# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 4 Experimental Results and Analysis
### TUM RGB-D Dataset Evaluation  
The TUM RGB-D dataset includes real indoor scenes with human activity and static objects, and is designed to assess system accuracy and stability in high-dynamic indoor environments. In this experiment, we conducted a comparative analysis of the impact of dynamic contexts on the performance of the proposed SLAM system versus ORB-SLAM3 in a monocular setting across six image sequences from the TUM RGB-D dataset. The performance of the different systems across the sequences is summarized in Tables 5-7, where bold font indicates the best results in each metric. As presented in Table 7, compared to ORB-SLAM3, the proposed System(D) achieves an average reduction of 28% in ATE and 4.27% in RPE. The enhanced System(D+O) achieves an average reduction of 24% in ATE and 8.27% in RPE. These quantitative results verify that our methods effectively mitigate cumulative errors induced by dynamic feature mismatches. Furthermore, the experimental data in Tables 5 and 6 reveal that both System(D) and System(D+O) exhibit lower standard deviation (Std) values, demonstrating the high adaptability and stability of our proposed approaches in dynamic environments. Similarly, Figures 3 and 5 visually demonstrate that both System(D) and System(D+O) exhibit narrower standard deviation ranges compared to ORB-SLAM3, indicating more stable performance. From a specific scenario analysis, walking sequences such as w_halfsphere, w_rpy, and w_static contain significant overall motion by individuals, causing notable dynamic interference. Traditional methods are prone to feature matching failures and drift estimation due to the presence of dynamic points. In contrast, the proposed approaches exhibit greater accuracy improvements in these highly dynamic sequences, indicating that our dynamic point elimination mechanism has good suppression capabilities for widespread disturbances. \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 & 0.026 & **0.023** & 0.011 & **0.025** & **0.023** & 0.010 & 0.027 & 0.026 & **0.008** \\ s\_static & 0.323 & 0.323 & 0.006 & 0.312 & 0.312 & **0.003** & **0.285** & **0.285** & **0.003** \\ s\_xyz & 0.031 & 0.026 & 0.017 & 0.027 & 0.021 & 0.017 & **0.024** & **0.019** & **0.015** \\ w\_halfsphere & 0.044 & 0.038 & 0.023 & 0.032 & 0.027 & 0.017 & **0.030** & **0.026** & **0.014** \\ w\_rpy & 0.455 & 0.454 & 0.033 & **0.137** & **0.128** & 0.048 & 0.138 & 0.138 & **0.011** \\ w\_static & 0.730 & 0.730 & 0.003 & **0.368** & **0.368** & 0.003 & 0.640 & 0.640 & 0.003 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative evaluation of Absolute Trajectory Error (ATE) for monocular SLAM systems. In the experimental results for the fre3_walking_rpy(w_rpy) sequence, Figure 3 shows that the trajectory error of ORB-SLAM is in the range of 0.019-0.035, 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. The accuracy of System(D+O) is almost the same as that of System(D). Figure 4 presents the trajectory deviation map in comparison to ground truth, showing that ORB-SLAM3 experiences significant drift, while System(D) offers the most accurate trajectory estimation. In these results, System(D+O) exhibits marginally lower performance compared to System(D).