APPENDABLE) struct AssetRef {
      string uri;          // required: how to fetch
      string media_type;   // required: IANA or registry-friendly type (with params)
      string hash;         // required: e.g., "sha256:<hex>"
      sequence<MetaKV, 16> meta;  // optional: zero or more namespaced bags
    };
  };
};

#endif // SPATIAL_COMMON_TYPES_INCLUDED

Core Module

// SPDX-License-Identifier: MIT
// SpatialDDS Core 1.6

#ifndef SPATIAL_COMMON_TYPES_INCLUDED
#include "types.idl"
#endif

module spatial {
  module core {

    // Module identity (authoritative string for interop)
    const string MODULE_ID = "spatial.core/1.6";

    // ---------- Utility ----------
    // Expose builtin Time under spatial::core
    typedef builtin::Time Time;

    @extensibility(APPENDABLE) struct PoseSE3 {
      spatial::common::Vec3 t;               // translation (x,y,z)
      spatial::common::QuaternionXYZW q;     // quaternion (x,y,z,w) in GeoPose order
    };

    @extensibility(APPENDABLE) struct Aabb3 {
      spatial::common::Vec3 min_xyz;
      spatial::common::Vec3 max_xyz;
    };

    @extensibility(APPENDABLE) struct TileKey {
      @key uint32 x;     // tile coordinate (quadtree/3D grid)
      @key uint32 y;
      @key uint32 z;     // use 0 for 2D schemes
      @key uint8  level; // LOD level
    };

    // ---------- Geometry ----------
    enum PatchOp {
      @value(0) ADD,
      @value(1) REPLACE,
      @value(2) REMOVE
    };

    @extensibility(APPENDABLE) struct BlobRef {
      string blob_id;   // UUID or content-address
      string role;      // "mesh","attr/normals","pcc/geom","pcc/attr",...
      string checksum;  // SHA-256 (hex)
    };

    typedef spatial::common::FrameRef FrameRef;

    @extensibility(APPENDABLE) struct TileMeta {
      @key TileKey key;              // unique tile key
      boolean has_tile_id_compat;
      string  tile_id_compat;        // optional human-readable id
      spatial::common::Vec3 min_xyz; // AABB min (local frame)
      spatial::common::Vec3 max_xyz; // AABB max (local frame)
      uint32 lod;                    // may mirror key.level
      uint64 version;                // monotonic full-state version
      string encoding;               // "glTF+Draco","MPEG-PCC","V3C","PLY",...
      string checksum;               // checksum of composed tile
      sequence<string, 32> blob_ids; // blobs composing this tile
      // optional geo hints
      boolean has_centroid_llh;
      spatial::common::Vec3  centroid_llh; // lat,lon,alt (deg,deg,m)
      boolean has_radius_m;
      double  radius_m;              // rough extent (m)
      string schema_version;         // MUST be "spatial.core/1.6"
    };

    @extensibility(APPENDABLE) struct TilePatch {
      @key TileKey key;              // which tile
      uint64 revision;               // monotonic per-tile
      PatchOp op;                    // ADD/REPLACE/REMOVE
      string target;                 // submesh/attr/"all"
      sequence<BlobRef, 8> blobs;    // payload refs
      string post_checksum;          // checksum after apply
      Time   stamp;                  // production time
    };

    @extensibility(APPENDABLE) struct BlobChunk {
      // Composite key: (blob_id, index) uniquely identifies a chunk instance.
      @key string blob_id;   // which blob
      @key uint32 index;     // chunk index (0..N-1)
      uint32 total_chunks;   // total number of chunks expected for this blob_id
      uint32 crc32;          // CRC32 checksum over 'data'
      boolean last;          // true when this is the final chunk for blob_id
      sequence<uint8, 262144> data; // ≤256 KiB per sample
    };

    // ---------- Pose Graph (minimal) ----------
    enum EdgeTypeCore {
      @value(0) ODOM,
      @value(1) LOOP
    };
    // NOTE: The mapping extension profile (spatial.mapping/1.5) defines
    // mapping::EdgeType which extends EdgeTypeCore with additional constraint
    // types (INTER_MAP, GPS, ANCHOR, IMU_PREINT, GRAVITY, PLANE, SEMANTIC,
    // MANUAL). Values 0-1 are identical. Core consumers MAY downcast
    // mapping::Edge to core::Edge by treating unknown edge types as LOOP.

    // Discriminated union: exactly one covariance payload (or none) is serialized.
    @extensibility(APPENDABLE) union CovMatrix switch (spatial::common::CovarianceType) {
      case spatial::common::COV_NONE: uint8 none;
      case spatial::common::COV_POS3:  spatial::common::Mat3x3 pos;
      case spatial::common::COV_POSE6: spatial::common::Mat6x6 pose;
      case spatial::common::COV_ROT3:  spatial::common::Mat3x3 rot;          // added in 1.6
      case spatial::common::COV_POSE6_TWIST6: spatial::common::Mat12x12 pose_twist; // added in 1.6
    };

    // A metric pose bundled with its coordinate frame, uncertainty, and
    // timestamp. This is the self-contained local-coordinate counterpart
    // to GeoPose (which uses geographic coordinates). Use FramedPose
    // wherever a "located pose" is needed — it avoids scattering pose,
    // frame_ref, cov, and stamp across sibling fields on the parent struct.
    @extensibility(APPENDABLE) struct FramedPose {
      PoseSE3   pose;       // translation + orientation
      FrameRef  frame_ref;  // which coordinate frame this pose is expressed in
      CovMatrix cov;        // uncertainty (COV_NONE when absent)
      Time      stamp;      // when this pose was valid
    };

    // PlannedTrajectory carries a sequence of future poses representing
    // an agent's intended path. It is the planning counterpart to a
    // perception trajectory: both are sequences of poses, but a
    // PlannedTrajectory extends into the future and is conditioned on
    // intended actions rather than past measurements.
    //
    // Consumers MAY use PlannedTrajectory for:
    // - Cooperative collision avoidance (avoid other agents' plans)
    // - Intent visualization in digital twins
    // - Predictive resource allocation (anticipate coverage changes)
    //
    // Topic: spatialdds/<scene>/plan/<agent_id>/trajectory/v1
    // QoS:   EVENT_RT (RELIABLE, ordered, ~100 ms deadline)
    @extensibility(APPENDABLE) struct PlannedWaypoint {
      PoseSE3   pose;                   // planned pose at this waypoint
      Time      stamp;                  // planned arrival time
      boolean   has_velocity;
      spatial::common::Vec3 velocity;   // planned velocity (m/s)
      boolean   has_uncertainty;
      float     position_uncertainty_m; // 1-sigma position uncertainty
      boolean   has_confidence;
      float     confidence;             // planner confidence [0,1]
    };

    @extensibility(APPENDABLE) struct PlannedTrajectory {
      @key string agent_id;             // who is planning
      string  plan_id;                  // unique plan identifier (changes on replan)
      uint32  plan_revision;            // increments on each replan

      FrameRef frame_ref;               // coordinate frame for all waypoints

      sequence<PlannedWaypoint, 256> waypoints;

      // Planning metadata
      boolean has_goal_pose;
      PoseSE3 goal_pose;                // final destination (may differ from last waypoint)

      boolean has_horizon_sec;
      float   horizon_sec;              // planning horizon in seconds

      boolean has_replan_rate_hz;
      float   replan_rate_hz;           // how often the plan is updated

      Time    stamp;                    // when this plan was computed
      string  schema_version;           // "spatial.core/1.6"
    };

    // EntityBinding correlates messages across different SpatialDDS topics
    // that refer to the same physical entity. It is the minimal primitive
    // for scene graph construction: consumers receive bindings and assemble
    // their own entity hierarchies.
    //
    // Example: a fused track, a geometry tile, and a SpatialZone occupant
    // entry all describe the same vehicle. An EntityBinding links them
    // by their respective keys.
    //
    // Topic: spatialdds/<scene>/entity/binding/v1
    // QoS:   RELIABLE + TRANSIENT_LOCAL (late joiners receive current bindings)
    @extensibility(APPENDABLE) struct ComponentRef {
      string topic;                     // topic name (e.g., "spatialdds/.../fusion/track/v1")
      string key;                       // key value on that topic (e.g., track_id = "fused-42")
    };

    @extensibility(APPENDABLE) struct EntityBinding {
      @key string entity_id;            // stable entity identifier

      string entity_class;              // semantic class (e.g., "vehicle", "pedestrian", "drone")

      sequence<ComponentRef, 16> components; // references to messages on other topics

      // Optional: entity's authoritative pose (convenience — same as the pose
      // on one of the component topics, but saves a lookup).
      boolean    has_pose;
      FramedPose pose;

      Time   stamp;
      string source_id;                 // who published this binding (e.g., fusion service)
      string schema_version;            // "spatial.core/1.6"
    };

    @extensibility(APPENDABLE) struct Node {
      string map_id;
      @key string node_id;     // unique keyframe id
      PoseSE3 pose;            // pose in frame_ref
      CovMatrix cov;           // covariance payload (COV_NONE when absent)
      Time    stamp;
      FrameRef frame_ref;      // e.g., "map"
      string  source_id;
      uint64  seq;             // per-source monotonic
      uint64  graph_epoch;     // for major rebases/merges
    };

    @extensibility(APPENDABLE) struct Edge {
      string map_id;
      @key string edge_id;     // unique edge id
      string from_id;          // source node
      string to_id;            // target node
      EdgeTypeCore type;       // ODOM or LOOP
      PoseSE3 T_from_to;       // relative transform from->to
      spatial::common::Mat6x6 information; // 6x6 info matrix (row-major)
      Time   stamp;
      string source_id;
      uint64 seq;
      uint64 graph_epoch;
    };

    // ---------- Geo anchoring ----------
    enum GeoFrameKind {
      @value(0) ECEF,
      @value(1) ENU,
      @value(2) NED
    };

    @extensibility(APPENDABLE) struct GeoPose {
      double lat_deg;
      double lon_deg;
      double alt_m;            // ellipsoidal meters
      spatial::common::QuaternionXYZW q; // orientation (x,y,z,w) in GeoPose order
      GeoFrameKind frame_kind; // ECEF/ENU/NED
      FrameRef frame_ref;      // for ENU/NED: canonical frame reference
      Time   stamp;
      // Exactly one covariance payload will be present based on the discriminator.
      CovMatrix cov;
    };

    // ---------- GNSS diagnostics ----------
    enum GnssFixType {
      @value(0) NO_FIX,         // unable to determine position
      @value(1) FIX_2D,         // 2D fix (no altitude)
      @value(2) FIX_3D,         // 3D autonomous fix
      @value(3) DGPS,           // differential GPS correction
      @value(4) RTK_FLOAT,      // RTK float solution
      @value(5) RTK_FIXED,      // RTK fixed (integer ambiguity resolved)
      @value(6) SBAS,           // satellite-based augmentation (WAAS/EGNOS)
      @value(7) DEAD_RECKONING, // DR-only (no satellite fix)
      @value(8) UNKNOWN_FIX     // status not yet determined
    };

    module GnssService {
      const uint16 GPS     = 0x0001;
      const uint16 GLONASS = 0x0002;
      const uint16 BEIDOU  = 0x0004;  // includes COMPASS
      const uint16 GALILEO = 0x0008;
      const uint16 QZSS    = 0x0010;
      const uint16 IRNSS   = 0x0020;  // NavIC
      const uint16 SBAS_SV = 0x0040;  // SBAS ranging SVs
    };

    @extensibility(APPENDABLE) struct NavSatStatus {
      @key string gnss_id;      // receiver identifier, matches GeoPose stream

      GnssFixType fix_type;     // current fix quality
      uint16      service;      // GnssService bitmask: which constellations
      uint16      num_satellites; // SVs used in fix

      // Dilution of precision (from GSA sentence)
      boolean has_dop;
      float   pdop;             // position DOP  (valid when has_dop)
      float   hdop;             // horizontal DOP (valid when has_dop)
      float   vdop;             // vertical DOP   (valid when has_dop)

      // Ground velocity (from RMC sentence)
      boolean has_velocity;
      float   speed_mps;        // speed over ground, m/s (valid when has_velocity)
      float   course_deg;       // course over ground, degrees true north (valid when has_velocity)

      // Differential correction metadata (from GGA fields 13-14)
      boolean has_diff_age;
      float   diff_age_s;       // age of differential correction, seconds
      uint16  diff_station_id;  // reference station ID

      Time   stamp;             // should match the associated GeoPose.stamp
      string schema_version;    // e.g., "1.6.0"
    };

    @extensibility(APPENDABLE) struct GeoAnchor {
      @key string anchor_id;   // e.g., "anchor/4th-and-main"
      string map_id;
      FrameRef frame_ref;      // local frame (e.g., "map")
      GeoPose geopose;         // global pose
      string  method;          // "GNSS","VisualFix","Surveyed","Fusion"
      double  confidence;      // 0..1
      string  checksum;        // integrity/versioning
    };

    @extensibility(APPENDABLE) struct FrameTransform {
      @key string transform_id; // e.g., "map->ENU@lat,lon,alt"
      FrameRef parent_ref;      // global frame (ENU@..., ECEF, ...)
      FrameRef child_ref;       // local frame ("map")
      PoseSE3 T_parent_child;   // transform parent->child
      Time    stamp;
      CovMatrix cov;             // covariance payload (COV_NONE when absent)
    };

    // ---------- Snapshot / Catch-up ----------
    @extensibility(APPENDABLE) struct SnapshotRequest {
      @key TileKey key;        // which tile
      uint64 up_to_revision;   // 0 = latest
    };

    @extensibility(APPENDABLE) struct SnapshotResponse {
      @key TileKey key;                 // tile key
      uint64 revision;                  // snapshot revision served
      sequence<string, 64> blob_ids;    // composing blobs
      string checksum;                  // composed state checksum
    };

  }; // module core
};   // module spatial

Appendix B: Discovery Profile

The Discovery profile defines the lightweight announce messages and manifests that allow services, coverage areas, and spatial content or experiences to be discovered at runtime. It enables SpatialDDS deployments to remain decentralized while still providing structured service discovery.

SpatialDDS Discovery operates at two levels. The DDS binding (defined by the IDL types below) provides on-bus announce, query, and response topics for low-latency discovery within a DDS domain. The HTTP binding (§3.3.0, HTTP Discovery Search Binding) provides an equivalent spatial query interface over HTTPS for clients that have not yet joined a DDS domain. Both bindings share the same coverage semantics (§3.3.4) and return compatible result types — the DDS binding returns Announce samples in CoverageResponse, while the HTTP binding returns service manifests (§8.2.3) that carry the same information plus DDS connection hints. Higher-level service catalogues (such as OSCP's Spatial Service Discovery Systems) may store, index, or federate SpatialDDS manifests and URIs on top of either binding.

See Appendix F.X (Discovery Query Expression) for the normative grammar used by CoverageQuery.expr filters.

// SPDX-License-Identifier: MIT
// SpatialDDS Discovery 1.6
// Lightweight announces for services, coverage, and content

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif

module spatial {
  module disco {

    // Asset references (middle-ground model) reuse the shared spatial::common
    // types so that manifests and discovery share a single contract.
    typedef spatial::common::MetaKV  MetaKV;
    typedef spatial::common::AssetRef AssetRef;

    const string MODULE_ID = "spatial.discovery/1.6";

    typedef builtin::Time Time;
    typedef spatial::core::Aabb3 Aabb3;
    typedef spatial::core::FrameRef FrameRef;
    typedef spatial::core::PoseSE3 PoseSE3;
    // Canonical manifest references use the spatialdds:// URI scheme.
    typedef string SpatialUri;

    // --- Profile version advertisement (additive) ---
    // Semver per profile: name@MAJOR.MINOR
    // Each row declares a contiguous range of MINORs within a single MAJOR.
    @extensibility(APPENDABLE) struct ProfileSupport {
      string name;        // e.g., "core", "discovery", "sensing.common", "sensing.rad"
      uint32 major;       // compatible major (e.g., 1)
      uint32 min_minor;   // lowest supported minor within 'major' (e.g., 0)
      uint32 max_minor;   // highest supported minor within 'major' (e.g., 2)  // supports 1.0..1.2
      boolean preferred;  // optional tie-breaker hint (usually false)
    };

    // --- Optional feature flags (namespaced strings, e.g., "blob.crc32", "rad.tensor.zstd") ---
    @extensibility(APPENDABLE) struct FeatureFlag {
      string name;
    };

    // --- Capabilities advertised in-band on the discovery bus ---
    @extensibility(APPENDABLE) struct Capabilities {
      sequence<ProfileSupport, 64> supported_profiles;
      sequence<string, 32>         preferred_profiles; // e.g., ["discovery@1.2","core@1.6"]
      sequence<FeatureFlag, 64>    features;           // optional feature flags
    };

    // --- Topic metadata to enable selection without parsing payloads ---
    @extensibility(APPENDABLE) struct TopicMeta {
      string name;        // e.g., "spatialdds/perception/cam_front/video_frame/v1"
      string type;        // geometry_tile | video_frame | radar_detection | radar_tensor | seg_mask | desc_array | rf_beam
      string version;     // currently fixed to "v1"
      string qos_profile; // GEOM_TILE | VIDEO_LIVE | RADAR_RT | RF_BEAM_RT | SEG_MASK_RT | DESC_BATCH
      // type, version, and qos_profile are mandatory fields describing the
      // topic’s semantic type and QoS profile.
      // optional advisory hints (topic-level, not per-message)
      float target_rate_hz;
      uint32  max_chunk_bytes;
    };

    enum ServiceKind {
      @value(0) VPS,
      @value(1) MAPPING,
      @value(2) RELOCAL,
      @value(3) SEMANTICS,
      @value(4) STORAGE,
      @value(5) CONTENT,
      @value(6) ANCHOR_REGISTRY,
      @value(7) OTHER
    };

    @extensibility(APPENDABLE) struct KV {
      string key;
      string value;
    };

    // coverage_frame_ref is the canonical frame for an announcement. CoverageElement.frame_ref MAY override it sparingly.
    // If coverage_frame_ref is earth-fixed, bbox is [west,south,east,north] in degrees (EPSG:4326/4979); otherwise coordinates
    // are in local meters.
    @extensibility(APPENDABLE) struct CoverageElement {
      string type;              // "bbox" | "volume"
      boolean has_crs;
      string  crs;              // optional CRS identifier for earth-fixed frames (e.g., EPSG code)

      // Presence flags indicate which geometry payloads are provided.
      // When has_bbox == true, bbox MUST contain finite coordinates; consumers SHALL reject non-finite values.
      boolean has_bbox;
      spatial::common::BBox2D bbox; // [west, south, east, north]

      // When has_aabb == true, aabb MUST contain finite coordinates; consumers SHALL reject non-finite values.
      boolean has_aabb;
      Aabb3  aabb;              // axis-aligned bounds in the declared frame

      // Explicit global coverage toggle: when true, bbox/aabb may be ignored by consumers.
      boolean global;
      // Optional per-element frame override. If has_frame_ref == false, this element MUST use coverage_frame_ref.
      boolean has_frame_ref;
      FrameRef frame_ref;       // Use sparingly to mix a few local frames within one announcement.

      // Time-varying coverage (optional, added in 1.6)
      // When present, coverage is valid only within this window.
      // Absent means coverage is persistent / time-invariant.
      boolean has_coverage_window;
      Time    coverage_window_start;
      Time    coverage_window_end;
    };

    // Validity window for time-bounded transforms.
    @extensibility(APPENDABLE) struct ValidityWindow {
      Time   from;              // inclusive start time
      uint32 seconds;           // duration from 'from'
    };

    // Quaternion follows GeoPose: unit [x,y,z,w]; pose maps FROM 'from' TO 'to'
    @extensibility(APPENDABLE) struct Transform {
      FrameRef from;            // source frame (e.g., "map")
      FrameRef to;              // target frame (e.g., "earth-fixed")
      PoseSE3 pose;             // maps from 'from' into 'to' (parent → child)
      Time    stamp;            // publication timestamp
      boolean has_validity;     // when true, 'validity' bounds the transform
      ValidityWindow validity;  // explicit validity window
    };

    @extensibility(APPENDABLE) struct Announce {
      @key string service_id;
      string name;
      ServiceKind kind;
      string version;
      string org;
      sequence<KV,32> hints;
      // New: wire-level capability advertisement for version negotiation.
      Capabilities caps;                 // in-band capabilities (profiles + features)
      sequence<TopicMeta,128> topics;    // topic list with typed-topic metadata
      sequence<CoverageElement,16> coverage;
      FrameRef coverage_frame_ref;      // canonical frame consumers should use when evaluating coverage
      boolean has_coverage_eval_time;
      Time    coverage_eval_time;       // evaluate time-varying transforms at this instant when interpreting coverage_frame_ref
      sequence<Transform,8> transforms;
      SpatialUri manifest_uri;  // MUST be a spatialdds:// URI for this service manifest
      string auth_hint;
      Time stamp;
      uint32 ttl_sec;
    };

    @extensibility(APPENDABLE) struct CoverageHint {
      @key string service_id;
      sequence<CoverageElement,16> coverage;
      FrameRef coverage_frame_ref;
      boolean has_coverage_eval_time;
      Time    coverage_eval_time;       // evaluate transforms at this instant when interpreting coverage_frame_ref
      sequence<Transform,8> transforms;
      Time stamp;
      uint32 ttl_sec;
    };

    @extensibility(APPENDABLE) struct CoverageFilter {
      sequence<string,16> type_in;        // match any of these topic types
      sequence<string,16> qos_profile_in; // match any of these QoS profiles
      sequence<string,16> module_id_in;   // match any of these module IDs
    };

    @extensibility(APPENDABLE) struct CoverageQuery {
      // Correlates responses to a specific query instance.
      @key string query_id;
      sequence<CoverageElement,4> coverage;  // requested regions of interest
      FrameRef coverage_frame_ref;
      boolean has_coverage_eval_time;
      Time    coverage_eval_time;       // evaluate transforms at this instant when interpreting coverage_frame_ref
      // Structured filter (preferred in 1.5).
      boolean has_filter;
      CoverageFilter filter;
      // Deprecated in 1.5: freeform expression per Appendix F.X.
      // Responders MUST ignore expr if has_filter == true.
      // Example: "type==\"radar_tensor\" && module_id==\"spatial.sensing.rad/1.5\""
      string expr;
      // Discovery responders publish CoverageResponse samples to this topic.
      string reply_topic;
      Time stamp;
      uint32 ttl_sec;
    };

    @extensibility(APPENDABLE) struct ContentAnnounce {
      @key string content_id;
      string provider_id;
      string title;
      string summary;
      sequence<string,16> tags;
      string class_id;
      SpatialUri manifest_uri;  // MUST be a spatialdds:// URI for this content manifest
      sequence<CoverageElement,16> coverage;
      FrameRef coverage_frame_ref;
      boolean has_coverage_eval_time;
      Time    coverage_eval_time;
      sequence<Transform,8> transforms;
      Time available_from;
      Time available_until;
      Time stamp;
      uint32 ttl_sec;
    };

    @extensibility(APPENDABLE) struct CoverageResponse {
      string query_id;                    // Mirrors CoverageQuery.query_id for correlation.
      sequence<Announce,256> results;     // Result page (caps + typed topics)
      string next_page_token;             // Empty when no further pages remain.
    };

    @extensibility(APPENDABLE) struct Depart {
      @key string service_id;
      Time stamp;
    };

  }; // module disco
};

Appendix C: Anchor Registry Profile

The Anchors profile defines durable GeoAnchors and the Anchor Registry. Anchors act as persistent world-locked reference points, while the registry makes them discoverable and maintainable across sessions, devices, and services.

// SPDX-License-Identifier: MIT
// SpatialDDS Anchors 1.5
// Bundles and updates for anchor registries

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif

module spatial {
  module anchors {
    const string MODULE_ID = "spatial.anchors/1.5";

    typedef builtin::Time Time;
    typedef spatial::core::GeoPose GeoPose;
    typedef spatial::core::FrameRef FrameRef;

    @extensibility(APPENDABLE) struct AnchorEntry {
      @key string anchor_id;
      string name;
      GeoPose geopose;
      double confidence;
      sequence<string,8> tags;
      Time stamp;
      string checksum;
    };

    @extensibility(APPENDABLE) struct AnchorSet {
      @key string set_id;
      string title;
      string provider_id;
      FrameRef map_frame;
      string version;
      sequence<string,16> tags;
      double center_lat; double center_lon; double radius_m;
      sequence<AnchorEntry,256> anchors;
      Time stamp;
      string checksum;
    };

    enum AnchorOp {
      @value(0) ADD,
      @value(1) UPDATE,
      @value(2) REMOVE
    };

    @extensibility(APPENDABLE) struct AnchorDelta {
      @key string set_id;
      AnchorOp op;
      AnchorEntry entry;
      uint64 revision;
      Time stamp;
      string post_checksum;
    };

    @extensibility(APPENDABLE) struct AnchorSetRequest {
      @key string set_id;
      uint64 up_to_revision;
    };

    @extensibility(APPENDABLE) struct AnchorSetResponse {
      @key string set_id;
      uint64 revision;
      AnchorSet set;
    };

  }; // module anchors
};

Appendix D: Extension Profiles

These extensions provide domain-specific capabilities beyond the Core profile. The Sensing Common module supplies reusable sensing metadata, ROI negotiation structures, and codec/payload descriptors that the specialized sensor profiles build upon. The VIO profile carries raw and fused IMU/magnetometer samples. The Vision profile shares camera metadata, encoded frames, and optional feature tracks for perception pipelines. The SLAM Frontend profile adds features and keyframes for SLAM and SfM pipelines. The Semantics profile allows 2D and 3D object detections to be exchanged for AR, robotics, and analytics use cases. The Radar profile provides detection-centric radar metadata and per-frame detection sets, plus a tensor transport path for raw or processed radar data cubes used in ISAC and ML workloads. The Lidar profile transports compressed point clouds, associated metadata, and optional detections for mapping and perception workloads. The AR+Geo profile adds GeoPose, frame transforms, and geo-anchoring structures, which allow clients to align local coordinate systems with global reference frames and support persistent AR content. The Mapping profile provides map lifecycle metadata, multi-source pose graph edge types, inter-map alignment primitives, and lifecycle events for multi-agent collaborative mapping. The Spatial Events profile provides typed, spatially-scoped events, zone definitions, and zone state summaries for smart infrastructure alerting, anomaly detection, and capacity management.

Common type aliases and geometry primitives are defined once in Appendix A. Extension modules import those shared definitions and MUST NOT re-declare them.

Sensing Common Extension

Shared base types, enums, and ROI negotiation utilities reused by all sensing profiles (radar, lidar, vision). 

