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: 1.2
Date: 12 September 2025
Author: James Jackson [Open AR Cloud] – james.jackson [at] openarcloud [dot] org
Contents
- Introduction
- IDL Profiles
- Example Manifests
- Operational Scenarios
- Conclusion
- Future Directions
- Glossary of Acronyms
- References
- Appendices
- Appendix A: Core Profile
- Appendix B: Discovery Profile
- Appendix C: Anchor Registry Profile
- Appendix D: Extension Profiles
- Appendix E: Provisional Extension Examples
1. Introduction
SpatialDDS is a lightweight, standards-based protocol 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 VIO sensors, SLAM frontend features, semantic detections, AR+Geo, and provisional Neural/Agent profiles.
This profile-based design keeps the protocol lean and interoperable, while letting communities adopt only the pieces they need.
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, while optional Extensions (VIO, SLAM Frontend, Semantics, AR+Geo) and provisional profiles (Neural, Agent) add domain-specific capabilities. Implementers adopt only what they need, keeping deployments lean and interoperable. - 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.
2. 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.
2.1 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.
2.2 Discovery
The Discovery profile adds a minimal, lightweight way to announce services, anchors, content, and registries in the real world. It complements DDS’s built-in participant and topic discovery by describing what a service does, where it operates, and how to learn more. Announcements are deliberately simple—service kind, coarse coverage (via geohash or a bounding-box array [min_lon, min_lat, max_lon, max_lat]), and a pointer to a manifest for richer details. This keeps the bus lean while enabling clients to discover and connect to services such as VPS, mapping, anchor registries, semantics, or AR content providers without requiring heavy registries or complex protocols.
2.3 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.
2.4 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 and manifests 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
- 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.
- 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.
3. Example Manifests
While SpatialDDS keeps its on-bus messages small and generic, richer details about services, maps, and experiences are provided out-of-band through manifests. A manifest is a lightweight JSON document referenced by a manifest_uri in a discovery announce. Manifests let providers describe capabilities, formats, coverage shapes, entry points, and assets without bloating the real-time data stream. The examples here show four common cases: a Visual Positioning Service (VPS) manifest that defines request/response topics and limits, a Mapping Service manifest that specifies tiling scheme and encodings, a Content/Experience manifest that lists anchors, tiles, and media for AR experiences, and an Anchors manifest that enumerates localization anchors with associated assets. Together they illustrate how manifests complement the DDS data plane by carrying descriptive metadata and policy.
A) VPS Manifest
This manifest describes a Visual Positioning Service (VPS). It specifies the service identifier, version, coverage area, and the topics used for queries and responses. It also lists supported input encodings and response types, allowing clients to determine compatibility before interacting with the service.
{
"service_id": "svc:vps:acme/sf-downtown",
"profiles": ["Core", "SLAM Frontend", "AR+Geo"],
"request": {
"features_topic": "feat.keyframe",
"image_blob_role": "image/jpeg",
"prior_topic": "geo.fix"
},
"response": {
"rich": "pg.nodegeo",
"minimal": "geo.fix"
},
"limits": { "max_fps": 10, "max_image_px": 1920 },
"auth": { "scheme": "oauth2", "issuer": "https://auth.acme.com" },
"coverage": { "geohash": ["9q8y","9q8z"] }
}
B) Mapping Service Manifest
This manifest describes a Mapping service that publishes geometry tiles for a given coverage area. It defines the service identifier, version, supported encodings, and the DDS topics used for requesting and receiving tile data. It enables clients to subscribe to live or cached geometry without ambiguity about formats or endpoints.
{
"service_id": "svc:mapping:acme/sf-downtown",
"version": "1.0.0",
"provider": { "id": "acme-maps", "org": "Acme Maps Inc." },
"title": "Acme Downtown Map Service",
"summary": "Tiled 3D meshes for SF downtown area",
"profiles": ["Core"],
"topics": {
"meta": "geom.tile.meta",
"patch": "geom.tile.patch",
"blob": "geom.tile.blob"
},
"tile_scheme": "quadtree",
"encodings": ["glTF+Draco", "LASzip"],
"lod_range": [12, 18],
"coverage": {
"geohash": ["9q8y","9q8z"],
"polygon_uri": "https://cdn.acme.example/downtown_poly.geojson"
},
"auth": { "scheme": "none" },
"terms": { "license": "CC-BY-4.0" }
}
C) Content/Experience Manifest
This manifest describes a spatial content or experience service. It declares a content identifier, version, anchor bindings, and optional dependencies on other manifests. This allows AR applications to discover and attach experiences to shared anchors while keeping the actual content assets (e.g., 3D models, media) external to DDS.
