WiFi-based Global Localization in Large-Scale Environments Leveraging Structural Priors from osmAG

2025-08-13

AI Summary

To address the challenge of large-scale indoor global localization under GPS-denied conditions—particularly the degraded accuracy and low spatial efficiency in areas lacking fingerprint data—this paper proposes a novel localization framework integrating OpenStreetMap-based Area Graphs (osmAG) geometric-topological priors with physics-informed WiFi signal propagation modeling. We innovatively incorporate a wall-attenuation model to enhance radio signal characterization and synergistically combine osmAG-guided AP collaborative positioning with KNN-based fingerprint matching. In the offline phase, AP localization error is reduced to 3.79 m. Online localization achieves mean errors of 3.12 m in fingerprint-covered regions and 3.83 m in non-fingerprint regions—substantially outperforming conventional approaches. The method effectively mitigates the “robot kidnapping” problem and significantly improves spatial generalization capability.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Local Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Global localization is essential for autonomous robotics, especially in indoor environments where the GPS signal is denied. We propose a novel WiFi-based localization framework that leverages ubiquitous wireless infrastructure and the OpenStreetMap Area Graph (osmAG) for large-scale indoor environments. Our approach integrates signal propagation modeling with osmAG's geometric and topological priors. In the offline phase, an iterative optimization algorithm localizes WiFi Access Points (APs) by modeling wall attenuation, achieving a mean localization error of 3.79 m (35.3% improvement over trilateration). In the online phase, real-time robot localization uses the augmented osmAG map, yielding a mean error of 3.12 m in fingerprinted areas (8.77% improvement over KNN fingerprinting) and 3.83 m in non-fingerprinted areas (81.05% improvement). Comparison with a fingerprint-based method shows that our approach is much more space efficient and achieves superior localization accuracy, especially for positions where no fingerprint data are available. Validated across a complex 11,025 &m^2& multi-floor environment, this framework offers a scalable, cost-effective solution for indoor robotic localization, solving the kidnapped robot problem. The code and dataset are available at https://github.com/XuMa369/osmag-wifi-localization.

Problem

• Develop WiFi-based global localization for large indoor spaces
• Integrate OpenStreetMap structural data to improve WiFi AP positioning
• Achieve accurate robot localization without fingerprint data dependency

Innovation

• WiFi-based localization using osmAG structural priors
• Iterative AP localization with wall attenuation modeling
• Real-time robot localization via augmented osmAG map