Spatial Skeleton-enhanced Location Tracking for Indoor Localization

Abstract

Map information can assist indoor localization to avoid improbable cases and achieve accurate location estimation. In this paper, we proposed an automatic method to extract useful information from an indoor map as spatial skeleton database (SSD). Then, based on conventional probabilistic fingerprinting technique and SSD, we proposed spatial skeleton-based dynamic probabilistic fingerprinting database (S-DFD) to filter out reference points (RPs) in fingerprinting database according to the previous target location and the walking distance between RPs. Finally, we proposed a spatial skeleton-based particle filter tracking (S-PT) based on conventional particle filter, which can use SSD to construct realistic particle transition model. The combination of SSD, S-DFD and S-PT are called spatial skeleton-enhanced location tracking for indoor localization (SELT). According to the result of the experiment, SELT can achieve accurate location estimation.