{
"content_id": "xp:sculpture-walk:met-foyer",
"version": "1.0.2",
"provider": { "id": "svc:content:museum-inc", "org": "Museum Inc." },
"title": "AR Sculpture Walk",
"summary": "Guided AR overlays for five sculptures in the main foyer.",
"tags": ["ar", "museum", "tour"],
"profiles_required": ["Core", "AR+Geo"],
"availability": {
"from": "2025-09-01T09:00:00Z",
"until": "2025-12-31T23:59:59Z",
"local_tz": "America/New_York"
},
"coverage": { "geohash": ["dr5ru9","dr5rua"], "polygon_uri": "https://cdn.museum.example/foyer_poly.geojson" },
"entrypoints": {
"anchors": [
{ "anchor_id": "anchor/met-foyer/north-plinth", "hint": "Start here" },
{ "anchor_id": "anchor/met-foyer/central", "hint": "Checkpoint 2" }
]
},
"runtime_topics": {
"subscribe": ["geo.tf", "geo.anchor", "geom.tile.meta", "geom.tile.patch"],
"optional": ["semantics.det.3d.set"]
},
"assets": [
{ "type": "image", "role": "poster", "uri": "https://cdn.museum.example/img/poster.jpg" },
{ "type": "audio", "role": "narration", "uri": "https://cdn.museum.example/audio/room_intro.mp3", "lang": "en" }
]
}
D) Anchors Manifest
This manifest enumerates durable localization anchors for a zone and links them to relocalization or scene-alignment assets. Each anchor entry supplies an anchor_id, a simplified GeoPose (latitude, longitude, altitude, quaternion), and whatever metadata or asset descriptors the publisher wants to expose (timestamps, quality hints, coverage tags, etc.). Top-level fields mirror the publisher's registry structure—no default frame assumptions or cache semantics are imposed by the specification.
{
"schema": "https://example.org/spatialdds/anchor-manifest.schema.json#v1",
"zone_id": "knossos:palace",
"zone_title": "Knossos Palace Archaeological Site",
"coverage": {
"geohash": ["sv8wkf", "sv8wkg"],
"bbox": [
25.1608,
35.2965,
25.1665,
35.3002
]
},
"anchors": [
{
"anchor_id": "square:statue-east",
"geopose": {
"lat_deg": 35.29802,
"lon_deg": 25.16305,
"alt_m": 110.2,
"qw": 1,
"qx": 0,
"qy": 0,
"qz": 0
},
"assets": [
{
"kind": "features:ORB:v1",
"uri": "https://registry.example/anchors/statue-east/orb_v1.bin",
"count": 2048,
"descriptor_bytes": 32,
"patch_frame": "anchor-local",
"hash": "sha256:placeholder...",
"bytes": 65536
},
{
"kind": "geom:pcd:lod1",
"uri": "https://registry.example/anchors/statue-east/patch_lod1.las",
"points": 12000,
"hash": "sha256:placeholder...",
"bytes": 480000
}
],
"stamp": "2025-09-07T15:45:00Z"
},
{
"anchor_id": "central-court:north",
"geopose": {
"lat_deg": 35.29761,
"lon_deg": 25.16391,
"alt_m": 109.8,
"qw": 0.707,
"qx": 0,
"qy": 0,
"qz": 0.707
},
"assets": [
{
"kind": "features:SuperPoint:v1",
"uri": "https://registry.example/anchors/central-court-n/superpoint_v1.npz",
"count": 1500,
"descriptor_bytes": 256,
"hash": "sha256:placeholder...",
"bytes": 220000
},
{
"kind": "geom:mesh:lod0",
"uri": "https://registry.example/anchors/central-court-n/patch_lod0.glb",
"triangles": 8000,
"hash": "sha256:placeholder...",
"bytes": 350000
}
],
"stamp": "2025-09-08T11:12:13Z"
}
],
"stamp": "2025-09-12T22:55:00Z"
}
4. Operational Scenarios: From SLAM to AI World Models
SpatialDDS is designed to be practical and flexible across real-world deployments. The following scenarios illustrate how the Core, Discovery, Anchors, and Extension profiles can be combined in different ways to support robotics, AR/XR, smart city, IoT, and AI-driven applications. Each scenario lists the profiles involved and the key DDS topics flowing in and out, showing how the schema maps onto actual use cases. Optional profiles such as Neural and Agent are marked clearly, allowing implementers to see future directions without requiring them in the baseline.
