Semantic SLAM With More Accurate Point Cloud Map in Dynamic Environments

Static environment is a prerequisite for most existing vision-based SLAM (simultaneous localization and mapping) systems to work properly, which greatly limits the use of SLAM in real-world environments. The quality of the global point cloud map constructed by the SLAM system in a dynamic environment is related to the camera pose estimation and the removal of noise blocks in the local point cloud maps. Most dynamic SLAM systems mainly improve the accuracy of camera localization, but rarely study on noise blocks removal. In this paper, we proposed a novel semantic SLAM system with a more accurate point cloud map in dynamic environments. We obtained the masks and bounding boxes of the dynamic objects in the images by BlitzNet. The mask of a dynamic object was extended by analyzing the depth statistical information of the mask in the bounding box. The islands generated by the residual information of dynamic objects were removed by a morphological operation after geometric segmentation. With the bounding boxes, the images can be quickly divided into environment regions and dynamic regions, so the depth-stable matching points in the environment regions are used to construct epipolar constraints to locate the static matching points in the dynamic regions. In order to verify the preference of our proposed SLAM system, we conduct the experiments on the TUM RGB-D datasets. Compared with the state-of-the-art dynamic SLAM systems, the global point cloud map constructed by our system is the best.