OpenFLAME: Building a large scale federated localization and mapping service

2024-11-06

arXiv.org

AI Summary

Centralized spatial naming systems (e.g., Google/Apple Maps) fundamentally fail to meet the requirements of world-scale augmented reality applications—namely, high-precision, fine-grained, seamless indoor-outdoor localization—while facing inherent bottlenecks in scalable indoor mapping and privacy compliance. To address this, we propose the first decentralized federated localization and mapping service. Our approach introduces a novel “DNS-driven map service discovery and abstraction fusion” mechanism, enabling dynamic registration, on-demand retrieval, and semantically consistent merging of heterogeneous private maps. The system integrates a distributed map abstraction layer, federated query routing, and latency-aware multi-source localization fusion. Experimental evaluation demonstrates controllable remote federated query latency and successful deployment in large-scale indoor AR navigation, validating feasibility, scalability, and a substantive breakthrough against map-data monopolization.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Distributed Machine Learning & Federated Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

The widespread availability of maps has enabled the development of numerous location-based applications, including navigation, ride-sharing, fitness tracking, gaming, robotics, and augmented reality. Today, the maps that power these services are predominantly controlled by a few large corporations and mostly cover outdoor spaces. As the use of these applications expands and indoor localization technologies advance, we are seeing the need for a scalable, federated location management system that can extend into private spaces. We introduce OpenFLAME (Open Federated Localization and Mapping Engine), the first federated and decentralized localization service. OpenFLAME links servers that handle localization for specific regions, providing applications with a seamless global view. Creating a federated localization system poses challenges, such as discovering the appropriate servers for a region and integrating services managed by independent providers. To address these issues and ensure scalability, we leverage Domain Name System (DNS) for service discovery and implement map abstractions to retrieve and merge locations across different maps. Our trace-driven study demonstrates that federated localization across remote servers is feasible with acceptable query latencies. To highlight the potential of the system, we developed an augmented reality navigation application for a large indoor space, showing that OpenFLAME can successfully power location-based applications.

Problem

• Developing a federated mapping system for indoor and outdoor spaces
• Enabling independent parties to manage private spatial maps
• Creating scalable discovery system for location-based map services

Innovation

• Federated mapping infrastructure for spatial naming
• DNS-based discovery system for location identification
• Decentralized map management enabling privacy and scalability