# An Improved Visual SLAM Based on Map Point Reliability under Dynamic Environments
## 3 Experiments
### Comparison with the State of the Art  
Firstly, we will introduce the robustness and real-time performance of our SLAM on this dataset and compare it with the other state-of-the-art real-time SLAM methods based on the consistency principle. The V-SLAM methods based on the consistency principle used in the comparison experiments are DVO [39], StaticFusion [51], SPWSLAM [38], MotionRemove [41] and DSLAM [42]. The results are shown in Tables 1-3. The results in Tables 1-3 show that our SLAM achieves the best performance in the whole highly dynamic sequences compared with the state-of-the-art methods. In the five slightly dynamic sequences, the proposed method also achieves the best performance in four sequences, except in fr3/s/xyz, the ATE.RMSE of the proposed method is higher than that of DSLAM. However, the average ATE.RMSE of the proposed method reduces to 0.0255 m, which is about 37.35% less than DSLAM (relative value). In terms of the Rot.RMSE, DSLAM can obtain the second-best performance. However, the average Rot.RMSE of DSLAM increases by about 111.62% (relative value) compared with the proposed method. This result shows that DSLAM cannot handle camera rotation well. In addition, compared with ORB-SLAM3, our method can outperform ORB-SLAM3. The average Rot.RMSE, Trans.RMSE and ATE.RMSE of the proposed method reduce by about 75.63%, 48.81% and 63.31% (relative value), respectively. The main reason is that by screening each new map point using the distance threshold, our SLAM can significantly reduce the number of erroneous map points generated due to mismatches in a purely rotational environment. In addition, as the camera keeps still, this method can also eliminate the wrong map points generated by triangulation due to two frames being too close to each other. The comparison results of the trajectory between the proposed method and ORB-SLAM3 are shown in Figures 7 and 8. Some examples of the working process of the proposed method and ORB-SLAM3 in fr3/w/xyz are shown in Figure 9.  
In Figures 7 and 8, the blue line in the figure above is the trajectory of the V-SLAM system, the black line is the ground truth trajectory, and the red line segments are used to connect the same timestamp points at two trajectories. So, the longer the red segment is, the larger the error trajectory is at that timestamp. The results in Figures 7 and 8 show that the error trajectory of our proposed method is less than that of ORB-SLAM3, especially in the sequences fr3/s/rpy, fr3/w/static, fr3/w/half, and fr3/w/xyz, where the improvements of the proposed method are 60% higher than ORB-SLAM3 (see Table 3). The results in Figure 9 show that the proposed method can deal with different types of map points, which are marked in different colors and sizes. However, ORB-SLAM3 uses all map points indiscriminately, ignoring possible potential errors between map points and their real-world landmarks. This makes it perform poorly in dynamic environments. In order to show the performance of the proposed method in dynamic environments, we also compare our proposed method with the state-of-the-art V-SLAM methods based on deep learning, which are not suited for real-time applications. These methods include Co-fusion [52], MaskFusion [34], DynaSLAM [28], EM-Fusion [29], DS-SLAM [30] and RDS-SLAM [23]. To make the paper more concise, here only the ATE.RMSE of these methods is given. The results are listed in Table 4.  
Figure 8: Comparison of ATE results between our method and ORB-SLAM3 in highly dynamic sequences. Figure 9: Some examples of the working process of the proposed method and ORB-SLAM3 in fr3/w/xyz: (**a**) environment with people moving at low speed; (**b**) environment with people moving at high speed. In our method, the red feature point means it connects to a D-MP; the green feature point means it connects to an R-MP or a U-MP. The larger the green feature point is, the more reliable the map point is. In our method, there are significantly fewer feature points on the people.