Robust Indoor Mobile Localization with a Semantic Augmented Route Network Graph

In recent years, using smartphones to determine pedestrian locations in indoor environments is an extensively promising technique for improving context-aware applications. However, the applicability and accuracy of the conventional approaches are still limited due to infrastructure-dependence, and there is seldom consideration of the semantic information inherently existing in maps. In this paper, a semantically-constrained low-complexity sensor fusion approach is proposed for the estimation of the user trajectory within the framework of the smartphone-based indoor pedestrian localization, which takes into account the semantic information of indoor space and its compatibility with user motions. The user trajectory is established by pedestrian dead reckoning (PDR) from the mobile inertial sensors, in which the proposed semantic augmented route network graph with adaptive edge length is utilized to provide semantic constraint for the trajectory calibration using a particle filter algorithm. The merit of the proposed method is that it not only exploits the knowledge of the indoor space topology, but also exhausts the rich semantic information and the user motion in a specific indoor space for PDR accumulation error elimination, and can extend the applicability for diverse pedestrian step length modes. Two experiments are conducted in the real indoor environment to verify of the proposed approach. The results confirmed that the proposed method can achieve highly acceptable pedestrian localization results using only the accelerometer and gyroscope embedded in the phones, while maintaining an enhanced accuracy of 1.23 m, with the indoor semantic information attached to each pedestrian's motion.