# DFD-SLAM: Visual SLAM with Deep Features in Dynamic Environment
## 4 Results
### Test on TUM-VI Dataset
The findings indicated that in scenes characterized by frequent frame rotations, HFNet descriptors underperformed compared to manually designed Rotated BRIEF descriptors. For instance, in the _corridor_2 scene, using only HFNet descriptors is prone to tracking loss. While IMU data and relocalization can mitigate tracking loss, precision remains compromised. Despite the enhanced robustness of manually designed descriptors, they do not match the effectiveness of HFNet in terms of matching. Our DFD-SLAM(HB) strategy effectively combines the strengths of both, resulting in improved system performance. In our tests, we found that DFD-SLAM(H) performed best in the _corridor_3 sequence. We think the main reason for this is that, in this sequence, even using descriptors generated by HFNet alone does not result in frame loss during frame rotations. However, such rotations typically cause frame loss in most other sequences. This is primarily because the dramatic plane rotations in this sequence do not involve significant viewpoint changes, and there are many features in these scenes, allowing the system to relocalize accurately. As a result, the final absolute trajectory error remains low. Most tracking processes without dramatic frame rotations use descriptors generated by HFNet, which perform better in feature matching. Moreover, the sequence involves many scene changes. As shown in Figure 5, HFNet demonstrates more significant advantages at these corners. This leads to the method using only descriptors generated by HFNet performing better overall. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{3}{c}{**TUM-VI (ATE)**} \\ \cline{2-4}  & **DFD-SLAM(H)** & **DFD-SLAM(B)** & **DFD-SLAM(HB)** \\ \hline corridor1 & 0.024 & 0.031 & **0.018** \\ corridor2 & 0.052 & 0.027 & **0.015** \\ corridor3 & **0.068** & 0.247 & 0.112 \\ corridor4 & 0.196 & 0.201 & **0.183** \\ corridor5 & 0.039 & 0.058 & **0.027** \\ average & 0.076 & 0.113 & **0.071** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation experimental result of absolute trajectory error in TUM-VI. Figure 5: Matching performance of DFD-SLAM and ORB-SLAM3 under varying lighting and scene conditions. The first row shows the matching performance of ORB-SLAM3 using its strategies. The second row illustrates the matching performance of DFD-SLAM using HFNet for feature point extraction and descriptor matching. In most cases, the deep-features-based extraction method still holds advantages. We designed an ablation study to further investigate the improvement in loop closure performance of DFD-SLAM using HFNet for global descriptor generation. To eliminate the assistance of the IMU, the ablation experiment was conducted exclusively under cam0 of TUM-VI for a purely monocular test, ensuring that results were solely influenced by tracking and loop closure. By comparing DFD-SLAM and ORB-SLAM3 under different modes, we found that in most cases, the use of HFNet for global descriptor generation yields better loop closure performance, as shown in Table 3. DFD-SLAM (BOW) refers to the version that still uses bag-of-words descriptions for loop closure. DFD-SLAM (HFNet) refers to the version that uses global descriptors generated by HFNet for loop closure. Particularly in the _Magistrale5_ sequence, all methods relying on bag-of-words descriptions failed to detect loop closures successfully, whereas methods utilizing global descriptors achieved timely and effective loop closure detection. Furthermore, the performance of DFD-SLAM with loop closure detection activated surpasses that of ORB-SLAM3 with loop closure detection activated in terms of accuracy. Additionally, we found that utilizing global descriptors for loop closure detection provides a significantly greater improvement in accuracy compared to using BOW-based methods, both within DFD-SLAM and relative to ORB-SLAM3.