ORB-SLAM3 Dynamic Scene Reconstruction based on fused YOLOV5

With the development of enterprise intelligence and automation, the inspection of power distribution room is not limited to manual work. This paper proposes a semantic segmentation method based on improved ORB-SLAM3 and YOLOV5 fusion to eliminate dynamic features. ORB-SLAM3 is used to realize scene reconstruction to generate dense point cloud maps. In this paper, all the differences between ORB-SLAM2 and ORBSLAM3 are compared, and the dense point cloud generation and loop detection modules are added to make the mapping more accurate. In real scenes, the depth sensor is inevitably disturbed by dynamic objects when obtaining real environmental information. Compared with dynamic SLAM for object detection, the semantic segmentation module of YOLOV5 is used in this paper, which can remove dynamic objects while minimizing the loss of effective features. In addition, TensorRT is used to accelerate the construction of the map, and the semantic map is generated, which makes a prerequisite for the subsequent conversion of the map into a two-dimensional occupancy map. Experimental results show that compared with the original ORB-SLAM2/3, the improved ORBSLAM3 greatly improves the accuracy in dynamic scenes. A more real-time and accurate scene reconstruction function of power distribution room is realized.