Core SLAM/SfM Scenarios
These scenarios cover the foundational use cases for spatial mapping and localization. They show how devices and services exchange features, images, pose graphs, and geometry tiles to support SLAM and structure-from-motion pipelines, either on-device, at the edge, or in multi-agent systems.
- On-device Visual(-Inertial) SLAM
A single device runs its own SLAM, fusing camera and IMU, publishing nodes/edges. - Profiles: Core (Pose Graph, VIO)
- Topics In: raw IMU/camera
-
Topics Out: pg.node, pg.edge, geo.tf
-
Device → Edge Distributed SLAM
A mobile device streams features/images to an edge server for map building. - Profiles: Core, SLAM Frontend
- Topics In: feat.keyframe, BlobChunk (images)
-
Topics Out: pg.node, pg.edge, geom.tile.*
-
Multi-Agent SLAM with Global Alignment
Multiple devices contribute to a shared map, aligning through anchors. - Profiles: Core, Anchors
- Topics In: pg.node/edge from peers
-
Topics Out: pg.edge (loop closures), geo.tf (frame alignment)
-
Offline SfM / Batch Reconstruction
A service reconstructs geometry from stored images/features. - Profiles: Core, SLAM Frontend
- Topics In: BlobChunk (image sets), feat.keyframe
- Topics Out: geom.tile.meta/patch/blob
Service Scenarios
These scenarios describe how SpatialDDS supports services that go beyond local SLAM, such as Visual Positioning Services (VPS), cooperative relocalization, map delivery, and anchor registries. Discovery messages and manifests play a key role here, allowing clients to find and interact with services dynamically.
- Relocalization / Place Recognition
A service matches incoming features against a prior map for relocalization. - Profiles: Core, SLAM Frontend
- Topics In: feat.keyframe
-
Topics Out: geo.fix, pg.nodegeo
-
VPS — Features-only Query
Client sends features; service returns a pose or node with geo anchor. - Profiles: Core, SLAM Frontend
- Topics In: feat.keyframe
-
Topics Out: geo.fix or pg.nodegeo
-
VPS — Image-only Query
Client sends an image; service extracts features and returns pose. - Profiles: Core
- Topics In: BlobChunk (role=“image/jpeg”)
-
Topics Out: geo.fix or pg.nodegeo
-
Cooperative VPS / Crowd Relocalization
Devices share queries and matches to improve coverage. - Profiles: Core, Discovery
- Topics In: feat.keyframe, geo.fix
-
Topics Out: shared pg.edge or consensus geo.fix
-
Mapping Service Consumption (Discovery)
Clients discover and fetch map tiles for their area of interest. - Profiles: Core, Discovery
- Topics In: disco.service, geom.tile.meta/patch/blob
-
Topics Out: local cache of geometry
-
Anchor Registry Subscription (Discovery)
Clients subscribe to a registry of persistent anchors. - Profiles: Core, Anchors, Discovery
- Topics In: anchors.set, anchors.delta
- Topics Out: geo.tf (local → anchor/world alignment)
Consumer Scenarios
These scenarios focus on AR clients and applications that consume maps, anchors, and semantics. They show how SpatialDDS delivers persistent content alignment, semantic overlays, and shared localization for end-user experiences.
