# DI-SLAM: A Real-Time Enhanced RGB-D SLAM for Dynamic Indoor Environments
## 5 Conclusions  
To improve SLAM precision in scenarios with moving elements, we proposed an enhanced SLAM system that integrates multi-view geometry and object detection techniques. Developed upon the ORB-SLAM3 framework, we implement a dynamic feature filtering \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{4}{c}{**ORB-SLAM3**} & \multicolumn{4}{c}{**Ours**} & \multicolumn{4}{c}{**Improvement (\%)**} \\ \cline{2-10}  & **RMSE** & **Mean** & **Std** & **RMSE** & **Mean** & **Std** & **RMSE** & **Mean** & **Std** \\ \hline s\_static & 0.0095 & 0.0085 & 0.0043 & 0.0074 & 0.0065 & 0.0034 & 22.11 & 23.53 & 20.93 \\ w\_half & 0.2920 & 0.2418 & 0.1636 & 0.0329 & 0.0285 & 0.0165 & 88.73 & 88.21 & 89.91 \\ w\_xyz & 0.6502 & 0.5525 & 0.3429 & 0.0191 & 0.0166 & 0.0094 & 97.06 & 97.00 & 97.26 \\ w\_static & 0.2226 & 0.2046 & 0.0878 & 0.0077 & 0.0068 & 0.0036 & 96.54 & 96.68 & 95.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ATE comparison with the ORB-SLAM3 algorithms on 4 sequences. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{4}{c}{**RDS-SLAM**} & \multicolumn{4}{c}{**Ours**} \\ \cline{2-10}  & **RMSE** & **Max** & **Mean** & **Std** & **RMSE** & **Max** & **Mean** & **Std** \\ \hline s\_static & 0.0070 & 0.0272 & 0.0062 & 0.0032 & 0.0074 & 0.0280 & 0.0065 & 0.0035 \\ w\_half & 0.0317 & 0.1626 & 0.0273 & 0.0161 & 0.0329 & 0.0927 & 0.0285 & 0.0165 \\ w\_xyz & 0.0092 & 0.0231 & 0.0085 & 0.0037 & 0.0077 & 0.0237 & 0.0068 & 0.0036 \\ w\_static & 0.0163 & 0.0637 & 0.0142 & 0.0079 & 0.0191 & 0.0499 & 0.0166 & 0.0094 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ATE comparison with the RDS-SLAM algorithms on 4 sequences. Figure 9: Comparative evaluation of trajectory estimations in dynamic sequences between the proposed algorithm and RDS-SLAM: (**a**) orb-slam3:fr3_w_half, (**b**) orb-slam3:fr3_w_static, (**c**) orb-slam3:fr3_w_xyz, (**d**) ours:fr3_w_half, (**e**) ours:fr3_w_static, (**f**) ours:fr3_w_xyz, (**g**) rds-slam:fr3_w_half, (**h**) rds-slam:fr3_w_static, and (**i**) rds-slam:fr3_w_xyz. thread using an improved object detection network to identify possible dynamic features. During the tracking process, dynamic feature points are filtered based on a combined dynamic mask generated from both multi-view geometry and object detection results, while static features are retained for pose estimation. Extensive experiments on dynamic sequence datasets demonstrate that our approach significantly reduces visual localization errors caused by moving objects. For instance, on the walking_xyz sequence, our method reduces the RMSE from 0.6502 m (ORB-SLAM3) to 0.0191 m, achieving a 97.06% improvement. Compared to RDS-SLAM, it yields a further reduction of approximately 16.3%. Across all tested dynamic sequences, our method achieves an average RMSE improvement of 76.11% over ORB-SLAM3. Moreover, in highly dynamic indoor scenes such as walking_xyz and walking_half, our method significantly outperforms ORB-SLAM3 in both accuracy and robustness and demonstrates competitive performance compared to RDS-SLAM, in most cases. However, the current evaluation is limited to indoor dynamic environments. Future works will focus on extending the proposed system to outdoor dynamic scenarios, where challenges such as scale variation, lighting changes, and complex motion patterns are expected to further test the adaptability and robustness of our approach. Conceptualization, [PERSON] and C.X. ; Software, [PERSON]; Validation, [PERSON]; Data curation, J.H. ; Writing--original draft, W.W.; Writing--review and editing, [PERSON]; Supervision, C.X. All authors have read and agreed to the published version of the manuscript. This work was partly supported by the National Natural Science Foundation of China under Grant 52272435. Not applicable.