# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 4 Experimental Results and Analysis
### Evaluation on the KITTI Dataset
Further comparison between System(D) and System(D+O) reveals that because the KITTI dataset primarily consists of vehicular scenes, with potential dynamic objects mostly being vehicles, System(D) employs a strategy of globally removing dynamic objects, which requires high accuracy in dynamic object recognition. Any misclassification can lead to a significant performance degradation in the system. In scenes with a higher presence of static vehicles and pedestrians and with fewer dynamic vehicles (such as sequences 00, 08, 09, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**System(D)**} & \multicolumn{2}{c}{**System(D+O)**} \\ \cline{2-5}  & **ATE (RMSE)** & ** RPE (Mean)** & **ATE (RMSE)** & ** RPE (Mean)** \\ \hline KITTI 00 & **1.63\%** & \(-13.54\%\) & \(-5.47\%\) & \(-\)**4.30\%** \\ KITTI 01 & 8.81\% & **9.43\%** & **21.23\%** & 7.94\% \\ KITTI 02 & \(11.48\%\) & \(21.35\%\) & **18.27\%** & **28.04\%** \\ KITTI 03 & \(-\)**9.12\%** & \(-\)**4.61\%** & \(-44.76\%\) & \(-16.70\%\) \\ KITTI 04 & 23.50\% & 7.63\% & **34.80\%** & **17.44\%** \\ KITTI 05 & **10.74\%** & \(-\)**8.08\%** & \(-2.16\%\) & \(-8.37\%\) \\ KITTI 06 & \(-\)**6.14\%** & \(-2.09\%\) & \(-7.93\%\) & **4.55\%** \\ KITTI 07 & **16.86\%** & 7.26\% & 15.84\% & **24.50\%** \\ KITTI 08 & **6.86\%** & **5.58\%** & 4.64\% & 4.67\% \\ KITTI 09 & **1.92\%** & **41.98\%** & 1.82\% & 39.60\% \\ KITTI 10 & **7.03\%** & **30.67\%** & 1.46\% & **28.28\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative performance improvement rates of ORB-SLAM3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sequence** & **Static Objects** & **Dynamic Objects** & **ATE (RMSE)** & ** RPE (Mean)** \\ \hline KITTI 00 & Many & Few & DO\(<3<\) D & \(3<\) D \(<\) DO \\ KITTI 01 & None & Many & D \(<\) DO \(<3\) & DO \(<\) D \(<3\) \\ KITTI 02 & Many & Medium & D \(<\) DO \(<3\) & D \(<\) DO \(<3\) \\ KITTI 03 & Few & Few & \(3<\) DO \(<\) D & \(3<\) DO \(<\) D \\ KITTI 04 & None & Many & D \(<\) DO \(<3\) & D \(<\) DO \(<3\) \\ KITTI 05 & Many & Medium & DO \(<\) D \(<3\) & \(3<\) D = DO \\ KITTI 06 & Many & Few & \(3<\) DO \(<\) D & D \(<3\) \(<\) DO \\ KITTI 07 & Many & Many & DO \(<\) D \(<3\) & D \(<\) DO \(<3\) \\ KITTI 08 & Many & Medium & DO \(<\) D \(<3\) & DO \(<\) D \(<3\) \\ KITTI 09 & Many & Medium & DO \(<\) D \(<3\) & DO \(<\) D \(<3\) \\ KITTI 10 & Medium & Few & DO \(<\) D \(<3\) & DO \(<\) D \(<3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Impact analysis of potential dynamic objects on SLAM system performance. and 10), System(D+O) outperforms System(D) in both trajectory and relative pose accuracy. This indicates that its fine-grained dual-mask method effectively prevents performance fluctuations caused by the misremoval of static objects, demonstrating stronger stability. In scenarios with a high density of dynamic objects but a lack of static ones (such as sequences 01 and 04), System(D) exhibits superior performance due to the absence of misjudged static object interference, as its coarse-grained elimination strategy is more advantageous in such contexts. In summary, the experimental results demonstrate that the dual-mask dynamic object elimination method proposed in this paper can effectively reduce pose estimation errors in dynamic scenes. The proposed system achieves accuracy surpassing that of ORB-SLAM3 in most KITTI sequences, exhibiting robustness and versatility. Even in complex boundary scenarios with many static objects and fewer dynamic ones, the performance of our system remains stable, further validating the key role of the proposed dual-mask strategy in enhancing accuracy and stability in dynamic SLAM systems.