# DFD-SLAM: Visual SLAM with Deep Features in Dynamic Environment
## 4 Results
### Test on TUM-VI Dataset  
We conducted detailed tests and comparisons on the TUM-VI dataset by isolating the semantic thread of YOLOv8 and comparing it with ORB-SLAM3 [16], VINS-Mono [2], and HFNet-SLAM [7]. ORB-SLAM3 and VINS-Mono are classic works in the field of visual SLAM, while HFNet-SLAM utilizes deep-learning-based feature extraction for front-end odometry tracking. Table 1 illustrates that SLAM systems utilizing deep-learning-based feature extraction achieve superior tracking accuracy overall. It was observed that DFD-SLAM excels over ORB-SLAM3 in scenarios with significant viewpoint changes or lighting variations, as depicted in Figure 5. Compared to the original ORB-SLAM3, our improvements show enhancements across various scenarios in the TUM-VI dataset. By calculating the improvement rate of DFD-SLAM over ORB-SLAM3 based on the data in Table 1, we found that the average improvement (boost) for different sequences is 29.24% in the _corridor_ sequences, 40.07% in the _magistrale_ sequences, 28.75% in the _room_ sequences, and 35.26% in the _slides_ sequences. Additionally, compared to these advanced visual SLAM systems, we maintain an advantage in 14 out of the 20 sequences. This demonstrates the superiority of our system. Our method can estimate frame rotation angles and select appropriate descriptors for matching, allowing DFD-SLAM to provide better accuracy in _corridor_ sequences with frequent frame rotations. In other scenarios, although frame rotations do not result in tracking loss, they degrade matching performance and impact overall accuracy. DFD-SLAM's targeted strategy effectively identifies these frames and compensates appropriately. In the _room_ scenario, due to the smaller environment and timely loop closures, there are no significant accuracy differences between ORB-SLAM3, HFNet-SLAM, and DFD-SLAM. It is only in sequences with fewer frame rotations where systems utilizing deep learning feature descriptors perform better. Overall, our method demonstrates excellent performance in \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{4}{c}{**TUM-VI (ATE)**} \\ \cline{2-5}  & **ORB-SLAM3** & **VINS-Mono** & **HFNet-SLAM** & **DFD-SLAM** \\ \hline corridor1 & 0.04 & 0.63 & 0.023 & **0.018** \\ corridor2 & 0.02 & 0.95 & 0.048 & **0.015** \\ corridor3 & 0.31 & 1.56 & **0.036** & 0.112 \\ corridor4 & **0.17** & 0.25 & 0.227 & 0.183 \\ corridor5 & 0.03 & 0.77 & 0.051 & **0.027** \\ average & 0.11 & 0.83 & 0.077 & **0.071** \\ \hline Magistrale1 & 0.56 & 2.19 & **0.130** & 0.144 \\ Magistrale2 & 0.52 & 3.11 & 0.471 & **0.319** \\ Magistrale3 & 4.89 & **0.40** & 2.903 & 2.478 \\ Magistrale4 & 0.13 & 5.12 & 0.184 & **0.113** \\ Magistrale5 & 1.03 & **0.85** & 0.874 & 0.956 \\ Magistrale6 & 1.30 & 2.29 & 0.604 & **0.547** \\ average & 1.41 & 2.33 & 0.861 & **0.760** \\ \hline Room1 & 0.01 & 0.07 & **0.008** & **0.008** \\ Room2 & 0.02 & 0.07 & 0.012 & **0.009** \\ Room3 & 0.04 & 0.11 & **0.013** & **0.013** \\ Room4 & 0.01 & 0.04 & 0.016 & **0.011** \\ Room5 & 0.02 & 0.20 & 0.012 & **0.008** \\ Room6 & 0.01 & 0.08 & **0.006** & 0.012 \\ average & 0.02 & 0.10 & 0.011 & **0.010** \\ \hline Slides1 & 0.97 & 0.68 & 0.414 & **0.402** \\ Slides2 & 1.06 & 0.84 & 0.803 & **0.776** \\ Slides3 & 0.69 & 0.69 & 0.611 & **0.549** \\ average & 0.91 & 0.74 & 0.609 & **0.576** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Absolute trajectory error results tested in TUM-VI. various indoor scenarios and effectively addresses the limitations of state-of-the-art deep learning feature extraction methods, leading to superior performance in specific scenarios. To further evaluate the impact of the frame rotation strategy on system performance, we conducted detailed ablation experiments in the _corridor_ scene. The specific data are shown in the Table 2. The experiments compared DFD-SLAM using only ORB descriptors (DFD-SLAM(B)), only HFNet descriptors (DFD-SLAM(H)), and the combination of frame rotation detection (DFD-SLAM(HB)).