TraIL: Pinpoint Trajectory for Indoor Localization

Abstract

Indoor localization on smartphones is an enabler for a number of ubiquitous and mobile computing applications attracting worldwide attentions. Many location-based services rely on WiFi fingerprinting approaches to achieve a reasonable accuracy. However, there is still room for improvement due to the prevalent-existing errors (e.g., 8∼12 m). In this study, we devise and implement a high-accuracy indoor localization solution leveraging the WiFi-based method and pedestrian mobility provided by smartphones. Our basic idea is that WiFi-only localization can generate rough but absolute positions, while user motion is able to bring accurate but relative locations. Taking both sides into account simultaneously, we design techniques to refine the raw WiFi positions in the process of laying the precise local trajectory appropriately down to the absolute coordinate using a novel least median of squares (LMS) fit algorithm. We develop a prototype system, named TraIL, and conduct comprehensive experiments in a building along different shaped routes. The evaluation results show that TraIL can achieve 80% improvement on average error with respect to WiFi-only indoor localization.