INS/MPS/LiDAR Integrated Navigation System Using Federated Kalman Filter in an Indoor Environment

Taehoon Lee, Byungjin Lee, Jaehyun Yun, Sangkyung Sung

Abstract:	Abstract— In this paper, we propose a method to integrate data from Inertial Navigation System (INS), Magnetic Pose Estimation System (MPS), and Laser Imaging Detection and Ranging (LiDAR) using a Federated Kalman Filter (FKF). We adaptively adjusted the information sharing factor using the Mahalanobis distance to maintain navigation performance in indoor environments with mirrors that contaminate LiDAR measurements. By adaptively adjusting the information sharing factor, we can adjust the weight of each local filter. To validate navigation performance, we conducted UGV driving tests in various indoor environments. We conducted experiments by driving a UGV on a course with a diameter of 3.6 meters. UGVs are equipped with LiDAR, MPS receivers, and IMUs to measure data. We used four 1-meter diameter MPS coils. An optical motion capture device, the Optitrack, was used as reference data. Keywords—INS, MPS, LiDAR, FKF.
Published in:	2023 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 24 - 27, 2023 Hyatt Regency Hotel Monterey, CA
Pages:	160 - 166
Cite this article:	Lee, Taehoon, Lee, Byungjin, Yun, Jaehyun, Sung, Sangkyung, "INS/MPS/LiDAR Integrated Navigation System Using Federated Kalman Filter in an Indoor Environment," 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, April 2023, pp. 160-166. https://doi.org/10.1109/PLANS53410.2023.10140065
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