- AR Client Map Consumption
An AR headset consumes geometry and anchors to render content. - Profiles: Core, Anchors
- Topics In: geom.tile.*, geo.anchor, geo.tf
-
Topics Out: none
-
Semantics-Assisted Mapping
A client enriches tiles with object detections for smarter AR overlays. - Profiles: Core, Semantics
- Topics In: geom.tile.blob
-
Topics Out: semantics.det.3d.set
-
AR Client with VPS + Anchor Registry
A client uses VPS fixes plus anchors for persistent localization. - Profiles: Core, Anchors, Discovery
- Topics In: feat.keyframe or image blobs, anchors.set
- Topics Out: geo.tf
Lifecycle / Recovery Scenario
This scenario illustrates how a device or client can quickly catch up with the current state of the world after joining late or recovering from a failure. By fetching cached tiles and anchors, clients can synchronize efficiently without disrupting live streams.
- Catch-Up & Recovery (Reality Feed Style)
A late joiner fetches cached tiles/anchors to sync quickly. - Profiles: Core, Anchors
- Topics In: geom.tile.meta/patch/blob, anchors.set
- Topics Out: resumed pg.node/edge
AI & World-Model Extensions
These scenarios extend SpatialDDS beyond SLAM and AR into the realm of AI agents, neural maps, and digital twins. They demonstrate how AI perception services, planning agents, and predictive twin backends can plug into the same bus, consuming and enriching the shared world model. Neural and Agent profiles are optional extensions, and scenarios that use them are marked accordingly.
- VLM/Detector as a Perception Service
An AI model consumes images and publishes 2D/3D detections. - Profiles: Core, Semantics (+ SLAM Frontend if features in)
- Topics In: geom.tile.blob, optionally feat.keyframe
-
Topics Out: semantics.det.2d.set, semantics.det.3d.set
-
Captioning / Visual QA Agent
A vision-language model provides captions/labels tied to anchors or tiles. - Profiles: Core, Semantics
- Topics In: geom.tile.blob, geo.anchor
-
Topics Out: semantics.det.2d.set (with captions), agent.answer (optional)
-
Neural Map — Remote View Synthesis (optional Neural extension)
Thin clients request rendered views from a neural map service. - Profiles: Core (+ Neural if adopted)
- Topics In: neural.view.req
-
Topics Out: neural.view.resp with images
-
Neural Map — Asset Streaming (optional Neural extension)
Neural assets (e.g., Gaussian splats) are streamed as blobs for local rendering. - Profiles: Core (+ Neural if adopted)
- Topics In: geom.tile.meta/patch (encoding=“nerf”/“gaussians”)
-
Topics Out: none
-
Digital Twin Ingest (Realtime → Twin Backend)
A digital twin backend ingests SpatialDDS streams for persistent modeling. - Profiles: Core, Semantics, Anchors
- Topics In: pg.node/edge, geom.tile.*, geo.anchor, semantics.det.3d.set
-
Topics Out: none
-
Digital Twin → SpatialDDS (Predictive Overlays)
A twin service publishes predictions or overlays back to clients. - Profiles: Core, Semantics
- Topics In: none (internal twin logic)
-
Topics Out: semantics.det.3d.set, geom.tile.*
-
Route/Task Planning Agent (optional Agent extension)
An AI agent consumes world state and publishes goals or routes. - Profiles: Core (+ Agent if adopted)
- Topics In: pg.node, geo.tf, semantics.det.3d.set, geo.anchor
-
Topics Out: task.route, task.goal, or agent.task/status
-
Human-in-the-Loop Labeling & Training Data Capture
Detections are corrected by humans and fed back for model improvement. - Profiles: Core, Semantics
- Topics In: geom.tile.blob, semantics.det.* (proposals)
- Topics Out: semantics.det.* (corrected), data.capture.meta
Taken together, these scenarios show how SpatialDDS functions as a real-time bus for spatial world models. From raw sensing and SLAM pipelines to AR content, digital twins, and AI-driven perception and planning, the protocol provides a common substrate that lets diverse systems interoperate without heavy gateways or custom formats. This positions SpatialDDS as a practical foundation for AI world models that are grounded in the physical world.
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, 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, future work could align with ontologies and scene graphs 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
Lightweight tasking and coordination schemas could evolve into a standard Agent profile, supporting multi-agent planning and human-AI collaboration at scale. - 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.
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. Glossary of Acronyms
AI – Artificial Intelligence
AR – Augmented Reality
DDS – Data Distribution Service (OMG standard middleware)
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)
8. 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
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.
