# An Improved Visual SLAM Based on Map Point Reliability under Dynamic Environments
## 3 Experiments
### Ablation Experiments  
To demonstrate the effectiveness of our improvements in the proposed method, we also perform two ablation experiments: one is the ORB-SLAM3 with only the proposed UMPP strategy as described in Section 2.3 (called ORB-UMPP), and the other is the ORB-SLAM3 with only the proposed NUMP strategy as described in Section 2.2 (called ORB-NUMPP). The results of the ablation experiments are shown in Table 7.  
From Table 7, we can see that the performance improvement of ORB-NUPP and ORB-UMPP is positive when compared with ORB-SLAM3 in highly dynamic sequences. This means that the proposed strategies of NUPP and UMPP can effectively reduce the number of dynamic map points in highly dynamic environments and increase the robustness of V-SLAM. When it comes to slightly dynamic sequences 'rpy' and'static', ORB-UMPP and ORB-NUPP can also significantly reduce the number of wrong m \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{3}{c}{**Our Method**} & \multicolumn{3}{c}{**ORB-SLAM3**} \\ \cline{3-6}  & & **Median (ms)** & **Mean (ms)** & **Median (ms)** & **Mean (ms)** \\ \hline \multirow{2}{*}{Slightly} & f2/d/person & 17.84 & 19.74 & 18.50 & 19.40 \\  & f3/s/static & 17.45 & 18.44 & 16.61 & 17.01 \\  & f3/s/xyz & 16.61 & 17.13 & 16.48 & 17.23 \\  & f3/s/my & 16.75 & 17.10 & 16.10 & 16.48 \\  & f3/s/half & 17.45 & 18.44 & 17.75 & 18.58 \\ Highly & f3/s/static & 14.89 & 15.72 & 15.15 & 16.71 \\  & f3/s/xyz & 15.21 & 16.79 & 15.82 & 16.29 \\  & f3/w/rpy & 16.29 & 16.67 & 15.40 & 15.98 \\  & f3/w/half & 15.60 & 17.55 & 17.13 & 17.84 \\
**Average** & 16.45 & 17.51 & 16.55 & 17.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the real-time performance in dynamic environments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{3}{c}{**ATE.RMSE of Trajectory Alignment (m)**} \\ \cline{3-5}  & & **ORB-SLAM3** & **ORB-UMPP** & **ORB-NUMPP** & **Our Method** \\ \hline \multirow{2}{*}{Slightly} & f2/d/person & 0.0081 & 0.0110 & 0.0079 & 0.0074 \\  & f3/s/static & 0.0106 & 0.0079 & 0.0076 & 0.0066 \\  & f3/s/xyz & 0.0198 & 0.0256 & 0.0195 & 0.0151 \\  & f3/s/my & 0.0592 & 0.0278 & 0.0235 & 0.0219 \\  & f3/s/half & 0.0224 & 0.0448 & 0.0217 & 0.0161 \\ Highly & f3/w/static & 0.0249 & 0.0227 & 0.0202 & 0.0079 \\  & f3/w/xyz & 0.1493 & 0.0499 & 0.1120 & 0.0243 \\  & f3/w/rpy & 0.2257 & 0.1756 & 0.1621 & 0.1121 \\  & f3/w/half & 0.1057 & 0.0793 & 0.0659 & 0.0181 \\
**Average** & 0.0695 & 0.0494 & 0.0489 & 0.0255 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The results of ablation experiments. \begin{table}
\begin{tabular}{l l} \hline \hline \multirow{2}{*}{**System**} & \multicolumn{2}{c}{**Average Time Consumption of per**} & **Hardware Platform** \\  & **Frame (ms)** & **Frames (ms)** \\ \hline Co-Fusion [52] & 83.3 & 1 CPU,1 GPU \\ DS-SLAM [30] & 76.46 & Intel Core 17 CPU, P4000 GPU \\ DynSLAM [28] & 78.36 & Titan X GPU \\ RD5-SLAM [23] & 206.13 & GeForce RTX 2080 Ti GPU \\ Mask Fusion [34] & 200.00 & Intel Core CPU, 2 Nvidia GTX Titan X GPU \\ EM-Fusion [29] & 106–257 & Intel Xeon Silver 4112 CPU, Nvidia GTX 1080 Ti GPU \\ DS-SLAM [34] & 30.65 & Intel Core 5-340 CPU \\ SPWSLAM [38] & 45 & Intel Core 17-4790 CPU \\ StaticFusion [51] & 30 & Intel Core 17 370 CPU, GeForce GTX 1070 GPU \\ DVD [39] & 727.48 & Intel Core 56 0 CPU \\ ORB-SLAM3 & 17.28 & Intel Core 15 10000 CPU \\ Our method & 17.51 & Intel Core 15 10400 CPU \\ \hline \hline \end{tabular} Note: All data of state-of-the-art SLAM methods are obtained from their original papers. The running times of other SLAM are obtained from the corresponding papers, except the time of ORB-SLAM3 is obtained with the same computer that we use. Co-fusion does not provide specific information about the hardware used in the original paper, but only the number of CPU and GPU.  
\end{table} 
Table 5: Comparison of the real-time performance in dynamic environments. the mismatch in a pure rotation environment and wrong triangulation.