Error State Estimation of Localization Using 3D Point Cloud According to the Surrounding Environment

Abstract

In autonomous vehicles, it is desirable to drive while estimating the error state in places where the error in the localization using LiDAR becomes large. In this study, we have developed a new method to estimate the error state of the localization in real time, which enables more accurate localization in real environments. The effectiveness of the proposed method was verified in places including environments with few features and where there are errors in the measurement of 3D point clouds. It was confirmed that the enlargement of the pose error in those places could be grasped by the error ellipse calculated by the proposed method.