The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLVI-3/W1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-3/W1-2022, 249–254, 2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-3/W1-2022, 249–254, 2022
22 Apr 2022
22 Apr 2022


Y. Yu1, W. Shi1, R. Chen2, and L. Chen2 Y. Yu et al.
  • 1Land Surveying and Geo-Informatics Department (LSGI), The Hong Kong Polytechnic University, Hong Kong, 999077, China
  • 2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430000, China

Keywords: Self-localization, Wi-Fi APs, indoor pedestrian network, floor identification, bias estimation, iteration unscented Kalman filter

Abstract. The acquisition of locations of Wi-Fi access points (APs) in urban buildings plays an important role in smart city applications, such as indoor navigation and social media data mining. This paper proposes a crowdsourcing-based approach for self-localization of Wi-Fi APs with the assistance of indoor pedestrian network (AP Detector). The features extracted from local opportunity signals are adopted for floor identification, and the crowdsourced indoor trajectories are segmented and matched with extracted indoor pedestrian network for the further trajectory calibration. In addition, the iteration unscented Kalman filter is applied for the location and bias estimation of local Wi-Fi FTM stations using the constructed Wi-Fi ranging model. The experimental results indicate that the proposed AP Detector can realize accurate location estimation of Wi-Fi APs, which also provides an effective way for autonomous construction of indoor navigation database and hybrid localization.