# DM-SLAM: Monocular SLAM in Dynamic Environments
## 3 Results
### Overall Evaluation Using TUM RGB-D Dataset  
It is worth noting that the determination of static objects in the DM-SLAM system stems from the solution of the model in the grid. Therefore, DM-SLAM is not suitable for the situation where massive moving objects exist in the scene, resulting in the static background blocked in various parts of images. The most scenes in TUM RGB-D dataset are indoor and the motion situation is not too complicated. At the same time, sequence fr3_walking includes many different types of camera motions, such as rpy, xyz, and half_sphere, where we can observe the performance of the SLAM system more comprehensively. Therefore, we chose TUM sequence fr3_walking to compare ORB-SLAM2 with DM-SLAM. Tables 2-4 show quantitative comparison results of ORB-SLAM2 and DM-SLAM in sequences fr3_walking and fr1_xyz. As [PERSON], [PERSON] and [PERSON] proposed in [1], we run each sequence ten times and show RMSE (RootMeanSquareError), mean, media, S.D. results, to account for non-deterministic nature of the system. In order to compare the basic performance of DRLRSAC, we have added alignment with sequence fr1_xyz, which is dominated by static scenes. RMSE, Mean Error, Median Error, and Standard Deviation (S.D.) indicates SLAM errors from different aspects,  
Figure 5: Solution result in grids containing mixed model. Red circles: eight features solving Grid model, blue circles: all matching feature points, and yellow circles: inliers of the Grid model. but RMSE and S.D. are more relevant because they can better indicate the robustness and stability of the system. The calculation method of improvements refers to literature [17]. As we can see from Tables 2-4 there is obvious improvement when compared DM-SLAM with ORB-SLAM2. In sequence fr3_walking, the RMSE and S.D. improvement values of ATE can reach up to 96.42% and 98.45%. DM-SLAM errors are mainly caused by outliers, mismatches, and numerical calculations. We think that improvement depends on the ratio between the errors caused by the moving object and the above errors. In fr1_xyz, the improvement in performance is not obvious and we hypothesized that when the scene is mainly static, the distinction of DM-SLAM for dynamic features is not necessary for ORB-SLAM2 system. Figure 7 shows the ATE and results of metric rotational drift (RPE) from ORB-SLAM2 and DM-SLAM respectively. It can be seen that the overall error of DM-SLAM is much smaller than ORB-SLAM2. In experiments, when the number of feature points extracted per frame (default) is 1000, ORB-SLAM2 cannot work (while DM-SLAM works). For comparison, we set the number of feature points extracted per frame 3000, which improves the data association between frames and robustness of the system. Figure 6: The features used for solving camera pose in the tracking thread, which are marked with green circle. (**a–f**) show performance of static features extraction of our algorithm in several typical motion scenes. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequences**} & \multicolumn{6}{c}{**ORB-SLAM2**} & \multicolumn{6}{c}{**DM-SLAM**} & \multicolumn{6}{c}{**Improvements**} \\ \cline{2-13}  & **RMSE** & **MEAN** & **MEDIAN** & **S.D.** & **RMSE** & **MEAN** & **MEDIAN** & **S.D.** & **RMSE** & **MEAN** & **S.D.** & **RMSE** \\ \hline fr3\_walking\_xyz & 0.3292 & 0.3078 & 0.2942 & 0.1161 & 0.1429 & 0.1298 & 0.1352 & 0.0537 & 56.59\% & 57.83\% & 54.04\% & 53.74\% \\ fr3\_walking\_ry & 2.9432 & 2.7549 & 2.8641 & 1.8461 & 0.1055 & 0.1016 & 0.0864 & 0.0285 & 96.42\% & 96.31\% & 96.98\% & 98.45\% \\ fr3\_walking\_half & 0.5211 & 0.4534 & 0.3867 & 0.2379 & 0.1548 & 0.1254 & 0.1481 & 0.0716 & 69.77\% & 72.34\% & 61.71\% & 69.91\% \\ fr1\_xyz & 0.0492 & 0.0467 & 0.0439 & 0.0151 & 0.0285 & 0.0271 & 0.0277 & 0.0086 & 42.01\% & 41.97\% & 36.91\% & 43.05\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of metrics absolute trajectory error (ATE).