# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 6 Conclusions  
This paper introduces a monocular ORB-SLAM3 system optimized for dynamic environments. The proposed system is aimed at improving the localization accuracy and mapping robustness of visual SLAM in dynamic scenarios. By integrating semantic segmentation with YOLOv11, morphological dilation operations, and a geometric reprojection consistency discrimination mechanism, the proposed method achieves a dual-mask approach for precise identification and effective filtering of dynamic features, helping to mitigate the interference caused by dynamic objects on traditional SLAM systems. Experimental results conducted on the KITTI and TUM RGB-D datasets demonstrate that the proposed method improves global trajectory accuracy across various dynamic scenes. Employing a dynamic object elimination mechanism reduces the influence of dynamic targets on trajectory estimation. In particular, this approach exhibits superiority over traditional methods in highly dynamic scenarios, where it helps to improve robustness. However, a notable limitation of this method is that the improvement in efficiency is relatively marginal, meaning that it may lead to a slight decrease in accuracy in scenarios \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**Semantic Segmentation**} & \multicolumn{2}{c}{**Geometric**} \\ \cline{2-4}  & **CPU** & **GPU** & **Consistency Check** \\ \hline KITTI & 360.78 ms & 15.79 ms & 0.78 ms \\ TUM & 317.87 ms & 13.36 ms & 0.83 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Time evaluation. with a large number of potentially dynamic objects but few truly dynamic ones. These issues primarily stem from misclassifications of dynamic objects, where some static feature points are incorrectly identified as dynamic during the geometric consistency verification phase. In contrast, the dual-mask approach proposed in this study demonstrates enhanced robustness; even when dynamic points are mistakenly detected, removal is confined to local regions rather than entire objects, helping to preserve the overall stability and robustness of the system. Overall, the proposed system shows improvements in both ATE and RPE, effectively overcoming the cumulative errors associated with mismatches in dynamic environments. This provides strong support for high-precision localization and mapping in practical applications such as autonomous driving and robotic navigation. In terms of time and computational cost, classical dynamic SLAM methods typically introduce additional geometric constraints prior to the feature matching stage to determine whether candidate regions produced by instance segmentation truly correspond to dynamic objects. For example, DS-SLAM employs optical flow to implement a simplified geometric consistency check based on ORB-SLAM2, offering a lightweight mechanism to distinguish dynamic objects. On the other hand, DynaSLAM utilizes depth information to perform a more complex multi-view geometric analysis for dynamic object detection. In contrast, the method proposed in this paper leverages the existing optimization process within the SLAM pipeline to identify dynamic points, thereby avoiding additional computational overhead while maintaining reliable outlier detection capability. This leads to improved overall computational efficiency as well as better accuracy. Future research should consider further optimizing the system's real-time performance and local motion models to enhance its stability and accuracy in highly dynamic environments. The method proposed in this paper not only offers a superior solution for SLAM systems operating in dynamic environments but also provides important guidance and insights for future research and applications of SLAM systems. Conceptualization, Z.L., C.J., and Y.S. ; methodology, Z.L. and C.J. ; software, C.J. and L.H. ; validation, C.J. ; data curation, C.J. ; writing--original draft preparation, C.J. and H.S. ; writing--review and editing, Z.L., C.J., and Y.S. ; supervision, Z.L. and Y.S.