In recent years, high-precision 3D environmental maps have attracted the attention of researchers in various fields and have been put to practical use. For the autonomous movement of mobile robots, it is common to create an environmental map in advance and use it for localization. In this study, to investigate the usefulness of 3D environmental maps, we scanned physical environments using two different simultaneous localization and mapping (SLAM) approaches, specifically a wearable 3D scanner and a 3D LiDAR mounted on a robot. We used the scan data to create 3D environmental maps consisting of 3D point clouds. Wearable 3D scanners can be used to generate high-density and high-precision 3D point-cloud maps. The application of high-precision maps to the field of autonomous navigation is expected to improve the accuracy of self-localization. Navigation experiments were conducted using a robot, which was equipped with the maps obtained from the two approaches described. Autonomous navigation was achieved in this manner, and the performance of the robot using each type of map was assessed by requiring it to halt at specific landmarks set along the route. The high-density colored environmental map generated from the wearable 3D scanner’s data enabled the robot to perform autonomous navigation easily with a high degree of accuracy, showing potential for usage in digital twin applications.

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