# DI-SLAM: A Real-Time Enhanced RGB-D SLAM for Dynamic Indoor Environments
## 4 Experiments
### Positioning Accuracy Analysis  
To assess the effectiveness of our proposed algorithm, we first compared it with ORB-SLAM3, as our method extends the ORB-SLAM3 framework. We evaluated both systems using four sequences from the TUM dataset: sitting_static, walking_halfsphere, walking_xyz, and walking_static.  
Figure 8a-i illustrate the comparison of the absolute trajectory error between the proposed algorithm (middle section), ORB-SLAM3 (upper section), and RDS-SLAM (lower section) on the datasets fr3_walking_halfsphere, fr3_walking_static, and fr3_walking_xyz. The black curve represents the ground truth of the camera's movement path; the blue curve denotes the trajectory estimation from ORB-SLAM3, RDS-SLAM, or the proposed DI-SLAM algorithm; and the red line segments indicate the absolute error between the estimated and ground truth trajectories.  
In our evaluation, we adopt three absolute trajectory error (ATE) metrics--RMSE, Mean, and Std--to measure the system's localization accuracy. Additionally, to provide a more intuitive understanding of the experimental outcomes and to supplement the results presented in Figure 8, we illustrate the absolute pose errors of the proposed algorithm in comparison with two other methods in Figure 9a-i. The corresponding data comparing our method with the original ORB-SLAM3 are reported in Table 2, while the comparative results with the state-of-the-art RDS-SLAM are presented in Table 3.  
The results from the experiments indicate that it achieves significant improvements in pose estimation accuracy for highly dynamic scenarios when compared to the original ORB-SLAM3. In low dynamic scene dataset sequences, the RMSE of the absolute trajectory error demonstrates a 22.11% decrease relative to ORB-SLAM3. In high-dynamic scene datasets, the proposed algorithm reduces the RMSE of the absolute trajectory error by an average  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **MAP/\%** & **Precision/\%** & **Recall/\%** & **Params/M** & **Each Frame/ms** \\ \hline Improved\_Yolov5s & 69.8 & 74.7 & 67.3 & 6.5 & 10.7 \\ Yolov5s & 65.6 & 65.9 & 65.4 & 7.4 & 12.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance testing of object detection networks on a GPU.  
of 94.11% compared to ORB-SLAM3, which reveals that the DI-SLAM system proves to be more superior than ORB-SLAM3 in scenarios ranging from low to high dynamics, delivering significantly more precise trajectory estimates across the board.  
Additionally, we compared our DI-SLAM system with the state-of-the-art dynamic SLAM algorithm, RDS-SLAM, and the experimental results are presented in Table 3. In static environments, both methods demonstrate reliable performance. However, as the presence of dynamic elements in the scene increases, trajectory errors also grow. Compared to RDS-SLAM, our method performs slightly better on the walking_xyz sequence. In other dynamic scenarios, although the estimation accuracy of both approaches is comparable, our method shows a marginally inferior overall performance. We attribute this discrepancy primarily to the semantic segmentation approach adopted by RDS-SLAM, which enables effective pixel-level separation of dynamic and static regions. Nevertheless, this segmentation method introduces additional image processing overhead. Furthermore, RDS-SLAM employs a mature dynamic-static data association strategy, granting it a notable advantage in tracking and handling outliers. In contrast, our method relies on multi-view geometry and object detection techniques for outlier filtering. While this approach may be slightly less effective in highly dynamic scenes, it does not significantly affect the overall system performance. Overall, the DI-SLAM system demonstrates clear advantages in specific scenarios (e.g., walking_xyz), and with further algorithmic refinement, it is expected to compete with--and potentially surpass--existing methods in a broader range of dynamic environments.