In recent years, automated driving has been attracting attention, and various companies and institutions have been actively conducting research on this technology. In particular, automatic driving requires high-precision location estimation, which requires a 3D point cloud map with high-precision information on the road surroundings. SLAM, which simultaneously estimates self-position and maps the environment, has been attracting attention as a method for constructing 3D point cloud maps, but it is known that 3D point cloud maps constructed by SLAM have residual position errors. However, it is known that 3D point cloud maps constructed by SLAM and other methods have a problem of residual positional errors. Therefore, in this study, we aimed to detect degraded locations in 3D point cloud maps by focusing on errors in the translational direction of point clouds, taking advantage of point cloud clutter.