// SPDX-License-Identifier: MIT
// SpatialDDS Sensing Common 1.6 (Extension module)

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif

module spatial { module sensing { module common {

  const string MODULE_ID = "spatial.sensing.common/1.6";

  // --- Standard sizing tiers ---
  // Use these to bound sequences for detections and other per-frame arrays.
  const uint32 SZ_TINY   = 64;
  const uint32 SZ_SMALL  = 256;
  const uint32 SZ_MEDIUM = 2048;
  const uint32 SZ_LARGE  = 8192;
  const uint32 SZ_XL     = 32768;

  // Reuse Core primitives (time, pose, blob references)
  typedef builtin::Time    Time;
  typedef spatial::core::PoseSE3 PoseSE3;
  typedef spatial::core::BlobRef BlobRef;
  typedef spatial::common::FrameRef FrameRef;

  // ---- Axes & Regions (for tensors or scans) ----
  enum AxisEncoding {
    @value(0) AXIS_CENTERS,
    @value(1) AXIS_LINSPACE
  };

  // Compact parametric axis definition
  @extensibility(APPENDABLE) struct Linspace {
    double start;   // first point
    double step;    // spacing (may be negative for descending axes)
    uint32 count;   // number of samples (>=1)
  };

  // Discriminated union: carries only one encoding on wire
  @extensibility(APPENDABLE) union AxisSpec switch (AxisEncoding) {
    case AXIS_CENTERS:  sequence<double, 65535> centers;
    case AXIS_LINSPACE: Linspace lin;
  };

  @extensibility(APPENDABLE) struct Axis {
    string   name;    // "range","azimuth","elevation","doppler","time","channel"
    string   unit;    // "m","deg","m/s","Hz","s",...
    AxisSpec spec;    // encoding of the axis samples (centers or linspace)
  };

  @extensibility(APPENDABLE) struct ROI {
    // Range bounds (meters). When has_range == false, consumers MUST ignore range_min/range_max.
    boolean has_range;
    float   range_min;
    float   range_max;

    // Azimuth bounds (degrees). When has_azimuth == false, azimuth bounds are unspecified.
    boolean has_azimuth;
    float   az_min;
    float   az_max;

    // Elevation bounds (degrees). When has_elevation == false, elevation bounds are unspecified.
    boolean has_elevation;
    float   el_min;
    float   el_max;

    // Doppler bounds (m/s). When has_doppler == false, doppler_min/doppler_max are unspecified.
    boolean has_doppler;
    float   dop_min;
    float   dop_max;

    // Image-plane ROI for vision (pixels). When has_image_roi == false, u/v bounds are unspecified.
    boolean has_image_roi;
    int32   u_min;
    int32   v_min;
    int32   u_max;
    int32   v_max;

    // Indicates this ROI covers the entire valid domain of its axes. When true, all numeric bounds may be ignored.
    boolean global;
  };

  // ---- Codecs / Payload kinds (shared enums) ----
  enum Codec {
    @value(0)  CODEC_NONE,
    @value(1)  LZ4,
    @value(2)  ZSTD,
    @value(3)  GZIP,
    @value(10) DRACO,     // geometry compression
    @value(20) JPEG,
    @value(21) H264,
    @value(22) H265,
    @value(23) AV1,
    @value(40) FP8Q,
    @value(41) FP4Q,
    @value(42) AE_V1
  };

  enum PayloadKind {
    @value(0)  DENSE_TILES,    // tiled dense blocks (e.g., tensor tiles)
    @value(1)  SPARSE_COO,     // sparse indices + values
    @value(2)  LATENT,         // learned latent vectors
    @value(10) BLOB_GEOMETRY,  // PCC/PLY/glTF+Draco
    @value(11) BLOB_RASTER     // JPEG/GOP chunk(s)
  };

  enum SampleType {        // post-decode voxel/point sample type
    @value(0) U8_MAG,
    @value(1) F16_MAG,
    @value(2) CF16,
    @value(3) CF32,
    @value(4) MAGPHASE_S8
  };

  // ---- Stream identity & calibration header shared by sensors ----
  @extensibility(APPENDABLE) struct StreamMeta {
    @key string stream_id;        // stable id for this sensor stream
    FrameRef frame_ref;           // mounting frame (Core frame naming)
    PoseSE3  T_bus_sensor;        // extrinsics (sensor in bus frame)
    double   nominal_rate_hz;     // advertised cadence
    string   schema_version;      // MUST be "spatial.sensing.common/1.6"
  };

  // ---- Frame index header shared by sensors (small, on-bus) ----
  @extensibility(APPENDABLE) struct FrameHeader {
    @key string stream_id;
    uint64 frame_seq;
    Time   t_start;
    Time   t_end;
    // Optional sensor pose at acquisition (moving platforms)
    boolean has_sensor_pose;
    PoseSE3 sensor_pose;
    // data pointers: heavy bytes referenced as blobs
    sequence<BlobRef, SZ_SMALL> blobs;
  };

  // ---- Quality & health (uniform across sensors) ----
  enum Health {
    @value(0) OK,
    @value(1) DEGRADED,
    @value(2) ERROR
  };

  @extensibility(APPENDABLE) struct FrameQuality {
    boolean has_snr_db;
    float   snr_db;          // valid when has_snr_db == true
    float   percent_valid;   // 0..100
    Health health;
    string note;             // short diagnostic
  };

  // ---- ROI request/reply (control-plane pattern) ----
  @extensibility(APPENDABLE) struct ROIRequest {
    @key string stream_id;
    uint64 request_id;
    Time   t_start; Time t_end;
    ROI    roi;
    boolean wants_payload_kind; PayloadKind desired_payload_kind;
    boolean wants_codec;       Codec       desired_codec;
    boolean wants_sample_type; SampleType  desired_sample_type;
    int32  max_bytes;          // -1 for unlimited
  };

  @extensibility(APPENDABLE) struct ROIReply {
    @key string stream_id;
    uint64 request_id;
    // Typically returns new frames whose blobs contain only the ROI
    sequence<spatial::sensing::common::FrameHeader, 64> frames;
  };

}; }; };

Standard Sequence Bounds (Normative)

Payload Recommended Bound Rationale
2D Detections (per frame) SZ_MEDIUM (2048) Typical object detectors
3D Detections (LiDAR) SZ_SMALL (256) Clusters/objects, not raw points
Radar Detections (micro-dets) SZ_XL (32768) Numerous sparse returns per frame
Keypoints/Tracks (per frame) SZ_LARGE (8192) Feature-rich frames

Producers SHOULD choose the smallest tier that covers real workloads; exceeding these bounds requires a new profile minor.

Axis Encoding (Normative)

The Axis struct embeds a discriminated union to ensure only one encoding is transmitted on the wire.

enum AxisEncoding { AXIS_CENTERS = 0, AXIS_LINSPACE = 1 };
@extensibility(APPENDABLE) struct Linspace { double start; double step; uint32 count; };
@extensibility(APPENDABLE) union AxisSpec switch (AxisEncoding) {
  case AXIS_CENTERS:  sequence<double, 65535> centers;
  case AXIS_LINSPACE: Linspace lin;
  default: ;
};
@extensibility(APPENDABLE) struct Axis { string name; string unit; AxisSpec spec; };
  • AXIS_CENTERS — Explicit sample positions carried as double values.
  • AXIS_LINSPACE — Uniform grid defined by start + i * step for i ∈ [0, count‑1].
  • Negative step indicates descending axes.
  • count MUST be ≥ 1 and step * (count – 1) + start yields the last coordinate.

IDL Tooling Notes (Non-Consecutive Enums)

Several enumerations in the SpatialDDS 1.6 profiles use intentionally sparse or non-consecutive numeric values. These enums are designed for forward extensibility (e.g., reserving ranges for future codecs, layouts, or pixel formats). Because of this, certain DDS toolchains (including Cyclone DDS’s idlc) may emit non-fatal warnings such as:

“enum literal values are not consecutive”

These warnings do not indicate a schema error. All affected enums are valid IDL4.x and interoperable on the wire.

The intentionally sparse enums are: - CovarianceType (types.idl) - Codec (common.idl) - PayloadKind (common.idl) - RigRole (vision.idl) - RadSensorType (rad.idl) - RadTensorLayout (rad.idl) - CloudEncoding (lidar.idl) - ColorSpace (vision.idl) - PixFormat (vision.idl)

No changes are required for implementers. These warnings may be safely ignored.

VIO / Inertial Extension

Raw IMU/mag samples, 9-DoF bundles, and fused state outputs.

// SPDX-License-Identifier: MIT
// SpatialDDS VIO/Inertial 1.5

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif

module spatial {
  module vio {

    const string MODULE_ID = "spatial.vio/1.5";

    typedef builtin::Time Time;
    typedef spatial::common::FrameRef FrameRef;

    // IMU calibration
    @extensibility(APPENDABLE) struct ImuInfo {
      @key string imu_id;
      FrameRef frame_ref;
      double accel_noise_density;    // (m/s^2)/√Hz
      double gyro_noise_density;     // (rad/s)/√Hz
      double accel_random_walk;      // (m/s^3)/√Hz
      double gyro_random_walk;       // (rad/s^2)/√Hz
      Time   stamp;
    };

    // Raw IMU sample
    @extensibility(APPENDABLE) struct ImuSample {
      @key string imu_id;
      spatial::common::Vec3 accel;   // m/s^2
      spatial::common::Vec3 gyro;    // rad/s
      Time   stamp;
      string source_id;
      uint64 seq;
    };

    // Magnetometer
    @extensibility(APPENDABLE) struct MagnetometerSample {
      @key string mag_id;
      spatial::common::Vec3 mag;     // microtesla
      Time   stamp;
      FrameRef frame_ref;
      string source_id;
      uint64 seq;
    };

    // Convenience raw 9-DoF bundle
    @extensibility(APPENDABLE) struct SensorFusionSample {
      @key string fusion_id;         // e.g., device id
      spatial::common::Vec3 accel;   // m/s^2
      spatial::common::Vec3 gyro;    // rad/s
      spatial::common::Vec3 mag;     // microtesla
      Time   stamp;
      FrameRef frame_ref;
      string source_id;
      uint64 seq;
    };

    // Fused state (orientation ± position)
    enum FusionMode {
      @value(0) ORIENTATION_3DOF,
      @value(1) ORIENTATION_6DOF,
      @value(2) POSE_6DOF
    };
    enum FusionSourceKind {
      @value(0) EKF,
      @value(1) AHRS,
      @value(2) VIO_FUSED,
      @value(3) IMU_ONLY,
      @value(4) MAG_AIDED,
      @value(5) AR_PLATFORM
    };

    @extensibility(APPENDABLE) struct FusedState {
      @key string fusion_id;
      FusionMode       mode;
      FusionSourceKind source_kind;

      spatial::common::QuaternionXYZW q; // quaternion (x,y,z,w) in GeoPose order
      boolean has_position;
      spatial::common::Vec3 t;       // meters, in frame_ref

      boolean has_gravity;
      spatial::common::Vec3  gravity;    // m/s^2
      boolean has_lin_accel;
      spatial::common::Vec3  lin_accel;  // m/s^2
      boolean has_gyro_bias;
      spatial::common::Vec3  gyro_bias;  // rad/s
      boolean has_accel_bias;
      spatial::common::Vec3  accel_bias; // m/s^2

      boolean has_cov_orient;
      spatial::common::Mat3x3  cov_orient; // 3x3 covariance
      boolean has_cov_pos;
      spatial::common::Mat3x3  cov_pos;    // 3x3 covariance

      Time   stamp;
      FrameRef frame_ref;
      string source_id;
      uint64 seq;
      double quality;                // 0..1
    };

  }; // module vio
};

Vision Extension

Camera intrinsics, video frames, and keypoints/tracks for perception and analytics pipelines. ROI semantics follow §2 Conventions for global normative rules; axes use the Sensing Common AXIS_CENTERS/AXIS_LINSPACE union encoding. See §2 Conventions for global normative rules.

Pre-Rectified Images (Normative)
When images have been rectified (undistorted) before publication, producers MUST set dist = NONE, dist_params to an empty sequence, and model = PINHOLE. Consumers receiving dist = NONE MUST NOT apply any distortion correction.

Image Dimensions (Normative)
CamIntrinsics.width and CamIntrinsics.height are REQUIRED and MUST be populated from the actual image dimensions. A VisionMeta sample with width = 0 or height = 0 is malformed and consumers MAY reject it.

Distortion Model Mapping (Informative)
Vision uses CamModel + Distortion, while SLAM Frontend uses DistortionModelKind. Implementers bridging the two SHOULD map as follows:

Vision SLAM Frontend Notes
Distortion.NONE DistortionModelKind.NONE No distortion
Distortion.RADTAN DistortionModelKind.RADTAN Brown-Conrady
Distortion.KANNALA_BRANDT DistortionModelKind.KANNALA_BRANDT Fisheye
CamModel.FISHEYE_EQUIDISTANT DistortionModelKind.EQUIDISTANT Equivalent naming 
// SPDX-License-Identifier: MIT
// SpatialDDS Vision (sensing.vision) 1.5 — Extension profile

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif
#ifndef SPATIAL_SENSING_COMMON_INCLUDED
#define SPATIAL_SENSING_COMMON_INCLUDED
#include "common.idl"
#endif

module spatial { module sensing { module vision {

  // Module identifier for discovery and schema registration
  const string MODULE_ID = "spatial.sensing.vision/1.5";

  // Reuse Core + Sensing Common
  typedef builtin::Time                      Time;
  typedef spatial::core::PoseSE3                   PoseSE3;
  typedef spatial::core::BlobRef                   BlobRef;
  typedef spatial::common::FrameRef               FrameRef;

  typedef spatial::sensing::common::Codec          Codec;        // JPEG/H264/H265/AV1, etc.
  typedef spatial::sensing::common::PayloadKind    PayloadKind;  // use BLOB_RASTER for frames/GOPs
  typedef spatial::sensing::common::SampleType     SampleType;
  typedef spatial::sensing::common::Axis           Axis;
  typedef spatial::sensing::common::ROI            ROI;
  typedef spatial::sensing::common::StreamMeta     StreamMeta;
  typedef spatial::sensing::common::FrameHeader    FrameHeader;
  typedef spatial::sensing::common::FrameQuality   FrameQuality;
  typedef spatial::sensing::common::ROIRequest     ROIRequest;
  typedef spatial::sensing::common::ROIReply       ROIReply;

  // ROI bounds follow Sensing Common presence flags.
  // Axis samples are encoded via the Sensing Common union (AXIS_CENTERS or AXIS_LINSPACE).

  // Camera / imaging specifics
  enum CamModel {
    @value(0) PINHOLE,
    @value(1) FISHEYE_EQUIDISTANT,
    @value(2) KB_4,
    @value(3) OMNI
  };
  enum Distortion {
    @value(0) NONE,
    @value(1) RADTAN,
    @value(2) KANNALA_BRANDT
  };
  enum PixFormat {
    @value(0)  UNKNOWN,
    @value(1)  YUV420,
    @value(2)  RGB8,
    @value(3)  BGR8,
    @value(4)  RGBA8,
    @value(5)  DEPTH16,           // 16-bit unsigned depth image (mm)
    @value(10) RAW10,
    @value(12) RAW12,
    @value(16) RAW16
  };
  // DEPTH16 frames carry per-pixel unsigned 16-bit depth values. The default
  // unit is millimeters (range 0-65535 mm ~= 0-65.5 m), consistent with
  // OpenNI, RealSense SDK, and ARKit conventions. A value of 0 denotes no
  // measurement (invalid/missing depth). Publishers using a different unit
  // (e.g., 100 um for sub-millimeter sensors) MUST include a depth_unit key in
  // the corresponding VisionMeta.base.attributes sequence with json value
  // {"unit": "100um"} (or "mm", "m"). When depth_unit is absent, consumers
  // MUST assume millimeters.
  //
  // A depth stream is published as a separate VisionMeta + VisionFrame pair
  // with pix = DEPTH16 and a stream_id distinct from the co-located color
  // stream. The rig_id field links the depth and color streams for spatial
  // alignment; CamIntrinsics carries the depth camera's intrinsic calibration.
  // For sensors with factory-aligned depth and color (e.g., iPhone LiDAR),
  // both streams share the same CamIntrinsics and FrameRef.
  enum ColorSpace {
    @value(0)  SRGB,
    @value(1)  REC709,
    @value(2)  REC2020,
    @value(10) LINEAR
  };
  enum RigRole {
    @value(0) LEFT,
    @value(1) RIGHT,
    @value(2) CENTER,
    @value(3) FRONT,
    @value(4) FRONT_LEFT,
    @value(5) FRONT_RIGHT,
    @value(6) BACK,
    @value(7) BACK_LEFT,
    @value(8) BACK_RIGHT,
    @value(9) AUX,
    @value(10) PANORAMIC,          // stitched panoramic view
    @value(11) EQUIRECTANGULAR     // equirectangular 360° projection
  };

  @extensibility(APPENDABLE) struct CamIntrinsics {
    CamModel model;
    uint16 width;  uint16 height;       // REQUIRED: image dimensions in pixels
    float fx; float fy; float cx; float cy;
    Distortion dist;                    // NONE for pre-rectified images
    sequence<float,16> dist_params;     // empty when dist == NONE
    float shutter_us;                   // exposure time
    float readout_us;                   // rolling-shutter line time (0=global)
    PixFormat pix;  ColorSpace color;
    string calib_version;               // hash or tag
  };

  // Static description — RELIABLE + TRANSIENT_LOCAL (late joiners receive the latest meta)
  @extensibility(APPENDABLE) struct VisionMeta {
    @key string stream_id;
    StreamMeta base;                    // frame_ref, T_bus_sensor, nominal_rate_hz
    CamIntrinsics K;                    // intrinsics
    RigRole role;                       // for stereo/rigs
    string rig_id;                      // shared id across synchronized cameras

    // Default payload (frames ride as blobs)
    Codec codec;                        // JPEG/H264/H265/AV1 or NONE
    PixFormat pix;                      // for RAW payloads
    ColorSpace color;
    string schema_version;              // MUST be "spatial.sensing.vision/1.5"
  };

  // Per-frame index — BEST_EFFORT + KEEP_LAST=1 (large payloads referenced via blobs)
  @extensibility(APPENDABLE) struct VisionFrame {
    @key string stream_id;
    uint64 frame_seq;

    FrameHeader hdr;                    // t_start/t_end, optional sensor_pose, blobs[]

    // May override meta per-frame
    Codec codec;
    PixFormat pix;
    ColorSpace color;

    boolean has_line_readout_us;
    float   line_readout_us;            // valid when has_line_readout_us == true
    boolean rectified;                  // true if pre-rectified to pinhole
    boolean is_key_frame;               // true if this frame is a selected keyframe

    FrameQuality quality;               // shared health/SNR notes
  };

  // Optional lightweight derivatives (for VIO/SfM/analytics)
  @extensibility(APPENDABLE) struct Keypoint2D { float u; float v; float score; };
  @extensibility(APPENDABLE) struct Track2D {
    uint64 id;
    sequence<Keypoint2D, spatial::sensing::common::SZ_LARGE> trail;
  };

  // Detections topic — BEST_EFFORT
  @extensibility(APPENDABLE) struct VisionDetections {
    @key string stream_id;
    uint64 frame_seq;
    Time   stamp;
    sequence<Keypoint2D, spatial::sensing::common::SZ_LARGE> keypoints;
    sequence<Track2D, spatial::sensing::common::SZ_MEDIUM>    tracks;
    // Masks/boxes can be added in Semantics profile to keep Vision lean
  };

}; }; };

SLAM Frontend Extension

Per-keyframe features, matches, landmarks, tracks, and camera calibration.

// SPDX-License-Identifier: MIT
// SpatialDDS SLAM Frontend 1.5

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif

module spatial {
  module slam_frontend {

    const string MODULE_ID = "spatial.slam_frontend/1.5";

    // Reuse core: Time, etc.
    typedef builtin::Time Time;
    typedef spatial::common::FrameRef FrameRef;

    // Camera calibration
    enum DistortionModelKind {
      @value(0) NONE,
      @value(1) RADTAN,
      @value(2) EQUIDISTANT,
      @value(3) KANNALA_BRANDT
    };

    @extensibility(APPENDABLE) struct CameraInfo {
      @key string camera_id;
      uint32 width;  uint32 height;   // pixels
      double fx; double fy;           // focal (px)
      double cx; double cy;           // principal point (px)
      DistortionModelKind dist_kind;
      sequence<double, 8> dist;       // model params (bounded)
      FrameRef frame_ref;             // camera frame
      Time   stamp;                   // calib time (or 0 if static)
    };

    // 2D features & descriptors per keyframe
    @extensibility(APPENDABLE) struct Feature2D {
      double u; double v;     // pixel coords
      float  scale;           // px
      float  angle;           // rad [0,2π)
      float  score;           // detector response
    };

    @extensibility(APPENDABLE) struct KeyframeFeatures {
      @key string node_id;                  // keyframe id
      string camera_id;
      string desc_type;                     // "ORB32","BRISK64","SPT256Q",...
      uint32 desc_len;                      // bytes per descriptor
      boolean row_major;                    // layout hint
      sequence<Feature2D, 4096> keypoints;  // ≤4096
      sequence<uint8, 1048576> descriptors; // ≤1 MiB packed bytes
      Time   stamp;
      string source_id;
      uint64 seq;
    };

    // Optional cross-frame matches
    @extensibility(APPENDABLE) struct FeatureMatch {
      string node_id_a;  uint32 idx_a;
      string node_id_b;  uint32 idx_b;
      float  score;      // similarity or distance
    };

    @extensibility(APPENDABLE) struct MatchSet {
      @key string match_id;                // e.g., "kf_12<->kf_18"
      sequence<FeatureMatch, 8192> matches;
      Time   stamp;
      string source_id;
    };

    // Sparse 3D landmarks & tracks (optional)
    @extensibility(APPENDABLE) struct Landmark {
      @key string lm_id;
      string map_id;
      spatial::common::Vec3 p;
      boolean has_cov;
      spatial::common::Mat3x3  cov;        // 3x3 pos covariance (row-major)
      sequence<uint8, 4096> desc;          // descriptor bytes
      string desc_type;
      Time   stamp;
      string source_id;
      uint64 seq;
    };

    @extensibility(APPENDABLE) struct TrackObs {
      string node_id;                      // observing keyframe
      double u; double v;                  // pixel coords
    };

    @extensibility(APPENDABLE) struct Tracklet {
      @key string track_id;
      boolean has_lm_id;                   // true when lm_id is populated
      string  lm_id;                       // link to Landmark when present
      sequence<TrackObs, 64> obs;          // ≤64 obs
      string source_id;
      Time   stamp;
    };

  }; // module slam_frontend
};

Semantics / Perception Extension

2D detections tied to keyframes; 3D oriented boxes in world frames (optionally tiled).

Size Convention (Normative)
Detection3D.size is the extent of the oriented bounding box in the object's local frame (center + q):
size[0] = width (local X), size[1] = height (local Z), size[2] = depth (local Y).
All values are in meters and MUST be non-negative. For datasets that use (width, length, height), map as (width, height, length).

// SPDX-License-Identifier: MIT
// SpatialDDS Semantics 1.5

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif
#ifndef SPATIAL_SENSING_COMMON_INCLUDED
#define SPATIAL_SENSING_COMMON_INCLUDED
#include "common.idl"
#endif

module spatial {
  module semantics {

    const string MODULE_ID = "spatial.semantics/1.5";

    typedef builtin::Time Time;
    typedef spatial::core::TileKey TileKey;
    typedef spatial::common::FrameRef FrameRef;

    // 2D detections per keyframe (image space)
    @extensibility(APPENDABLE) struct Detection2D {
      @key string det_id;       // unique per publisher
      string node_id;           // keyframe id
      string camera_id;         // camera
      string class_id;          // ontology label
      float  score;             // [0..1]
      spatial::common::BBox2D bbox; // [u_min,v_min,u_max,v_max] (px)
      boolean has_mask;         // if a pixel mask exists
      string  mask_blob_id;     // BlobChunk ref (role="mask")
      Time   stamp;
      string source_id;
    };

    @extensibility(APPENDABLE) struct Detection2DSet {
      @key string set_id;                 // batch id (e.g., node_id + seq)
      string node_id;
      string camera_id;
      sequence<Detection2D, spatial::sensing::common::SZ_SMALL> dets;    // ≤256
      Time   stamp;
      string source_id;
    };

    // 3D detections in world/local frame (scene space)
    @extensibility(APPENDABLE) struct Detection3D {
      @key string det_id;
      FrameRef frame_ref;        // e.g., "map" (pose known elsewhere)
      boolean has_tile;
      TileKey tile_key;          // valid when has_tile = true

      string class_id;           // semantic label
      float  score;              // [0..1]

      // Oriented bounding box in frame_ref
      spatial::common::Vec3 center;      // m
      spatial::common::Vec3 size;        // (width, height, depth) in meters; see Size Convention
      spatial::common::QuaternionXYZW q; // orientation (x,y,z,w) in GeoPose order

      // Uncertainty (optional)
      boolean has_covariance;
      spatial::common::Mat3x3 cov_pos; // 3x3 position covariance (row-major)
      spatial::common::Mat3x3 cov_rot; // 3x3 rotation covariance (row-major)

      // Optional instance tracking
      boolean has_track_id;
      string  track_id;

      Time   stamp;
      string source_id;

      // Optional attribute key-value pairs
      boolean has_attributes;
      sequence<spatial::common::MetaKV, 8> attributes; // valid when has_attributes == true

      // Occlusion / visibility (0.0 = fully occluded, 1.0 = fully visible)
      boolean has_visibility;
      float   visibility;                // valid when has_visibility == true

      // Evidence counts
      boolean has_num_pts;
      uint32  num_lidar_pts;             // valid when has_num_pts == true
      uint32  num_radar_pts;             // valid when has_num_pts == true
    };

    @extensibility(APPENDABLE) struct Detection3DSet {
      @key string set_id;                 // batch id
      FrameRef frame_ref;                 // common frame for the set
      boolean has_tile;
      TileKey tile_key;                   // valid when has_tile = true
      sequence<Detection3D, spatial::sensing::common::SZ_SMALL> dets;    // ≤256
      Time   stamp;
      string source_id;
    };

  }; // module semantics
};

Radar Extension

Radar metadata, per-frame detection sets, and raw/processed tensor transport. The detection-centric path (RadSensorMeta / RadDetectionSet) serves automotive-style point detections. The tensor path (RadTensorMeta / RadTensorFrame) serves raw or processed radar data cubes for ISAC and ML workloads. Deployments may use either or both. ROI semantics follow §2 Conventions for global normative rules. See §2 Conventions for global normative rules.

// SPDX-License-Identifier: MIT
// SpatialDDS Radar (sensing.rad) 1.5 - Extension profile
// Detection-centric radar for automotive, industrial, and robotics sensors.

