# Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow
## 4 Experimental Results
### Analysis of Experimental Results
#### Analysis of system runtime  
In addition to localization accuracy, the real-time performance of the system operation is also an important metric for evaluating SLAM systems. The system was tested for runtime on four highly dynamic sequences and one low-dynamic sequence. As shown in Table 10, compared with the systems with the same level of positioning accuracy, the average tracking time per frame of the proposed improved algorithm is much smaller than that of DynaSLAM, and the detection speed is relatively faster due to the algorithm's lightweight processing of the semantic segmentation network; although the processing speed is slightly slower than that of the ORB-SLAM2 on each frame of the image, the localization accuracy is greatly improved. While guaranteeing positioning accuracy, it meets the real-time demand and realizes a balance between accuracy and time consumption. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{3}{c}{**Average Tracking Time per Frame/s**} \\ \cline{2-4}  & **ORB-SLAM2** & **DynaSLAM** & **Proposed Algorithm** \\ \hline walking\_xyz & 0.0283 & 1.1148 & 0.1256 \\ walking\_rpy & 0.0188 & 1.1152 & 0.1152 \\ walking\_halfsphere & 0.0193 & 1.1193 & 0.1154 \\ walking\_static & 0.0218 & 1.1501 & 0.1142 \\ sitting\_static & 0.0175 & 1.1415 & 0.1120 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of average tracking time. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**Without Background Repair**} & \multicolumn{2}{c}{**With Background Repair**} & \multicolumn{2}{c}{**Lift Rate/\%**} \\ \cline{2-6}  & **ATE** & **RPE(T)** & **ATE** & **RPE(T)** & **ATE** & **RPE(T)** \\ \hline walking\_xyz & 0.0196 & 0.0283 & 0.0168 & 0.0242 & 14.2900 & 14.4900 \\ walking\_rpy & 0.1209 & 0.1711 & 0.0380 & 0.0550 & 68.5700 & 67.8600 \\ walking\_halfsphere & 0.5199 & 0.7562 & 0.0077 & 0.0112 & 98.5200 & 98.5200 \\ walking\_static & 0.0087 & 0.0125 & 0.0062 & 0.0092 & 28.7400 & 26.4000 \\ sitting\_static & 0.0067 & 0.0097 & 0.0066 & 0.0096 & 1.4900 & 1.0300 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of errors. 3.4 Mapping Experiments in an Indoor Dynamic Environment Experiments of Dense Point Cloud Map Construction  
(1) The TUM RGB-D data  
The dense point cloud map construction experiments were conducted using highly dynamic sequences in the TUM RGB-D dataset, namely, \"walking_xyz\", \"walking_ryy\", \"walking_halfspere\", and \"walking_static\" sequences, which contain dynamic characters, and the camera carries out the corresponding positional motion. For these four sequences, the ORB-SLAM2 algorithm and the proposed algorithm were used to build the maps, and the results are shown in Figures 16 and 17, respectively. As can be seen in Figure 16, the ORB-SLAM2 algorithm does not perform any processing for the dynamic target, which leads to many overlapping shadows as a result of image building. The main reason for this phenomenon is that the ORB-SLAM2 system records the point cloud of each frame, while in the above four sequences, the characters are always moving, and due to the change in the camera's position, the system detects and records the point cloud data at different positions, thus forming a series of overlapping shadows. In addition, in the highly dynamic sequence, the existence and movement of the dynamic target will affect the camera position estimation, leading to errors in the position estimation, so the constructed  
In addition to the map overlap being affected, these overlapping shadows will obscure the point cloud information of the static background, resulting in incomplete and erroneous map information, which will lead to inaccurate robot localization and path planning or even choosing the wrong path, thus reducing the robot's navigation accuracy and efficiency.