## 4 Experiment and Results Analysis
### Pose Estimation Error Analysis Experiment
Various metrics, including root mean square error (RMSE), standard deviation (STD), mean error, and median error, were utilized to provide a thorough evaluation of the algorithm's performance. Tables 5-7 present the ATE, RPE, and absolute error comparisons between the proposed method and other algorithms. As shown in Tables 5 and 6, the improved algorithm incorporates dynamic feature point rejection into the traditional ORB-SLAM framework, achieving significant enhancements over ORB-SLAM3 in highly dynamic scenarios (e.g., the walking_half and walking_xyz datasets). Specifically, the ATE RMSE is reduced by 93.34% and 94.43%, while the RPE RMSE is reduced by 45.8% and 68.31%, effectively mitigating the impact of dynamic objects on global trajectory estimation and local pose estimation. These improvements can be attributed to the integration of the multi-scale convolutional module (MSFConv), which enhances the detection of fast-moving objects, and the LK optical flow method, which accurately filters dynamic feature points. In low-dynamic scenarios, such as sitting_static and sitting_xyz, where dynamic objects are almost stationary, ORB_SLAM3 benefits from retaining all feature points in the scene. In contrast, the proposed algorithm occasionally misclassifies some static feature points as dynamic and removes them, reducing the number of usable feature points for tracking. This leads to varied performance in these environments. For example, in the sitting_xyz dataset, the ATE RMSE increased by 16.92% compared to ORB-SLAM3. However, in the sitting_static dataset, the ATE and RPE RMSEs were reduced by 26.85% \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**ORB\_SLAM3**} & \multicolumn{3}{c}{**This Paper**} \\  & **RMSE** & **STD** & **Mean** & **Median** & **RMSE** & **STD** & **Mean** & **Median** \\ \hline sitting\_static & 0.0159 & 0.0036 & 0.0154 & 0.0160 & **0.0054** & 0.0026 & 0.0048 & 0.0043 \\ sitting\_xyz & 0.0177 & 0.0093 & 0.0150 & 0.0129 & **0.0116** & 0.0060 & 0.0100 & 0.0085 \\ walking\_half & 0.0286 & 0.0210 & 0.0193 & 0.0136 & **0.0155** & 0.0096 & 0.0121 & 0.0099 \\ walking\_static & 0.0084 & 0.0074 & 0.0039 & 0.0015 & **0.0056** & 0.0029 & 0.0048 & 0.0041 \\ walking\_xyz & 0.0385 & 0.0358 & 0.0285 & 0.0237 & **0.0122** & 0.0071 & 0.0099 & 0.0083 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of Relative Pose Error (RPE) (Unit m). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**ORB\_SLAM3**} & \multicolumn{3}{c}{**This Paper**} \\  & **RMSE** & **STD** & **Mean** & **Median** & **RMSE** & **STD** & **Mean** & **Median** \\ \hline sitting\_static & 0.0159 & 0.0036 & 0.0154 & 0.0160 & **0.0054** & 0.0026 & 0.0048 & 0.0043 \\ sitting\_xyz & 0.0177 & 0.0093 & 0.0150 & 0.0129 & **0.0116** & 0.0060 & 0.0100 & 0.0085 \\ walking\_half & 0.0286 & 0.0210 & 0.0193 & 0.0136 & **0.0155** & 0.0096 & 0.0121 & 0.0099 \\ walking\_static & 0.0084 & 0.0074 & 0.0039 & 0.0015 & **0.0056** & 0.0029 & 0.0048 & 0.0041 \\ walking\_xyz & 0.0385 & 0.0358 & 0.0285 & 0.0237 & **0.0122** & 0.0071 & 0.0099 & 0.0083 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of RMSE Absolute Errors with Other Algorithms (m).