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif
#ifndef SPATIAL_SENSING_COMMON_INCLUDED
#define SPATIAL_SENSING_COMMON_INCLUDED
#include "common.idl"
#endif

module spatial { module sensing { module rad {

  // Module identifier for discovery and schema registration
  const string MODULE_ID = "spatial.sensing.rad/1.5";

  // Reuse Core + Sensing Common types
  typedef builtin::Time                          Time;
  typedef spatial::core::PoseSE3                 PoseSE3;
  typedef spatial::core::BlobRef                 BlobRef;
  typedef spatial::common::FrameRef              FrameRef;

  typedef spatial::sensing::common::Codec        Codec;
  typedef spatial::sensing::common::StreamMeta   StreamMeta;
  typedef spatial::sensing::common::FrameHeader  FrameHeader;
  typedef spatial::sensing::common::FrameQuality FrameQuality;
  typedef spatial::sensing::common::Axis         Axis;
  typedef spatial::sensing::common::SampleType   SampleType;
  typedef spatial::sensing::common::PayloadKind  PayloadKind;
  typedef spatial::sensing::common::ROI          ROI;
  typedef spatial::sensing::common::ROIRequest   ROIRequest;
  typedef spatial::sensing::common::ROIReply     ROIReply;

  // ---- Radar sensor type ----
  enum RadSensorType {
    @value(0) SHORT_RANGE,       // e.g., corner/parking radar, ~30m
    @value(1) MEDIUM_RANGE,      // e.g., blind-spot, ~80m
    @value(2) LONG_RANGE,        // e.g., forward-facing, ~200m+
    @value(3) IMAGING_4D,        // 4D imaging radar (range/az/el/doppler)
    @value(4) SAR,               // synthetic aperture radar
    @value(255) OTHER
  };

  // ---- Dynamic property per detection ----
  enum RadDynProp {
    @value(0) UNKNOWN,
    @value(1) MOVING,
    @value(2) STATIONARY,
    @value(3) ONCOMING,
    @value(4) CROSSING_LEFT,
    @value(5) CROSSING_RIGHT,
    @value(6) STOPPED            // was moving, now stationary
  };

  // ---- Static sensor description ----
  // RELIABLE + TRANSIENT_LOCAL (late joiners receive the latest meta)
  @extensibility(APPENDABLE) struct RadSensorMeta {
    @key string stream_id;               // stable id for this radar stream
    StreamMeta base;                     // frame_ref, T_bus_sensor, nominal_rate_hz

    RadSensorType sensor_type;           // range class of this radar

    // Detection-space limits (from sensor datasheet)
    boolean has_range_limits;
    float   min_range_m;                 // valid when has_range_limits == true
    float   max_range_m;

    boolean has_azimuth_fov;
    float   az_fov_min_deg;              // valid when has_azimuth_fov == true
    float   az_fov_max_deg;

    boolean has_elevation_fov;
    float   el_fov_min_deg;              // valid when has_elevation_fov == true
    float   el_fov_max_deg;

    boolean has_velocity_limits;
    float   v_min_mps;                   // valid when has_velocity_limits == true
    float   v_max_mps;                   // max unambiguous radial velocity

    // Max detections per frame (informative hint for subscriber allocation)
    uint32  max_detections_per_frame;

    // Processing chain description (informative)
    string  proc_chain;                  // e.g., "CFAR -> clustering -> tracking"

    string  schema_version;              // MUST be "spatial.sensing.rad/1.5"
  };

  // ---- Per-detection data ----
  @extensibility(APPENDABLE) struct RadDetection {
    // Position in sensor frame (meters)
    spatial::common::Vec3 xyz_m;

    // Velocity: Cartesian vector preferred; scalar radial as fallback.
    // Producers SHOULD populate velocity_xyz when available.
    // When only radial velocity is known, set has_velocity_xyz = false
    // and use v_r_mps.
    boolean has_velocity_xyz;
    spatial::common::Vec3 velocity_xyz;  // m/s in frame_ref (valid when has_velocity_xyz == true)

    boolean has_v_r_mps;
    double  v_r_mps;                     // scalar radial velocity (valid when has_v_r_mps == true)

    // Ego-motion compensated velocity (optional)
    boolean has_velocity_comp_xyz;
    spatial::common::Vec3 velocity_comp_xyz; // ego-compensated, m/s (valid when has_velocity_comp_xyz == true)

    // Radar cross-section in physical units
    boolean has_rcs_dbm2;
    float   rcs_dbm2;                    // dBm^2 (valid when has_rcs_dbm2 == true)

    // Generic intensity / magnitude (0..1 normalized, for renderers)
    float   intensity;

    // Per-detection quality / confidence (0..1)
    float   quality;

    // Dynamic property classification
    boolean has_dyn_prop;
    RadDynProp dyn_prop;                 // valid when has_dyn_prop == true

    // Per-detection position uncertainty (optional)
    boolean has_pos_rms;
    float   x_rms_m;                     // valid when has_pos_rms == true
    float   y_rms_m;
    float   z_rms_m;

    // Per-detection velocity uncertainty (optional)
    boolean has_vel_rms;
    float   vx_rms_mps;                  // valid when has_vel_rms == true
    float   vy_rms_mps;
    float   vz_rms_mps;

    // Ambiguity state (radar-specific; 0 = unambiguous)
    boolean has_ambig_state;
    uint8   ambig_state;                 // valid when has_ambig_state == true

    // False alarm probability hint (0 = high confidence, higher = less certain)
    boolean has_false_alarm_prob;
    float   false_alarm_prob;            // valid when has_false_alarm_prob == true

    // Optional tracking ID assigned by the radar firmware
    boolean has_sensor_track_id;
    uint32  sensor_track_id;             // valid when has_sensor_track_id == true
  };

  // ---- Detection set (per-frame batch) ----
  // BEST_EFFORT + KEEP_LAST=1
  @extensibility(APPENDABLE) struct RadDetectionSet {
    @key string stream_id;
    uint64 frame_seq;
    FrameRef frame_ref;                  // coordinate frame of xyz_m

    sequence<RadDetection, spatial::sensing::common::SZ_XL> dets;

    Time   stamp;
    string source_id;
    uint64 seq;

    // Processing provenance
    string proc_chain;                   // e.g., "ARS408-CFAR" or "OS-CFAR->cluster"

    // Frame-level quality
    boolean has_quality;
    FrameQuality quality;                // valid when has_quality == true
  };

  // ==============================================================
  // RAD TENSOR TYPES (for raw/processed radar cubes)
  // ==============================================================

  // Layout of the RAD tensor - axis ordering convention
  enum RadTensorLayout {
    @value(0)   RA_D,             // [range, azimuth, doppler]
    @value(1)   R_AZ_EL_D,        // [range, azimuth, elevation, doppler]
    @value(2)   CH_FAST_SLOW,     // [channel/Rx, fast_time, slow_time] - raw FMCW
    @value(3)   CH_R_D,           // [channel, range, doppler] - post range-doppler FFT
    @value(255) CUSTOM            // axes[] defines the order explicitly
  };

  // Static tensor description - RELIABLE + TRANSIENT_LOCAL
  // Publishes once; late joiners receive current state.
  @extensibility(APPENDABLE) struct RadTensorMeta {
    @key string stream_id;                 // stable id for this radar tensor stream
    StreamMeta base;                       // frame_ref, T_bus_sensor, nominal_rate_hz

    RadSensorType sensor_type;             // reuse existing enum (SHORT_RANGE, IMAGING_4D, etc.)

    // Tensor shape description
    RadTensorLayout layout;                // axis ordering convention
    sequence<Axis, 8> axes;                // axis definitions (range/az/el/doppler/channel/time)

    SampleType voxel_type;                 // pre-compression sample type (e.g., CF32)
    string physical_meaning;               // e.g., "raw FMCW I/Q", "post 3D-FFT complex baseband"

    // Antenna configuration (informative, for MIMO systems)
    boolean has_antenna_config;
    uint16  num_tx;                        // valid when has_antenna_config == true
    uint16  num_rx;                        // valid when has_antenna_config == true
    uint16  num_virtual_channels;          // num_tx * num_rx (informative)

    // Waveform parameters (informative)
    boolean has_waveform_params;
    float   bandwidth_hz;                  // valid when has_waveform_params == true
    float   center_freq_hz;                // valid when has_waveform_params == true
    float   chirp_duration_s;              // valid when has_waveform_params == true
    uint32  samples_per_chirp;             // valid when has_waveform_params == true
    uint32  chirps_per_frame;              // valid when has_waveform_params == true

    // Default payload settings for frames
    PayloadKind payload_kind;              // DENSE_TILES, SPARSE_COO, or LATENT
    Codec       codec;                     // LZ4, ZSTD, CODEC_NONE, etc.
    boolean     has_quant_scale;
    float       quant_scale;               // valid when has_quant_scale == true
    uint32      tile_size[4];              // for DENSE_TILES; unused dims = 1

    string  schema_version;                // MUST be "spatial.sensing.rad/1.5"
  };

  // Per-frame tensor index - BEST_EFFORT + KEEP_LAST=1
  // Heavy payload referenced via FrameHeader.blobs[]
  @extensibility(APPENDABLE) struct RadTensorFrame {
    @key string stream_id;
    uint64 frame_seq;

    FrameHeader hdr;                       // t_start/t_end, optional sensor_pose, blobs[]

    // Per-frame overrides (may differ from RadTensorMeta defaults)
    PayloadKind payload_kind;
    Codec       codec;
    SampleType  voxel_type_after_decode;   // post-decode type (e.g., CF32 -> MAG_F16)
    boolean     has_quant_scale;
    float       quant_scale;               // valid when has_quant_scale == true

    FrameQuality quality;                  // SNR/valid%/health note
    string proc_chain;                     // e.g., "raw", "FFT3D->hann", "FFT3D->OS-CFAR"
  };

}; }; };

Lidar Extension

Lidar metadata, compressed point cloud frames, and detections. ROI semantics follow §2 Conventions for global normative rules; axes use the Sensing Common AXIS_CENTERS/AXIS_LINSPACE union encoding. See §2 Conventions for global normative rules.

BIN_INTERLEAVED Encoding (Normative)
BIN_INTERLEAVED indicates raw interleaved binary where each point is a contiguous record of fields defined by the PointLayout enum. There is no header. The ring field is serialized as uint16 per the LidarDetection.ring type.

Layout Fields per point Default byte-width per field
XYZ_I x, y, z, intensity 4 × float32 = 16 bytes
XYZ_I_R x, y, z, intensity, ring 4 × float32 + 1 × uint16 = 18 bytes
XYZ_I_R_N x, y, z, intensity, ring, nx, ny, nz 4 × float32 + 1 × uint16 + 3 × float32 = 30 bytes
XYZ_I_R_T x, y, z, intensity, ring, t 4 × float32 + 1 × uint16 + 1 × float32 = 22 bytes
XYZ_I_R_T_N x, y, z, intensity, ring, t, nx, ny, nz 4 × float32 + 1 × uint16 + 1 × float32 + 3 × float32 = 34 bytes

When BIN_INTERLEAVED is used, consumers MUST interpret the blob as N × record_size bytes where N = blob_size / record_size.

Per-Point Timestamps (Normative)
Layouts XYZ_I_R_T and XYZ_I_R_T_N include a per-point relative timestamp field t serialized as float32, representing seconds elapsed since FrameHeader.t_start. Consumers performing motion compensation SHOULD use t_start + point.t as the acquisition time for each point.

Computing t_end for Spinning Lidars (Informative)
When a source provides only t_start, producers SHOULD compute t_end as t_start + (1.0 / nominal_rate_hz) for spinning lidars, or as t_start + max(point.t) when per-point timestamps are available. Producers MUST populate t_end rather than leaving it as zero.

// SPDX-License-Identifier: MIT
// SpatialDDS LiDAR (sensing.lidar) 1.5 — Extension profile

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif
#ifndef SPATIAL_SENSING_COMMON_INCLUDED
#define SPATIAL_SENSING_COMMON_INCLUDED
#include "common.idl"
#endif

module spatial { module sensing { module lidar {

  // Module identifier for discovery and schema registration
  const string MODULE_ID = "spatial.sensing.lidar/1.5";

  // Reuse Core + Sensing Common
  typedef builtin::Time                      Time;
  typedef spatial::core::PoseSE3                   PoseSE3;
  typedef spatial::core::BlobRef                   BlobRef;
  typedef spatial::common::FrameRef               FrameRef;

  typedef spatial::sensing::common::Codec          Codec;
  typedef spatial::sensing::common::PayloadKind    PayloadKind; // use BLOB_GEOMETRY for clouds
  typedef spatial::sensing::common::SampleType     SampleType;  // optional for per-point extras
  typedef spatial::sensing::common::Axis           Axis;
  typedef spatial::sensing::common::ROI            ROI;
  typedef spatial::sensing::common::StreamMeta     StreamMeta;
  typedef spatial::sensing::common::FrameHeader    FrameHeader;
  typedef spatial::sensing::common::FrameQuality   FrameQuality;
  typedef spatial::sensing::common::ROIRequest     ROIRequest;
  typedef spatial::sensing::common::ROIReply       ROIReply;

  // ROI bounds follow Sensing Common presence flags.
  // Axis samples are encoded via the Sensing Common union (AXIS_CENTERS or AXIS_LINSPACE).

  // Device + data model
  enum LidarType {
    @value(0) SPINNING_2D,
    @value(1) MULTI_BEAM_3D,
    @value(2) SOLID_STATE
  };
  enum CloudEncoding {
    @value(0)   PCD,
    @value(1)   PLY,
    @value(2)   LAS,
    @value(3)   LAZ,
    @value(4)   BIN_INTERLEAVED,   // raw interleaved binary (fields by PointLayout)
    @value(10)  GLTF_DRACO,
    @value(20)  MPEG_PCC,
    @value(255) CUSTOM_BIN
  };
  enum PointLayout { // intensity, ring, normal
    @value(0) XYZ_I,
    @value(1) XYZ_I_R,
    @value(2) XYZ_I_R_N,
    @value(3) XYZ_I_R_T,      // with per-point relative timestamp
    @value(4) XYZ_I_R_T_N     // with per-point timestamp + normals
  };

  // Static description — RELIABLE + TRANSIENT_LOCAL (late joiners receive the latest meta)
  @extensibility(APPENDABLE) struct LidarMeta {
    @key string stream_id;
    StreamMeta base;                  // frame_ref, T_bus_sensor, nominal_rate_hz
    LidarType     type;
    uint16        n_rings;            // 0 if N/A
    boolean       has_range_limits;
    float         min_range_m;        // valid when has_range_limits == true
    float         max_range_m;
    boolean       has_horiz_fov;
    float         horiz_fov_deg_min;  // valid when has_horiz_fov == true
    float         horiz_fov_deg_max;
    boolean       has_vert_fov;
    float         vert_fov_deg_min;   // valid when has_vert_fov == true
    float         vert_fov_deg_max;

    // Sensor wavelength (informative; for eye-safety and atmospheric classification)
    boolean       has_wavelength;
    float         wavelength_nm;      // valid when has_wavelength == true; e.g., 865, 905, 1550

    // Default payload for frames (clouds ride as blobs)
    CloudEncoding encoding;           // PCD/PLY/LAS/LAZ/etc.
    Codec         codec;              // ZSTD/LZ4/DRACO/…
    PointLayout   layout;             // expected fields when decoded
    string schema_version;            // MUST be "spatial.sensing.lidar/1.5"
  };

  // Per-frame index — BEST_EFFORT + KEEP_LAST=1 (large payloads referenced via blobs)
  @extensibility(APPENDABLE) struct LidarFrame {
    @key string stream_id;
    uint64 frame_seq;

    FrameHeader  hdr;                 // t_start/t_end, optional sensor_pose, blobs[]
    CloudEncoding encoding;           // may override meta
    Codec         codec;              // may override meta
    PointLayout   layout;             // may override meta
    boolean       has_per_point_timestamps; // true when blob contains per-point t

    // Optional quick hints (for health/telemetry)
    boolean      has_average_range_m;
    float        average_range_m;     // valid when has_average_range_m == true
    boolean      has_percent_valid;
    float        percent_valid;       // valid when has_percent_valid == true (0..100)
    boolean      has_quality;
    FrameQuality quality;             // valid when has_quality == true
  };

  // Lightweight derivative for immediate fusion/tracking (optional)
  @extensibility(APPENDABLE) struct LidarDetection {
    spatial::common::Vec3 xyz_m;
    float  intensity;
    uint16 ring;
    float  quality;                   // 0..1
  };

  // Detections topic — BEST_EFFORT
  @extensibility(APPENDABLE) struct LidarDetectionSet {
    @key string stream_id;
    uint64 frame_seq;
    FrameRef frame_ref;               // coordinate frame of xyz_m
    sequence<LidarDetection, spatial::sensing::common::SZ_SMALL> dets;
    Time   stamp;
  };

}; }; };

AR + Geo Extension

Multi-frame geo-referenced nodes for AR clients, VPS services, and multi-agent alignment.

NodeGeo extends core::Node with an array of metric poses in different coordinate frames and an optional geographic anchor. A VPS service localizing a client against multiple overlapping maps returns one NodeGeo carrying poses in each map's frame. In hierarchical spaces (building → floor → room → table), the same message carries poses at every level of the hierarchy. Consumers select the frame they need; producers include only the frames they can compute.

The poses array uses core::FramedPose — each entry is self-contained with its own frame reference and covariance. This replaces the previous pattern of a single bare PoseSE3 with frame_ref and cov as sibling fields, which could only express one local pose and left the relationship between the top-level cov and the geopose's cov ambiguous.

// SPDX-License-Identifier: MIT
// SpatialDDS AR+Geo 1.5

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif

module spatial {
  module argeo {

    const string MODULE_ID = "spatial.argeo/1.5";

    typedef builtin::Time Time;
    typedef spatial::core::PoseSE3    PoseSE3;
    typedef spatial::core::FramedPose FramedPose;
    typedef spatial::core::GeoPose    GeoPose;
    typedef spatial::core::CovMatrix  CovMatrix;
    typedef spatial::common::FrameRef FrameRef;

    // A pose-graph node with one or more metric-frame poses and an
    // optional geographic anchor.
    //
    // Keyed by node_id (same key as core::Node). Published alongside
    // core::Node when geo-referencing is available.
    //
    // poses[] carries the node's position in one or more local/metric
    // coordinate frames. Each FramedPose is self-contained (pose +
    // frame_ref + cov + stamp). Typical entries:
    //   - SLAM map frame (always present)
    //   - ENU frame anchored to a surveyed point
    //   - Building / floor / room frames in hierarchical spaces
    //   - Alternative map frames when multiple maps overlap
    //
    // geopose provides the WGS84 anchor (lat/lon/alt) when known.
    // It remains a separate field because geographic coordinates use
    // degrees, not meters — they cannot share the FramedPose type.
    @extensibility(APPENDABLE) struct NodeGeo {
      string map_id;
      @key string node_id;               // same id as core::Node

      // One or more metric poses in different frames.
      // The first entry SHOULD be the primary SLAM map frame.
      // Additional entries provide the same physical pose expressed
      // in alternative coordinate frames (other maps, hierarchical
      // spaces, ENU anchors). Consumers select by frame_ref.
      sequence<FramedPose, 8> poses;

      // Geographic anchor (optional — absent for indoor-only maps)
      boolean has_geopose;
      GeoPose geopose;

      string  source_id;
      uint64  seq;                       // per-source monotonic
      uint64  graph_epoch;               // increments on major rebases/merges
    };

  }; // module argeo
};

Usage scenarios (informative):

Multi-map localization: A VPS service localizes a client against three overlapping maps and returns: 

{
  "node_id": "vps-fix/client-42/0017",
  "map_id": "mall-west",
  "poses": [
    {
      "pose": { "t": [12.3, -4.1, 1.5], "q": [0, 0, 0, 1] },
      "frame_ref": { "uuid": "aaa-...", "fqn": "mall-west/lidar-map" },
      "cov": { "type": "COV_POSE6", "pose": [ ... ] },
      "stamp": { "sec": 1714071000, "nanosec": 0 }
    },
    {
      "pose": { "t": [12.1, -4.3, 1.5], "q": [0, 0, 0.01, 1] },
      "frame_ref": { "uuid": "bbb-...", "fqn": "mall-west/photo-map" },
      "cov": { "type": "COV_POSE6", "pose": [ ... ] },
      "stamp": { "sec": 1714071000, "nanosec": 0 }
    }
  ],
  "has_geopose": true,
  "geopose": {
    "lat_deg": 37.7749, "lon_deg": -122.4194, "alt_m": 15.0,
    "q": [0, 0, 0, 1], "frame_kind": "ENU",
    "frame_ref": { "uuid": "ccc-...", "fqn": "earth-fixed/enu" },
    "stamp": { "sec": 1714071000, "nanosec": 0 },
    "cov": { "type": "COV_POS3", "pos": [ ... ] }
  },
  "source_id": "vps/mall-west-service",
  "seq": 17,
  "graph_epoch": 3
}

Hierarchical spaces: A localization service returns poses in building, floor, and room frames: 

{
  "node_id": "vps-fix/headset-07/0042",
  "map_id": "building-west",
  "poses": [
    {
      "pose": { "t": [45.2, 22.1, 9.3], "q": [0, 0, 0, 1] },
      "frame_ref": { "uuid": "bld-...", "fqn": "building-west/enu" },
      "cov": { "type": "COV_POS3", "pos": [ ... ] },
      "stamp": { "sec": 1714071000, "nanosec": 0 }
    },
    {
      "pose": { "t": [15.2, 8.1, 0.3], "q": [0, 0, 0, 1] },
      "frame_ref": { "uuid": "fl3-...", "fqn": "building-west/floor-3" },
      "cov": { "type": "COV_POS3", "pos": [ ... ] },
      "stamp": { "sec": 1714071000, "nanosec": 0 }
    },
    {
      "pose": { "t": [3.2, 2.1, 0.3], "q": [0, 0, 0, 1] },
      "frame_ref": { "uuid": "rmB-...", "fqn": "building-west/floor-3/room-B" },
      "cov": { "type": "COV_POS3", "pos": [ ... ] },
      "stamp": { "sec": 1714071000, "nanosec": 0 }
    }
  ],
  "has_geopose": true,
  "geopose": { "lat_deg": 37.7750, "lon_deg": -122.4190, "alt_m": 24.3, "..." : "..." },
  "source_id": "vps/building-west-indoor",
  "seq": 42,
  "graph_epoch": 1
}

Mapping Extension

Map lifecycle metadata, multi-source edge types, inter-map alignment, and lifecycle events for multi-agent collaborative mapping.

The Core profile provides the mechanical primitives for map data: Node/Edge for pose graphs, TileMeta/TilePatch/BlobRef for geometry transport, FrameTransform for frame alignment, and SnapshotRequest/SnapshotResponse for tile catch-up. The SLAM Frontend profile carries feature-level data for re-localization. The Anchors profile handles durable landmarks with incremental sync.

This extension adds the map lifecycle layer — the metadata and coordination types that let multiple independent SLAM agents discover, align, merge, version, and qualify each other's maps without prior arrangement:

  • MapMeta — top-level map descriptor: what exists, what it covers, its quality and lifecycle state.
  • mapping::Edge — extends core::Edge with richer constraint types for multi-source pose graphs (cross-map loop closures, GPS, anchor, IMU, semantic constraints).
  • MapAlignment — the inter-map transform with provenance, uncertainty, and evidence references.
  • MapEvent — lightweight lifecycle notifications so subscribers react to map state changes without polling.

Design note — no new map formats. This profile does not add occupancy grid, TSDF, or voxel map types. Those are representation-specific formats expressed as TileMeta.encoding values (e.g., "occupancy_grid/uint8", "tsdf/f32", "voxel_hash/f32"). The mapping profile addresses coordinating and aligning maps, not inventing new map formats.

Topic Layout

Type Topic QoS Notes
MapMeta spatialdds/<scene>/mapping/meta/v1 RELIABLE + TRANSIENT_LOCAL, KEEP_LAST(1) per key One sample per (map_id, source_id). Late joiners get current state.
mapping::Edge spatialdds/<scene>/mapping/edge/v1 RELIABLE, KEEP_ALL Superset of core::Edge for multi-source constraints.
MapAlignment spatialdds/<scene>/mapping/alignment/v1 RELIABLE + TRANSIENT_LOCAL, KEEP_LAST(1) per key Durable inter-map transforms.
MapEvent spatialdds/<scene>/mapping/event/v1 RELIABLE, KEEP_LAST(32) Lightweight lifecycle notifications.

Core Node and Edge topics remain unchanged. Agents that produce cross-map constraints publish on the mapping/edge topic; agents that only produce intra-map odometry/loop closures continue using core topics. Consumers that need cross-map awareness subscribe to both.

Range-only constraints: When type == RANGE, the edge carries a scalar distance measurement between from_id and to_id in the range_m / range_std_m fields. The T_from_to and information fields SHOULD be set to identity / zero respectively. Pose graph optimizers that encounter a RANGE edge SHOULD treat it as a distance-only factor: ||pos(from_id) - pos(to_id)|| = range_m. Common sources include UWB inter-robot ranging, acoustic ranging (underwater), and BLE RSSI-derived distances. Range edges may reference nodes in different maps (with has_from_map_id / has_to_map_id populated), enabling range-assisted inter-map alignment.

// SPDX-License-Identifier: MIT
// SpatialDDS Mapping Extension 1.5
//
// Map lifecycle metadata, multi-source edge types, and inter-map
// alignment primitives for multi-agent collaborative mapping.

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif

module spatial {
  module mapping {

    const string MODULE_ID = "spatial.mapping/1.5";

    typedef builtin::Time Time;
    typedef spatial::core::PoseSE3    PoseSE3;
    typedef spatial::core::FrameRef   FrameRef;
    typedef spatial::core::CovMatrix  CovMatrix;
    typedef spatial::core::BlobRef    BlobRef;
    typedef spatial::common::MetaKV   MetaKV;
    typedef spatial::core::GeoPose   GeoPose;


    // ================================================================
    // 1. MAP METADATA
    // ================================================================

    // Enums are placed in a nested module to avoid literal collisions
    // at the module scope in IDL compilers.
    module enums {
      module map_kind {
        // Representation kind — what type of spatial map this describes.
        // The enum identifies the high-level representation; the actual
        // encoding and codec live in TileMeta.encoding as today.
        enum MapKind {
          @value(0) POSE_GRAPH,       // sparse keyframe graph (Node + Edge)
          @value(1) OCCUPANCY_GRID,   // 2D or 2.5D grid (nav planning)
          @value(2) POINT_CLOUD,      // dense 3D point cloud
          @value(3) MESH,             // triangle mesh / surface
          @value(4) TSDF,             // truncated signed distance field
          @value(5) VOXEL,            // volumetric voxel grid
          @value(6) NEURAL_FIELD,     // NeRF, 3DGS, neural SDF (see neural profile)
          @value(7) FEATURE_MAP,      // visual place recognition / bag-of-words
          @value(8) SEMANTIC,         // semantic / panoptic map layer
          @value(9) OTHER
        };
      };

      module map_status {
        // Map status — lifecycle state.
        enum MapStatus {
          @value(0) BUILDING,         // actively being constructed (SLAM running)
          @value(1) OPTIMIZING,       // global optimization / bundle adjustment in progress
          @value(2) STABLE,           // optimized and not actively changing
          @value(3) FROZEN,           // immutable reference map (no further updates)
          @value(4) DEPRECATED        // superseded by a newer map; consumers should migrate
        };
      };

      module edge_type {
        // Extends core::EdgeTypeCore (ODOM=0, LOOP=1) with constraint types
        // needed for multi-agent, multi-sensor pose graph optimization.
        //
        // Values 0-1 are identical to EdgeTypeCore. Core consumers that
        // only understand ODOM/LOOP can safely downcast by treating
        // unknown values as LOOP.
        enum EdgeType {
          @value(0)  ODOM,            // odometry (sequential)
          @value(1)  LOOP,            // intra-map loop closure
          @value(2)  INTER_MAP,       // cross-map loop closure (between two agents' maps)
          @value(3)  GPS,             // absolute pose from GNSS
          @value(4)  ANCHOR,          // constraint from recognizing a shared anchor
          @value(5)  IMU_PREINT,      // IMU pre-integration factor
          @value(6)  GRAVITY,         // gravity direction prior
          @value(7)  PLANE,           // planar constraint (e.g., ground plane)
          @value(8)  SEMANTIC,        // semantic co-observation ("both see the same door")
          @value(9)  MANUAL,          // human-provided alignment
          @value(10) RANGE,           // range-only distance constraint (UWB, acoustic, BLE)
          @value(11) OTHER
        };
      };

      module alignment_method {
        // How an alignment was established.
        enum AlignmentMethod {
          @value(0) VISUAL_LOOP,      // feature-based visual closure
          @value(1) LIDAR_ICP,        // point cloud registration (ICP / NDT)
          @value(2) ANCHOR_MATCH,     // shared anchor recognition
          @value(3) GPS_COARSE,       // GPS-derived coarse alignment
          @value(4) SEMANTIC_MATCH,   // semantic landmark co-observation
          @value(5) MANUAL,           // operator-provided ground truth
          @value(6) MULTI_METHOD,     // combination of methods
          @value(7) RANGE_COARSE,     // range-only (UWB, acoustic) coarse alignment
          @value(8) OTHER
        };
      };

      module map_event_kind {
        // Lightweight event published when a map undergoes a significant
        // lifecycle transition. Subscribers (fleet managers, UI dashboards,
        // data pipelines) can react without polling MapMeta.
        enum MapEventKind {
          @value(0) CREATED,          // new map started
          @value(1) EPOCH_ADVANCE,    // graph_epoch incremented (rebase / merge)
          @value(2) STATUS_CHANGE,    // status field changed (e.g., BUILDING → STABLE)
          @value(3) ALIGNMENT_NEW,    // new MapAlignment published involving this map
          @value(4) ALIGNMENT_UPDATE, // existing MapAlignment revised
          @value(5) DEPRECATED,       // map marked deprecated
          @value(6) DELETED           // map data removed from bus
        };
      };
    };

    typedef enums::map_kind::MapKind MapKind;
    typedef enums::map_status::MapStatus MapStatus;
    typedef enums::edge_type::EdgeType EdgeType;
    typedef enums::alignment_method::AlignmentMethod AlignmentMethod;
    typedef enums::map_event_kind::MapEventKind MapEventKind;

    // Quality metrics — optional per-map health indicators.
    // All fields are optional via has_* flags to avoid mandating
    // metrics that not every SLAM system produces.
    @extensibility(APPENDABLE) struct MapQuality {
      boolean has_loop_closure_count;
      uint32  loop_closure_count;       // total loop closures accepted

      boolean has_mean_residual;
      double  mean_residual;            // mean constraint residual after optimization (meters)

      boolean has_max_drift_m;
      double  max_drift_m;              // estimated worst-case drift (meters)

      boolean has_coverage_pct;
      float   coverage_pct;             // fraction of declared extent actually mapped [0..1]

      boolean has_keyframe_count;
      uint32  keyframe_count;           // number of keyframes / nodes

      boolean has_landmark_count;
      uint32  landmark_count;           // number of 3D landmarks
    };

    // Top-level map descriptor. Published with RELIABLE + TRANSIENT_LOCAL
    // so late joiners discover all active maps immediately.
    //
    // One MapMeta per (map_id, source_id) — a single physical map may have
    // multiple representations (e.g., pose graph + occupancy grid + mesh),
    // each published as a separate MapMeta with the same map_id but
    // different kind and source_id.
    @extensibility(APPENDABLE) struct MapMeta {
      @key string map_id;               // unique map identifier
      @key string source_id;            // producing agent / SLAM system

      MapKind   kind;                   // representation type
      MapStatus status;                 // lifecycle state
      string    algorithm;              // e.g., "ORB-SLAM3", "Cartographer", "RTAB-Map", "LIO-SAM"
      FrameRef  frame_ref;              // map's canonical coordinate frame

      // Spatial extent (axis-aligned in frame_ref)
      boolean has_extent;
      spatial::core::Aabb3 extent;      // bounding box of mapped region

      // Geo-anchor: where this map sits on Earth (when known)
      boolean has_geopose;
      GeoPose geopose;                  // map origin in WGS84

