Error Covariance Estimation of 3D Point Cloud Registration Considering Surrounding Environment

To realize autonomous vehicle safety, it is important to accurately estimate the vehicle's pose. As one of the localization techniques, 3D point cloud registra-tion is commonly used. However, pose errors are likely to occur when there are few features in the sur-rounding environment. Although many studies have been conducted on estimating error distribution of 3D point cloud registration, the real environment is not re-flected. This paper presents real-time error covariance estimation in 3D point cloud registration according to the surrounding environment. The proposed method provides multiple initial poses for iterative optimiza-tion in the registration method. Using converged poses in multiple searches, the error covariance reflecting the real environment is obtained. However, the ini-tial poses were limited to directions in which the pose error was likely to occur. Hence, the limited search efficiently determined local optima of the registration. In addition, the process was conducted within 10 Hz, which is laser imaging detection and ranging (LiDAR) period; however, the execution time exceeded 100 ms in some places. Therefore, further improvement is necessary.