Robot Localization and Reconstruction based on 3D Point Cloud

Abstract

The 3D point cloud is widely used in robot fields because of its accurate positioning results and dense environment information. However, most of the existing methods are real-time positioning and 3D reconstruction in unknown environments. In some scenes that require multiple regular operations, such as robot patrol and maintenance, the stability of the system is slightly insufficient. At present, the methods in this field are mostly based on fixed starting points or manual positioning, with an insufficient degree of automation. In this paper, a real-time robot localization and reconstruction system based on 3D vision is proposed, which includes pose estimation, environment reconstruction, and relocalization based on a 3D point cloud. First, a more accurate pose estimation method is applied for 3D environment reconstruction, using the coordinate transformation of the point cloud and the point cloud matching of the key frames. Then, a new point cloud segmentation method is proposed for local map maintenance to realize point cloud map display and human-robot interaction under real-time network transmission. Finally, a new robot relocalization method is proposed for map updating when the mapping is interrupted or repeated. The M2DGR dataset and real robot test were used to verify the accuracy and effect of the system, where the results showed that our method had a good performance.