Unsupervised pedestrian trajectory reconstruction from IMU sensors

This paper presents a pedestrian navigation algorithm based on a foot-mounted 9DOF Inertial Measurement Unit, which provides accelerations, angular rates and magnet-ics along 3-axis during the motion. Most of algorithms used worldwide are based on stance detection to reduce the tremendous integration errors, from acceleration to displacement. As the crucial part is to detect stance phase precisely, we introduced a cyclic left-to-right style Hidden Markov Model that is able to appropriately model the periodic nature of signals. Stance detection is then made unsupervised by using a suited learning algorithm. Then, assisted by a simplified error-state Kalman filter, trajectory can be reconstructed. Experimental results show that the proposed algorithm can provide more accurate location, compared to competitive algorithms, w.r.t. ground-truth obtained from OpenStreet Map.

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