Abstract
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.
Highlights
In the past few years, the range of location-based services has been progressively extended from outdoor to indoor environments, as well as to applications such as path finding, emergency planning, and object tracking
The outdoor localization services can be provided by GPS with a reliable accuracy, but in indoor spaces which are GPS-denied, alternative technology needs to be explored
Many existing indoor localization methods rely on dedicated infrastructure such as Wi-Fi access points [1], ultrasonic networks [2], synthetic aperture radar (SAR) [3], Bluetooth [4], ultra-wideband (UWB) [5,6], or magnetic fields [7]
Summary
In the past few years, the range of location-based services has been progressively extended from outdoor to indoor environments, as well as to applications such as path finding, emergency planning, and object tracking. By constructing each node with the indoor landmark (e.g., corners, doors, stairs) and each edge with the routes between the landmarks, the semantic augmented route network graph was input as a prior map for the trajectory calibration using a particle filter algorithm, providing geometric (location, length of edges), topological (connectivity and orientation), and semantic information (human cognition). In this way, the rich semantic information can be exploited to avoid the localization errors caused by purely depending on geometric coordinates in the conventional methods.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
