# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 4 Experimental Results and Analysis
### Evaluation on the KITTI Dataset
\begin{table}
\begin{tabular}{l 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 KITTI 00 & 6.24 & 5.43 & **3.05** & 6.58 & 5.59 & 3.44 & **6.14** & **5.35** & 3.09 \\ KITTI 01 & 295.24 & 264.75 & 129.86 & **232.57** & **205.63** & **108.08** & 269.23 & 241.76 & 118.03 \\ KITTI 02 & 24.47 & 19.17 & 15.21 & **20.00** & **16.03** & **13.91** & 21.66 & 16.16 & 14.01 \\ KITTI 03 & **0.79** & **0.55** & 0.56 & 1.14 & 0.93 & 0.65 & 0.86 & 0.69 & **0.52** \\ KITTI 04 & 1.56 & 1.36 & 0.76 & **1.01** & **0.85** & **0.55** & 1.19 & 1.00 & 0.64 \\ KITTI 05 & 6.32 & 5.88 & 2.31 & 6.46 & 5.97 & 2.46 & **5.64** & **5.19** & **2.22** \\ KITTI 06 & **14.86** & **12.91** & **7.35** & 16.04 & 14.00 & 7.85 & 15.77 & 13.81 & 7.62 \\ KITTI 07 & 2.94 & 2.16 & 1.77 & 2.47 & 2.25 & **0.99** & **2.44** & **1.90** & 1.41 \\ KITTI 08 & 64.38 & 50.01 & 40.54 & 61.39 & 47.09 & 39.37 & **59.96** & **46.18** & **38.25** \\ KITTI 09 & 9.03 & 8.09 & 4.00 & 8.87 & 7.93 & **3.96** & **8.86** & **7.90** & 4.00 \\ KITTI 10 & 8.15 & 6.63 & 4.72 & 8.03 & 6.54 & 4.65 & **7.57** & **6.29** & **4.24** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative evaluation of Absolute Trajectory Error (ATE) for monocular SLAM systems. \begin{table}
\begin{tabular}{l 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 KITTI 00 & 0.186 & **0.096** & 0.154 & 0.339 & 0.100 & 0.306 & **0.144** & 0.109 & **0.109** \\ KITTI 01 & 2.026 & 1.902 & 0.948 & 2.079 & 1.751 & 1.302 & **1.962** & **1.723** & **0.869** \\ KITTI 02 & 0.163 & 0.162 & 0.103 & 0.151 & **0.117** & **0.095** & **0.147** & 0.127 & 0.097 \\ KITTI 03 & **0.024** & **0.019** & **0.014** & 0.030 & 0.023 & 0.020 & 0.025 & 0.020 & 0.016 \\ KITTI 04 & 0.058 & 0.048 & 0.033 & **0.047** & **0.039** & **0.026** & 0.054 & 0.044 & 0.032 \\ KITTI 05 & **0.321** & **0.224** & **0.230** & 0.376 & 0.243 & 0.286 & 0.346 & 0.242 & 0.247 \\ KITTI 06 & 0.409 & 0.253 & 0.334 & **0.403** & **0.241** & **0.321** & 0.499 & 0.258 & 0.327 \\ KITTI 07 & 1.858 & 0.264 & 1.814 & **0.345** & **0.199** & **0.279** & 0.989 & 0.245 & 1.439 \\ KITTI 08 & 0.355 & 0.284 & 0.213 & 0.343 & 0.271 & 0.210 & **0.339** & **0.268** & **0.208** \\ KITTI 09 & 0.248 & 0.248 & 0.199 & 0.252 & **0.150** & 0.203 & **0.242** & **0.144** & **0.195** \\ KITTI 10 & 0.067 & 0.067 & 0.047 & 0.067 & 0.048 & 0.048 & **0.062** & **0.046** & **0.041** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative evaluation of Relative Pose Error (RPE) for monocular SLAM systems. Conversely, in scenarios with many dynamic objects, such as sequences 01, 04, and 07, the dynamic SLAM system significantly outperforms ORB-SLAM3 in terms of both ATE and RPE, further validating the effectiveness of the dynamic object elimination mechanism in complex dynamic environments. To quantify the degree of system performance improvement, this paper uses the following improvement rate (enhancement rate) calculation equation:  
\[\eta=\frac{o-r}{o}\times 100\% \tag{5}\]  
where \(o\) represents the error value of ORB-SLAM3, \(r\) represents the error value of the proposed method in this paper, and \(\eta\) is the percentage of improvement. As shown in Table 4, based on this evaluation, System(D) achieves an average improvement of 6.69% in absolute trajectory accuracy and 8.69% in relative pose estimation accuracy compared to ORB-SLAM3, while System(D+O) achieves an average improvement of 3.43% and 11.42%, respectively. These results demonstrate the comprehensive advantage of the proposed systems in dynamic environments.