A New Point-Cloud-Based LiDAR/IMU Localization Method with Uncertainty Evaluation

Ali Hassani and Mathieu Joerger

Abstract:	This paper describes the design, analysis, and experimental evaluation of a new spherical-grid-based (SGB) localization algorithm. This method combines a light detection and ranging (LiDAR)’s spherically-parametrized point cloud with measurements from an inertial measurement units (IMU) to estimate the position and orientation of a moving vehicle. It also quantifies navigation uncertainty. This grid-based method does not require feature extraction and data association, which are necessary steps in landmark-based localization. In addition, we developed an automated testbed to analyze the probabilistic performance of a landmark-based method and of the new spherical gridbased algorithm. The sample and analytical error distributions for both methods are evaluated in a lab environment.
Published in:	Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021) September 20 - 24, 2021 Union Station Hotel St. Louis, Missouri
Pages:	636 - 651
Cite this article:	Hassani, Ali, Joerger, Mathieu, "A New Point-Cloud-Based LiDAR/IMU Localization Method with Uncertainty Evaluation," Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), St. Louis, Missouri, September 2021, pp. 636-651. https://doi.org/10.33012/2021.17905
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