LIWO-SLAM: A LiDAR, IMU, and Wheel Odometry Simultaneous Localization and Mapping System for GNSS-Denied Environments Based on Factor Graph Optimization

Eva Reitbauer, Christoph Schmied, Fabian Theurl, Manfred Wieser

Peer Reviewed

Abstract:	The paper presents a sensor fusion algorithm for Simultaneous Localization and Mapping (SLAM) for a wheeled robot which fuses LiDAR, IMU, and wheel odometry using a factor graph. Building on the existing algorithm LIO-SAM, a detailed derivation is given for including an odometry factor for a four-wheel-independent steering and four-wheel-independent driving (4WIS4WID) robot. The algorithm is tested and evaluated using datasets collected at a tunnel research facility. The results show that in comparison to an Extended Kalman Filter, drift can be significantly reduced.
Published in:	Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023) September 11 - 15, 2023 Hyatt Regency Denver Denver, Colorado
Pages:	1669 - 1683
Cite this article:	Reitbauer, Eva, Schmied, Christoph, Theurl, Fabian, Wieser, Manfred, "LIWO-SLAM: A LiDAR, IMU, and Wheel Odometry Simultaneous Localization and Mapping System for GNSS-Denied Environments Based on Factor Graph Optimization," Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023), Denver, Colorado, September 2023, pp. 1669-1683. https://doi.org/10.33012/2023.19216
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