Multi hypothesis Kalman Filter for indoor pedestrian navigation based on topological maps
Location Based Services (LBS) require the knowledge of a user's position for manifold purposes in indoor and outdoor environments. For those applications several methods can be used, such as a Global Navigation Satellite System (GNSS) or a wireless localization system. In this paper a topological map is used to implement a indoor localization system for smartphone users. Benefit of this kind of map is that the position is reduced to one dimension, which simplifies the localization process in a considerable way. The map consists of vertices, which could be important points such as crossings or a ending point of a path, and edges, which represent ways between the vertices. The localization process utilizes different sensors of the smartphone, such as the accelerometer, the pressure sensor, Bluetooth Low Energy (BLE) and the integrated rotation sensor. To combine all of these information an Extended Kalman Filter (EKF) is introduced. To overcome crucial sensor information and thus to increase the robustness of the localization a multi hypotheses approach is implemented. Here different possible user positions on the topological map are instantiated and weighted. The implemented Kalman Filter is evaluated in several experiments which are recorded in an office building.
2016 IEEE/ION Position, Location and Navigation Symposium (PLANS)