Seamless Positioning and Mapping Using an Adaptive GNSS/INS/LIDAR/Wheel Odometry Integration Based on Factor Graph Optimization

Eva Buchmayer, Fabian Theurl, Karin Mascher, Christoph Schmied, Franziska Huebl

Peer Reviewed

Abstract:	This paper presents LIWO-GO, an extension of the algorithm LIWO-SLAM, which incorporates GNSS in a factor graph for Simultaneous Localization and Mapping (SLAM). To ensure a seamless transitions from outdoor to indoor environments, the GNSS observations must be properly weighted. For this, an adaptive weighting scheme and a trust score are used. The trust score is based on the combination of a hybrid autoencoder for GNSS SNR values with a map-based approach using the SLAM map. To evaluate the algorithm, a tracked robot was equipped with a dual-antenna GNSS receiver, an IMU, and a LiDAR. A test dataset was collected where the robot was steered along a route that contains both outdoor and indoor environments. The trajectory obtained by LIWO-GO is compared to a reference trajectory. The results show that with the trust score and the adaptive weighting scheme, the position estimation of the robot can be improved.
Published in:	Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024) September 16 - 20, 2024 Hilton Baltimore Inner Harbor Baltimore, Maryland
Pages:	2409 - 2423
Cite this article:	Buchmayer, Eva, Theurl, Fabian, Mascher, Karin, Schmied, Christoph, Huebl, Franziska, "Seamless Positioning and Mapping Using an Adaptive GNSS/INS/LIDAR/Wheel Odometry Integration Based on Factor Graph Optimization," Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024), Baltimore, Maryland, September 2024, pp. 2409-2423. https://doi.org/10.33012/2024.19919
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