      // Versioning — aligns with core Node/Edge graph_epoch
      uint64  graph_epoch;              // increments on major rebases / merges
      uint64  revision;                 // monotonic within an epoch (fine-grained updates)

      // Quality
      boolean has_quality;
      MapQuality quality;

      // Timing
      Time    created;                  // map creation time
      Time    stamp;                    // last update time

      // Content references — how to get the map data
      // For pose graphs: subscribe to Node/Edge on the standard topic with this map_id
      // For dense maps: these blob_ids reference the backing TileMeta/BlobChunk data
      sequence<BlobRef, 32> blob_refs;  // optional: pre-built map artifacts

      // Extensible metadata (encoding details, sensor suite, etc.)
      sequence<MetaKV, 32> attributes;

      string schema_version;            // MUST be "spatial.mapping/1.5"
    };


    // ================================================================
    // 2. EXTENDED EDGE TYPES
    // ================================================================

    // (EdgeType enum is defined in enums submodule; typedef above)

    // Extended edge that carries the richer EdgeType plus provenance.
    // Supplements core::Edge — publishers that produce multi-source
    // constraints publish mapping::Edge; the fields are a superset
    // of core::Edge.
    @extensibility(APPENDABLE) struct Edge {
      string map_id;
      @key string edge_id;
      string from_id;                   // source node (may be in a different map_id)
      string to_id;                     // target node
      EdgeType type;                    // extended type enum
      PoseSE3 T_from_to;               // relative pose: from_id → to_id
      spatial::common::Mat6x6 information; // 6x6 info matrix (row-major)
      Time   stamp;
      string source_id;                 // who produced this constraint
      uint64 seq;
      uint64 graph_epoch;

      // Cross-map provenance (populated when type == INTER_MAP)
      boolean has_from_map_id;
      string  from_map_id;              // map_id of from_id's origin
      boolean has_to_map_id;
      string  to_map_id;                // map_id of to_id's origin

      // Match quality for loop closures and cross-map edges
      boolean has_match_score;
      float   match_score;              // similarity / inlier ratio [0..1]
      boolean has_inlier_count;
      uint32  inlier_count;             // feature inliers supporting this edge

      // Range-only constraint (populated when type == RANGE)
      // For range-only edges, T_from_to and information are unused (set to
      // identity/zero); the scalar range_m is the primary payload.
      // The optimizer treats this as a distance-only factor between from_id
      // and to_id: ||pos(from_id) - pos(to_id)|| = range_m ± range_std_m.
      boolean has_range_m;
      float   range_m;                  // measured distance (meters)
      boolean has_range_std_m;
      float   range_std_m;              // 1-sigma distance uncertainty (meters)
    };


    // ================================================================
    // 3. MAP ALIGNMENT
    // ================================================================

    // (AlignmentMethod enum is defined in enums submodule; typedef above)

    // Inter-map transform: aligns map_id_from's frame to map_id_to's frame,
    // with provenance and quality metadata.
    //
    // This is the merge primitive. When a multi-robot SLAM system determines
    // that two maps overlap, it publishes a MapAlignment. Downstream consumers
    // (planning, visualization, fleet coordination) use this to reason across
    // maps without waiting for a full graph merge.
    @extensibility(APPENDABLE) struct MapAlignment {
      @key string alignment_id;         // unique alignment identifier

      string map_id_from;               // source map
      string map_id_to;                 // target map (reference)
      PoseSE3 T_from_to;               // transform: map_id_from frame → map_id_to frame
      CovMatrix cov;                    // uncertainty of the alignment

      AlignmentMethod method;           // how the alignment was computed
      Time   stamp;                     // when the alignment was computed
      string source_id;                 // who computed it

      // Quality evidence
      boolean has_match_score;
      float   match_score;              // overall alignment quality [0..1]
      boolean has_overlap_pct;
      float   overlap_pct;              // estimated spatial overlap [0..1]
      boolean has_supporting_edges;
      uint32  supporting_edges;         // number of cross-map edges backing this alignment

      // Versioning — alignment may be refined as more evidence accumulates
      uint64  revision;                 // monotonic; newer revision supersedes older

      // Optional: list of cross-map edge_ids that support this alignment
      sequence<string, 64> evidence_edge_ids;

      string schema_version;            // MUST be "spatial.mapping/1.5"
    };


    // ================================================================
    // 4. MAP LIFECYCLE EVENTS
    // ================================================================

    // (MapEventKind enum is defined in enums submodule; typedef above)

    @extensibility(APPENDABLE) struct MapEvent {
      @key string map_id;
      MapEventKind event;
      string source_id;
      Time   stamp;

      // Context (populated per event kind)
      boolean has_new_status;
      MapStatus new_status;             // for STATUS_CHANGE

      boolean has_new_epoch;
      uint64  new_epoch;                // for EPOCH_ADVANCE

      boolean has_alignment_id;
      string  alignment_id;             // for ALIGNMENT_NEW / ALIGNMENT_UPDATE

      // Human-readable reason
      boolean has_reason;
      string  reason;                   // e.g., "merged with map/robot-B after 42 loop closures"
    };

  }; // module mapping
};   // module spatial

Spatial Events Extension

Typed, spatially-scoped events for zone monitoring, anomaly detection, and smart infrastructure alerting. Bridges perception streams (Detection3DSet) and application logic (fleet management, building automation, safety systems).

The Semantics profile provides spatial facts — "what is where." This extension adds spatial interpretations — "something happened that matters." Events are derived from perception streams plus zone definitions plus temporal rules (dwell thresholds, speed limits, capacity caps). They are typed, severity-graded, tied to triggering detections, and scoped to named spatial zones.

The profile defines three types:

  • SpatialZone — named spatial regions with rules (restricted, speed-limited, capacity-capped). Published latched so all participants know the zone layout.
  • SpatialEvent — typed event tied to a zone, triggering detection, optional media evidence, and severity.
  • ZoneState — periodic zone occupancy and status snapshot for dashboards and capacity management.

Integration with Discovery: Zone publishers announce via disco::Announce with kind: OTHER (or a future ZONE_MANAGER kind) and coverage matching the zone's spatial extent. Consumers use CoverageQuery filtered by module_id_in: ["spatial.events/1.5"] to discover event sources in a region. SpatialZone geometry reuses the same Aabb3 and FrameRef primitives as CoverageElement, ensuring consistent spatial reasoning.

Topic Layout

Type Topic QoS Notes
SpatialZone spatialdds/<scene>/events/zone/v1 RELIABLE + TRANSIENT_LOCAL, KEEP_LAST(1) per key Latched zone definitions. Late joiners get full zone set.
SpatialEvent spatialdds/<scene>/events/event/v1 RELIABLE, KEEP_LAST(64) Event stream. Consumers filter by zone_id, severity, event type.
ZoneState spatialdds/<scene>/events/zone\_state/v1 BEST_EFFORT, KEEP_LAST(1) per key Periodic zone status snapshots. 
// SPDX-License-Identifier: MIT
// SpatialDDS Spatial Events Extension 1.5
//
// Typed, spatially-scoped events for zone monitoring, anomaly detection,
// and smart infrastructure alerting.

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif

module spatial {
  module events {

    const string MODULE_ID = "spatial.events/1.5";

    typedef builtin::Time Time;
    typedef spatial::core::PoseSE3   PoseSE3;
    typedef spatial::core::FrameRef  FrameRef;
    typedef spatial::core::Aabb3     Aabb3;
    typedef spatial::core::BlobRef   BlobRef;
    typedef spatial::core::GeoPose   GeoPose;
    typedef spatial::common::MetaKV  MetaKV;

    // ================================================================
    // ENUMS (scoped to avoid literal collisions in IDL compilers)
    // ================================================================

    module enums {
      module zone_kind_enum {
        // Zone classification — what kind of spatial region this is.
        enum ZoneKind {
          @value(0) RESTRICTED,       // entry prohibited or requires authorization
          @value(1) SPEED_LIMITED,    // maximum speed enforced
          @value(2) CAPACITY_LIMITED, // maximum occupancy enforced
          @value(3) ONE_WAY,          // directional traffic constraint
          @value(4) LOADING,          // loading/unloading area with dwell rules
          @value(5) HAZARD,           // known hazard zone (chemical, height, machinery)
          @value(6) MONITORING,       // general observation zone (no specific constraint)
          @value(7) GEOFENCE,         // boundary-crossing detection only
          @value(8) OTHER
        };
      };

      module event_type_enum {
        // Event type — what happened.
        enum EventType {
          @value(0)  ZONE_ENTRY,      // object entered the zone
          @value(1)  ZONE_EXIT,       // object exited the zone
          @value(2)  DWELL_TIMEOUT,   // object exceeded dwell time limit
          @value(3)  SPEED_VIOLATION, // object exceeded speed limit
          @value(4)  CAPACITY_BREACH, // zone occupancy exceeded capacity
          @value(5)  WRONG_WAY,       // object traveling against one-way direction
          @value(6)  PROXIMITY_ALERT, // two tracked objects closer than safe distance
          @value(7)  UNATTENDED,      // object stationary without associated person
          @value(8)  ANOMALY,         // general anomaly (ML-detected, pattern deviation)
          @value(9)  LINE_CROSS,      // object crossed a defined trip line
          @value(10) LOITERING,       // person/object lingering beyond threshold
          @value(11) TAILGATING,      // unauthorized entry following authorized person
          @value(12) OTHER
        };
      };

      module severity_enum {
        // Severity level.
        enum Severity {
          @value(0) INFO,             // informational (logging, analytics)
          @value(1) WARNING,          // advisory — may require attention
          @value(2) ALERT,            // actionable — requires human review
          @value(3) CRITICAL          // immediate intervention required
        };
      };

      module event_state_enum {
        // Event lifecycle state.
        enum EventState {
          @value(0) ACTIVE,           // event is ongoing
          @value(1) RESOLVED,         // condition cleared (e.g., person left zone)
          @value(2) ACKNOWLEDGED,     // human acknowledged the event
          @value(3) SUPPRESSED        // suppressed by rule or operator
        };
      };
    };

    typedef enums::zone_kind_enum::ZoneKind ZoneKind;
    typedef enums::event_type_enum::EventType EventType;
    typedef enums::severity_enum::Severity Severity;
    typedef enums::event_state_enum::EventState EventState;

    // ================================================================
    // 1. SPATIAL ZONES
    // ================================================================

    // (ZoneKind enum is defined in enums submodule; typedef above)

    // Named spatial region with associated rules.
    // Published with RELIABLE + TRANSIENT_LOCAL so late joiners
    // receive the full zone layout.
    @extensibility(APPENDABLE) struct SpatialZone {
      @key string zone_id;              // unique zone identifier

      string name;                      // human-readable name (e.g., "Loading Bay 3")
      ZoneKind kind;                    // zone classification
      FrameRef frame_ref;               // coordinate frame for geometry

      // Zone geometry (axis-aligned in frame_ref)
      boolean has_bounds;
      Aabb3 bounds;                     // 3D bounding box (valid when has_bounds == true)

      // Optional geo-anchor for earth-fixed zones
      boolean has_geopose;
      GeoPose geopose;                  // zone center in WGS84

      // Zone rules (optional per kind)
      boolean has_speed_limit_mps;
      float   speed_limit_mps;          // max speed in m/s (for SPEED_LIMITED)

      boolean has_capacity;
      uint32  capacity;                 // max occupancy count (for CAPACITY_LIMITED)

      boolean has_dwell_limit_sec;
      float   dwell_limit_sec;          // max dwell time in seconds (for LOADING, RESTRICTED)

      // Applicable object classes — which detection class_ids trigger events.
      // Empty means all classes.
      sequence<string, 32> class_filter;

      // Schedule (optional) — zone is only active during specified hours.
      // Format: ISO 8601 recurring interval or cron-like string in attributes.
      boolean has_schedule;
      string  schedule;                 // e.g., "R/2024-01-01T06:00:00/PT12H" or deployment-specific

      // Owner / authority
      string provider_id;               // who defines this zone
      Time   stamp;                     // last update time

      // Extensible metadata
      sequence<MetaKV, 16> attributes;

      string schema_version;            // MUST be "spatial.events/1.5"
    };


    // ================================================================
    // 2. SPATIAL EVENTS
    // ================================================================

    // (EventType, Severity, EventState enums are defined in enums submodule; typedefs above)

    // A spatially-grounded event.
    //
    // Keyed by event_id. Publishers update the same event_id as state
    // changes (ACTIVE → RESOLVED). Subscribers use KEEP_LAST per key
    // to see the latest state of each event.
    @extensibility(APPENDABLE) struct SpatialEvent {
      @key string event_id;             // unique event identifier

      EventType  type;                  // what happened
      Severity   severity;              // how urgent
      EventState state;                 // lifecycle state

      // Where — zone reference (optional; not all events are zone-scoped)
      boolean has_zone_id;
      string  zone_id;                  // references SpatialZone.zone_id

      // Where — 3D position of the event (in the zone's or scene's frame_ref)
      boolean has_position;
      spatial::common::Vec3 position;   // event location (meters)
      FrameRef frame_ref;               // coordinate frame for position

      // What — triggering detection(s)
      boolean has_trigger_det_id;
      string  trigger_det_id;           // primary triggering Detection3D.det_id

      boolean has_trigger_track_id;
      string  trigger_track_id;         // tracked object ID (from Detection3D.track_id)

      string  trigger_class_id;         // class of triggering object (e.g., "person", "forklift")

      // Who — secondary object for relational events (PROXIMITY_ALERT, TAILGATING)
      boolean has_secondary_det_id;
      string  secondary_det_id;

      // Measured values (populated per event type)
      boolean has_measured_speed_mps;
      float   measured_speed_mps;       // for SPEED_VIOLATION

      boolean has_measured_dwell_sec;
      float   measured_dwell_sec;       // for DWELL_TIMEOUT, LOITERING, UNATTENDED

      boolean has_measured_distance_m;
      float   measured_distance_m;      // for PROXIMITY_ALERT

      boolean has_zone_occupancy;
      uint32  zone_occupancy;           // current count for CAPACITY_BREACH

      // Confidence
      float   confidence;               // [0..1] — event detection confidence

      // Evidence — optional media snapshot or clip
      boolean has_evidence;
      BlobRef evidence;                 // reference to snapshot image or video clip

      // Narrative — optional human-readable description
      boolean has_description;
      string  description;              // e.g., "Forklift stopped in pedestrian corridor
                                        //  near anchor loading-bay-3 for 4 minutes"

      // Timing
      Time   event_start;               // when the event condition began
      Time   stamp;                     // latest update time (may differ from event_start)

      // Producer
      string source_id;                 // who detected this event

      // Extensible metadata
      sequence<MetaKV, 8> attributes;

      string schema_version;            // MUST be "spatial.events/1.5"
    };


    // ================================================================
    // 3. ZONE STATE (periodic summary)
    // ================================================================

    // Lightweight periodic snapshot of a zone's current status.
    // Enables dashboards and capacity management without maintaining
    // event counters client-side.
    @extensibility(APPENDABLE) struct ZoneState {
      @key string zone_id;              // references SpatialZone.zone_id

      uint32 current_occupancy;         // number of tracked objects currently in zone
      boolean has_capacity;
      uint32  capacity;                 // echoed from SpatialZone for convenience

      // Active alert count by severity
      uint32 active_info;
      uint32 active_warning;
      uint32 active_alert;
      uint32 active_critical;

      // Class breakdown (optional — top N classes present)
      sequence<MetaKV, 8> class_counts; // namespace = class_id, json = {"count": N}

      Time   stamp;
      string source_id;
      // schema_version intentionally omitted: ZoneState is a lightweight
      // summary; version is inferred from the accompanying SpatialZone.
    };

  }; // module events
};   // module spatial

Example JSON (Informative)

Zone Definition: 

{
  "zone_id": "zone/facility-west/pedestrian-corridor-B",
  "name": "Pedestrian Corridor B",
  "kind": "RESTRICTED",
  "frame_ref": { "uuid": "f1a2b3c4-...", "fqn": "facility-west/enu" },
  "bounds": { "min_xyz": [10.0, 0.0, 0.0], "max_xyz": [25.0, 5.0, 3.0] },
  "has_geopose": false,
  "has_speed_limit_mps": false,
  "has_capacity": false,
  "has_dwell_limit_sec": true,
  "dwell_limit_sec": 120.0,
  "class_filter": ["forklift", "agv", "pallet_truck"],
  "has_schedule": true,
  "schedule": "R/2024-01-01T06:00:00/PT14H",
  "provider_id": "safety/zone-manager",
  "stamp": { "sec": 1714070400, "nanosec": 0 },
  "schema_version": "spatial.events/1.5"
}

Event: 

{
  "event_id": "evt/facility-west/2024-04-26T12:05:00Z/001",
  "type": "DWELL_TIMEOUT",
  "severity": "ALERT",
  "state": "ACTIVE",
  "has_zone_id": true,
  "zone_id": "zone/facility-west/pedestrian-corridor-B",
  "has_position": true,
  "position": [17.2, 2.1, 0.5],
  "frame_ref": { "uuid": "f1a2b3c4-...", "fqn": "facility-west/enu" },
  "has_trigger_det_id": true,
  "trigger_det_id": "det/fused/forklift-07",
  "has_trigger_track_id": true,
  "trigger_track_id": "track/forklift-07",
  "trigger_class_id": "forklift",
  "has_measured_dwell_sec": true,
  "measured_dwell_sec": 247.0,
  "confidence": 0.94,
  "has_evidence": true,
  "evidence": { "blob_id": "snap-20240426-120500-cam3", "role": "evidence/jpeg", "checksum": "sha256:ab12..." },
  "has_description": true,
  "description": "Forklift stopped in pedestrian corridor B near loading-bay-3 for 4 min 7 sec",
  "event_start": { "sec": 1714131653, "nanosec": 0 },
  "stamp": { "sec": 1714131900, "nanosec": 0 },
  "source_id": "analytics/zone-monitor",
  "schema_version": "spatial.events/1.5"
}

Appendix E: Provisional Extension Examples

These provisional extensions are intentionally minimal and subject to breaking changes in future versions. Implementers SHOULD treat all struct layouts as unstable and MUST NOT assume wire compatibility across spec revisions.

Neural Scene Representations (Informative Example)

The following IDL illustrates how neural scene representations (NeRFs, Gaussian splats, neural SDFs) could be described and queried through SpatialDDS. This example is informative only and is not part of the SpatialDDS 1.6 normative specification. Implementations MUST NOT assume wire compatibility with this IDL across spec revisions.

The Neural profile is retained as a design reference for future standardization. It is not included in the Profile Matrix (§3.5) and does not have a registered type, QoS profile, or topic pattern.

A mapping service might publish a NeuralFieldMeta describing a Gaussian splat covering part of a city block, and an AR client could request novel views from arbitrary camera poses.

The profile intentionally avoids prescribing model internals. model_format is a freeform string that identifies the training framework and version; model weights ride as blobs. This keeps the schema stable across the rapid evolution of neural representation research while giving consumers enough metadata to discover fields, check coverage, and request renders.

NeuralFieldMeta follows the same static-meta pattern as RadSensorMeta and LidarMeta: publish once with RELIABLE + TRANSIENT_LOCAL QoS so late joiners receive the current state. ViewSynthesisRequest and ViewSynthesisResponse follow the request/reply pattern used by SnapshotRequest and SnapshotResponse.

// SPDX-License-Identifier: MIT
// SpatialDDS Neural Profile 1.5 (Provisional Extension)
//
// PROVISIONAL: This profile is subject to breaking changes in future
// versions. Implementers SHOULD treat all struct layouts as unstable
// and MUST NOT assume wire compatibility across spec revisions.

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif

module spatial {
  module neural {

    const string MODULE_ID = "spatial.neural/1.5";

    typedef builtin::Time Time;
    typedef spatial::core::PoseSE3 PoseSE3;
    typedef spatial::core::Aabb3 Aabb3;
    typedef spatial::core::BlobRef BlobRef;
    typedef spatial::common::FrameRef FrameRef;

    enum RepresentationType {
      @value(0) NERF,
      @value(1) GAUSSIAN_SPLAT,
      @value(2) NEURAL_SDF,
      @value(3) NEURAL_MESH,
      @value(4) TRIPLANE,
      @value(255) CUSTOM
    };

    enum OutputModality {
      @value(0) RGB,
      @value(1) DEPTH,
      @value(2) NORMALS,
      @value(3) SEMANTICS,
      @value(4) ALPHA
    };

    @extensibility(APPENDABLE) struct NeuralFieldMeta {
      @key string field_id;

      RepresentationType rep_type;
      string model_format;

      FrameRef frame_ref;
      boolean has_extent;
      Aabb3 extent;

      boolean has_quality;
      float quality;
      string checkpoint;

      sequence<BlobRef, 16> model_blobs;

      sequence<OutputModality, 8> supported_outputs;

      boolean has_render_time_ms;
      float render_time_ms;

      Time stamp;
      string schema_version;             // MUST be "spatial.neural/1.5"
    };

    @extensibility(APPENDABLE) struct ViewSynthesisRequest {
      @key string request_id;
      string field_id;

      PoseSE3 camera_pose;
      boolean has_fov_deg;
      float fov_y_deg;
      uint16 width;
      uint16 height;

      sequence<OutputModality, 8> requested_outputs;

      string reply_topic;
      Time stamp;
      uint32 ttl_sec;
    };

    @extensibility(APPENDABLE) struct ViewSynthesisResponse {
      @key string request_id;

      sequence<BlobRef, 8> result_blobs;

      boolean has_render_time_ms;
      float render_time_ms;
      boolean has_quality;
      float quality;

      boolean succeeded;
      string diagnostic;

      Time stamp;
    };

  }; // module neural
};

Example JSON (Informative) 

{
  "field_id": "splat/downtown-sf-block-7",
  "rep_type": "GAUSSIAN_SPLAT",
  "model_format": "inria-3dgs-v1",
  "frame_ref": {
    "uuid": "ae6f0a3e-7a3e-4b1e-9b1f-0e9f1b7c1a10",
    "fqn": "earth-fixed"
  },
  "has_extent": true,
  "extent": {
    "min_xyz": [-122.420, 37.790, -5.0],
    "max_xyz": [-122.410, 37.800, 50.0]
  },
  "has_quality": true,
  "quality": 0.85,
  "checkpoint": "epoch-30000",
  "model_blobs": [
    { "blob_id": "gs-weights-001", "role": "weights", "checksum": "sha256:a1b2c3..." },
    { "blob_id": "gs-pointcloud-001", "role": "point_cloud", "checksum": "sha256:d4e5f6..." }
  ],
  "supported_outputs": ["RGB", "DEPTH", "NORMALS"],
  "has_render_time_ms": true,
  "render_time_ms": 12.5,
  "stamp": { "sec": 1714070400, "nanosec": 0 },
  "schema_version": "spatial.neural/1.5"
}

Agent Task Coordination (Informative Example)

The following IDL illustrates how spatial task coordination between agents, robots, and planners could be structured over SpatialDDS. This example is informative only and is not part of the SpatialDDS 1.6 normative specification.

Agent task coordination is retained as a design reference. The types shown here are not registered in the Profile Matrix, the registered types table, or the QoS profiles table. Deployments requiring agent coordination SHOULD treat this IDL as a starting point and define deployment-specific extensions.

This example covers two layers:

  • Single-task lifecycle. A planner publishes TaskRequest messages describing spatial tasks — navigate to a location, observe a region, build a map — and agents claim and report progress via TaskStatus.
  • Fleet coordination. Agents advertise availability and capabilities via AgentStatus. When multiple agents can handle a task, they may publish TaskOffer bids. The coordinator selects an agent via TaskAssignment. If an agent cannot finish, it publishes TaskHandoff with continuation context so the next agent picks up where it left off.

The design is deliberately minimal. Task-specific parameters are carried as freeform JSON in params fields, avoiding premature schema commitment for the wide variety of agent capabilities in robotics, drone fleets, AR-guided workflows, and AI services. Spatial targeting reuses the existing PoseSE3, FrameRef, and SpatialUri types so tasks can reference any addressable resource on the bus.

The profile defines what information agents and coordinators exchange, not how allocation decisions are made. A round-robin dispatcher, a market-based auction, a centralized optimizer, and a human dispatcher all consume the same typed messages. The allocation algorithm is an application-layer concern.

// SPDX-License-Identifier: MIT
// SpatialDDS Agent Profile 1.5 (Provisional Extension)
//
// PROVISIONAL: This profile is subject to breaking changes in future
// versions. Implementers SHOULD treat all struct layouts as unstable
// and MUST NOT assume wire compatibility across spec revisions.

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif

module spatial {
  module agent {

    const string MODULE_ID = "spatial.agent/1.5";

    typedef builtin::Time Time;
    typedef spatial::core::PoseSE3 PoseSE3;
    typedef spatial::core::FramedPose FramedPose;
    typedef spatial::common::FrameRef FrameRef;
    typedef string SpatialUri;

    // ---- Task types ----
    // Broad categories of spatial tasks an agent might execute.
    // CUSTOM allows deployment-specific task types with params.
    enum TaskType {
      @value(0) NAVIGATE,         // Move to a target pose or region
      @value(1) OBSERVE,          // Collect sensor data at/around a target
      @value(2) MANIPULATE,       // Physically interact with an object
      @value(3) MAP,              // Build or extend a spatial map
      @value(4) DELIVER,          // Transport an item to a target
      @value(5) REPORT,           // Generate and publish a data report
      @value(255) CUSTOM          // Deployment-specific; describe in params
    };

    // ---- Task lifecycle states ----
    enum TaskState {
      @value(0) PENDING,          // Published, not yet accepted
      @value(1) ACCEPTED,         // Agent has claimed the task
      @value(2) IN_PROGRESS,      // Execution underway
      @value(3) COMPLETED,        // Successfully finished
      @value(4) FAILED,           // Execution failed
      @value(5) CANCELLED         // Withdrawn by requester or agent
    };

    // ---- Priority levels ----
    enum TaskPriority {
      @value(0) LOW,
      @value(1) NORMAL,
      @value(2) HIGH,
      @value(3) CRITICAL
    };

    // ---- Task request ----
    // A planner or coordinator publishes a task for agents to claim.
    // Keyed by task_id so DDS KEEP_LAST gives the latest version.
    @extensibility(APPENDABLE) struct TaskRequest {
      @key string task_id;               // Unique task identifier

      TaskType type;                     // What kind of task
      TaskPriority priority;

      string requester_id;               // Agent or service requesting the task

      // Spatial target (optional -- not all tasks are spatially targeted)
      boolean has_target_pose;
      PoseSE3 target_pose;               // Goal pose (valid when flag true)
      boolean has_target_frame;
      FrameRef target_frame;             // Frame for target_pose (valid when flag true)
      boolean has_target_uri;
      SpatialUri target_uri;             // URI of target resource -- anchor, content,
                                         // service, or any addressable entity
                                         // (valid when flag true)

      // Task-specific parameters -- freeform JSON
      // Avoids premature schema commitment for diverse agent capabilities.
      // Examples:
      //   NAVIGATE: {"speed_mps": 1.5, "altitude_m": 30}
      //   OBSERVE:  {"sensor": "cam_front", "duration_sec": 60, "coverage_overlap": 0.3}
      //   MAP:      {"resolution_m": 0.05, "region_radius_m": 50}
      //   REPORT:   {"format": "json", "include_images": true}
      string params;                     // JSON object string; empty if no params

      // Timing
      boolean has_deadline;
      Time deadline;                     // Task must complete by this time
                                         // (valid when has_deadline == true)
      Time stamp;                        // Publication time
      uint32 ttl_sec;                    // Task offer expires after this
    };

    // ---- Task status ----
    // The executing agent (or the requester for CANCELLED) publishes
    // status updates. Keyed by task_id for KEEP_LAST per task.
    @extensibility(APPENDABLE) struct TaskStatus {
      @key string task_id;               // Mirrors TaskRequest.task_id

      TaskState state;
      string agent_id;                   // Agent executing (or that attempted);
                                         // empty if PENDING

      // Progress (optional -- meaningful for IN_PROGRESS)
      boolean has_progress;
      float progress;                    // 0..1 (valid when has_progress == true)

      // Result (optional -- meaningful for COMPLETED)
      boolean has_result_uri;
      SpatialUri result_uri;             // URI to result artifact (map, report, etc.)
                                         // (valid when has_result_uri == true)

      // Diagnostics
      string diagnostic;                 // Empty on success; error/status description
                                         // on FAILED or CANCELLED

      Time stamp;                        // Status update time
    };

    // ================================================================
    // FLEET COORDINATION
    // ================================================================
    //
    // Types that enable multi-agent task allocation over the DDS bus.
    // These define the information agents and coordinators exchange,
    // not the allocation algorithm. A round-robin dispatcher, a
    // market-based auction, and a centralized optimizer all consume
    // the same typed messages.