// SPDX-License-Identifier: MIT
// SpatialDDS Core 1.2
module spatial {
module core {
// ---------- Utility ----------
struct Time {
int32 sec; // seconds since UNIX epoch (UTC)
uint32 nsec; // nanoseconds [0..1e9)
};
struct PoseSE3 {
double t[3]; // translation (x,y,z)
double q[4]; // quaternion (x,y,z,w) in GeoPose order
};
@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 { ADD = 0, REPLACE = 1, REMOVE = 2 };
@appendable struct BlobRef {
string blob_id; // UUID or content-address
string role; // "mesh","attr/normals","pcc/geom","pcc/attr",...
string checksum; // SHA-256 (hex)
};
@appendable struct TileMeta {
@key TileKey key; // unique tile key
string tile_id_compat; // optional human-readable id
double min_xyz[3]; // AABB min (local frame)
double max_xyz[3]; // 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
double centroid_llh[3]; // lat,lon,alt (deg,deg,m) or NaN
double radius_m; // rough extent (m) or NaN
};
@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
};
@appendable struct BlobChunk {
@key string blob_id; // which blob
uint32 index; // chunk index (0..N-1)
sequence<uint8, 262144> data; // ≤256 KiB per sample
boolean last; // true on final chunk
};
// ---------- Pose Graph (minimal) ----------
enum EdgeTypeCore { ODOM = 0, LOOP = 1 };
@appendable struct Node {
string map_id;
@key string node_id; // unique keyframe id
PoseSE3 pose; // pose in frame_id
double cov[36]; // 6x6 covariance (row-major); NaN if unknown
Time stamp;
string frame_id; // e.g., "map"
string source_id;
uint64 seq; // per-source monotonic
uint64 graph_epoch; // for major rebases/merges
};
@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
double information[36]; // 6x6 info matrix (row-major)
Time stamp;
string source_id;
uint64 seq;
uint64 graph_epoch;
};
// ---------- Geo anchoring ----------
enum GeoFrameKind { ECEF = 0, ENU = 1, NED = 2 };
@appendable struct GeoPose {
double lat_deg;
double lon_deg;
double alt_m; // ellipsoidal meters
double q[4]; // orientation (x,y,z,w) in GeoPose order
GeoFrameKind frame_kind; // ECEF/ENU/NED
string frame_ref; // for ENU/NED: "@lat,lon,alt"
Time stamp;
double cov[9]; // 3x3 pos covariance (m^2), row-major; NaN if unknown
};
@appendable struct GeoAnchor {
@key string anchor_id; // e.g., "anchor/4th-and-main"
string map_id;
string frame_id; // local frame (e.g., "map")
GeoPose geopose; // global pose
string method; // "GNSS","VisualFix","Surveyed","Fusion"
double confidence; // 0..1
string checksum; // integrity/versioning
};
@appendable struct FrameTransform {
@key string transform_id; // e.g., "map->ENU@lat,lon,alt"
string parent_frame; // global frame (ENU@..., ECEF, ...)
string child_frame; // local frame ("map")
PoseSE3 T_parent_child; // transform parent->child
Time stamp;
double cov[36]; // 6x6 covariance; NaN if unknown
};
// ---------- Snapshot / Catch-up ----------
@appendable struct SnapshotRequest {
@key TileKey key; // which tile
uint64 up_to_revision; // 0 = latest
};
@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.
