Spherical Grid-Based IMU/Lidar Localization and Uncertainty Evaluation Using Signal Quantization

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 in an iterative extended Kalman filter to estimate the position and orientation of a moving vehicle. An analytical bound is derived from the vehicle’s state estimation error, which accounts for both 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 a landmark-based method in an indoor environment, whereas an outdoor experiment verifies the practicality of the method in a realistic driving scenario.