Abstract
In this paper, an optical flow based step length estimation algorithm for indoor pedestrian navigation is proposed. To address the challenge of interferences arising from hands shaking during walking, the pose of a smartphone is computed by attitude and heading reference system (AHRS) algorithm and used to improve the performance of optical flow algorithm. Moreover, the motion information of pedestrians can be captured by calculating the alteration and relevance between sequential pixels and frames of camera snapshots when steps are detected. Accordingly, online training and calibration of step length estimation in pedestrian dead-reckoning system (PDR) are accomplished. To verify the performance of proposed step length estimation algorithm, several field tests with a smartphone were conducted in various environments. Experimental results show that the proposed algorithm achieves accurate performance in terms of step length estimation.
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