  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.