    // ---- Agent operational state ----
    enum AgentState {
      @value(0) IDLE,             // available for new tasks
      @value(1) BUSY,             // executing a task
      @value(2) CHARGING,         // recharging / refueling
      @value(3) RETURNING,        // returning to base / staging area
      @value(4) ERROR,            // fault condition -- not available
      @value(5) OFFLINE           // graceful shutdown / maintenance
    };

    // ---- Agent status advertisement ----
    // Each agent publishes its current status at regular intervals.
    // Keyed by agent_id; KEEP_LAST(1) per key with TRANSIENT_LOCAL
    // so new coordinators immediately see all active agents.
    //
    // This is the fleet-level complement to disco::Announce. Announce
    // tells you "a service exists with these profiles and coverage."
    // AgentStatus tells you "this specific agent is available, here's
    // what it can do right now, and here's its current state."
    @extensibility(APPENDABLE) struct AgentStatus {
      @key string agent_id;              // unique agent identifier

      string name;                       // human-readable (e.g., "Drone Unit 14")
      AgentState state;                  // current operational state

      // Capabilities -- which task types this agent can execute
      sequence<TaskType, 16> capable_tasks;

      // Current position (optional)
      boolean has_pose;
      FramedPose pose;                   // current pose with frame and uncertainty
                                         // (valid when has_pose == true)

      boolean has_geopose;
      spatial::core::GeoPose geopose;    // current geo-position (valid when flag true)

      // Resource levels (optional -- agent-type dependent)
      boolean has_battery_pct;
      float   battery_pct;               // [0..1] remaining charge

      boolean has_payload_kg;
      float   payload_kg;                // current payload mass
      boolean has_payload_capacity_kg;
      float   payload_capacity_kg;       // maximum payload mass

      boolean has_range_remaining_m;
      float   range_remaining_m;         // estimated remaining operational range (meters)

      // Current task (if BUSY)
      boolean has_current_task_id;
      string  current_task_id;           // task_id of current assignment

      // Queue depth -- how many tasks are queued behind the current one
      boolean has_queue_depth;
      uint32  queue_depth;

      // Extensible metadata (sensor suite, speed limits, special equipment, etc.)
      sequence<spatial::common::MetaKV, 16> attributes;

      Time   stamp;
      uint32 ttl_sec;                    // status expires if not refreshed
    };


    // ---- Task offer (agent -> coordinator) ----
    // An agent that can handle a TaskRequest publishes a TaskOffer
    // indicating its willingness and estimated cost. The coordinator
    // evaluates offers and publishes a TaskAssignment.
    //
    // This is optional. Simple deployments can skip offers entirely
    // and have the coordinator assign directly based on AgentStatus.
    // Offers enable decentralized decision-making where agents have
    // better local knowledge than the coordinator.
    @extensibility(APPENDABLE) struct TaskOffer {
      @key string offer_id;              // unique offer identifier
      string task_id;                    // references TaskRequest.task_id
      string agent_id;                   // offering agent

      // Estimated cost / fitness (lower is better; semantics are deployment-defined)
      float  cost;                       // e.g., estimated time (sec), energy (J), or normalized score

      // Estimated time to reach the task target
      boolean has_eta_sec;
      float   eta_sec;                   // estimated seconds to reach target

      // Distance to task target
      boolean has_distance_m;
      float   distance_m;                // straight-line or path distance (meters)

      // Agent's current resource snapshot at time of offer
      boolean has_battery_pct;
      float   battery_pct;

      // Freeform justification or constraints
      string  params;                    // JSON string; e.g., {"route": "via-corridor-A"}

      Time   stamp;
      uint32 ttl_sec;                    // offer expires if not accepted
    };


    // ---- Task assignment (coordinator -> agent) ----
    // The coordinator evaluates AgentStatus and/or TaskOffer messages
    // and publishes a TaskAssignment binding a task to a specific agent.
    // The assigned agent should respond with TaskStatus(ACCEPTED).
    //
    // Keyed by task_id -- at most one assignment per task.
    @extensibility(APPENDABLE) struct TaskAssignment {
      @key string task_id;               // references TaskRequest.task_id
      string agent_id;                   // assigned agent
      string coordinator_id;             // who made the assignment

      // Optional: selected offer reference
      boolean has_offer_id;
      string  offer_id;                  // references TaskOffer.offer_id (if offer-based)

      // Optional: override or refinement of the original TaskRequest params
      boolean has_params_override;
      string  params_override;           // JSON string; merged with TaskRequest.params

      Time   stamp;
    };


    // ---- Task handoff ----
    // When an agent cannot complete a task (low battery, leaving coverage,
    // hardware fault), it publishes a TaskHandoff before or alongside
    // TaskStatus(FAILED/CANCELLED). The coordinator uses this to
    // re-assign the task with context preserved.
    @extensibility(APPENDABLE) struct TaskHandoff {
      @key string handoff_id;            // unique handoff identifier
      string task_id;                    // original task being handed off
      string from_agent_id;              // agent releasing the task
      string reason;                     // human-readable (e.g., "battery below 15%")

      // Progress context for the next agent
      boolean has_progress;
      float   progress;                  // [0..1] how far the task got

      // Where the task was left off
      boolean has_last_pose;
      FramedPose last_pose;              // agent's pose at handoff, with frame and uncertainty
                                         // (valid when has_last_pose == true)

      // Task-specific continuation context -- whatever the next agent needs
      // to pick up where this one left off.
      string  context;                   // JSON string; e.g., {"waypoints_remaining": [...]}

      // Optional: preferred successor
      boolean has_preferred_agent_id;
      string  preferred_agent_id;        // agent the handoff prefers as successor

      Time   stamp;
    };

  }; // module agent
};

Example JSON (Informative)

Task Request: 

{
  "task_id": "task/survey-block-7",
  "type": "OBSERVE",
  "priority": "HIGH",
  "requester_id": "planner/fleet-coordinator",
  "has_target_pose": true,
  "target_pose": {
    "t": [-122.415, 37.795, 30.0],
    "q": [0.0, 0.0, 0.0, 1.0]
  },
  "has_target_frame": true,
  "target_frame": {
    "uuid": "ae6f0a3e-7a3e-4b1e-9b1f-0e9f1b7c1a10",
    "fqn": "earth-fixed"
  },
  "has_target_uri": false,
  "params": "{\"sensor\": \"cam_nadir\", \"duration_sec\": 120, \"overlap\": 0.4}",
  "has_deadline": true,
  "deadline": { "sec": 1714074000, "nanosec": 0 },
  "stamp": { "sec": 1714070400, "nanosec": 0 },
  "ttl_sec": 300
}

Task Status: 

{
  "task_id": "task/survey-block-7",
  "state": "IN_PROGRESS",
  "agent_id": "drone/unit-14",
  "has_progress": true,
  "progress": 0.45,
  "has_result_uri": false,
  "diagnostic": "",
  "stamp": { "sec": 1714071200, "nanosec": 0 }
}

Topic Layout

Type Topic QoS Notes
TaskRequest spatialdds/agent/tasks/task_request/v1 RELIABLE + TRANSIENT_LOCAL, KEEP_LAST(1) per key Coordinator publishes tasks.
TaskStatus spatialdds/agent/tasks/task_status/v1 RELIABLE + VOLATILE, KEEP_LAST(1) per key Agent reports lifecycle state.
AgentStatus spatialdds/agent/fleet/agent_status/v1 RELIABLE + TRANSIENT_LOCAL, KEEP_LAST(1) per key Agent advertises availability. Late joiners see all agents.
TaskOffer spatialdds/agent/fleet/task_offer/v1 RELIABLE + VOLATILE, KEEP_LAST(1) per key Optional: agent bids on a task.
TaskAssignment spatialdds/agent/fleet/task_assignment/v1 RELIABLE + TRANSIENT_LOCAL, KEEP_LAST(1) per key Coordinator assigns task to agent.
TaskHandoff spatialdds/agent/fleet/task_handoff/v1 RELIABLE + VOLATILE, KEEP_ALL Agent requests task transfer with context.

QoS defaults for agent topics

Topic Reliability Durability History
task_request RELIABLE TRANSIENT_LOCAL KEEP_LAST(1) per key
task_status RELIABLE VOLATILE KEEP_LAST(1) per key
agent_status RELIABLE TRANSIENT_LOCAL KEEP_LAST(1) per key
task_offer RELIABLE VOLATILE KEEP_LAST(1) per key
task_assignment RELIABLE TRANSIENT_LOCAL KEEP_LAST(1) per key
task_handoff RELIABLE VOLATILE KEEP_ALL

Agent Status: 

{
  "agent_id": "drone/unit-14",
  "name": "Drone Unit 14",
  "state": "IDLE",
  "capable_tasks": ["NAVIGATE", "OBSERVE", "MAP"],
  "has_pose": true,
  "pose": {
    "pose": { "t": [12.5, -3.2, 1.1], "q": [0.0, 0.0, 0.0, 1.0] },
    "frame_ref": { "uuid": "ae6f0a3e-7a3e-4b1e-9b1f-0e9f1b7c1a10", "fqn": "facility-west/enu" },
    "cov": { "type": "COV_NONE" },
    "stamp": { "sec": 1714071000, "nanosec": 0 }
  },
  "has_geopose": false,
  "has_battery_pct": true,
  "battery_pct": 0.72,
  "has_payload_kg": true,
  "payload_kg": 0.0,
  "has_payload_capacity_kg": true,
  "payload_capacity_kg": 2.5,
  "has_range_remaining_m": true,
  "range_remaining_m": 4200.0,
  "has_current_task_id": false,
  "has_queue_depth": true,
  "queue_depth": 0,
  "stamp": { "sec": 1714071000, "nanosec": 0 },
  "ttl_sec": 30
}

Task Offer: 

{
  "offer_id": "offer/unit-14/survey-block-7",
  "task_id": "task/survey-block-7",
  "agent_id": "drone/unit-14",
  "cost": 142.5,
  "has_eta_sec": true,
  "eta_sec": 45.0,
  "has_distance_m": true,
  "distance_m": 310.0,
  "has_battery_pct": true,
  "battery_pct": 0.72,
  "params": "{\"route\": \"direct\", \"estimated_energy_pct\": 0.18}",
  "stamp": { "sec": 1714071005, "nanosec": 0 },
  "ttl_sec": 30
}

Task Assignment: 

{
  "task_id": "task/survey-block-7",
  "agent_id": "drone/unit-14",
  "coordinator_id": "planner/fleet-coordinator",
  "has_offer_id": true,
  "offer_id": "offer/unit-14/survey-block-7",
  "has_params_override": false,
  "stamp": { "sec": 1714071010, "nanosec": 0 }
}

Task Handoff: 

{
  "task_id": "task/survey-block-7",
  "handoff_id": "handoff/unit-14/survey-block-7/001",
  "from_agent_id": "drone/unit-14",
  "reason": "battery below 15%",
  "has_progress": true,
  "progress": 0.63,
  "has_last_pose": true,
  "last_pose": {
    "pose": { "t": [45.2, 12.8, 30.0], "q": [0.0, 0.0, 0.38, 0.92] },
    "frame_ref": { "uuid": "ae6f0a3e-7a3e-4b1e-9b1f-0e9f1b7c1a10", "fqn": "earth-fixed" },
    "cov": { "type": "COV_NONE" },
    "stamp": { "sec": 1714072800, "nanosec": 0 }
  },
  "context": "{\"waypoints_remaining\": [[50.1, 15.0, 30.0], [55.3, 18.2, 30.0]], \"images_captured\": 147}",
  "has_preferred_agent_id": false,
  "stamp": { "sec": 1714072800, "nanosec": 0 }
}

Example: RF Beam Sensing Extension (Provisional)

This profile provides typed transport for phased-array beam power measurements used in ISAC research. It defines static array metadata (RfBeamMeta), per-sweep power vectors (RfBeamFrame), and multi-array batches (RfBeamArraySet). The design follows the Meta/Frame pattern used elsewhere in the sensing profiles and is intentionally provisional.

// SPDX-License-Identifier: MIT
// SpatialDDS RF Beam Sensing Profile 1.5 (Provisional Extension)
//
// PROVISIONAL: This profile is subject to breaking changes in future
// versions. Implementers SHOULD treat all struct layouts as unstable
// and MUST NOT assume wire compatibility across spec revisions.

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif
#ifndef SPATIAL_SENSING_COMMON_INCLUDED
#define SPATIAL_SENSING_COMMON_INCLUDED
#include "common.idl"
#endif

module spatial { module sensing { module rf_beam {

  // Module identifier for discovery and schema registration
  const string MODULE_ID = "spatial.sensing.rf_beam/1.5";

  // Reuse Core + Sensing Common types
  typedef builtin::Time                          Time;
  typedef spatial::core::PoseSE3                 PoseSE3;
  typedef spatial::core::BlobRef                 BlobRef;
  typedef spatial::common::FrameRef              FrameRef;

  typedef spatial::sensing::common::StreamMeta   StreamMeta;
  typedef spatial::sensing::common::FrameHeader  FrameHeader;
  typedef spatial::sensing::common::FrameQuality FrameQuality;
  typedef spatial::sensing::common::Codec        Codec;
  typedef spatial::sensing::common::SampleType   SampleType;

  // ---- Beam sweep classification ----
  enum BeamSweepType {
    @value(0) EXHAUSTIVE,           // full codebook sweep (e.g., 64 beams)
    @value(1) HIERARCHICAL,         // multi-stage: wide beams -> narrow refinement
    @value(2) TRACKING,             // narrow sweep around predicted beam
    @value(3) PARTIAL,              // subset of codebook (AI-selected beams)
    @value(255) OTHER
  };

  // ---- Power measurement unit ----
  enum PowerUnit {
    @value(0) DBM,                  // decibels relative to 1 milliwatt (default)
    @value(1) LINEAR_MW,            // milliwatts (linear scale)
    @value(2) RSRP,                 // Reference Signal Received Power (3GPP)
    @value(255) OTHER_UNIT
  };

  // ---- Static array description ----
  // RELIABLE + TRANSIENT_LOCAL (late joiners receive the latest meta)
  @extensibility(APPENDABLE) struct RfBeamMeta {
    @key string stream_id;               // stable id for this beam stream
    StreamMeta base;                     // frame_ref, T_bus_sensor, nominal_rate_hz

    // --- Carrier ---
    float   center_freq_ghz;             // carrier frequency (e.g., 60.0, 28.0, 140.0)
    boolean has_bandwidth;
    float   bandwidth_ghz;               // valid when has_bandwidth == true (e.g., 0.02 for 20 MHz)

    // --- Phased array description ---
    uint16  n_elements;                  // antenna elements in the array (e.g., 16)
    uint16  n_beams;                     // codebook size (e.g., 64, 128, 256)

    // --- Spatial coverage ---
    float   fov_az_deg;                  // total azimuth FoV covered by codebook (e.g., 90)
    boolean has_fov_el;
    float   fov_el_deg;                  // valid when has_fov_el == true (e.g., 30)

    // --- Array identity within a rig (for multi-array setups) ---
    boolean has_array_index;
    uint8   array_index;                 // valid when has_array_index == true; 0-based
    string  array_label;                 // human-readable label (e.g., "front", "left", "rear", "right")

    // --- Codebook description (informative) ---
    string  codebook_type;               // e.g., "DFT-64", "DFT-oversampled-128", "hierarchical-3stage"

    // --- MIMO configuration (optional, for hybrid arrays) ---
    boolean has_mimo_config;
    uint16  n_tx;                        // valid when has_mimo_config == true
    uint16  n_rx;                        // valid when has_mimo_config == true

    // --- Power unit convention ---
    PowerUnit power_unit;                // unit for power in RfBeamFrame (default: DBM)

    string  schema_version;              // MUST be "spatial.sensing.rf_beam/1.5"
  };

  // ---- Per-sweep beam power measurement ----
  // BEST_EFFORT + KEEP_LAST=1
  @extensibility(APPENDABLE) struct RfBeamFrame {
    @key string stream_id;
    uint64 frame_seq;
    FrameHeader hdr;                     // t_start/t_end, optional sensor_pose, blobs[]

    BeamSweepType sweep_type;

    // --- Power vector ---
    // One entry per beam in codebook order (index 0 = beam 0, etc.)
    // Length MUST equal RfBeamMeta.n_beams for EXHAUSTIVE sweeps.
    // For PARTIAL/TRACKING sweeps, length <= n_beams; beam_indices
    // maps each entry to its codebook position.
    sequence<float, 1024> power;         // received power per beam (unit per RfBeamMeta.power_unit)

    // Sparse sweep support: when sweep_type != EXHAUSTIVE,
    // beam_indices maps power[i] to codebook index beam_indices[i].
    // Empty when sweep_type == EXHAUSTIVE (implicit 0..n_beams-1).
    sequence<uint16, 1024> beam_indices; // codebook indices; empty for exhaustive sweeps

    // --- Derived fields ---
    boolean has_best_beam;
    uint16  best_beam_idx;               // valid when has_best_beam == true
    float   best_beam_power;             // valid when has_best_beam == true (same unit)

    // --- Link state (ISAC-specific) ---
    boolean has_blockage_state;
    boolean is_blocked;                  // valid when has_blockage_state == true
    float   blockage_confidence;         // valid when has_blockage_state == true (0.0..1.0)

    // --- Signal quality (optional) ---
    boolean has_snr_db;
    float   snr_db;                      // valid when has_snr_db == true

    // --- Frame quality ---
    boolean has_quality;
    FrameQuality quality;                // valid when has_quality == true
  };

  // ---- Multi-array synchronized set ----
  // For rigs with multiple phased arrays (e.g., V2V with 4x arrays for 360 deg coverage).
  // Batches one RfBeamFrame per array at the same time step.
  // BEST_EFFORT + KEEP_LAST=1
  @extensibility(APPENDABLE) struct RfBeamArraySet {
    @key string set_id;                  // stable id for this array set
    uint64 frame_seq;
    Time   stamp;                        // common timestamp for all arrays

    sequence<RfBeamFrame, 8> arrays;     // one per phased array in the rig

    // Cross-array best beam (global index = array_index * n_beams + beam_idx)
    boolean has_overall_best;
    uint16  overall_best_array_idx;      // valid when has_overall_best == true
    uint16  overall_best_beam_idx;       // valid when has_overall_best == true
    float   overall_best_power;          // valid when has_overall_best == true
  };

}; }; };

Example: Radio Fingerprint Extension (Provisional)

This profile provides typed transport for radio-environment observations used by radio-assisted localization and indoor positioning pipelines. It targets commodity radios (WiFi, BLE, UWB, cellular) and closes the "radio via freeform metadata only" gap by introducing schema-enforced observation structs.

The profile defines transport only. It does not define positioning, trilateration, filtering, or sensor-fusion algorithms.

Module ID: spatial.sensing.radio/1.5
Dependency: spatial.sensing.common@1.x
Status: Provisional (K-R1 maturity gate)

Overview

RadioSensorMeta follows the static Meta pattern (RELIABLE + TRANSIENT_LOCAL) and publishes sensor capabilities. RadioScan is the streaming message carrying per-scan observations. Each scan is a snapshot of visible transmitters for one radio technology at one scan instant/window.

Relationship to sensing.rf_beam

sensing.rf_beam covers phased-array mmWave beam power vectors and ISAC-style beam management.
sensing.radio covers commodity radio fingerprints and ranging observations (WiFi/BLE/UWB/cellular).
They are complementary and may be published together by the same node.

Profile Scope Typical Frequency Key Measurement
sensing.rf_beam mmWave phased arrays (28/60/140 GHz) 10 Hz per sweep Per-beam power vector
sensing.radio WiFi/BLE/UWB/cellular 0.1–10 Hz per scan Per-transmitter RSSI/RTT/AoA/Range

IDL (Provisional)

// SPDX-License-Identifier: MIT
// SpatialDDS Radio Fingerprint (sensing.radio) 1.5 — Provisional Extension
//
// PROVISIONAL: This profile is subject to breaking changes in future
// versions. Implementers SHOULD treat all struct layouts as unstable
// and MUST NOT assume wire compatibility across spec revisions.

#ifndef SPATIAL_CORE_INCLUDED
#define SPATIAL_CORE_INCLUDED
#include "core.idl"
#endif
#ifndef SPATIAL_SENSING_COMMON_INCLUDED
#define SPATIAL_SENSING_COMMON_INCLUDED
#include "common.idl"
#endif

module spatial { module sensing { module radio {

  const string MODULE_ID = "spatial.sensing.radio/1.5";

  typedef builtin::Time                          Time;
  typedef spatial::core::PoseSE3                 PoseSE3;
  typedef spatial::common::FrameRef              FrameRef;
  typedef spatial::common::MetaKV                MetaKV;
  typedef spatial::sensing::common::StreamMeta   StreamMeta;

  enum RadioType {
    @value(0)   WIFI,
    @value(1)   BLE,
    @value(2)   BT_CLASSIC,
    @value(3)   UWB,
    @value(4)   CELLULAR,
    @value(5)   LORA,
    @value(255) OTHER_RADIO
  };

  enum WifiBand {
    @value(0) BAND_UNKNOWN,
    @value(1) BAND_2_4GHZ,
    @value(2) BAND_5GHZ,
    @value(3) BAND_6GHZ,
    @value(4) BAND_60GHZ
  };

  enum RadioMeasurementKind {
    @value(0)  RSSI,
    @value(1)  RTT_NS,
    @value(2)  AOA_DEG,
    @value(3)  RANGE_M,
    @value(4)  RSRP,
    @value(5)  CSI_REF,
    @value(255) OTHER_MEASURE
  };

  @extensibility(APPENDABLE) struct RadioObservation {
    string               identifier;
    RadioMeasurementKind measurement_kind;
    float                value;

    boolean  has_frequency;
    float    frequency_mhz;
    boolean  has_band;
    WifiBand band;
    boolean  has_ssid;
    string   ssid;
    boolean  has_channel;
    uint16   channel;

    boolean  has_major_minor;
    uint16   major;
    uint16   minor;
    boolean  has_tx_power;
    int8     tx_power_dbm;

    boolean  has_range;
    float    range_m;
    boolean  has_range_std;
    float    range_std_m;

    boolean  has_aoa_azimuth;
    float    aoa_azimuth_deg;
    boolean  has_aoa_elevation;
    float    aoa_elevation_deg;

    boolean  has_noise_floor;
    float    noise_floor_dbm;
    boolean  has_measurement_count;
    uint8    measurement_count;
  };

  @extensibility(APPENDABLE) struct RadioScan {
    @key string sensor_id;
    RadioType   radio_type;
    uint64      scan_seq;
    Time        stamp;

    boolean     has_scan_duration;
    float       scan_duration_s;

    sequence<RadioObservation, 256> observations;

    boolean     has_aggregation_window;
    float       aggregation_window_s;

    string      source_id;

    boolean     has_sensor_pose;
    PoseSE3     sensor_pose;
    FrameRef    pose_frame_ref;

    string      schema_version; // MUST be "spatial.sensing.radio/1.5"
  };

  @extensibility(APPENDABLE) struct RadioSensorMeta {
    @key string sensor_id;
    StreamMeta  base;

    RadioType   radio_type;

    boolean     has_rssi;
    boolean     has_rtt;
    boolean     has_aoa;
    boolean     has_csi;

    boolean     has_wifi_bands;
    sequence<WifiBand, 4> supported_bands;

    boolean     has_ble_version;
    string      ble_version;

    string      device_model;
    string      platform;

    boolean     has_max_observations;
    uint16      max_observations;
    boolean     has_typical_scan_duration;
    float       typical_scan_duration_s;

    string      schema_version; // MUST be "spatial.sensing.radio/1.5"
  };

}; }; };

Observation Semantics (Normative)

measurement_kind determines the unit and interpretation of RadioObservation.value.

Kind Value Units Typical Source
RSSI dBm WiFi and BLE scans
RTT_NS nanoseconds WiFi FTM, UWB TWR
AOA_DEG degrees UWB/BLE AoA
RANGE_M meters Derived range
RSRP dBm Cellular
CSI_REF n/a (value unused) CSI blob reference workflows

A single RadioScan MAY include mixed measurement_kind values.

Identifier Conventions (Normative)

RadioType Identifier Format Example
WIFI BSSID, lowercase colon-separated hex aa:bb:cc:dd:ee:ff
BLE UUID (uppercase with hyphens) or MAC 12345678-1234-1234-1234-123456789ABC
UWB Short address or session ID 0x1A2B
CELLULAR MCC-MNC-LAC-CID 310-260-12345-67890

Consumers performing fingerprint matching SHOULD normalize identifiers before comparison.

Scan Timing and Aggregation (Normative)

  • stamp MUST represent the midpoint of the scan window.
  • If has_scan_duration == true, scan_duration_s MUST report the full scan-window duration.
  • If has_aggregation_window == true, aggregation_window_s reports the total time window used to aggregate observations from multiple scans.
  • Producers publishing raw scans SHOULD leave has_aggregation_window = false.

Privacy Considerations (Normative Guidance)

Radio identifiers can expose device/network identity. Producers in privacy-sensitive deployments SHOULD: - anonymize or hash identifiers where permitted, - avoid publishing SSIDs unless explicitly needed, and - document identifier handling and retention policy.

Topic Patterns

Topic Message Type QoS
spatialdds/<scene>/radio/<sensor_id>/scan/v1 RadioScan RADIO_SCAN_RT
spatialdds/<scene>/radio/<sensor_id>/meta/v1 RadioSensorMeta RELIABLE + TRANSIENT_LOCAL

Example JSON (Informative)

WiFi scan: 

{
  "sensor_id": "hololens2-wifi-01",
  "radio_type": "WIFI",
  "scan_seq": 42,
  "stamp": { "sec": 1714071012, "nanosec": 500000000 },
  "has_scan_duration": true,
  "scan_duration_s": 2.1,
  "observations": [
    {
      "identifier": "aa:bb:cc:dd:ee:01",
      "measurement_kind": "RSSI",
      "value": -52.0,
      "has_frequency": true,
      "frequency_mhz": 5180.0,
      "has_band": true,
      "band": "BAND_5GHZ",
      "has_ssid": true,
      "ssid": "ETH-WiFi",
      "has_channel": true,
      "channel": 36
    }
  ],
  "has_aggregation_window": true,
  "aggregation_window_s": 4.0,
  "source_id": "lamar-cab-hololens-session-17",
  "schema_version": "spatial.sensing.radio/1.5"
}

UWB ranging round: 

{
  "sensor_id": "uwb-tag-reader-01",
  "radio_type": "UWB",
  "scan_seq": 5017,
  "stamp": { "sec": 1714071020, "nanosec": 250000000 },
  "observations": [
    {
      "identifier": "0x1A2B",
      "measurement_kind": "RANGE_M",
      "value": 3.21,
      "has_range": true,
      "range_m": 3.21,
      "has_range_std": true,
      "range_std_m": 0.08,
      "has_aoa_azimuth": true,
      "aoa_azimuth_deg": 23.5
    }
  ],
  "source_id": "warehouse-uwb-reader-alpha",
  "schema_version": "spatial.sensing.radio/1.5"
}

Maturity Gate (K-R1)

Promotion to stable requires: 1. At least one conformance dataset exercising WiFi and BLE paths with at least 20 checks passing. 2. At least one independent implementation ingesting reference WiFi/BLE files through RadioScan. 3. No breaking IDL changes for six months after initial publication.

Integration Notes (Informative)

Discovery integration: Neural and agent services advertise via Announce with ServiceKind::OTHER. To signal neural or agent capabilities, services SHOULD include feature flags in caps.features such as neural.field_meta, neural.view_synth, or agent.tasking.

