# Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow
## 4 Experimental Results
### Analysis of Experimental Results
#### Analysis of system runtime
Due to the presence of overlapping shadows of dynamic targets, the map of the whole scene appears to be very confusing, and due to the continuous changes in the position and movement state of dynamic targets, the overlapping shadows produced by them will also change, which makes the stability and reliability of the map greatly reduced. Moreover, the overlapping shadows occupy the whole scene, which means the information of the whole scene cannot be displayed completely, and even the outline of any object in the scene cannot be displayed clearly, which greatly reduces the readability of the map. In addition, these heavy shadows will also be recognized as obstacles, affecting the navigation accuracy of the subsequent mobile robot when it carries out navigation. Figure 21 shows the result of octree map construction by the proposed algorithm, which utilizes a combination of semantic segmentation and the optical flow method to eliminate dynamic feature points during feature extraction, thus effectively filtering out the  
Figure 20: Octree map construction using the ORB-SLAM2 algorithm. (**a**) The “walking_xyz” sequence; (**b**) the “walking_rpy” sequence; (**c**) the “walking_halfsphere” sequence; (**d**) the “walking_static” sequence. dynamic targets in the scene. Compared with the ORB-SLAM2 algorithm, the octree map constructed by the proposed algorithm can better filter out the information of dynamic targets. In addition, the proposed algorithm also carries out background repair for the parts obscured by dynamic targets, so the information in the scene can be clearly expressed in the map without the heavy shadows produced by moving people. The basic outline in the scene is very clear and the readability of the map is very high, which is very favorable for both robot navigation and map reuse. (2) The experimental platform data  
In Figure 22, (a) shows the octree map constructed by the ORB-SLAM2 algorithm, and (b) shows the octree map constructed by the proposed algorithm. By comparing the axonometric view, it can be seen that the octree map constructed by the ORB-SLAM2 algorithm contains dynamic target information, which will appear as a series of heavy shadows, and the environmental information of the map itself cannot be seen at all and does not have any effective information, which greatly reduces the map's readability and practicality. The octree map constructed by the proposed algorithm completely constructs the static background information in the scene, directly and effectively eliminates the dynamic target information, and displays the occupancy of the nodes, which can be used in the actual environment. Figure 21: Octree map construction using the proposed algorithm. (a) The “walking_xyz” sequence; (b) the “walking_rpy” sequence; (c) the “walking_halfsphere” sequence; (d) the “walking_static” sequence. Experiments of Semantic Map Construction  
(1) The TUM RGB-D data  
Semantic map construction experiments were conducted based on the successful establishment of dense point cloud maps and octree maps. Experiments were conducted using highly dynamic sequences in the TUM RGB-D dataset, namely, \"walking_xyz\" and \"walking_static\" sequences, which both contain dynamic characters, and the camera carried out corresponding positional movements. For these two sequences, semantic mapping was performed using the proposed algorithm, and the results of the semantic point cloud map and semantic octree map are shown in Figures 23 and 24.