LAUSR

The development of simultaneous localization and mapping (SLAM) technology plays an important role in robot navigation and autonomous vehicle innovation. The ORB-SLAM2 is a unified SLAM solution for monocular, binocular, and RGBD cameras which constructs a sparse feature point map for real-time positioning. However, a sparse map based approach cannot effectively meet the requirements of robot navigation, environment reconstruction, and other tasks. In this paper, a dense mapping thread is added to the existing ORB-SLAM2 system. The depth map and color image obtained by the stereo matching of a binocular camera are used to generate a three-dimensional point cloud for keyframes; then, the point cloud is fused by tracking and optimizing the motion track of a feature frame to obtain a real-time point cloud map. Through the experiments conducted on the KITTI dataset and the real environment under the ROS, it is proved that the proposed system constructs a clear three-dimensional point cloud map while constructing an accurate trajectory.