Application of 3D point cloud map and image identification to mobile robot navigation

Abstract

This paper presents application of the combination of mapping, image identification, and indoor navigation for the omnidirectional wheeled mobile robot (WMR). The mapping part uses the point clouds converted from the depth map of the LiDAR camera to construct the indoor 3D point cloud map. Combining this map with the You Only Look Once ver.4 (YOLOv4) network, the door number and the doorknob can be displayed on the map by image identification. The navigation part applies 3D point cloud map, image, and depth map to an omnidirectional WMR so that it can know the current position and plan the path to the designated location. The proposed control scheme can make the robot perform indoor navigation and reach designated location.

Non-local collaborative denoising via weighted nuclear norm minimization for point cloud

“…Point cloud is one of the basic shape representations in computer graphics. With the continuous advancement of 3D scanning technology and sensor technology, 3D point cloud data have been widely applied in numerous fields such as reverse engineering [1], autonomous navigation [2], and virtual reality [3]. However, even high-precision 3D laser scanners cannot avoid noise in the process of measuring data.…”