|Abstract:||This paper describes the design, analysis, and experimental evaluation of a spherical-grid-based localization algorithm that leverages quantization theory to bound navigation uncertainty. This algorithm integrates data from Light Detection And Ranging (LiDAR) and inertial measuring units (IMU) in an iterative extended Kalman filter to estimate the position and orientation of a moving vehicle. An analytical bound is derived on the vehicle’s state estimation error, which accounts for both the random measurement noise and the loss of localization information caused by gridding. The performance of the proposed approach is analyzed and compared with that of a brute-force spherical grid-based method and of a landmark-based method in an indoor environment.|
Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022)
September 19 - 23, 2022
Hyatt Regency Denver
|Pages:||367 - 380|
|Cite this article:||
Hassani, Ali, Joerger, Mathieu, "Spherical Grid-Based IMU/LiDAR Localization and Uncertainty Evaluation Using Signal Quantization," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 367-380.
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