LocMe: Human locomotion and map exploitation based indoor localization

State-of-the-art indoor localization techniques can reach high localization accuracy, but rely on widely deployed infrastructure which may be absent in less developed regions. To achieve infrastructure-free indoor localization, researchers have proposed to use the inertial sensors on the mobile devices to update the users potential locations by walking steps. Such methods are known to accumulate errors and degrade drastically over time. To compensate for it, either a wall-constraint that eliminates possible steps going through the walls, or landmarks correspond to the special user activities, are usually employed. However, these approaches still suffer from slow convergence and cannot detect floor changes automatically without the information from other users. In this paper, we propose LocMe, an indoor localization service based on human locomotion detection and map exploitation. By synergizing both the locomotion-constraint and wall-constraint, LocMe can significantly increase the converging speed of localization and automatically detect the floor changes, with negligible extra complexity. Our field tests show that LocMe can achieve a median localization error of 1.1 m, which is over 68% lower than the localization algorithm with only the wall-constraint in the same test condition.