WiFi and Vision-Integrated Fingerprint for Smartphone-Based Self-Localization in Public Indoor Scenes

Abstract

Smartphone-based indoor localization systems are increasingly needed in various types of applications. This article proposes a novel WiFi and vision-integrated fingerprint (Wi-Vi fingerprint) for accurate and robust indoor localization. The method consists of two steps of fingerprint mapping and fingerprint localization. In the mapping step, the Wi-Vi fingerprints for all the sampling sites are computed by using EXIT signs as landmarks. In the localization step, a multiscale localization strategy is proposed, which includes coarse localization from weighted access points (WAPs)-based WiFi matching, the Gaussian weighted KNN (GW-KNN)-based image-level localization from holistic visual features, and finally, the metric localization for refinement. The proposed method has been tested in an indoor office building of 12 000 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and a mega-mall of 7200 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with different types of smartphones. The experimental results demonstrate that the proposed method can achieve 95% and 98% site recognition rates from image-level localization. The final localization errors after metric localization are less than a half meter on average.