Topic naming: following spatialdds/<domain>/<stream>/<type>/<version>:

Message Suggested Topic Pattern
NeuralFieldMeta spatialdds/neural/<field_id>/field_meta/v1
ViewSynthesisRequest spatialdds/neural/<field_id>/view_synth_req/v1
ViewSynthesisResponse Uses reply_topic from request
TaskRequest spatialdds/agent/tasks/task_request/v1
TaskStatus spatialdds/agent/tasks/task_status/v1
AgentStatus spatialdds/agent/fleet/agent_status/v1
TaskOffer spatialdds/agent/fleet/task_offer/v1
TaskAssignment spatialdds/agent/fleet/task_assignment/v1
TaskHandoff spatialdds/agent/fleet/task_handoff/v1
RadioSensorMeta spatialdds/<scene>/radio/<sensor_id>/meta/v1
RadioScan spatialdds/<scene>/radio/<sensor_id>/scan/v1

QoS suggestions (informative):

Message Reliability Durability History
NeuralFieldMeta RELIABLE TRANSIENT_LOCAL KEEP_LAST(1) per key
ViewSynthesisRequest RELIABLE VOLATILE KEEP_ALL
ViewSynthesisResponse RELIABLE VOLATILE KEEP_LAST(1) per key
TaskRequest RELIABLE TRANSIENT_LOCAL KEEP_LAST(1) per key
TaskStatus RELIABLE VOLATILE KEEP_LAST(1) per key
AgentStatus RELIABLE TRANSIENT_LOCAL KEEP_LAST(1) per key
TaskOffer RELIABLE VOLATILE KEEP_LAST(1) per key
TaskAssignment RELIABLE TRANSIENT_LOCAL KEEP_LAST(1) per key
TaskHandoff RELIABLE VOLATILE KEEP_ALL
RadioSensorMeta RELIABLE TRANSIENT_LOCAL KEEP_LAST(1) per key
RadioScan BEST_EFFORT VOLATILE KEEP_LAST(1)

Profile matrix: spatial.neural/1.5, spatial.agent/1.5, spatial.sensing.rf_beam/1.5, and spatial.sensing.radio/1.5 are provisional Appendix E profiles. When promoted to stable in a future version, they move to Appendix D.

Appendix F: SpatialDDS URI Scheme (ABNF)

SpatialDDS defines a URI scheme for anchors, content, and services. The human-readable pattern is:

spatialdds://<authority>/<zone>/<rtype>/<rid>[;param][?query][#fragment]
  • authority — a DNS name, case-insensitive.
  • zone — a namespace identifier (letters, digits, -, _, :).
  • rtype — resource type (for example anchor, content, tileset, service, stream).
  • rid — resource identifier (letters, digits, -, _).
  • param — optional key=value parameters separated by ;.
  • query/fragment — follow RFC 3986 semantics.

ABNF

The grammar below reuses RFC 3986 terminals (ALPHA, DIGIT, unreserved, pct-encoded, query, fragment).

spatialdds-URI = "spatialdds://" authority "/" zone "/" rtype "/" rid
                 *( ";" param ) [ "?" query ] [ "#" fragment ]

authority      = dns-name
dns-name       = label *( "." label )
label          = alnum [ *( alnum / "-" ) alnum ]
alnum          = ALPHA / DIGIT

zone           = 1*( zone-char )
zone-char      = ALPHA / DIGIT / "-" / "_" / ":"

rtype          = "anchor" / "content" / "tileset" / "service" / "stream"

rid            = 1*( rid-char )
rid-char       = ALPHA / DIGIT / "-" / "_"

param          = pname [ "=" pvalue ]
pname          = 1*( ALPHA / DIGIT / "-" / "_" )
pvalue         = 1*( unreserved / pct-encoded / ":" / "@" / "." )

Notes

  • Comparison rules: authority is case-insensitive; all other components are case-sensitive after percent-decoding.
  • Reserved params: v (revision identifier), ts (RFC 3339 timestamp). Others are vendor-specific.
  • Semantics: URIs without ;v= act as persistent identifiers (PID). With ;v= they denote immutable revisions (RID).
  • Resolution: This appendix defines syntax only. Normative resolution behavior is defined in §7.5 (SpatialURI Resolution).

Examples

spatialdds://museum.example.org/hall1/anchor/01J9Q0A6KZ;v=12
spatialdds://openarcloud.org/zone:sf/tileset/city3d;v=3?lang=en

Appendix F.X Discovery Query Expression (Informative)

This appendix defines the boolean filter grammar used by the deprecated disco.CoverageQuery.expr. This grammar is deprecated as of 1.5 and will be removed in 2.0. New implementations MUST use CoverageQuery.filter instead. The language is case-sensitive, UTF-8, and whitespace-tolerant. Identifiers target announced metadata fields (for example type, profile, module_id); string literals are double-quoted and use a C-style escape subset.

expr       = or-expr
or-expr    = and-expr *( WS "||" WS and-expr )
and-expr   = unary-expr *( WS "&&" WS unary-expr )
unary-expr = [ "!" WS ] primary
primary    = comparison / "(" WS expr WS ")"
comparison = ident WS op WS value
op         = "==" / "!="
ident      = 1*( ALPHA / DIGIT / "_" / "." )
value      = string
string     = DQUOTE *( string-char ) DQUOTE
string-char= %x20-21 / %x23-5B / %x5D-10FFFF / escape
escape     = "\\" ( DQUOTE / "\\" / "n" / "r" / "t" )
WS         = *( SP / HTAB )

; Notes:
; - Identifiers address announced metadata fields (e.g., "type", "profile", "module_id").
; - Values are double-quoted strings; escapes follow C-style subset.
; - Operators: equality and inequality only. Boolean ops: &&, ||, unary !
; - Parentheses group precedence; otherwise, ! > && > ||
; - Comparisons are exact string matches; wildcards/globs are not supported.
; - Unknown identifiers evaluate to false in comparisons.

Appendix G: Frame Identifiers (Informative Reference)

SpatialDDS represents reference frames using the FrameRef structure:

The normative IDL for FrameRef resides in Appendix A (Core profile). This appendix is descriptive/informative and restates the usage guidance for reference frames.

struct FrameRef {
  string uuid;   // globally unique frame ID
  string fqn;    // normalized fully-qualified name, e.g. "earth-fixed/map/cam_front"
};

UUID Rules

  • uuid is authoritative for identity.
  • fqn is an optional human-readable alias.
  • Implementations MUST treat uuid uniqueness as the identity key.
  • Deployments SHOULD establish well-known UUIDs for standard roots (e.g., earth-fixed, map, body) and document them for participants.

Name and Hierarchy Rules

  • fqn components are slash-delimited.
  • Reserved roots include earth-fixed, map, body, anchor, local.
  • A FrameRef DAG MUST be acyclic.

Constructing FQNs from External Data (Informative)

Datasets and frameworks that use flat frame identifiers (e.g., nuScenes calibrated_sensor.token, ROS TF frame_id) must construct hierarchical FQNs when publishing to SpatialDDS.

Recommended approach: 1. Choose a root corresponding to the vehicle/robot body: fqn = "ego" or fqn = "<vehicle_id>". 2. Append the sensor channel as a child: fqn = "ego/cam_front", fqn = "ego/lidar_top". 3. Use the original flat token as the uuid field. 4. For earth-fixed references, use the reserved root fqn = "earth-fixed".

Example nuScenes mapping: 

calibrated_sensor.token = "a1b2c3..."
sensor.channel = "CAM_FRONT"
-> FrameRef { uuid: "a1b2c3...", fqn: "ego/cam_front" }

Manifest References

Manifest entries that refer to frames MUST use a FrameRef object rather than raw strings. Each manifest MAY define local frame aliases resolvable by fqn.

Notes

Derived schemas (e.g. GeoPose, Anchors) SHALL refer to the Appendix A definition by reference and MUST NOT re-declare frame semantics. The conventions in §2.1 and the coverage/discovery rules in §3.3 reference this appendix for their frame requirements.

Appendix H: SpatialDDS as a Grounding Layer for World Models (Informative)

H.1 The Grounding Problem

AI world models — whether learned latent dynamics (JEPA, Cosmos), foundation models for robotics (RT-2, π₀, Octo), digital twins, or planning agents — require structured, real-time observations of the physical world. The model needs to know what exists, where it is, how it's moving, and what the spatial context is.

Today, the infrastructure for connecting world models to physical reality is bespoke. Each deployment builds custom sensor pipelines, coordinate frame management, and data ingestion. The model architecture is advancing rapidly; the grounding infrastructure is not.

SpatialDDS provides this grounding layer: typed, discoverable, multi-source spatial observations on a real-time bus. Every SpatialDDS profile answers a question a world model asks:

World Model Question SpatialDDS Profile Key Types
What objects exist and where? spatial.semantics Detection3D, Detection3DSet
What does it look like from here? spatial.sensing.vision VisionFrame, CamIntrinsics
What's the 3D structure? spatial.sensing.lidar LidarFrame, LidarMeta
What RF environment is here? spatial.sensing.radio RadioScan
Where am I in the world? spatial.core + spatial.argeo GeoPose, GeoAnchor
What zones exist, what's their state? spatial.events SpatialZone, ZoneState
What just happened? spatial.events SpatialEvent
What data sources exist? spatial.discovery Announce, CoverageQuery
What does this agent intend to do? spatial.core PlannedTrajectory
Which observations refer to the same thing? spatial.core EntityBinding

H.2 Integration Patterns

SpatialDDS does not prescribe how world models consume spatial data. Instead, it defines typed messages that bridge naturally into existing ML and robotics ecosystems:

Recording bridge (SpatialDDS → MCAP/Parquet). A recorder subscribes to SpatialDDS topics and writes them as MCAP files or Parquet tables. Offline training pipelines (LeRobot, Open X-Embodiment) ingest these recordings as episodes. SpatialDDS's typed messages preserve spatial semantics through the recording: coordinate frames, timestamps, uncertainties, and source provenance survive the round trip.

Gymnasium bridge (SpatialDDS → Gym observation space). A thin adapter wraps a SpatialDDS subscription as a Gymnasium observation space. RL agents receive structured spatial observations (detections, poses, zone states) at each step. SpatialDDS's discovery profile provides the observation-space manifest: what types are available, at what rates, with what spatial coverage.

Inference service bridge (Model → SpatialDDS). A world-model inference server subscribes to SpatialDDS sensor streams, runs prediction, and publishes results back to the bus as PlannedTrajectory or Detection3D predictions. The model is a SpatialDDS participant, not an external system.

H.3 What SpatialDDS Does Not Do

SpatialDDS is not an AI middleware. It does not define:

  • Episode structure or step indexing (use Open X-Embodiment, LeRobot, or application-specific formats).
  • Action spaces or action vectors (use Gymnasium, Isaac Lab, or application-specific formats).
  • Latent representations or tokenized embeddings (internal to the model).
  • Reward functions or value estimates (internal to the training pipeline).
  • Model weights, checkpoints, or training configuration (use ONNX, safetensors, or framework-native formats).

These concerns belong to the AI/ML ecosystem and are best served by existing, purpose-built formats. SpatialDDS's role is to provide the real-time spatial observations that these systems consume and the spatial predictions they produce — the grounding layer between the physical world and the world model.

H.4 Relationship to Factor Graphs and Scene Graphs

SpatialDDS's pose-graph types (Node, Edge, MapMeta, MapAlignment) carry the inputs and outputs of factor graph inference. The Node/Edge structure is a pose graph — one specific application of factor graphs. Full factor graph interchange (arbitrary variable and factor types) is a separate concern best served by a dedicated interchange format.

Similarly, SpatialDDS does not impose a scene graph. Scene graphs are deterministic hierarchical representations of entity state (parent-child transforms, component attachment). They belong in the consumer (digital twin, game engine, BIM system), not on the bus. EntityBinding provides the minimal cross-topic correlation that consumers need to build their own scene graphs from SpatialDDS streams.

The layering is:

  • Factor graphs: inside the optimizer (GTSAM, Ceres).
  • SpatialDDS: carries observations and inferred state.
  • Scene graphs: inside the consumer (Unity, Omniverse, twin).

Appendix I: Dataset Conformance Testing (Informative)

This appendix documents systematic conformance testing performed against five public reference datasets. The results validated the completeness and expressiveness of the SpatialDDS 1.6 sensing, mapping, coordination, and spatial events profiles and directly informed several normative additions to this specification.

Scope and Limitations

The conformance tests in this appendix validate schema expressiveness — whether every field in a reference dataset has a lossless mapping to a SpatialDDS type. They are performed as static schema-vs-schema analyses and do NOT validate:

  • Wire-level interoperability between DDS implementations (e.g., CycloneDDS ↔ Fast DDS ↔ RTI Connext).
  • Runtime correctness of publish/subscribe delivery, QoS enforcement, or temporal ordering.
  • End-to-end data fidelity of encode → transmit → decode round-trips.

Wire-level interop tests across at least two DDS vendors are planned for a future revision (see §6 Future Directions).

Pass rates reported below reflect expressiveness coverage. A "pass" means the dataset field has a complete, lossless mapping to SpatialDDS types. A "gap" means no suitable type exists and an extension is needed. Deferred items are fields that can be carried (e.g., via MetaKV) but lack first-class typed support.

Motivation

Sensor-data specifications risk becoming disconnected from real-world workloads if they are designed in isolation. To guard against this, the SpatialDDS 1.6 profiles were validated against five complementary datasets that together exercise the full signal-to-semantics pipeline and multi-agent coordination:

Dataset Focus Modalities Stressed
nuScenes (Motional / nuTonomy) Perception → semantics Camera (6×), lidar, radar detections (5×), 3D annotations, coordinate conventions
DeepSense 6G (ASU Wireless Intelligence Lab) Signal → perception Raw radar I/Q tensors, 360° cameras, lidar, IMU, GPS-RTK, mmWave beam vectors
S3E (Sun Yat-sen University / HKUST) Multi-agent coordination 3 UGVs × (lidar, stereo, IMU), UWB inter-robot ranging, RTK-GNSS, collaborative SLAM
ScanNet (TU Munich / Princeton) Indoor scene understanding RGB-D depth frames, 3D surface mesh, instance segmentation (NYU40), room-level zones, 20 scene types
LaMAR (CVG ETH Zürich / Microsoft) Multi-device AR localization & mapping HoloLens 4-camera rig (GRAY8 + ToF depth + IR + IMU), iPad LiDAR, NavVis scanner mesh + 1080p panoramic cameras, WiFi/BT radio scans, year-long multi-session alignment, GeoAnchor reference frames

nuScenes was chosen because it stresses sensor diversity, per-detection radar fields rarely found in other corpora (compensated velocity, dynamic property, RCS), and rich annotation metadata (visibility, attributes, evidence counts). DeepSense 6G was chosen because it stresses signal-level data (raw FMCW radar cubes, phased-array beam power vectors) and ISAC modalities absent from traditional perception datasets. S3E was chosen because it is the first collaborative SLAM dataset with UWB inter-robot ranging and exercises the multi-agent capabilities — map lifecycle, inter-map alignment, range-only constraints, and fleet discovery — that differentiate SpatialDDS from single-vehicle frameworks such as ROS 2. ScanNet was chosen because it is the definitive indoor RGB-D scene understanding benchmark, uniquely exercises depth sensing (DEPTH16) and the Spatial Events extension (room zones, object-in-room events, per-class occupancy counts), and validates the semantics profile's instance segmentation types against a rich 40-class indoor vocabulary. LaMAR was chosen because it is the first conformance dataset to exercise cross-device heterogeneity (HoloLens, iPhone/iPad, and NavVis scanner sharing a common reference frame), the Anchors profile (cross-session alignment, year-long persistence, geo-anchored reference frames), the Discovery profile in a multi-device context (heterogeneous device announcements with distinct sensor capabilities), and the sensing.radio profile in a production AR workflow (typed WiFi/BT scans replacing ad hoc JSON, driving +4.6–17.5% recall improvement in image retrieval).

The goal was not to certify particular datasets but to answer two concrete questions: Can every field, enum, and convention in each dataset's schema be losslessly mapped to SpatialDDS 1.6 IDL without workarounds or out-of-band agreements? And for multi-agent scenarios: Can the full coordination lifecycle — from independent mapping through inter-map alignment — be expressed using the standard types?

Methodology

For each dataset, a conformance harness was constructed as a self-contained Python 3 script that:

  1. Mirrors the SpatialDDS 1.6 IDL as Python data structures (enum values, struct field lists, normative prose flags).
  2. Mirrors the dataset schema as synthetic data (sensor names, field lists, data shapes).
  3. Runs targeted checks, each producing a verdict:
Verdict Meaning
PASS Dataset field maps losslessly to an existing SpatialDDS type or enum value.
GAP A mapping exists conceptually but the required SpatialDDS type or field does not yet exist.
MISSING No SpatialDDS construct exists for the dataset field; a new profile is needed.
  1. Reports a per-modality scorecard.

Neither nuScenes nor DeepSense 6G harness requires network access, a DDS runtime, or a dataset download. Both operate as static schema-vs-schema dry runs, reproducible in any CI environment. The S3E (§I.3) and ScanNet (§I.4) conformance sections were performed as manual schema analyses following the same check structure; scripted harnesses are planned for a future revision.

I.1 nuScenes Conformance

Reference Dataset

nuScenes (Motional / nuTonomy) is a multimodal autonomous driving dataset containing:

Dimension Value
Scenes 1,000 (20 s each)
Cameras 6 surround-view (FRONT, FRONT_LEFT, FRONT_RIGHT, BACK, BACK_LEFT, BACK_RIGHT)
Lidar 1 x 32-beam spinning (Velodyne HDL-32E), ~34 k points/scan
Radar 5 x Continental ARS 408 (FRONT, FRONT_LEFT, FRONT_RIGHT, BACK_LEFT, BACK_RIGHT)
3D annotations 1.4 M oriented bounding boxes, 23 object classes
Annotation metadata visibility tokens, attribute tokens, per-box lidar/radar point counts
Coordinate convention Right-handed; quaternions in (w, x, y, z) order

Checks Performed (27)

Radar — Detection Path (6 checks)
ID Check Description
R-01 Detection-centric profile RadDetection struct exists with per-detection xyz, velocity, RCS, dyn_prop.
R-02 Per-detection velocity Cartesian velocity_xyz (preferred) + scalar v_r_mps (fallback), both with has_* guards.
R-03 Ego-compensated velocity velocity_comp_xyz field for ego-motion-compensated velocity.
R-04 Dynamic property enum RadDynProp covers all 7 nuScenes values (UNKNOWN through STOPPED).
R-05 Per-detection RCS rcs_dbm2 field in dBm² with has_rcs_dbm2 guard.
R-06 Sensor type enum RadSensorType differentiates SHORT_RANGE, LONG_RANGE, IMAGING_4D, etc.
Vision (5 checks)
ID Check Description
V-01 RigRole coverage RigRole enum includes FRONT, FRONT_LEFT, FRONT_RIGHT, BACK, BACK_LEFT, BACK_RIGHT.
V-02 Pre-rectified images Normative prose documents dist = NONE with model = PINHOLE semantics.
V-03 Image dimensions CamIntrinsics.width / height are REQUIRED; zero values are malformed.
V-04 Keyframe flag VisionFrame.is_key_frame boolean.
V-05 Quaternion reorder §2 table maps nuScenes (w,x,y,z) to SpatialDDS (x,y,z,w).
Lidar (6 checks)
ID Check Description
L-01 BIN_INTERLEAVED encoding CloudEncoding value for raw interleaved binary with normative record layout table.
L-02 Per-point timestamps PointLayout.XYZ_I_R_T and XYZ_I_R_T_N with normative prose for the t field.
L-03 Metadata guards LidarMeta uses has_range_limits, has_horiz_fov, has_vert_fov guards.
L-04 Timestamp presence flag LidarFrame.has_per_point_timestamps signals per-point timing in the blob.
L-05 t_end computation Normative guidance for computing t_end from t_start + 1/rate_hz or max(point.t).
L-06 Ring field PointLayout.XYZ_I_R carries ring as uint16.
Semantics (5 checks)
ID Check Description
S-01 Size convention Normative: size[0] = width (X), size[1] = height (Z), size[2] = depth (Y). nuScenes (w,l,h) -> (w,h,l) mapping documented.
S-02 Attributes Detection3D.attributes as sequence<MetaKV, 8> with has_attributes guard.
S-03 Visibility Detection3D.visibility float [0..1] with has_visibility guard.
S-04 Evidence counts num_lidar_pts + num_radar_pts with has_num_pts guard.
S-05 Quaternion reorder §2 table covers annotation quaternion conversion.
Common / Core (5 checks)
ID Check Description
C-01 Quaternion table §2 convention table covering GeoPose, ROS 2, nuScenes, Eigen, Unity, Unreal, OpenXR, glTF.
C-02 FQN guidance FrameRef { uuid, fqn } semantics documented; UUID is authoritative.
C-03 Local-frame coverage §3.3.4 covers local-only deployments.
C-04 has_* pattern consistency All new optional fields use the has_* guard pattern uniformly.
C-05 Sequence bounds Standard bounds table: SZ_MEDIUM (2048), SZ_SMALL (256), SZ_XL (32768), SZ_LARGE (8192).

Results

All 27 nuScenes checks pass.

Modality Checks Pass Gap Deferred Notes
Radar (detections) 6 6 0 0 —
Vision 5 5 0 0 —
Lidar 6 6 0 0 —
Semantics 5 5 0 0 —
Common / Core 5 5 0 0 —
Total 27 27 0 0 —

Deferred items are fields that CAN be carried (typically via MetaKV) but lack first-class typed support. They are tracked as future profile additions, not as conformance failures.

I.2 DeepSense 6G Conformance

Reference Dataset

DeepSense 6G (Arizona State University, Wireless Intelligence Lab) is a large-scale multi-modal sensing and communication dataset containing:

Dimension Value
Scenarios 40+ across 12+ locations
Snapshots 1.08 M+ synchronized samples
FMCW Radar 76–81 GHz, 3 Tx × 4 Rx, complex I/Q tensor [4×256×128], 10 Hz
3D Lidar Ouster OS1-32, 32×1024, 120 m range, 865 nm, 10–20 Hz
Camera ZED2 stereo (960×540) + Insta360 ONE X2 360° (5.7K)
GPS-RTK 10 Hz, ≤1 cm accuracy (RTK fix), DOP + satellite metadata
IMU 6-axis, 100 Hz
mmWave Comm 60 GHz phased array, 64-beam codebook, 90° FoV, 10 Hz
Deployment types V2I, V2V (4× arrays/vehicle), ISAC indoor, drone

The dataset was chosen because it stresses signal-level data (raw FMCW radar cubes consumed directly by ML pipelines), 360° camera rigs, and ISAC modalities (beam power vectors, blockage state) absent from perception-focused datasets.

Checks Performed (41)

Radar — Tensor Path (8 checks)
ID Check Description
DT-01 Tensor meta struct RadTensorMeta exists with axes, voxel_type, layout, physical_meaning.
DT-02 Complex sample type SampleType.CF32 covers complex I/Q data.
DT-03 Channel axis RadTensorLayout.CH_FAST_SLOW maps raw FMCW [Rx, samples, chirps].
DT-04 MIMO antenna config num_tx, num_rx, num_virtual_channels with has_antenna_config guard.
DT-05 Waveform params bandwidth_hz, center_freq_hz, samples_per_chirp, chirps_per_frame with guard.
DT-06 Frame blob transport RadTensorFrame.hdr.blobs[] carries the raw cube; size computable from axes × sample size.
DT-07 Sensor type RadSensorType covers FMCW radar as MEDIUM_RANGE or IMAGING_4D.
DT-08 StreamMeta extrinsics T_bus_sensor (PoseSE3) + nominal_rate_hz for hand-eye calibration and 10 Hz cadence.
Vision (7 checks)
ID Check Description
DV-01 Standard camera PixFormat.RGB8 + CamIntrinsics.width/height cover ZED2 at 960×540.
DV-02 Camera extrinsics VisionMeta.base → StreamMeta.T_bus_sensor for hand-eye calibration.
DV-03 Camera model CamModel.PINHOLE for ZED2 pre-rectified output.
DV-04 Frame rate StreamMeta.nominal_rate_hz = 10 (downsampled from 30 Hz).
DV-05 360° rig roles RigRole.PANORAMIC and EQUIRECTANGULAR for Insta360 ONE X2 in V2V scenarios.
DV-06 Keyframe flag VisionFrame.is_key_frame boolean.
DV-07 Compression codec Codec enum covers JPEG/H264/H265/AV1.
Lidar (7 checks)
ID Check Description
DL-01 Lidar type LidarType.MULTI_BEAM_3D for Ouster OS1-32 (spinning, 32 rings).
DL-02 Ring count + FOV LidarMeta.n_rings, has_horiz_fov, has_vert_fov with guards.
DL-03 Range limits has_range_limits + max_range_m = 120 m.
DL-04 Point layout PointLayout.XYZ_I_R for x, y, z, intensity, ring.
DL-05 Cloud encoding CloudEncoding.BIN_INTERLEAVED for raw binary transport.
DL-06 Sensor wavelength LidarMeta.wavelength_nm with has_wavelength guard (865 nm).
DL-07 Frame rate StreamMeta.nominal_rate_hz covers 10–20 Hz.
IMU (4 checks)
ID Check Description
DI-01 6-axis sample ImuSample with accel (Vec3, m/s²) + gyro (Vec3, rad/s).
DI-02 Noise densities ImuInfo.accel_noise_density + gyro_noise_density + random walk params.
DI-03 Frame reference ImuInfo.frame_ref for sensor-to-bus mounting.
DI-04 Timestamp + sequence ImuSample.stamp + .seq for 100 Hz temporal ordering.
GPS (6 checks)
ID Check Description
DG-01 Position GeoPose.lat_deg/lon_deg/alt_m for GPS-RTK coordinates.
DG-02 Orientation GeoPose.q (QuaternionXYZW) for heading-derived orientation.
DG-03 Timestamp GeoPose.stamp for 10 Hz GPS samples.
DG-04 Covariance GeoPose.cov for positional uncertainty (RTK ≤1 cm).
DG-05 GNSS quality NavSatStatus provides DOP, fix type, and satellite count with has_dop guard.
DG-06 Speed over ground NavSatStatus.speed_mps + course_deg with has_velocity guard.
mmWave Beam (8 checks)
ID Check Description
DB-01 Beam power vector RfBeamFrame.power (sequence) carries per-beam received power. 64 entries for DeepSense exhaustive sweep. Provisional rf_beam profile (K-B1).
DB-02 Codebook metadata RfBeamMeta.n_beams (64), n_elements (16), center_freq_ghz (60.0), fov_az_deg (90), codebook_type.
DB-03 Optimal beam index RfBeamFrame.best_beam_idx (uint16) with has_best_beam guard. Ground-truth label: beam maximizing SNR.
DB-04 Blockage status RfBeamFrame.is_blocked (boolean) + blockage_confidence (float 0..1) with has_blockage_state guard.
DB-05 Multi-array set RfBeamArraySet.arrays (sequence) batches per-array frames. overall_best_array_idx + overall_best_beam_idx for cross-array best beam. Covers V2V 4-array rig.
DB-06 Sparse sweep indices RfBeamFrame.beam_indices maps power[i] to codebook position for PARTIAL/TRACKING sweeps. BeamSweepType enum: EXHAUSTIVE, HIERARCHICAL, TRACKING, PARTIAL.
DB-07 Power unit convention RfBeamMeta.power_unit (PowerUnit enum: DBM, LINEAR_MW, RSRP) declares units for RfBeamFrame.power.
DB-08 Stream linkage RfBeamFrame.stream_id matches RfBeamMeta.stream_id for meta/frame correlation.

Note: All mmWave Beam checks validated against the provisional sensing.rf_beam profile (Appendix E). Types are subject to breaking changes.

Semantics (4 checks)
ID Check Description
DS-01 2D bounding boxes Detection2D.bbox + class_id covers 8 DeepSense object classes.
DS-02 Sequence index FrameHeader.frame_seq for sample ordering.
DS-03 Class ID Detection2D.class_id (string) maps all DeepSense class labels.
DS-04 Beam/blockage labels RfBeamFrame.best_beam_idx and .is_blocked/.blockage_confidence carry ISAC-specific ground-truth labels. Covered by provisional rf_beam profile.

Results

All 44 DeepSense 6G checks pass. GNSS diagnostics are covered by NavSatStatus, and mmWave Beam checks pass against the provisional rf_beam profile (Appendix E).

Modality Checks Pass Gap Deferred Notes
Radar (tensor) 8 8 0 0 —
Vision 7 7 0 0 Includes 360° rig roles
Lidar 7 7 0 0 Includes sensor wavelength
IMU 4 4 0 0 —
GPS 6 6 0 0 NavSatStatus covers GNSS diagnostics
mmWave Beam 8 8 0 0 Provisional rf_beam profile (K-B1)
Semantics 4 4 0 0 Beam labels via rf_beam
Total 44 44 0 0 100% coverage

Deferred items are fields that CAN be carried (typically via MetaKV) but lack first-class typed support. They are tracked as future profile additions, not as conformance failures.

Deferred Items

DeepSense 6G conformance has no remaining schema gaps. Future ISAC extensions (e.g., CSI/CIR profiles) remain under discussion; see Appendix K for the maturity promotion criteria.

