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

TL;DR

This paper introduces a WiFi-based localization method for large-scale indoor environments that leverages structural priors from osmAG, achieving higher accuracy and efficiency than traditional fingerprinting methods, especially in areas without fingerprint data.

Contribution

The paper presents a novel WiFi localization framework that integrates osmAG priors and signal modeling, improving accuracy and scalability over existing fingerprint-based approaches.

Findings

Achieved a mean localization error of 3.79 m in offline AP localization.

Improved real-time robot localization accuracy by 8.77% in fingerprinted areas.

Demonstrated superior accuracy and space efficiency in a large multi-floor environment.

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.