# DFD-SLAM: Visual SLAM with Deep Features in Dynamic Environment
## 4 Results
### Test on TUM-RGBD Dataset  
To validate the specific performance of DFD-SLAM in indoor dynamic environments, we compared it with several well-known dynamic SLAM systems on the TUM-RGBD dataset. This dataset provides comprehensive depth information and complete trajectory data, allowing us to verify the advantages of our novel removal method in RGBD mode. The TUM-RGBD dataset contains indoor sequences with dynamic objects, with the _Walk_ sequence being our main focus due to their high dynamic environment. Additionally, we used two _Sifting_ sequences to assess the system's performance in typical dynamic environments. We compared our system with some outstanding dynamic SLAM systems and conducted detailed comparisons of ATE and RPE. All the test data are provided in Tables 4 and 5. In our test data, we achieved the best results in five out of six sequences presented in Table 4. Additionally, in the relative pose error results shown in Table 5, our approach has certain advantages over many outstanding works. We also demonstrate significant advantages in highly dynamic scenarios. Calculations show that compared to ORB-SLAM3, our improvements result in a significant reduction in absolute trajectory error in highly dynamic sequences such as W/static, W/xyz, and W/rpy, with an average improvement (boost) of 91.57%. Our comparison includes Dyna-SLAM [27], DS-SLAM [29], Locrt [34], CDS-SLAM [9], and PR-SLAM [35]. Dyna-SLAM, DS-SLAM, Crowd-SLAM, CDS-SLAM, and PR-SLAM are all dynamic SLAM systems that perform feature point selection based on semantic information; this is similar to DFD-SLAM, which primarily uses semantic information for feature point selection in dynamic environments. Across the high-dynamic scenes of the TUM-RGBD dataset, DFD-SLAM demonstrated exceptional performance in all sequences, with the exception of \(W/half\), where its performance was average. Furthermore, detailed testing was conducted in our environment using open-source code. In Figure 7, we illustrate  
Figure 6: Comparison of loop closure detection in monocular mode. The final trajectory maps are shown in (**h,i**). The numbers annotated above indicate the positions where loop closure detection occurred in each system. Scenes (**a–g**) correspond to the occurrences of loop closure detection, where the second row indicates the frames where the systems correctly detected loop closures relative to the first row. the trajectories of ORB-SLAM3, DYNA-SLAM, CDS-SLAM, and DFD-SLAM, highlighting that our trajectory was notably smoother compared to those systems. In testing open-source systems, we found that DFD-SLAM shows a significant performance improvement over ORB-SLAM3 in dynamic environments. Additionally, it exhibits higher accuracy compared to other outstanding dynamic SLAM systems. When compared to DynaSLAM, DFD-SLAM performs better in all four highly dynamic sequences, with improvements of 10.34% in \(W/half\), 17.14% in \(W/rpy\), 17.67% in \(W/static\), and 56.25% in \(W/xyz\). In comparison with CDS-SLAM, DFD-SLAM is superior in two out of four highly dynamic sequences, with improvements of 45.28% in \(W/rpy\) and 46.15% in \(W/xyz\), while both systems perform similarly in \(W/static\). These data are also reflected in the trajectories shown in Figure 7. Compared to DynaSLAM, which is also based on semantic segmentation, our system demonstrates significantly better accuracy, as indicated by the more convergent red lines representing the error. This advantage is particularly evident in the \(W/rpy\) sequence, where the proportion of dynamic objects is the highest. In comparison with the advanced CDS-SLAM system, although our system shows a slight disadvantage in ATE data only in the \(W/half\) sequence, the actual tested trajectory of DFD-SLAM is more stable. CDS-SLAM, being based on object detection, still suffers from excessive removal or missed removal in its exclusion strategy, resulting in sudden errors in some parts of the \(W/half\) sequence trajectory.