3D Lidar Mapping Relative Accuracy Automatic Evaluation Algorithm
This addresses the challenge of accuracy assessment in HD map construction for autonomous driving, offering an automated solution but is incremental as it builds on existing SLAM technologies.
The paper tackles the problem of evaluating 3D lidar mapping accuracy for autonomous vehicles without ground truth poses by proposing an algorithm that detects ghosting in point clouds to identify inaccurate poses, achieving results such as detecting poses with errors below 0.1m and calculating accuracy metrics like P_acc.
HD (High Definition) map based on 3D lidar plays a vital role in autonomous vehicle localization, planning, decision-making, perception, etc. Many 3D lidar mapping technologies related to SLAM (Simultaneous Localization and Mapping) are used in HD map construction to ensure its high accuracy. To evaluate the accuracy of 3D lidar mapping, the most common methods use ground truth of poses to calculate the error between estimated poses and ground truth, however it's usually so difficult to get the ground truth of poses in the actual lidar mapping for autonomous vehicle. In this paper, we proposed a relative accuracy evaluation algorithm that can automatically evaluate the accuracy of HD map built by 3D lidar mapping without ground truth. A method for detecting the degree of ghosting in point cloud map quantitatively is designed to reflect the accuracy indirectly, which takes advantage of the principle of light traveling in a straight line and the fact that light can not penetrate opaque objects. Our experimental results confirm that the proposed evaluation algorithm can automatically and efficiently detect the bad poses whose accuracy are less than the set threshold such as 0.1m, then calculate the bad poses percentage P_bad in all estimated poses to obtain the final accuracy metric P_acc = 1 - P_bad.