# Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow
## 4 Experimental Results
### Analysis of Experimental Results
#### 4.3.2 Background Restoration Experiments  
(1) The TUM RGB-D data  
Three frames of images were randomly selected in the dataset \"walking_xyz\" sequence, and a comparison of the background restoration results is shown in Figure 12.  
Figure 11: Comparison of dynamic feature point rejection results. (**a**) The ORB-SLAM2 system; (**b**) the proposed improved algorithm.  
Figure 12: Comparison of background restoration results for TUM RGB-D data. (**a**) Original picture; (**b**) covering masks after semantic segmentation; (**c**) background restoration results.  
In Figure 12, (a) is the original image frame, (b) is the covering mask image after semantic segmentation, and (c) is the background repair image. As can be seen from frame 24, most of the background that was obscured by the character did not appear in the initial stage due to the limited background captured, so only a small portion of the background exposed by character movement could be repaired, such as the right silhouette of the left character. At the 141 st frame, some effective backgrounds accumulated, and they can be partially repaired, but because some parts had been occluded before, e.g., the left portion of the computer desk was occluded for most of the time during the preliminary stage, these parts cannot be repaired for the time being. At frame 751, by looking at the entire dataset, it is found that all the scenes currently displayed on the set were captured and can be repaired in their entirety, e.g., both monitors in the figure are displayed in their entirety. To summarize, initially, some areas cannot be completely repaired because the occluded parts up to this frame have not yet appeared completely in the field of view and do not have valid depth information. When the depth information is fully collected, most of the occluded background can be repaired.  
(2) The experimental platform data  
Further validation was performed using a video of a dynamic office scene captured by the mobile robot experiment platform. Two images were randomly selected that contained the dynamic target \"human\" in a moving state continuously walking and the dynamic target \"human\" in a relatively stationary state sitting in a chair. A comparison of the background restoration results is shown in Figure 13.  
In Figure 13, (a) is the original image frame and (b) is the background restoration. From the restoration results, most of the parts (in the green box) that have been occluded by the dynamic target character can be completely restored, which is due to the movement of the character, making the depth information acquisition complete. However, because some parts had been occluded before, such as the left part of the image (in the red box), where the  
Figure 13: Comparison of background restoration results for real scenes. (**a**) Original frame; (**b**) background restoration.  
dynamic target character is sitting in a chair, and the static background is occluded most of the time due to the presence of the mask, these parts cannot be repaired temporarily. To summarize, the background occluded by the dynamic target can be repaired efficiently.