# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 4 Experimental Results and Analysis
### TUM RGB-D Dataset Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{3}{c}{**ORB-SLAM3**} & \multicolumn{3}{c}{**System(D)**} & \multicolumn{3}{c}{**System(D+O)**} \\ \cline{2-10}  & **RMSE** & **Mean** & **Std** & **RMSE** & **Mean** & **Std** & **RMSE** & **Mean** & **Std** \\ \hline s\_halfsphere & 0.014 & 0.012 & 0.008 & 0.015 & 0.012 & 0.010 & **0.013** & **0.011** & **0.007** \\ s\_static & 0.010 & 0.006 & 0.008 & 0.010 & 0.005 & 0.008 & **0.008** & **0.004** & **0.006** \\ s\_xyz & 0.018 & 0.013 & 0.013 & 0.017 & **0.012** & 0.012 & **0.016** & **0.012** & **0.011** \\ w\_halfsphere & 0.031 & 0.018 & 0.026 & **0.020** & **0.015** & **0.012** & 0.020 & **0.015** & **0.012** \\ w\_rpy & **0.019** & 0.014 & **0.012** & **0.019** & **0.011** & 0.016 & 0.020 & 0.014 & 0.015 \\ w\_static & **0.008** & **0.005** & **0.006** & 0.009 & 0.006 & 0.007 & 0.010 & **0.005** & **0.006** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative evaluation of Relative Pose Error (RPE) for monocular SLAM systems. Figure 3: ATE and RPE results for SLAM systems on the fre3_walking_rpy dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{3}{c}{**System(D)**} & \multicolumn{3}{c}{**System(D+O)**} \\ \cline{2-5}  & **ATE (RMSE)** & **RPE (Mean)** & **ATE (RMSE)** & **RPE (Mean)** \\ \hline s\_halfsphere & **2.86\%** & \(-\)4.42\% & \(-\)6.05\% & **0.56\%** \\ s\_static & 3.39\% & 2.05\% & **11.65\%** & **21.65\%** \\ s\_xyz & 14.11\% & **6.47\%** & **24.29\%** & 6.35\% \\ w\_halfsphere & 28.50\% & **14.60\%** & **32.28\%** & **14.60\%** \\ w\_rpy & **69.87\%** & **20.73\%** & 69.54\% & 2.88\% \\ w\_static & **49.56\%** & \(-\)14.00\% & 12.27\% & **3.57\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative performance improvement rates of ORB-SLAM3. Conversely, sitting sequences such as s_halfsphere, s_rpy, and s_static are less dynamic, with only minor movements of the hands and head. In such scenarios, systems are more likely to misjudge local dynamics as global dynamics, leading to loss of static information. Therefore, the fine-grained dynamic discrimination strategy introduced in this paper is particularly crucial. In the experimental results for the fre3_sitting_xyz(s_xyz) sequence, the constrained local movements in this sitting scenario are primarily limited to head and hand motions, resulting in negligible dynamic interference. Figure 5 shows that the trajectory error of ORB-SLAM is in the range of 0.019-0.035 and that the trajectory error is relatively small, while the trajectory error of System(D) is in the error range is 0.018-0.03 and the trajectory error of System(D+O) is in the range of 0.017-0.043. As evidenced in Figure 6, the trajectory deviation map demonstrates no visually discernible differences between the compared systems. Thus, both System(D) and System(D+O) continue to exhibit enhancements in the ATE and RPE metrics compared with the ORB-SLAM3 system. When comparing the performance of System(D+O) and System(D), the former demonstrates superior ATE performance in the sitting sequences, indicating that its fine-grained dynamic point removal strategy can more effectively distinguish between local microdynamics and globally static regions, resulting in improved overall localization accuracy. System(D+O) also demonstrates measurable improvements over System(D), with enhancements of 3.18% in ATE and 8.15% in RPE. In contrast, the coarse-grained removal mechanism employed by System(D) is more prone to incorrectly eliminating static objects in such fine-grained scenarios, resulting in decreased accuracy. Conversely, in the highly dynamic walking sequences, the global removal strategy of System(D) exhibits greater advantages, as it can rapidly eliminate interfering features within large-scale moving regions. As a result, System(D) shows slightly better ATE performance in certain sequences compared to System(D+O).