# Improved Visual SLAM Algorithm Based on Dynamic Scenes
## 4 Experiments
### Dynamic Environment Trajectory Accuracy Validation Experiment  
In the TUM dataset, dynamic scene sequences are divided into two categories: high dynamic scenes and low dynamic scenes. Specifically, low dynamic scenes feature two people sitting at a table with minimal movements, while high dynamic scenes involve two people moving around the table with more significant movements. Additionally, these dynamic sub-sequences can be associated with four different types of camera motion: (1) hemisphere: the camera moves along a hemisphere with a diameter of 1 m; (2) XYZ: the camera moves along the x, y, and z axes; (3) RYY: the camera rotates around the roll, pitch, and yaw axes; (4) static: the camera is stationary. In this section, five sequences were selected for testing: fr3-walking_xyz, fr3-walking_halfsphere, fr3-walking_static, fr3-sitting_static, and fr3-sitting_halfsphere. Among these, fr3-walking_xyz, fr3-walking_halfsphere, and fr3-walking_static involve two people walking around, with the camera moving along the x, y, and z axes and performing hemisphere movement, representing high dynamic scenes; fr3-sitting_static and fr3-sitting_halfsphere feature people sitting on chairs, with the camera either stationary or  
Figure 5: Comparing feature point distribution between ORB and GCNv2, the scenes in figures (**a**,**b**) are cluttered with various objects, including computer screens, which made it difficult to obtain features. The images in (**c**,**d**) were taken from the corner of a table where the camera was moving, resulting in significant changes in viewpoint. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{3}{c}{**ORB-SLAM2**} & \multicolumn{3}{c}{**Improved SLAM**} & \multicolumn{3}{c}{**Promotion Rate(\%)**} \\ \cline{2-9}  & **RMSE** & **Mean** & **SD** & **RMSE** & **Mean** & **SD** & **RMSE** & **SD** \\ \hline fr1/xyz & 0.0096 & 0.0089 & 0.0053 & 0.0091 & 0.0082 & 0.0046 & 5.2 & 13.2 \\ fr1/desk & 0.0316 & 0.0259 & 0.0180 & 0.0274 & 0.0249 & 0.0163 & 13.3 & 9.4 \\ fr1/room & 0.0556 & 0.0503 & 0.0235 & 0.0445 & 0.0380 & 0.0132 & 19.9 & 43.8 \\ fr1/360 & 0.1591 & 0.1302 & 0.0915 & 0.1692 & 0.1500 & 0.0782 & \(-\)6.3 & 14.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ATE comparison between the improved SLAM algorithm and ORB-SLAM2. performing hemisphere movement, representing low dynamic scenes. We compared the ATE of the improved SLAM with ORB-SLAM2 to demonstrate the effectiveness in handling dynamic interference. Figures 6 and 7 display the ATE results for ORB-SLAM2 and the improved SLAM. It is evident that the improved SLAM had a significant impact on enhancing the positioning accuracy of VSLAM systems in dynamic indoor environments. We compared ORB-SLAM2 with the improved SLAM to assess the impact of dynamic objects on the system and to verify that the improved SLAM algorithm effectively removed dynamic objects. As shown in Table 4, experimental results indicated that the accuracy of the improved SLAM was significantly better than that of ORB-SLAM2, with a substantial reduction in ATE. The RMSEs for the fr3-walking_xyz, fr3-walking_halfsphere, fr3-walking_static, fr3-sitting_static, and fr3-sitting_halfsphere sequences were reduced by 97.59%, 93.35%, 98.51%, 57.17%, and 38.61%, respectively, indicating that the improved SLAM demonstrated better robustness in highly dynamic environments compared to low dynamic ones and exhibited enhanced robustness in handling various irregular camera motions across different sequences. Figure 6: Comparing the ATE of the improved SLAM, ORB-SLAM2 across five dynamic scene sequences from the fr3 dataset, (**a–e**) represent the trajectory maps of ORB-SLAM2, while (**f–j**) represent the trajectory maps of the improved SLAM. Figure 7: Shows the results for the fr3_walking_xyz sequence. Panels (**a,b**) illustrate the estimated trajectories compared to the ground truth, as well as the errors along the x, y, and z axes for ORB-SLAM2 and the improved SLAM.