OpenFLAME: Federated Localization & Mapping

• OpenFLAME is a federated mapping infrastructure that decouples global data via independent, region-specific map servers and DNS-based discovery.
• It uses waypoint-based transformations and coarse-to-fine localization to stitch together multi-source 3D scans without sharing complete map data.
• The system achieves low-latency, high-accuracy pose estimation for indoor/outdoor AR, robotics, and spatial computing while preserving privacy.

OpenFLAME is a federated localization and mapping infrastructure that enables decentralized, large-scale visual positioning and localization for modern location-based applications—including indoor and outdoor augmented reality (AR), robotics, and spatial computing. Its architecture permits independent hosting and control of region-specific map servers while supporting dynamic service discovery, privacy, and robust cross-map pose estimation.

1. Federation Model and System Architecture

OpenFLAME organizes localization and mapping as a federation of independently operated map servers, each responsible for a distinct geographic region. The granularity of regions is defined using the S2 spherical geometry library, which partitions the globe into hierarchical quadrilateral cells ("S2 cells"). Every server advertises its coverage by registering DNS geo-domain names derived from these cells.

The client localization workflow comprises:

• Coarse localization via device sensors (GPS, WiFi, BLE).
• Generation of DNS-compatible geo-domains using the S2 region coverer algorithm.
• DNS queries to identify relevant map servers for the device's position.
• Submission of location cues (images, sensor data) to those servers.
• Retrieval of pose estimates from map servers, each in their own local coordinate frames.

Coordination between disparate local map servers employs only a minimal set of public waypoints—landmarks with known positions—so that full map details need not be shared. The following formula transforms a waypoint from a map coordinate system R to an application frame A:

$W_A = P_A \cdot P_R^{-1} \cdot W_R$

where $W_R$ is the waypoint location in the map server's frame, $P_R$ and $P_A$ are pose transformations in the map and application frames, respectively, and $W_A$ is the transformed waypoint in the application coordinate system (Bharadwaj et al., 6 Nov 2024, Bharadwaj et al., 4 Oct 2025).

2. Federated Visual Positioning and Data Stitching

OpenFLAME's federated VPS approach allows organizations to independently capture and maintain 3D scans for spatial localization. This is crucial for privacy, regulatory compliance, and coverage of private indoor spaces that centralized VPS providers cannot reach.

Each VPS module functions autonomously. When clients move across spaces managed by different servers, OpenFLAME's dynamic VPS Stitcher uses the invariance of relative poses:

$O_D = C_D \cdot (C_{V_1}^{-1} \cdot O_{V_1})$

where $O_{V_1}$ is an object's pose in a VPS server's coordinate system and $C_D$/$C_{V_1}$ are the client's/device's poses in local and VPS frames. When transitioning to a new server,

$$T = C_{V_1} \cdot (C_D^{-1} \cdot C_D') \cdot C_{V_2}^{-1}$$

is computed to relate coordinate systems. This enables seamless data fusion and AR object anchoring across independent maps, all without exposing private map data (Bharadwaj et al., 4 Oct 2025).

3. Service Discovery and DNS Integration

Discovery of appropriate map servers is accomplished through the DNS infrastructure. Devices compute geo-domain names from their positional uncertainty, then issue parallel DNS queries. Custom TXT-based records (MNS for map name servers, MCNAME for direct server selection) allow multiple providers to advertise under the same region, overcoming standard DNS record constraints (e.g., CNAME exclusivity).

Caching mechanisms and parallel queries maintain low query latencies—responses occur in a few milliseconds, even at scale. Federation and DNS-based delegation protocols support local administrative control (e.g., subdividing a university campus by building), and negative caching prevents load imbalance during frequent location shifts (Bharadwaj et al., 6 Nov 2024).

4. Map Abstractions, Coordinate Systems, and Waypoints

OpenFLAME employs waypoints—landmarks with mapped positions—rather than globally aligned coordinate systems. Each provider can register waypoints tied to key physical features (doors, signs, etc.), which serve to merge and transition across coversages. Only the minimal waypoint set is shared, maintaining privacy of underlying scan data. Applications transform these waypoints as needed for AR rendering, using the transformation formula above.

This abstraction decouples map servers from global reference frames, facilitates scalable cross-provider federation, and supports overlapping coverage. It also prevents mandatory exposure of sensitive spatial data contained in full environment scans (Bharadwaj et al., 6 Nov 2024, Bharadwaj et al., 4 Oct 2025).

5. Privacy and Security Considerations

OpenFLAME prioritizes privacy by restricting information exchange to pose estimates and waypoints. Map servers retain control of raw scan data and never transmit complete maps. For sensitive environments, localization can be performed using derived features rather than direct images; more sensitive signals are only exchanged following client authentication with trusted servers.

The modular architecture allows each provider to implement privacy-preserving algorithms for place recognition (e.g., CLIP embeddings), semantic masking (e.g., YOLO for omitting dynamic features), and robust pose estimation without cross-server raw data leakage (Bharadwaj et al., 4 Oct 2025).

6. Performance, Scalability, and Real-World Use Cases

Trace-driven evaluations show that OpenFLAME achieves low-latency service discovery and localization. DNS query round-trips are typically milliseconds; localization server response times (including image upload and pose estimation) remain near one second. Median pose estimation errors are reported as 2.1 cm (translational) and <1° (rotational) in controlled indoor settings—a level matching or exceeding pre-placed marker systems (e.g., AprilTags).

The architecture supports AR navigation spanning outdoors to indoor transitions, robotics (autonomous navigation in private spaces), and vertical applications (gaming, fitness, logistics). Distributed map server control encourages broader and up-to-date coverage, while modular server implementations invite continuous innovation in the underlying localization algorithms (Bharadwaj et al., 6 Nov 2024).

7. Challenges, Limitations, and Future Directions

Key technical challenges addressed include: scalable discovery (solved via DNS geo-domain mapping and caching), cross-provider map merging (via waypoint-based transformations), and privacy (through minimal shared abstractions and secure feature localization). Some limitations remain—such as the complexity of configuration for multi-provider installations, the need for robust error score and confidence calibration across independent services, and achieving consistent quality control.

Future directions include advanced load balancing for heavily trafficked map servers, interoperability standards for federated waypoints and confidence metrics, and extensions to support real-time multi-modal localization (e.g., combining visual, inertial, and RF cues). A plausible implication is that as indoor AR and privacy-sensitive robotics proliferate, federated models like OpenFLAME will become increasingly dominant, especially where centralized global scans cannot meet regulatory or privacy requirements (Bharadwaj et al., 6 Nov 2024, Bharadwaj et al., 4 Oct 2025).

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OpenFLAME represents a comprehensive and scalable architecture for federated visual localization and mapping across indoor and outdoor domains. By leveraging DNS-based service discovery, waypoint-based map abstractions, and modular cross-provider stitching, it addresses the technical, privacy, and scalability constraints faced by contemporary AR and spatial computing applications.