# Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow
## 4 Experimental Results
### Analysis of Experimental Results  
#### 4.3.1 Dynamic Target Elimination Experiments  
(1) The TUM RGB-D data  
A single image in the TUM RGB-D dataset \"walking_xyz\" sequence was randomly selected for feature extraction in the ORB-SLAM2 system and the proposed system, and the results of processing this image are visualized as shown in Figure 9. \begin{table}
\begin{tabular}{c c} \hline \hline
**Library Functions/Software Configuration** & **Functions** \\ \hline ROS melodic & Robotics operating system \\ Eigen3.3.7 & Linear algebra operations \\ Pangolin 0.5 & Visualization programs for rotation matrices, \\  & translations, etc. \\ OpenCV 3.4.5 & Image algorithm processing \\ G2O & Graph optimization \\ DBoW 2 & Dictionary Library \\ PCL 1.11.1 & Processing point cloud data \\ OctoMap & Generating octree maps \\ VScode 1.46 & Code compiler and debugger \\ Anaconda 3.4.4.0 & Virtual environment manager \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Software configuration and functions. Figure 9: Comparison of feature extraction between ORB-SLAM2 and the proposed algorithm. (**a**) Original picture; (**b**) feature extraction effect of the ORB-SLAM2 system; (**c**) feature extraction effect of the ORB-SLAM2 system adding semantic segmentation; (**d**) feature extraction effect of the proposed algorithm. As can be seen from the experimental results in Figure 9b, the ORB-SLAM2 system extracts the feature points within the dynamic target \"human\", and as can be seen in Figure 9c, after adding the semantic segmentation network, the ORB-SLAM2 system covers the mask on the dynamic target \"human\" and then no longer extracts the feature points within the target \"human\", but still extracts the feature points at the edges of the human contour. As can be seen in Figure 9d, after adding the LK optical flow method to filter the feature points, the feature extraction result of this algorithm is significantly reduced compared with that of Figure 9c in the dynamic target edge contour, which proves that the feature points whose motion speed exceeds the set threshold are deleted and only the static feature points are left. In real scenarios, in addition to the prior dynamic target \"person\", there are also non-prior dynamic objects, such as books and cups moved by people in the \"desk with person\" sequence of the TUM RGB-D dataset, as shown in Figure 10. Figure 10a shows the result of extracting feature points by the ORB-SLAM2 system, and it can be learned that the ORB-SLAM2 system also extracts the features of the moving objects but does not do any processing. After filtering by LK optical flow method calculation in the improved algorithm, the feature points exceeding the threshold value are identified as dynamic feature points and are deleted, and the extraction results are shown in Figure 10b. The above experiments prove that the proposed improved SLAM system can effectively eliminate dynamic feature points, including the prior dynamic target \"human\" and the feature points on the non-prior dynamic target that moves with the human, to achieve the purpose of effectively eliminating dynamic targets. (2) The experimental platform data  
The dynamic office scene video captured by the mobile robot experiment platform was used to further verify the dynamic target feature point rejection effect. A frame is randomly  
Figure 10: Comparison of non-prior dynamic target feature extraction results. (**a**) The ORB-SLAM2 system; (**b**) the proposed improved algorithm. selected for feature extraction using the ORB-SLAM2 algorithm and the proposed improved algorithm, and the extraction results are shown in Figure 11.  
Comparing Figure 11a,b, the proposed improved algorithm can effectively filter out the moving parts of the dynamic targets in movement, such as the dynamic feature points on the limbs, and there is no similar phenomenon of clustering on dynamic targets as with the ORB-SLAM2 algorithm. It can reflect the information of static images well.