# Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow
## 4 Experimental Results
### Analysis of Experimental Results
#### Analysis of system runtime
The proposed algorithm incorporates semantic segmentation threads into the ORB-SLAM2 system and utilizes the semantic segmentation module and the optical flow module to eliminate moving and potential dynamic targets to build dense static scene point cloud maps. As shown in Figure 17, dynamic targets are culled and no longer obscure the static background, most of the static background point clouds are captured, and the ghosting problem is effectively improved. However, some noise can still be seen in the edge portion of the culled dynamic targets. This is because after semantic segmentation and optical flow detection, a small portion of the dynamic target feature points will be missed due to the accuracy of the segmentation and the setting of the threshold, and the missed portion will appear on the static background in the form of noise. Overall, the proposed algorithm, which eliminates dynamic targets effectively, improves the recognition of dense point cloud maps after reconstruction, the edge contours of objects in the scene are clearer, and the overlap between objects is lower. It eliminates the delamination due to the overlapping phenomenon, and the constructed maps overlap more, which enhances the readable performance of the maps. The experimental platform data  
To verify the effectiveness of the proposed dense map construction algorithm in real environments, it was tested using office desktop and office dynamic and static environments. A comparison of map building was carried out using the proposed algorithm and  
Figure 17: Dense point cloud 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. the ORB-SLAM2 algorithm, and the results of point cloud map building are shown in Figures 18 and 19. Figure 18 shows the static environment point cloud map constructed by the proposed algorithm. The scene does not produce heavy shadows, layering, distortion, stacking, or misalignment; the outlines of monitors, books, cups, cabinets, tables, etc. in the scene are clear; and the map is highly readable. Figure 19a shows the point cloud map of the dynamic environment constructed by the ORB-SLAM2 algorithm. The point cloud information in the scene is stacked and misaligned, and there is a large amount of noise. Due to the existence of dynamic targets, the scene is full of heavy shadows left by dynamic targets, the effective information of the static scene is blocked tightly, and the readability of the map is extremely poor. In contrast, the proposed algorithm can better eliminate the dynamic targets and the ghosting phenomenon when constructing the point cloud map, thus generating clear static scene information and significantly improving the readability of the map, as shown in Figure 19b. Figure 19: Dynamic office environment dense point cloud map. (**a**) The ORB-SLAM2 algorithm; (**b**) the proposed algorithm. Figure 18: Dense point cloud maps of static environments using the proposed algorithm. (**a**) Static desktop environments; (**b**) static office environments. Experiments of Octree Map Construction  
(1) The TUM RGB-D data  
Since point cloud maps cannot provide specific location information and occupy a large amount of memory, octree maps were used to save maps for visual SLAM construction. The octree map construction experiments were conducted using highly dynamic sequences in the TUM RGB-D dataset, namely, \"walking_xyz\", \"walking_rpy\", \"walking_halfspere\", and \"walking_static\" sequences, which all contain dynamic characters. For these four sequences, the map construction effect was compared using the ORB-SLAM2 algorithm and the proposed algorithm, and the results are shown in Figures 20 and 21, respectively. Figure 20 shows the result of the octree map construction of the ORB-SLAM2 algorithm, in which there are many noise points and dynamic target \"human\" information, resulting in many overlapping shadows.