Pedestrian Inertial Navigation System Augmented by Vision-Based Foot-to-foot Relative Position Measurements

Chi-Shih Jao, Yusheng Wang, Andrei M. Shkel

Abstract: In this paper, we investigate how self-contained pedestrian navigation can be augmented by the use of foot-to-foot visual observations. The main contribution is a measurement model that uses Zero velocity UpdaTe (ZUPT) and relative position measurements between the two shoes obtained from shoe-mounted feature patterns and cameras. This measurement model provides directly the compensation measurements for the three position states and three velocity states of a pedestrian. The involved features for detection are independent of surrounding environments, thus, the proposed system has a constant computational complexity in any context. The performance of the proposed system was compared to a standalone ZUPT method and a relative-distance-aided ZUPT method. Simulation results showed an improvement in accumulated navigation errors by over 90%. Real-world experiments were conducted, exhibiting a maximum improvement of 85% in accumulated errors, verifying validity of the approach.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 900 - 907
Cite this article: Jao, Chi-Shih, Wang, Yusheng, Shkel, Andrei M., "Pedestrian Inertial Navigation System Augmented by Vision-Based Foot-to-foot Relative Position Measurements," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 900-907.
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