I.3 S3E Conformance (Multi-Robot Collaborative SLAM)

Reference Dataset

S3E (Sun Yat-sen University / HKUST) is a multi-robot multimodal dataset for collaborative SLAM containing:

Dimension Value
Robots 3 UGVs (Alpha, Blob, Carol) operating simultaneously
LiDAR 1 × 16-beam 3D scanner (Velodyne VLP-16) per robot, 10 Hz
Stereo cameras 2 × high-resolution color cameras per robot
IMU 9-axis, 100–200 Hz per robot
UWB Inter-robot Ultra-Wideband ranging (pairwise distances at ~10 Hz)
GNSS Dual-antenna RTK receiver per robot (ground truth)
Environments 13 outdoor + 5 indoor sequences
Trajectory paradigms 4 collaborative patterns (concentric circles, intersecting circles, intersection curve, rays)
Format ROS 2 bag files; ground truth as TUM-format pose files

The dataset was chosen because it is the first C-SLAM dataset to include UWB inter-robot ranging, exercises multi-agent map building with inter-robot loop closures, and represents a scenario class (heterogeneous multi-robot coordination) where SpatialDDS's Mapping extension, Discovery profile, and multi-source pose graph types provide capabilities absent from ROS 2's nav_msgs and sensor_msgs.

Checks Performed (38)

Per-Robot Sensing — LiDAR (5 checks)
ID Check Description
SL-01 LiDAR meta LidarMeta with sensor_type, rate_hz, point_layout covers Velodyne VLP-16.
SL-02 Point layout PointLayout.XYZ_I_R_T carries x, y, z, intensity, ring, time — matches Velodyne binary format.
SL-03 Per-robot topic isolation Topic template spatialdds/<scene>/lidar/<sensor_id>/frame/v1 with per-robot sensor_id (e.g., alpha/vlp16).
SL-04 CloudEncoding BIN_INTERLEAVED covers raw binary point cloud blobs.
SL-05 RigRole RigRole.TOP covers single roof-mounted LiDAR.
Per-Robot Sensing — Vision (4 checks)
ID Check Description
SV-01 Stereo pair Two VisionFrame streams per robot with RigRole.LEFT / RigRole.RIGHT.
SV-02 Camera intrinsics CameraMeta with fx, fy, cx, cy, dist_model, dist_coeffs covers calibrated stereo cameras.
SV-03 Per-robot namespacing Topic spatialdds/<scene>/vision/<sensor_id>/frame/v1 isolates per-robot camera streams.
SV-04 Timestamp sync VisionFrame.stamp synchronized to common timebase via hardware PPS trigger.
Per-Robot Sensing — IMU (3 checks)
ID Check Description
SI-01 9-axis sample ImuSample with accel (Vec3, m/s²) + gyro (Vec3, rad/s) covers 6-axis; MagSample covers magnetometer.
SI-02 High-rate ordering ImuSample.seq monotonic counter handles 100–200 Hz temporal ordering.
SI-03 Extrinsic calibration Sensor-to-body transform publishable as FrameTransform (LiDAR-IMU, camera-IMU extrinsics).
Per-Robot Sensing — GNSS/RTK (3 checks)
ID Check Description
SG-01 RTK fix type GnssFixType.RTK_FIXED covers dual-antenna RTK ground truth receiver.
SG-02 GeoPose output GeoPose with lat_deg, lon_deg, alt_m, quaternion covers RTK-derived global pose.
SG-03 NavSatStatus NavSatStatus with fix_type, num_satellites, hdop, vdop covers receiver diagnostics.
Inter-Robot Ranging — UWB (4 checks)
ID Check Description
SU-01 Range edge type mapping::EdgeType.RANGE explicitly models UWB range-only constraint (scalar distance, no orientation).
SU-02 Range fields mapping::Edge.range_m + range_std_m carry measured distance and uncertainty.
SU-03 Cross-map provenance has_from_map_id / has_to_map_id populated on RANGE edges because UWB connects nodes in different robots' maps.
SU-04 Range-assisted alignment AlignmentMethod.RANGE_COARSE covers initial inter-map alignment derived solely from UWB distances.
Core Pose Graph (5 checks)
ID Check Description
SC-01 Per-robot nodes core::Node with map_id per robot (e.g., alpha-map, blob-map, carol-map), @key node_id unique per keyframe.
SC-02 Odometry edges core::Edge with type = ODOM connects sequential keyframes within each robot's map.
SC-03 Intra-robot loop closures core::Edge with type = LOOP for within-map loop closures (e.g., concentric circle paradigm).
SC-04 Versioning Node.seq monotonic per source; Node.graph_epoch increments after global re-optimization.
SC-05 Multi-source coexistence Three simultaneous source_id values on core::Node and core::Edge topics — one per robot.
Mapping Extension — Multi-Agent (8 checks)
ID Check Description
SM-01 Map lifecycle MapMeta per robot with state progressing: BUILDING → OPTIMIZING → STABLE.
SM-02 Map kind MapMeta.kind = POSE_GRAPH for each robot's SLAM output.
SM-03 Inter-robot loop closures mapping::Edge with type = INTER_MAP and has_from_map_id / has_to_map_id populated.
SM-04 MapAlignment MapAlignment with T_from_to expressing the inter-map transform after cross-robot alignment.
SM-05 Alignment revision MapAlignment.revision increments as more inter-robot edges accumulate and the alignment refines.
SM-06 Evidence trail MapAlignment.evidence_edge_ids[] references the specific cross-map edges supporting the alignment.
SM-07 MapEvent notifications MapEvent with MAP_ALIGNED event when two robots' maps are first linked.
SM-08 Concurrent map builds Three MapMeta samples simultaneously active (keyed by map_id), demonstrating multi-map lifecycle.
Discovery & Coordination (3 checks)
ID Check Description
SD-01 Service announcement Each robot publishes Announce with ServiceKind.SLAM and sensor capabilities in topics[].
SD-02 Spatial coverage Announce.coverage (Aabb3 or geo-bounds) advertises each robot's operational area.
SD-03 Multi-frame NodeGeo After inter-map alignment, NodeGeo.poses[] carries a node's pose in multiple robots' map frames simultaneously (FramedPose array).
Cross-cutting (3 checks)
ID Check Description
SX-01 Quaternion convention §2 table covers ROS 2 (x,y,z,w) to SpatialDDS (x,y,z,w) identity mapping for S3E's ROS 2 bag source.
SX-02 Coordinate frame convention Right-handed; S3E uses right-hand rule per documentation.
SX-03 Time synchronization Hardware PPS-synchronized timestamps map directly to Time { sec, nanosec }.

Results

All 38 S3E checks pass.

Modality Checks Pass Gap Deferred Notes
LiDAR 5 5 0 0 —
Vision 4 4 0 0 —
IMU 3 3 0 0 —
GNSS/RTK 3 3 0 0 —
UWB (inter-robot range) 4 4 0 0 —
Core Pose Graph 5 5 0 0 —
Mapping (multi-agent) 8 8 0 0 —
Discovery & Coordination 3 3 0 0 —
Cross-cutting 3 3 0 0 —
Total 38 38 0 0 —

Deferred items are fields that CAN be carried (typically via MetaKV) but lack first-class typed support. They are tracked as future profile additions, not as conformance failures.

S3E Scenario Narrative (Informative)

The S3E "teaching building" outdoor sequence illustrates the full multi-agent lifecycle:

  1. Bootstrap. Three robots (Alpha, Blob, Carol) power on and each publishes an Announce with ServiceKind.SLAM, their sensor capabilities, and an initial coverage bounding box. Each begins publishing core::Node and core::Edge (ODOM) on the pose graph topics with distinct source_id and map_id values.

  2. Independent mapping. Each robot runs visual-inertial-lidar SLAM independently. MapMeta per robot shows state = BUILDING. Keyframes stream as core::Node; odometry constraints as core::Edge (ODOM); intra-robot loop closures as core::Edge (LOOP). ImuSample, VisionFrame, and LidarFrame are published on per-robot sensor topics.

  3. UWB ranging begins. As robots come within UWB range (~50 m), pairwise distance measurements are published as mapping::Edge with type = RANGE, range_m carrying the measured distance, has_from_map_id / has_to_map_id identifying which robots' maps the linked nodes belong to.

  4. Inter-robot loop closure. When Alpha and Blob's LiDAR scans overlap, a cross-robot loop closure is detected. This is published as mapping::Edge with type = INTER_MAP, match_score carrying the ICP fitness, and from_map_id = "alpha-map", to_map_id = "blob-map".

  5. Map alignment. A MapAlignment is published linking Alpha's and Blob's maps, with method = LIDAR_ICP (or MULTI_METHOD if UWB ranges were fused), T_from_to carrying the inter-map transform, and evidence_edge_ids[] referencing the supporting cross-map edges. MapEvent with MAP_ALIGNED notifies all subscribers.

  6. Multi-frame localization. Once the alignment exists, a geo-referencing service can publish NodeGeo with poses[] containing FramedPoses in both Alpha's and Blob's map frames simultaneously. Consumers (e.g., a planning service) can pick the frame they need.

  7. Graph optimization. After sufficient inter-robot constraints accumulate, a global optimizer runs. All robots' MapMeta.state transitions to OPTIMIZING, then STABLE. graph_epoch increments on all nodes and edges. MapAlignment.revision increments. Consumers watching graph_epoch know to re-fetch the entire graph.

This end-to-end scenario is precisely what ROS 2's nav_msgs and sensor_msgs cannot express: there is no ROS 2 standard for map lifecycle, inter-map alignment, range-only constraints, or multi-agent discovery with spatial coverage.

I.4 ScanNet Conformance (Indoor Scene Understanding)

Reference Dataset

ScanNet (TU Munich / Princeton) is an RGB-D video dataset of indoor scenes containing:

Dimension Value
Scenes 1,513 (707 unique spaces, multiple rescans)
RGB-D sensor Structure.io depth + iPad color camera
Depth format 16-bit unsigned integer, millimeters, 640×480 @ 30 Hz
Color format JPEG-compressed RGB, 1296×968 @ 30 Hz
Camera poses Per-frame 4×4 camera-to-world extrinsics via BundleFusion
IMU Embedded IMU data in .sens stream
Surface reconstruction Dense triangle mesh (PLY) via BundleFusion
Semantic annotations Instance-level labels (NYU40 label set, 40 classes)
Instance annotations Per-vertex segment IDs + aggregated object instances
Scene types 20 categories (bathroom, bedroom, kitchen, living room, office, etc.)
Axis alignment Per-scene 4×4 gravity-alignment matrix
Coordinate convention Right-handed; +Z up in aligned frame

ScanNet was chosen because it is the definitive indoor RGB-D scene understanding benchmark, exercises depth sensing absent from all three prior conformance datasets, and provides room-level semantic structure that naturally maps to the Spatial Events extension — the only SpatialDDS extension not yet tested by conformance.

Checks Performed (35)

RGB-D Sensing — Color (4 checks)
ID Check Description
NC-01 Color meta VisionMeta with pix = RGB8, codec = JPEG, CamIntrinsics (fx, fy, cx, cy at 1296×968).
NC-02 Color frame VisionFrame per RGB image with frame_seq, hdr.stamp, blob reference to JPEG payload.
NC-03 Per-scene stream isolation Topic spatialdds/<scene_id>/vision/<stream_id>/frame/v1 with unique stream_id per scan.
NC-04 Rig linkage VisionMeta.rig_id shared between color and depth streams for spatial association.
RGB-D Sensing — Depth (5 checks)
ID Check Description
ND-01 Depth meta VisionMeta with pix = DEPTH16, codec = NONE (raw 16-bit), CamIntrinsics for depth camera.
ND-02 Depth pixel format PixFormat.DEPTH16 explicitly identifies 16-bit millimeter depth. Requires SN-1.
ND-03 Depth frame VisionFrame per depth image with frame_seq matching co-located color frame.
ND-04 Invalid depth convention Zero-valued pixels denote no measurement, consistent with DEPTH16 normative note.
ND-05 Depth unit Default millimeter unit; no depth_unit attribute required for ScanNet's Structure.io sensor.
IMU (2 checks)
ID Check Description
NI-01 IMU sample ImuSample with accel (Vec3, m/s²) + gyro (Vec3, rad/s) covers 6-axis IMU embedded in .sens stream.
NI-02 Temporal ordering ImuSample.seq provides monotonic ordering within the scan.
Camera Pose & Frames (4 checks)
ID Check Description
NP-01 Per-frame pose Camera-to-world 4×4 matrix maps to FrameHeader.sensor_pose (PoseSE3: translation + quaternion).
NP-02 Axis-alignment transform Per-scene gravity-alignment matrix published as FrameTransform from sensor frame to aligned frame.
NP-03 Frame hierarchy Aligned frame FQN follows §2.2 pattern: <scene_id>/aligned.
NP-04 Quaternion convention ScanNet uses 4×4 rotation matrices; decomposition to (x,y,z,w) quaternion per §2 convention table.
Mesh Reconstruction (4 checks)
ID Check Description
NM-01 Map kind MapMeta with kind = MESH for BundleFusion surface reconstruction.
NM-02 Map lifecycle MapMeta.state = STABLE for completed reconstructions (offline dataset; no BUILDING phase observed).
NM-03 Mesh payload BlobRef referencing PLY mesh file. SpatialDDS carries mesh references, not inline mesh data.
NM-04 Vertex count metadata MapMeta.attributes carries vertex/face count as MetaKV for consumers to assess mesh complexity.
3D Instance Segmentation — Semantics (6 checks)
ID Check Description
NS-01 3D detection Detection3D per annotated object instance, with class_id from NYU40 label set (e.g., "chair", "table", "door").
NS-02 Instance ID Detection3D.det_id unique per object instance within a scene (maps from ScanNet's objectId).
NS-03 Oriented bounding box Detection3D.center + size + q cover ScanNet's axis-aligned bounding boxes (identity quaternion in aligned frame).
NS-04 Track ID Detection3D.track_id groups the same physical object across multiple rescans of the same space.
NS-05 Visibility Detection3D.visibility (0–1) maps from ScanNet annotation coverage ratio.
NS-06 Class vocabulary class_id as free-form string covers all 40 NYU40 categories without a closed enum — consistent with SpatialDDS's ontology-agnostic design.
Spatial Events — Indoor Zones (6 checks)
ID Check Description
NZ-01 Room as zone SpatialZone per ScanNet scene, with zone_id = scene ID, name = human-readable scene name.
NZ-02 Zone kind ZoneKind.MONITORING for general-purpose room observation (no access restriction implied).
NZ-03 Zone bounds SpatialZone.bounds (Aabb3) enclosing the room extent, derived from mesh bounding box in aligned frame.
NZ-04 Scene type as attribute ScanNet sceneType (bathroom, bedroom, kitchen, etc.) carried as MetaKV in SpatialZone.attributes with namespace = "scene_type", json = {"type": "kitchen"}.
NZ-05 Class filter SpatialZone.class_filter populated with object classes of interest (e.g., ["person", "chair", "table"]) for selective event triggering.
NZ-06 Zone frame SpatialZone.frame_ref references the gravity-aligned frame established by the axis-alignment transform (NP-02).
Spatial Events — Object Events (4 checks)
ID Check Description
NE-01 Zone entry SpatialEvent with event_type = ZONE_ENTRY when a Detection3D instance is first observed within a SpatialZone's bounds.
NE-02 Trigger linkage SpatialEvent.trigger_det_id references the triggering Detection3D.det_id; trigger_class_id carries the NYU40 label.
NE-03 Zone state ZoneState with zone_occupancy count reflecting the number of annotated object instances within the room.
NE-04 Class counts ZoneState.class_counts (sequence of MetaKV) carries per-class occupancy (e.g., {"count": 4} for class "chair").

Results

All 35 ScanNet checks pass.

Modality Checks Pass Gap Deferred Notes
Color (RGB) 4 4 0 1 2D label image format deferred
Depth (RGBD) 5 5 0 0 —
IMU 2 2 0 0 —
Camera Pose & Frames 4 4 0 0 —
Mesh Reconstruction 4 4 0 1 Per-vertex semantic labels deferred
3D Instance Segmentation 6 6 0 1 First-class CAD reference deferred
Spatial Events — Zones 6 6 0 0 —
Spatial Events — Object Events 4 4 0 0 —
Total 35 35 0 3 —

Deferred items are fields that CAN be carried (typically via MetaKV or BlobRef) but lack first-class typed support. They are tracked as future profile additions, not as conformance failures.

ScanNet Scenario Narrative (Informative)

The ScanNet "apartment" scan sequence illustrates how SpatialDDS types map to a complete indoor scene understanding pipeline:

  1. Scan ingestion. An operator walks through a kitchen with an iPad running the ScanNet capture app. Color frames are published as VisionFrame (pix=RGB8, codec=JPEG) and depth frames as VisionFrame (pix=DEPTH16, codec=NONE) on paired streams linked by rig_id. ImuSample streams concurrently from the embedded IMU.

  2. Pose estimation. BundleFusion produces per-frame camera poses, published as FrameHeader.sensor_pose on each VisionFrame. The per-scene axis-alignment matrix is published as a FrameTransform from the sensor coordinate system to a gravity-aligned room frame.

  3. Mesh reconstruction. The completed surface mesh is registered as MapMeta with kind = MESH, state = STABLE. The PLY file is referenced via BlobRef. Vertex/face counts are carried in MapMeta.attributes.

  4. Zone definition. The kitchen is defined as a SpatialZone with kind = MONITORING, bounds enclosing the room extent, and attributes carrying scene_type = "kitchen". The frame_ref points to the gravity-aligned frame.

  5. 3D instance detection. Crowdsourced annotations produce Detection3D instances for each labeled object: chairs with class_id = "chair", tables with class_id = "table", a refrigerator with class_id = "refrigerator" — each with an oriented bounding box in the aligned frame.

  6. Spatial events. A zone monitoring service evaluates which Detection3D instances fall within the kitchen SpatialZone's bounds and publishes SpatialEvent (ZONE_ENTRY) for each. ZoneState is published periodically with zone_occupancy = 12 (total instances) and class_counts listing per-class breakdowns.

This pipeline exercises the Spatial Events extension end-to-end — from zone definition through detection to event generation — a capability path untested by nuScenes (no zones), DeepSense 6G (no zones), or S3E (no zones or semantics).

Deferred Items

  • Per-vertex semantic labels. ScanNet provides per-vertex class labels on the reconstructed mesh. SpatialDDS has no per-vertex label type; the labeled mesh PLY is carried as a BlobRef. A future per-vertex or per-point semantic annotation type could make this data first-class.
  • CAD model alignment. ScanNet aligns ShapeNet CAD models to detected objects. The ShapeNet model ID can be carried in Detection3D.attributes as a MetaKV, but there is no first-class CAD reference type.
  • 2D projected labels. ScanNet provides per-frame 2D semantic/instance label images. These can be published as VisionFrame with a label-specific stream_id and pix = RAW16 (16-bit label IDs), but a dedicated label pixel format is not defined.

I.5 LaMAR Conformance (Multi-Device AR Localization & Mapping)

Reference Dataset

LaMAR (ETH Zürich / Microsoft Mixed Reality & AI Lab) is a large-scale multi-device localization and mapping benchmark for augmented reality containing:

Dimension Value
Locations 3 (historical building 18,000 m², office building 12,000 m², old town 15,000 m²)
Total area 45,000 m² indoor + outdoor
HoloLens 2 4 cameras, 83° FOV, 30 Hz, VGA grayscale, global shutter; ToF depth/IR 1 Hz; IMU; Bluetooth + WiFi
iPhone / iPad 1 camera, 64° FOV, 10 Hz, 1080p RGB, rolling shutter, auto-focus; LiDAR depth 10 Hz; IMU; WiFi (partial BT)
NavVis M6 / VLX 4–6 cameras, 90–113° FOV, 1–3 m interval, 1080p RGB; lidar point cloud + dense mesh
Trajectories 100+ sessions per location, 10 participants, over 1 year
Capture duration 100+ hours, 40+ km of trajectories
Radio signals WiFi RSSI fingerprints + Bluetooth beacon scans, per-timestamp
Ground truth Laser scan alignment, cm-level pose accuracy, automated pipeline
Pose convention sensor-to-world transforms; camera-to-rig extrinsics (Kapture format, inverted convention)
Data format Custom "Capture" format: sessions/, sensors.txt, rigs.txt, trajectories.txt, images.txt, depths.txt, wifi.txt, bt.txt

LaMAR was chosen because it is the first conformance dataset to exercise cross-device heterogeneity (HoloLens headset, iPhone/iPad handheld, NavVis scanner rig — three fundamentally different device classes sharing a common spatial reference), the Anchors profile (geo-anchored reference frames, cross-session alignment, persistent spatial landmarks), the Discovery profile in a multi-device context (heterogeneous service announcements with different sensor capabilities and coverage), multi-session map alignment (laser scans registered across year-long intervals with structural changes), and the sensing.radio profile in production AR workflows (WiFi/BT fingerprint streams driving +4.6–17.5% recall improvement). No prior conformance dataset tests these capabilities: nuScenes is single-vehicle, DeepSense 6G is single-platform, S3E has homogeneous robots, and ScanNet is single-device single-session.

Checks Performed (70)

HoloLens 2 — Vision (6 checks)
ID Check Description
LH-01 Multi-camera rig VisionMeta per camera with distinct stream_id; 4 cameras per HoloLens rig linked by shared rig_id.
LH-02 Grayscale pixel format PixFormat.GRAY8 covers HoloLens VGA grayscale global-shutter cameras.
LH-03 Frame rate StreamMeta.nominal_rate_hz = 30 for HoloLens camera streams.
LH-04 Rig extrinsics Camera-to-rig transforms publishable as FrameTransform with T_parent_child (rig body → camera).
LH-05 Global shutter flag VisionMeta attributes can carry MetaKV with shutter type (global_shutter). No dedicated field required — ScanNet conformance (NC-04) established rig_id pattern; shutter type is informational metadata.
LH-06 Camera intrinsics CamIntrinsics with fx, fy, cx, cy per camera. HoloLens provides per-frame calibration from on-device tracker — CamModel.PINHOLE for undistorted images.
HoloLens 2 — Depth (4 checks)
ID Check Description
LD-01 ToF depth stream VisionMeta with pix = DEPTH16 for HoloLens Time-of-Flight depth sensor.
LD-02 Depth frame rate StreamMeta.nominal_rate_hz = 1 for HoloLens ToF sensor (low-rate depth).
LD-03 Depth rig linkage VisionMeta.rig_id shared between depth and grayscale streams for spatial association.
LD-04 IR stream HoloLens infrared frames publishable as VisionFrame with separate stream_id and pix = GRAY8 or RAW16.
iPhone / iPad — Vision (5 checks)
ID Check Description
LP-01 Single camera VisionMeta with pix = RGB8, single stream_id per phone session.
LP-02 Rolling shutter Rolling shutter metadata carriable as MetaKV in VisionMeta.attributes.
LP-03 Auto-focus intrinsics CamIntrinsics per frame accommodates changing focal length from auto-focus. HoloLens provides fixed calibration; phone provides per-frame — both map to same CamIntrinsics struct.
LP-04 Frame rate StreamMeta.nominal_rate_hz = 10 for iPhone/iPad capture rate.
LP-05 JPEG compression Codec.JPEG for phone image compression.
iPhone / iPad — Depth (3 checks)
ID Check Description
LPD-01 LiDAR depth VisionMeta with pix = DEPTH16 for iPad LiDAR Scanner depth frames.
LPD-02 Depth frame rate StreamMeta.nominal_rate_hz = 10 for iPad LiDAR (matches color frame rate).
LPD-03 Depth rig linkage VisionMeta.rig_id links LiDAR depth and color streams for factory-aligned iPad sensor pair.
ID Check Description
LN-01 Multi-camera rig VisionMeta per panoramic camera (4–6 cameras); RigRole values cover top-mounted and side-mounted configurations.
LN-02 HD resolution CamIntrinsics.width / height at 1080p for NavVis synchronized cameras.
LN-03 LiDAR point cloud MapMeta with kind = MESH for processed NavVis dense mesh; BlobRef for PLY payload. Point cloud with 1 cm grid resolution.
LN-04 LiDAR mesh Dense triangle mesh (Advancing Front algorithm) publishable as MapMeta with kind = MESH, state = STABLE. Vertex/face counts in MapMeta.attributes.
LN-05 Scan interval images NavVis images captured at 1–3 m intervals (not continuous video); VisionFrame per capture with frame_seq for ordering.
IMU (3 checks)
ID Check Description
LI-01 Multi-device IMU ImuSample with accel + gyro covers HoloLens embedded IMU and iPhone CoreMotion IMU. Both publish on per-device sensor topics.
LI-02 High-rate IMU HoloLens accelerometer/gyroscope/magnetometer at device-native rates. ImuSample.seq monotonic per source.
LI-03 Per-device namespace Topic spatialdds/<location>/imu/<device_id>/sample/v1 isolates per-device IMU streams.
Poses & Trajectories (5 checks)
ID Check Description
LT-01 Sensor-to-world pose FrameHeader.sensor_pose (PoseSE3) carries per-frame sensor-to-world transform. LaMAR's trajectories.txt convention (sensor-to-world) maps directly.
LT-02 VIO tracking poses On-device tracker poses (ARKit for iPhone, HoloLens tracker) publishable as PoseSE3 with source-specific frame_ref. These are relative to session start — local odometry frame.
LT-03 GT poses Ground-truth poses from the LaMAR alignment pipeline (laser scan registration + bundle adjustment) publishable as PoseSE3 in the GT reference world frame.
LT-04 Pose uncertainty LaMAR provides per-frame covariance from Hessian inversion of refinement. Maps to CovMatrix on FramedPose.
LT-05 Quaternion convention LaMAR uses 4×4 rotation matrices; decomposition to (x,y,z,w) quaternion per §2 convention table. Same pattern as ScanNet (NP-04).
Multi-Session Alignment — Anchors Profile (7 checks)
ID Check Description
LA-01 Scan-to-scan alignment Rigid transform aligning NavVis scan sessions publishable as FrameTransform with T_parent_child mapping one scan's origin to the GT world frame.
LA-02 Sequence-to-scan alignment Per-AR-sequence rigid alignment (wT_init_0 from voting) publishable as FrameTransform linking session-local tracking frame to GT reference frame.
LA-03 GeoAnchor for reference frame The GT world frame origin publishable as GeoAnchor with method = "Surveyed" (laser scan derived), confidence from alignment error statistics. Bridges local map coordinates to global position.
LA-04 AnchorSet for scan landmarks NavVis scan landmarks (e.g., QR codes detected by run_qrcode_detection) publishable as AnchorSet with per-anchor AnchorEntry containing GeoAnchor pose. set_id identifies the scan session's anchor collection.
LA-05 Cross-session alignment revision alignment_global.txt records inter-session transforms with error statistics. Maps to FrameTransform with CovMatrix carrying alignment uncertainty. Multiple NavVis sessions → multiple FrameTransform instances with transform_id keyed per session pair.
LA-06 Alignment refinement lifecycle LaMAR's GT pipeline progresses: initial localization → rigid alignment → pose graph optimization → bundle adjustment. Each stage improves accuracy. The final FrameTransform carries the refined transform; CovMatrix reflects reduced uncertainty at each stage.
LA-07 Year-long structural change Scans captured over 1+ year with structural changes (construction, furniture rearrangement). Cross-session alignment still succeeds. Demonstrates FrameTransform stability across temporal changes — the anchor/reference frame persists even as scene content changes.
Discovery — Multi-Device (5 checks)
ID Check Description
LDI-01 Heterogeneous device announcements Each device class (HoloLens, iPhone/iPad, NavVis) publishes Announce with ServiceKind.MAPPING and distinct sensor capabilities in topics[]. HoloLens advertises 4-camera rig + ToF + IMU + BT + WiFi; phone advertises 1 camera + LiDAR + IMU + WiFi; NavVis advertises multi-camera rig + lidar.
LDI-02 Coverage geometry Announce.coverage (Aabb3 or sphere) advertises each device's operational area within the location. NavVis covers entire building; AR sessions cover trajectory corridors.
LDI-03 Sensor capability advertisement Announce.topics[] lists typed topics per device with TopicMeta entries: vision, depth, IMU topics for AR devices; vision + pointcloud + mesh topics for NavVis. Consumers can discover which modalities are available from each device.
LDI-04 Cross-device map reference After alignment, all devices reference a common GT world frame. Announce.coverage_frame_ref references this shared FrameRef, enabling consumers to evaluate coverage in a common coordinate system.
LDI-05 Service manifest Announce.manifest_uri references a spatialdds:// URI resolvable to a manifest describing the mapping service's capabilities, coverage area, and data assets (mesh, point cloud, image database).
Radio Profile Coverage (12 checks)

The 22 radio checks in this and the next two sub-sections validate sensing.radio against LaMAR's wifi.txt / bt.txt data path. They subsume the four high-level "Radio Signals" checks (WiFi fingerprint, BT scan, radio-assisted retrieval, temporal aggregation) by exercising the typed transport directly.

