# DFD-SLAM: Visual SLAM with Deep Features in Dynamic Environment
## 4 Results
### Test on TUM-RGBD Dataset
Moreover, in the \(W/rpy\) sequence, the red lines representing the error are noticeably more convergent in DFD-SLAM compared to CDS-SLAM. Our removal strategy played a significant role as well. In the first row of Figure 8, the man on the left exhibited subtle rotation above the waist and movement of the hands in the few preceding frames while his lower body remained stationary on the chair. Our strategy accurately retained the feature points of the man's lower body. In most cases, moving objects were accurately selected and examined to determine which parts to remove. This precise removal strategy significantly improves accuracy in scenes where dynamic objects undergo local motion. In the last row of Figure 8, some dynamic SLAM methods based on semantic segmentation would remove all objects labeled as chairs upon detecting movement of the chair on the left, such as DS-SLAM. However, our method can precisely identify which specific chair in the scene has moved. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{8}{c}{**TUM-RGBD (ATE)**} \\ \cline{2-9}  & **O3** & **Dyna** & **DS** & **Crowd** & **Lccrf** & **CDS** & **PR** & **OURS** \\ \hline W/half & 0.424 & 0.029 & 0.030 & 0.026 & 0.028 & **0.019** & 0.025 & 0.026 \\ W/rpy & 0.726 & 0.035 & 0.044 & 0.044 & 0.035 & 0.053 & 0.034 & **0.029** \\ W/static & 0.022 & 0.006 & 0.008 & 0.007 & 0.011 & **0.005** & 0.006 & **0.005** \\ W/xyz & 0.825 & 0.016 & 0.024 & 0.020 & 0.016 & 0.013 & 0.017 & **0.007** \\ S/half & 0.019 & 0.018 & - & 0.020 & - & 0.013 & 0.015 & **0.011** \\ S/xyz & 0.012 & 0.012 & - & 0.018 & 0.009 & 0.011 & **0.007** & **0.007** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Absolute trajectory error results tested in TUM-RGBD. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{8}{c}{**TUM-RGBD (RPE)**} \\ \cline{2-9}  & **O3** & **Dyna** & **DS** & **Crowd** & **Lccrf** & **CDS** & **PR** & **OURS** \\ \hline W/half & 0.023 & 0.028 & 0.030 & 0.037 & 0.035 & 0.018 & **0.013** & 0.024 \\ W/rpy & 0.138 & 0.044 & 0.150 & 0.065 & 0.050 & 0.035 & **0.017** & 0.026 \\ W/static & 0.011 & 0.008 & 0.010 & 0.010 & 0.014 & 0.006 & 0.006 & **0.005** \\ W/xyz & 0.042 & 0.021 & 0.033 & 0.025 & 0.021 & 0.017 & 0.012 & **0.009**To further demonstrate the effectiveness of our proposed method, we conducted more detailed ablation experiments, and the results are presented in Table 6. Here, DFD-SLAM(H) indicates no use of any removal strategy; DFD-SLAM(HS) represents complete removal of feature points related to dynamic objects; DFD-SLAM(HSD) signifies the application of our precise removal method; and DFD-SLAM(BSD) signifies combining our removal strategy with the use of ORB descriptors, and not using HFNet descriptors. Notably, in scenarios featuring significant rotations, such as \(W/\mathit{rpy}\), employing HFNet-generated descriptors still yielded enhanced accuracy. This demonstrates that DFD-SLAM not only relies on superior removal strategies but also exhibits enhanced performance in feature point extraction descriptors, leading to overall accuracy improvements. Figure 7: Comparison of trajectories between outstanding dynamic SLAM systems and our method in highly dynamic environments. The first row shows the trajectory map for the \(W/\mathit{static}\) sequence, the second row for the \(W/\mathit{xyz}\) sequence, the third row for the \(W/\mathit{rpy}\) sequence, and the fourth row for the W/half sequence. The blue lines represent the system’s result trajectory, the black lines indicate the ground truth, and the red lines show the difference between the two. More prominent and numerous red lines indicate a higher absolute trajectory error, signifying lower tracking accuracy of the system.