// SPDX-License-Identifier: MIT
// SpatialDDS Discovery 1.2
// Lightweight announces for services, coverage, and content
module spatial {
module disco {
typedef spatial::core::Time Time;
enum ServiceKind {
VPS = 0,
MAPPING = 1,
RELOCAL = 2,
SEMANTICS = 3,
STORAGE = 4,
CONTENT = 5,
ANCHOR_REGISTRY = 6,
OTHER = 255
};
@appendable struct KV {
string key;
string value;
};
@appendable struct ServiceAnnounce {
@key string service_id;
string name;
ServiceKind kind;
string version;
string org;
sequence<string,16> rx_topics;
sequence<string,16> tx_topics;
sequence<KV,32> hints;
string manifest_uri;
string auth_hint;
Time stamp;
uint32 ttl_sec;
};
@appendable struct CoverageHint {
@key string service_id;
sequence<string,64> geohash;
double bbox[4]; // [min_lon, min_lat, max_lon, max_lat]
double center_lat; double center_lon; double radius_m;
Time stamp;
uint32 ttl_sec;
};
@appendable struct ContentAnnounce {
@key string content_id;
string provider_id;
string title;
string summary;
sequence<string,16> tags;
string class_id;
string manifest_uri;
double center_lat; double center_lon; double radius_m;
Time available_from;
Time available_until;
Time stamp;
uint32 ttl_sec;
};
}; // 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 1.2
// Bundles and updates for anchor registries
module spatial {
module anchors {
typedef spatial::core::Time Time;
typedef spatial::core::GeoPose GeoPose;
@appendable struct AnchorEntry {
@key string anchor_id;
string name;
GeoPose geopose;
double confidence;
sequence<string,8> tags;
Time stamp;
string checksum;
};
@appendable struct AnchorSet {
@key string set_id;
string title;
string provider_id;
string 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 { ADD=0, UPDATE=1, REMOVE=2 };
@appendable struct AnchorDelta {
@key string set_id;
AnchorOp op;
AnchorEntry entry;
uint64 revision;
Time stamp;
string post_checksum;
};
@appendable struct AnchorSetRequest {
@key string set_id;
uint64 up_to_revision;
};
@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 VIO profile carries raw and fused IMU/magnetometer samples. 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 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.
VIO / Inertial Extension
Raw IMU/mag samples, 9-DoF bundles, and fused state outputs.
// SPDX-License-Identifier: MIT
// SpatialDDS VIO/Inertial 1.2
module spatial {
module vio {
typedef spatial::core::Time Time;
// IMU calibration
@appendable struct ImuInfo {
@key string imu_id;
string frame_id;
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
@appendable struct ImuSample {
@key string imu_id;
double accel[3]; // m/s^2
double gyro[3]; // rad/s
Time stamp;
string source_id;
uint64 seq;
};
// Magnetometer
@appendable struct MagnetometerSample {
@key string mag_id;
double mag[3]; // microtesla
Time stamp;
string frame_id;
string source_id;
uint64 seq;
};
// Convenience raw 9-DoF bundle
@appendable struct SensorFusionSample {
@key string fusion_id; // e.g., device id
double accel[3]; // m/s^2
double gyro[3]; // rad/s
double mag[3]; // microtesla
Time stamp;
string frame_id;
string source_id;
uint64 seq;
};
// Fused state (orientation ± position)
enum FusionMode { ORIENTATION_3DOF = 0, ORIENTATION_6DOF = 1, POSE_6DOF = 2 };
enum FusionSourceKind { EKF = 0, AHRS = 1, VIO = 2, IMU_ONLY = 3, MAG_AIDED = 4, AR_PLATFORM = 5 };
@appendable struct FusedState {
@key string fusion_id;
FusionMode mode;
FusionSourceKind source_kind;
double q[4]; // quaternion (x,y,z,w) in GeoPose order
boolean has_position;
double t[3]; // meters, in frame_id
double gravity[3]; // m/s^2 (NaN if unknown)
double lin_accel[3]; // m/s^2 (NaN if unknown)
double gyro_bias[3]; // rad/s (NaN if unknown)
double accel_bias[3]; // m/s^2 (NaN if unknown)
double cov_orient[9]; // 3x3 covariance (NaN if unknown)
double cov_pos[9]; // 3x3 covariance (NaN if unknown)
Time stamp;
string frame_id;
string source_id;
uint64 seq;
double quality; // 0..1
};
}; // module vio
};
SLAM Frontend Extension
Per-keyframe features, matches, landmarks, tracks, and camera calibration.
