A Novel Map-Based Dead-Reckoning Algorithm for Indoor Localization

Step counting-based dead-reckoning has been widely accepted as a cheap and effective solution for indoor pedestrian tracking using a hand-held device equipped with motion sensors. To compensate for the accumulating error in a dead-reckoning tracking system, extra techniques are always fused together to form a hybrid system. In this paper, we first propose a map matching (MM) enhanced particle filter (PF) as a robust localization solution, in which MM utilizes the corridor information to calibrate the step direction estimation and PF is applied to filter out impossible locations. To overcome the dependency on manually input corridor information in the MM algorithm, as well as the computational complexity in combining two such algorithms, an improved PF is proposed. By better modelling of the location error, the improved PF calibrates the location estimation, as well as step direction estimation when the map information is available, while keeping the computational complexity the same as the original PF. Experimental results show that in a quite dense map constraint environment with corridors, the proposed methods have similar accuracy, but outperform the original PF in terms of accuracy. When only partial map constraints are applied to simulate a new testbed, the improved PF obtains the most robust and accurate results. Therefore, the improved PF is the recommended DR solution, which is adaptive to various indoor environments.