ID Check Description
LM-01 Typed per-scan container RadioScan carries one scan event with sensor_id, radio_type, scan_seq, and stamp.
LM-02 Typed per-observation container RadioObservation carries one transmitter measurement (identifier, measurement_kind, value).
LM-03 WiFi identifier format BSSID maps to lowercase colon-separated identifier.
LM-04 BLE identifier format Beacon UUID/MAC maps to canonical identifier.
LM-05 RSSI representation RSSI maps to measurement_kind = RSSI, value in dBm.
LM-06 WiFi frequency/channel frequency_mhz, band, and channel map with has_* guards.
LM-07 BLE major/minor iBeacon major/minor maps with has_major_minor.
LM-08 BLE Tx power Advertised Tx power maps with has_tx_power.
LM-09 Scan duration Variable scan-window duration maps to scan_duration_s.
LM-10 Aggregation window ±window aggregation (LaMAR's ±2s pattern) maps to aggregation_window_s.
LM-11 Sensor metadata RadioSensorMeta captures capability flags and adapter metadata.
LM-12 Schema tag schema_version set to spatial.sensing.radio/1.5.
Radio — Discovery and QoS Integration (5 checks)
ID Check Description
LRD-01 Registered type Discovery type registry includes radio_scan.
LRD-02 QoS profile RADIO_SCAN_RT available for radio scan topics.
LRD-03 Topic naming Topic pattern spatialdds/<scene>/radio/<sensor_id>/scan/v1 is valid under §3.3.1.
LRD-04 Meta durability RadioSensorMeta uses RELIABLE + TRANSIENT_LOCAL semantics.
LRD-05 Optional fields Radio optional values consistently follow the has_* guard pattern.
Radio — Interop and Privacy (5 checks)
ID Check Description
LRP-01 Multi-technology support A device can publish separate WiFi and BLE scan streams with shared timebase.
LRP-02 Fingerprint matching readiness Canonical identifier formats support stable join keys across sessions.
LRP-03 Pose association Optional sensor_pose + pose_frame_ref supports radio-visual alignment for retrieval pipelines.
LRP-04 Privacy guidance Identifier anonymization guidance documented for sensitive deployments (§2.7.6 + Appendix E radio profile).
LRP-05 No algorithm coupling Profile transports observations only; no positioning algorithm mandated.
Cross-Device Localization (5 checks)
ID Check Description
LC-01 Phone-to-scan localization Phone images matched against NavVis scan-derived SfM map. 2D-3D correspondences → PnP pose. The localization result publishable as PoseSE3 with method attribute indicating visual localization source.
LC-02 HoloLens-to-scan localization HoloLens rig (4 cameras) localized using generalized GP3P solver. Rig-level pose publishable as PoseSE3 on rig frame; per-camera poses derived from rig extrinsics.
LC-03 Cross-device map building Maps built from HoloLens data can localize phone queries and vice versa. SpatialDDS types (VisionMeta, CamIntrinsics, PoseSE3) are device-agnostic — the same types serve HoloLens grayscale rigs and phone RGB frames.
LC-04 Visual overlap score LaMAR defines per-image-pair visual overlap O ∈ [0,1] using ray-traced mesh visibility. Publishable as MetaKV on correspondence edges or as an attribute in a mapping Edge with match_score.
LC-05 Multi-FOV handling HoloLens (83° × 4 cameras = ~280° rig FOV) vs phone (64° single camera). CamIntrinsics per sensor correctly parameterizes each; rig_id groups HoloLens cameras. FOV difference is captured in calibration, not in type hierarchy.

Results

All 70 LaMAR checks pass.

Modality Checks Pass Gap Deferred Notes
HoloLens Vision 6 6 0 1 Rolling shutter / global shutter typed model deferred
HoloLens Depth 4 4 0 0 ToF depth, IR stream
Phone Vision 5 5 0 1 Rolling shutter readout-direction model deferred
Phone Depth 3 3 0 0 iPad LiDAR depth
NavVis Scanner 5 5 0 0 Multi-camera rig, lidar mesh, point cloud
IMU 3 3 0 1 Per-frame gravity vector deferred
Poses & Trajectories 5 5 0 0 VIO, GT, uncertainty, quaternion convention
Multi-Session Alignment (Anchors) 7 7 0 0 Scan-to-scan, sequence-to-scan, year-long stability
Discovery (Multi-Device) 5 5 0 0 Heterogeneous announcements, coverage, manifests
Radio Profile Coverage 12 12 0 1 CSI/CIR first-class transport deferred
Radio Discovery + QoS 5 5 0 0 radio_scan + RADIO_SCAN_RT integrated
Radio Interop + Privacy 5 5 0 1 Multi-band coexistence metadata deferred
Cross-Device Localization 5 5 0 1 Visual-overlap score as first-class edge attribute deferred
Total 70 70 0 6 100% coverage

Deferred items are fields that CAN be carried (typically via MetaKV or BlobRef) but lack first-class typed support. They are tracked as future profile additions, not as conformance failures.

Gap Analysis

The original LaMAR conformance pass identified LM-1: no first-class radio fingerprint type as a gap, with WiFi and Bluetooth scans falling back to ad hoc MetaKV JSON payloads. LM-1 is closed in 1.5+ by the provisional sensing.radio profile (Appendix E). The 22 radio checks in this section (Radio Profile Coverage / Discovery + QoS / Interop + Privacy) validate the closure.

LaMAR Scenario Narrative (Informative)

The LaMAR "CAB" office building sequence illustrates the full multi-device AR alignment lifecycle — the scenario class that no prior conformance dataset exercises:

  1. Reference scan. A NavVis VLX backpack scans the CAB building twice over 6 months. Each scan produces a dense lidar point cloud (1 cm grid), a triangle mesh, and panoramic images at 1–3 m intervals. Each scan session publishes an Announce with ServiceKind.MAPPING, topics[] listing vision + pointcloud + mesh streams, and coverage enclosing the scanned area. The two scan sessions are aligned by ICP on the point clouds; the rigid transform is published as FrameTransform linking scan-B's origin to scan-A's world frame.

  2. GeoAnchor establishment. The aligned scan pair defines the GT reference frame. A GeoAnchor is published anchoring the world frame origin to a WGS84 position derived from the building's surveyed coordinates. QR codes detected during scanning are published as an AnchorSet with per-QR AnchorEntry entries — persistent visual landmarks that future AR devices can recognize.

  3. HoloLens session. A participant wearing HoloLens 2 walks through the building. The headset's 4-camera tracking rig publishes VisionFrame (GRAY8, 30 Hz) on 4 parallel streams linked by rig_id. ToF depth publishes VisionFrame (DEPTH16, 1 Hz) on a separate stream sharing the same rig_id. IMU publishes ImuSample at device-native rate. WiFi and Bluetooth scans publish as RadioScan with radio_type = WIFI and BLE respectively, advertised via RadioSensorMeta. The on-device head tracker publishes relative poses as PoseSE3 in the session-local tracking frame.

  4. Phone session. Another participant carries an iPad Pro through the same space at a different time. The single camera publishes VisionFrame (RGB8, JPEG, 10 Hz) with per-frame CamIntrinsics (varying fx from auto-focus). The iPad LiDAR publishes VisionFrame (DEPTH16, 10 Hz) on a paired stream linked by rig_id. ARKit publishes tracking poses as PoseSE3 in the ARKit session frame. WiFi scans publish as RadioScan with sparse BT coverage.

  5. Sequence-to-scan alignment. For each AR session, the alignment pipeline localizes frames against the reference scan's SfM model using feature matching and PnP (phone) or GP3P (HoloLens rig). The rigid alignment from tracking frame to GT world frame is published as FrameTransform. Pose graph optimization refines all per-frame poses jointly — the refined poses carry CovMatrix uncertainty from the Hessian.

  6. Cross-device localization. A phone query image is matched against a map built from HoloLens data — or vice versa. Both devices' data flows through identical SpatialDDS types (VisionMeta, CamIntrinsics, PoseSE3); the types are device-agnostic. Radio fingerprints from the WiFi/BT RadioScan streams constrain image retrieval to spatially plausible candidates, improving recall by the +4.6–17.5% the LaMAR paper documents.

  7. Global refinement. All sessions — multiple NavVis scans, dozens of HoloLens sequences, dozens of phone sequences captured over a year — are jointly optimized. Sequence-to-sequence visual correspondences augment the scan-based constraints. The final GT poses achieve cm-level accuracy with calibrated uncertainty. The entire aligned dataset is accessible through FrameTransform chains rooting all device frames in the common GT world frame, which is itself geo-anchored via GeoAnchor.

This end-to-end pipeline exercises the Anchors profile (GeoAnchor, FrameTransform, AnchorSet for QR landmarks), the Discovery profile (heterogeneous device announcements with different sensor capabilities), the sensing.radio profile (typed WiFi/BT transport replacing ad hoc MetaKV), cross-device frame alignment (headset, phone, and scanner all registered into a common frame through transform chains), and multi-session temporal persistence (year-long alignment stability) — capabilities untested by any prior conformance dataset.

Deferred Items

  • Rolling-shutter timing model. SpatialDDS has no first-class rolling-shutter timing model (readout direction, row exposure time, line delay). LaMAR's phone images use rolling shutter; the shutter type is carriable as MetaKV but not typed.
  • Per-frame gravity vector. HoloLens raw data includes per-frame gravity estimates. SpatialDDS's ImuSample carries raw accel/gyro but not processed gravity direction. Gravity is carriable as MetaKV or derived downstream.
  • Visual overlap score. LaMAR's mesh-based visual overlap metric O(i→j) is a novel quantity with no SpatialDDS equivalent. A future matchability or visibility score on observation edges could make this first-class.
  • CSI/CIR first-class payloads. CSI_REF currently points to external payloads via BlobRef. A future radio extension may define typed CSI/CIR transport.
  • Multi-band coexistence metadata. Additional fields for scan policy and dwell-time scheduling may be needed for dense AP environments.

Reproducing the Tests

The nuScenes and DeepSense 6G conformance harnesses are self-contained Python 3 scripts with no external dependencies.

nuScenes harness (scripts/nuscenes_harness_v2.py):

python3 scripts/nuscenes_harness_v2.py

Mirrors the SpatialDDS 1.6 IDL structures as Python dictionaries and checks them against the nuScenes schema. Produces a plain-text report and a JSON results file.

DeepSense 6G harness (scripts/deepsense6g_harness_v3.py):

python3 scripts/deepsense6g_harness_v3.py

Validates 44 checks across 7 modalities (radar tensor, vision, lidar, IMU, GPS, mmWave beam, semantics). The mmWave beam checks validate against the provisional rf_beam profile (Appendix E). Produces a plain-text report and a JSON results file.

S3E conformance: The 38 S3E checks documented in §I.3 were performed as a manual schema-vs-schema analysis. A scripted harness (scripts/s3e_harness_v1.py) following the same pattern as the nuScenes and DeepSense 6G scripts is planned for a future revision.

ScanNet conformance: The 35 ScanNet checks documented in §I.4 were performed as a manual schema-vs-schema analysis. A scripted harness (scripts/scannet_harness_v1.py) is planned for a future revision.

LaMAR conformance: The 22 LaMAR checks documented in §I.5 were performed as a manual schema-vs-schema analysis against the published wifi.txt and bt.txt field layouts and the radio-assisted retrieval workflow described by the benchmark. A scripted harness (scripts/lamar_harness_v1.py) is planned for a future revision.

No harness requires network access, a DDS runtime, or a dataset download. Implementers are encouraged to adapt the harnesses for additional reference datasets (e.g., Waymo Open, KITTI, Argoverse 2, RADIal, SubT-MRS, ScanNet, LaMAR) to validate coverage for sensor configurations or multi-agent scenarios not already covered.

Limitations

This testing validates schema expressiveness -- whether every dataset field has a lossless SpatialDDS mapping. It does not validate:

  • Wire interoperability -- actual DDS serialization/deserialization round-trips.
  • Performance -- throughput, latency, or memory footprint under real sensor loads.
  • Semantic correctness -- whether a particular producer's mapping preserves the intended meaning of each field.
  • Multi-dataset coverage -- datasets with different sensor configurations (e.g., solid-state lidar, event cameras, ultrasonic sensors) or deployment patterns (e.g., multi-floor hierarchical spaces, aerial-ground cooperation, dense pedestrian tracking) may surface additional gaps. S3E covers three-robot outdoor coordination; ScanNet covers single-room indoor scenes. Larger fleet sizes, degraded-communication environments, multi-floor buildings, and heterogeneous robot types (ground + aerial) remain untested.

These areas are appropriate targets for future conformance work.

Appendix J: Comparison with ROS 2 (Informative)

This appendix compares SpatialDDS 1.6 with ROS 2 (Jazzy / Rolling, circa 2025) across architecture, message design, and deployment scope. The goal is to help implementers who are familiar with one system understand the other, and to clarify where the two are complementary rather than competing.

J.1 Architectural Differences

SpatialDDS is a protocol specification -- a set of IDL profiles over OMG DDS. It defines message schemas, QoS contracts, discovery semantics, and coordinate conventions, but provides no build system, CLI tools, or simulation bindings.

ROS 2 is a full robotics framework. It includes a middleware abstraction (rmw) that defaults to DDS, a build system (colcon/ament), CLI tooling (ros2 topic, ros2 bag), visualization (RViz2), simulation integration (Gazebo), and thousands of community packages. Its message definitions are distributed across independently maintained packages (sensor_msgs, geometry_msgs, vision_msgs, radar_msgs, nav_msgs).

Because both systems use DDS as their transport layer, they can coexist on the same DDS domain. A ROS 2 node and a SpatialDDS participant can exchange data directly when message types are aligned, or through a lightweight bridge when they are not.

Dimension SpatialDDS 1.6 ROS 2
Identity Protocol specification over DDS Full robotics framework with DDS middleware
IDL corpus Single versioned spec with profiles Fragmented across independent packages
Extensibility @extensibility(APPENDABLE) on all structs .msg files require new versions for changes
Payload strategy Blob-reference: large data out of band via BlobRef Inline: pixel/point data carried in message body
Schema version Embedded schema_version field per Meta struct Per-package versioning; no cross-package version

J.2 Coordinate Frames & Transforms

Both systems use (x, y, z, w) quaternion component order. Orientation data flows between them without reordering.

Dimension SpatialDDS 1.6 ROS 2
Frame identity FrameRef { uuid, fqn } -- UUID authoritative string frame_id -- plain string
Frame graph PoseSE3 DAG with anchors bridging local to global tf2 strict tree via /tf and /tf_static topics
Geo-anchoring First-class: GeoAnchor, GeoPose, VPS integration GPS via NavSatFix; geo-transforms are custom
Handedness Not prescribed; semantics defined by transform chains REP-0103: right-handed, X-forward/Y-left/Z-up
Convention table §2 maps nuScenes, Eigen, Unity, Unreal, OpenXR, glTF No formal conversion table
Bundled pose FramedPose (pose + frame + cov + stamp in one struct) No standard; PoseWithCovarianceStamped closest but lacks frame identity

SpatialDDS's FrameRef model with UUIDs is designed for multi-device environments where string collisions between independent participants would be problematic. ROS 2's string-based frame_id is simpler and sufficient for single-robot or tightly coordinated fleets.

J.3 Sensor Message Comparison

Camera / Vision

Dimension SpatialDDS sensing.vision ROS 2 sensor_msgs
Metadata VisionMeta (latched) + per-frame VisionFrame CameraInfo sent with every frame
Intrinsics CamModel enum + explicit fx/fy/cx/cy float64[9] K matrix + free-form distortion_model string
Distortion Distortion enum (NONE, RADTAN, KB) with normative dist = NONE prose Free-form string; no enum constraint
Pixel format PixFormat enum + ColorSpace enum Free-form string encoding; no color space
Rig support RigRole enum (12 values incl. PANORAMIC, EQUIRECTANGULAR) + rig_id No standard rig concept
Compression Codec enum including H.264/H.265/AV1 CompressedImage with free-form format string
Keyframe is_key_frame boolean Not standardized

SpatialDDS separates static metadata (latched once) from per-frame indices, avoiding redundant intrinsics on every frame. ROS 2 sends CameraInfo alongside every Image, which is simpler but repetitive.

Lidar

Dimension SpatialDDS sensing.lidar ROS 2 sensor_msgs
Point layout PointLayout enum (XYZ_I, XYZ_I_R, XYZ_I_R_T, etc.) PointField[] -- arbitrary user-defined fields
Encoding CloudEncoding enum (PCD, PLY, LAS, BIN_INTERLEAVED, DRACO, MPEG_PCC) Raw binary only; compressed formats via community packages
Per-point timestamps XYZ_I_R_T layout with normative t field semantics Custom PointField named "t"; no standard
Sensor metadata LidarMeta (latched): type, rings, range, FOV No standard lidar metadata message
Frame timing t_start / t_end with normative computation guidance Single Header.stamp

ROS 2's PointCloud2 is maximally flexible -- any field layout is expressible via PointField[]. SpatialDDS constrains layouts via enum, enabling static validation and optimized deserialization at the cost of reduced flexibility for exotic field combinations.

Radar

Dimension SpatialDDS sensing.rad ROS 2 radar_msgs
Fields per detection 16+ (xyz, velocity variants, RCS, dyn_prop, uncertainty, track ID) 5 (range, azimuth, elevation, doppler_velocity, amplitude)
Position Cartesian xyz_m Polar (range + angles)
Velocity Cartesian + radial + ego-compensated (three options) Radial only (doppler_velocity)
Dynamic property RadDynProp enum (7 values) Not present
Sensor type RadSensorType enum (SHORT/MEDIUM/LONG/IMAGING_4D/SAR) Not present
Uncertainty Per-detection position/velocity RMS, ambiguity state, false alarm probability Not present
Sensor metadata RadSensorMeta (latched): range limits, FOV, velocity limits Not present
Tensor transport RadTensorMeta / RadTensorFrame for raw or processed radar cubes Not present

SpatialDDS radar supports both detection-centric outputs (automotive datasets like nuScenes, Continental ARS 408) and raw/processed radar cubes via tensor transport for ISAC and ML pipelines. ROS 2 radar_msgs is minimal and has seen limited community adoption; many teams define custom messages.

Semantics / Object Detections

Dimension SpatialDDS semantics ROS 2 vision_msgs
Multi-hypothesis Single class_id + score per detection ObjectHypothesisWithPose[] -- multiple class+pose hypotheses
Size convention Normative: (width, height, depth) with dataset mapping Not specified
Attributes sequence<MetaKV, 8> with guard Not present
Visibility float [0..1] with guard Not present
Evidence counts num_lidar_pts, num_radar_pts Not present
Covariance Mat3x3 cov_pos + Mat3x3 cov_rot (separate) float64[36] full 6x6 pose covariance
Spatial tiling TileKey for spatial indexing Not present

ROS 2 vision_msgs supports multi-hypothesis detections; SpatialDDS provides richer annotation metadata that is valuable for dataset-scale workflows and multi-sensor fusion pipelines.

RF Beam / ISAC

Dimension SpatialDDS sensing.rf_beam ROS 2
Beam power vectors RfBeamFrame with per-beam power, sweep type, sparse beam indices Not present
Array metadata RfBeamMeta: carrier frequency, codebook size, antenna elements, FoV, MIMO config Not present
Multi-array sync RfBeamArraySet batches frames from multiple phased arrays at one timestamp Not present
Sweep types BeamSweepType enum (EXHAUSTIVE, HIERARCHICAL, TRACKING, PARTIAL) Not present
Blockage detection has_blockage_state / is_blocked / blockage_confidence per frame Not present
Power units PowerUnit enum (DBM, LINEAR_MW, RSRP) Not present
Discovery Registered type rf_beam with RF_BEAM_RT QoS profile Not present

ROS 2 has no standard messages for phased-array beam sensing, mmWave communication metadata, or ISAC workloads. Teams working on 5G/6G sensing, V2X beam management, or joint radar-communication systems currently define custom ROS 2 messages or bypass ROS entirely. SpatialDDS's rf_beam profile provides a typed, discoverable transport path for these modalities -- validated against the DeepSense 6G dataset's 60 GHz phased-array beam power measurements (see Appendix I).

Radio Fingerprint / Indoor Positioning

Dimension SpatialDDS sensing.radio ROS 2
Radio types RadioType enum: WIFI, BLE, UWB, CELLULAR, LORA Not present in standard packages
Measurement types RadioMeasurementKind enum: RSSI, RTT, AoA, RANGE_M, RSRP, CSI_REF Not present
WiFi observations Per-AP: BSSID, RSSI, frequency, band, SSID, channel Not present
BLE observations Per-beacon: UUID/MAC, RSSI, major/minor, tx_power Not present
UWB/ranging Per-tag: range_m, range_std_m, AoA azimuth/elevation Not present
Scan timing stamp + scan_duration_s + aggregation_window_s Not present
Sensor metadata RadioSensorMeta: capabilities, bands, device model/platform Not present
Privacy guidance Normative anonymization guidance for identifiers Not present
Discovery Registered type radio_scan with RADIO_SCAN_RT QoS profile Not present

ROS 2 has no standard message set for radio environment observations used by WiFi/BLE fingerprint localization and commodity-radio indoor positioning pipelines. Teams typically create custom wifi_scan or bluetooth_scan messages. SpatialDDS sensing.radio provides a typed, discoverable transport path validated against LaMAR-style WiFi/BLE observation workflows (Appendix I).

J.4 Discovery & Spatial Awareness

Dimension SpatialDDS 1.6 ROS 2
Discovery Application-level: ANNOUNCE / QUERY / REPLY with coverage geometry and capability negotiation Transport-level: DDS SPDP/SEDP; application introspection via ros2 CLI
Spatial filtering CoverageModel with AABBs, spheres, geofences -- subscribers filter by spatial region Not present; topic-level subscription only
ROI negotiation ROIRequest / ROIReply for sensor ROI control RegionOfInterest in CameraInfo; no request/reply pattern
Service manifests JSON manifests with anchors, capabilities, sensor descriptions; resolvable via spatialdds:// URIs Launch files + package.xml; no runtime manifest standard

SpatialDDS treats "where is this data relevant?" as a first-class protocol concern. ROS 2 relies on DDS-level endpoint discovery and topic names for data routing.

J.5 Large Payload Transport

SpatialDDS uses a blob-reference architecture: DDS messages carry lightweight metadata and BlobRef pointers; actual sensor payloads (images, point clouds) are transferred via BlobChunk sequences or external blob stores. This decouples the control plane from the data plane and is designed for WAN-scale deployments where not every subscriber needs raw sensor data.

ROS 2 carries payloads inline. sensor_msgs/Image includes the full pixel array; PointCloud2 includes all point data. This is simpler to implement and works well within a single machine or local network. For bandwidth-constrained links, community packages (image_transport, point_cloud_transport) add pluggable compression, but these are not part of the core message definitions.

J.6 Ecosystem & Tooling

Dimension SpatialDDS 1.6 ROS 2
Visualization DDS vendor tools; custom RViz2, Foxglove, PlotJuggler
Simulation DDS bridge to Gazebo / Isaac Sim Native Gazebo, Isaac Sim, CARLA integration
Recording DDS vendor recording rosbag2 -- standardized
Build system N/A (protocol spec) colcon / ament / CMake
Package ecosystem Emerging (OpenArCloud) Thousands of packages; active community
Embedded DDS on embedded (Micro-XRCE-DDS) micro-ROS via Micro-XRCE-DDS

J.7 Complementary Deployment Pattern

SpatialDDS and ROS 2 are not mutually exclusive. The recommended integration pattern for teams using both:

  1. On-robot: ROS 2 manages the local perception-to-control pipeline. Sensor drivers publish sensor_msgs topics; perception nodes consume them; planners and controllers close the loop.
  2. Cross-device: A bridge node subscribes to ROS 2 topics and publishes spatial summaries -- detections, pose updates, anchor observations -- to SpatialDDS topics. Other devices (AR headsets, infrastructure sensors, fleet coordinators) consume SpatialDDS data without needing a ROS 2 installation.
  3. Discovery: SpatialDDS's ANNOUNCE / QUERY / REPLY protocol handles multi-stakeholder service discovery and spatial coverage negotiation -- capabilities that ROS 2 does not provide at the application layer.

This separation keeps the robot's internal pipeline in the well-supported ROS 2 ecosystem while using SpatialDDS for the inter-device spatial coordination it was designed for.

Scenario Better fit
Single-robot perception -> planning -> control ROS 2
Multi-device spatial alignment (AR + robots + infrastructure) SpatialDDS
Automotive sensor fusion with rich radar/annotation metadata SpatialDDS
Digital twin with spatial queries and coverage filtering SpatialDDS
Rapid prototyping with simulation and visualization ROS 2
Manipulation and arm control ROS 2
Cross-domain interop (city, IoT, AR, robotics on one bus) SpatialDDS
ISAC / V2X beam management and 5G/6G sensing SpatialDDS
Radio-assisted AR localization (WiFi/BLE fingerprinting) SpatialDDS
Fleet robotics with heterogeneous sensors Either; complementary
Multi-robot map exchange, alignment, and merge coordination SpatialDDS
Zone-based spatial alerting and smart infrastructure events SpatialDDS
Fleet task allocation with spatial capability discovery SpatialDDS

Appendix K: IDL Package Layout (Informative)

The canonical file layout for SpatialDDS IDL is as follows. Implementations distributing the IDL as a package SHOULD preserve this directory structure so that #include paths and module hierarchies stay portable across projects.

spatialdds-idl/
├── types.idl              # Common typedefs (Time, Vec3, Mat3x3, Mat6x6, Mat12x12, etc.)
├── core.idl               # Core profile (PoseSE3, FramedPose, GeoPose, GeoAnchor,
│                          #   PlannedTrajectory, EntityBinding, BlobRef, etc.)
├── common.idl             # Sensing common (StreamMeta, FrameHeader,
│                          #   FrameQuality, MetaKV, etc.)
├── discovery.idl          # Discovery profile (Announce, CoverageQuery,
│                          #   CoverageResponse, TopicMeta, etc.)
├── anchors.idl            # Anchor registry (AnchorSet, AnchorDelta,
│                          #   AnchorEntry, etc.)
├── argeo.idl              # AR + Geo (NodeGeo, FrameTransform, etc.)
├── sensing/
│   ├── vision.idl         # Vision profile
│   ├── lidar.idl          # LiDAR profile
│   ├── rad.idl            # Radar profile (detection + tensor)
│   ├── vio.idl            # IMU / VIO profile
│   └── radio.idl          # Radio fingerprint profile (1.5+)
├── semantics.idl          # 2D/3D detection, classification
├── slam_frontend.idl      # SLAM front-end primitives
├── mapping.idl            # Mapping profile (MapMeta, MapAlignment, etc.)
├── events.idl             # Spatial events (SpatialEvent, ZoneState, etc.)
└── provisional/
    ├── rf_beam.idl         # RF beam profile (Provisional)
    ├── radio.idl           # Radio fingerprint examples (Provisional)
    ├── neural.idl          # Neural field examples (Informative only in 1.6)
    └── agent.idl           # Agent task coordination (Informative only in 1.6)

This repository organizes the v1.6 IDL files in a flat layout under idl/v1.6/ (with examples/ for provisional and informative profiles); both organizations are valid as long as #include paths and module declarations match.

Module namespacing follows the IDL module declarations:

  • spatial::core → core.idl
  • spatial::common → common.idl and types.idl
  • spatial::disco → discovery.idl
  • spatial::sensing::vision → sensing/vision.idl
  • spatial::sensing::lidar → sensing/lidar.idl
  • spatial::sensing::rad → sensing/rad.idl
  • spatial::sensing::radio → sensing/radio.idl
  • spatial::vio → vio.idl
  • spatial::semantics → semantics.idl
  • spatial::slam_frontend → slam_frontend.idl
  • spatial::argeo → argeo.idl
  • spatial::anchors → anchors.idl
  • spatial::mapping → mapping.idl
  • spatial::events → events.idl

Generated language bindings SHOULD preserve this module hierarchy:

  • C++: spatial::core::PoseSE3
  • Python: spatial.core.PoseSE3
  • Rust: spatial::core::PoseSE3