// SPDX-License-Identifier: MIT
// SpatialDDS SLAM Frontend 1.2
module spatial {
module slam_frontend {
// Reuse core: Time, etc.
typedef spatial::core::Time Time;
// Camera calibration
enum DistortionModelKind { NONE = 0, RADTAN = 1, EQUIDISTANT = 2, KANNALA_BRANDT = 3 };
@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)
string frame_id; // camera frame
Time stamp; // calib time (or 0 if static)
};
// 2D features & descriptors per keyframe
@appendable struct Feature2D {
double u; double v; // pixel coords
float scale; // px
float angle; // rad [0,2π)
float score; // detector response
};
@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
@appendable struct FeatureMatch {
string node_id_a; uint32 idx_a;
string node_id_b; uint32 idx_b;
float score; // similarity or distance
};
@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)
@appendable struct Landmark {
@key string lm_id;
string map_id;
double p[3];
double cov[9]; // 3x3 pos covariance; NaN if unknown
sequence<uint8, 4096> desc; // descriptor bytes
string desc_type;
Time stamp;
string source_id;
uint64 seq;
};
@appendable struct TrackObs {
string node_id; // observing keyframe
double u; double v; // pixel coords
};
@appendable struct Tracklet {
@key string track_id;
string lm_id; // optional link to Landmark
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).
// SPDX-License-Identifier: MIT
// SpatialDDS Semantics 1.2
module spatial {
module semantics {
typedef spatial::core::Time Time;
typedef spatial::core::TileKey TileKey;
// 2D detections per keyframe (image space)
@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]
float bbox[4]; // [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;
};
@appendable struct Detection2DSet {
@key string set_id; // batch id (e.g., node_id + seq)
string node_id;
string camera_id;
sequence<Detection2D, 256> dets; // ≤256
Time stamp;
string source_id;
};
// 3D detections in world/local frame (scene space)
@appendable struct Detection3D {
@key string det_id;
string frame_id; // 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_id
double center[3]; // m
double size[3]; // width,height,depth (m)
double q[4]; // orientation (x,y,z,w) in GeoPose order
// Uncertainty (optional; NaN if unknown)
double cov_pos[9]; // 3x3 position covariance
double cov_rot[9]; // 3x3 rotation covariance
// Optional instance tracking
string track_id;
Time stamp;
string source_id;
};
@appendable struct Detection3DSet {
@key string set_id; // batch id
string frame_id; // common frame for the set
boolean has_tile;
TileKey tile_key; // valid when has_tile = true
sequence<Detection3D, 128> dets; // ≤128
Time stamp;
string source_id;
};
}; // module semantics
};
AR + Geo Extension
Geo-fixed nodes for easy consumption by AR clients & multi-agent alignment.
// SPDX-License-Identifier: MIT
// SpatialDDS AR+Geo 1.2
module spatial {
module argeo {
typedef spatial::core::Time Time;
typedef spatial::core::PoseSE3 PoseSE3;
typedef spatial::core::GeoPose GeoPose;
@appendable struct NodeGeo {
string map_id;
@key string node_id; // same id as core::Node
PoseSE3 pose; // local pose in map frame
GeoPose geopose; // corresponding global pose (WGS84/ECEF/ENU/NED)
double cov[36]; // 6x6 covariance in local frame; NaN if unknown
Time stamp;
string frame_id; // local frame
string source_id;
uint64 seq;
uint64 graph_epoch;
};
}; // module argeo
};
Appendix E: Provisional Extension Examples
The following examples illustrate how provisional extensions might be used in practice. They are not normative and are provided only to show how Neural and Agent profiles could appear on the wire.
Example: Neural Extension (Provisional)
This example shows how a service might publish metadata for a Gaussian splat field covering part of a city block.
neural::NeuralFieldMeta {
field_id = "sf-market-01";
kind = GAUSSIANS;
encoding = "gsplat-2024";
min_x = -50; min_y = -20; min_z = 0;
max_x = 80; max_y = 40; max_z = 60;
base_res_x = 0; base_res_y = 0; base_res_z = 0; // not applicable
channels = 4; // RGBA
blob_ids = ["blob:shard01", "blob:shard02"];
revision = 12;
stamp = { sec=1700000000, nsec=0 };
}
Example: Agent Extension (Provisional)
This example shows how an AI planner could issue a navigation task and later update its status.
agent::Task {
task_id = "route-2025-001";
kind = "navigate";
subject_id = "robot-42";
inputs = ["geo.anchor:main-entrance"];
due = { sec=1700000500, nsec=0 };
notes = "Deliver package to lobby.";
}
agent::TaskStatus {
task_id = "route-2025-001";
status = RUNNING;
result_uri = "";
log = "En route, ETA 3 min.";
stamp = { sec=1700000520, nsec=0 };
}