## 4 Experiment
### Visualization  
The visualization results play a pivotal role in gauging the efficacy and robustness of our proposed PSMS-SLAM method. Figure 7 provides an in-depth illustration of the trajectory obtained through our system's operation on the M2 DGR dataset, offering a direct visual comparison against the ground truth. This trajectory visualization is a key metric for evaluating the accuracy and dependability of our SLAM system, providing a tangible  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Distance (km)** & **Time (s)** & **Scene Classification** \\ \hline walk 01 & 3.724 & 454 & static \\ Street 02 & 2.453 & 110 & dynamic, long straight \\ Street 06 & 5.067 & 485 & dynamic \\ Street 10 & 0.561 & 80 & static \\ Gate 03 & 0.393 & 27 & long straight \\ \hline \hline \end{tabular}
\end{table}
Table 1: M2 DGR dataset details.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**M2 DGR** & **Walk 01** & **Street 02** & **Street 06** & **Street 10** & **Gate 03** \\ \hline LeGO-LOAM [36] & 3.29 & 20.02 & 1.25 & 17.93 & 0.12 \\ VINS-Mono [6] & 57.90 & 24.16 & 124.36 & 23.57 & 6.25 \\ LVI-SAM [19] & 3.28 & 3.53 & 0.43 & 3.95 & 0.11 \\ PSMS-SLAM & 3.30 & 3.14 & 0.45 & 2.87 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of accuracy based on the absolute trajectory error (ATE) in meters on the M2 DGR dataset.  
representation of how well it reconstructs the environment and estimates the camera pose over time.  
In Figure 7, the trajectory traces the path followed by the camera throughout the dynamic scenes, offering a side-by-side comparison with the ground truth. Discrepancies between the estimated trajectory and the ground truth trajectory can be visually identified, providing insights into the system's performance under varying conditions and highlighting any potential challenges faced during the mapping and localization processes.  
Moving forward, Figure 8 delves into the visualization results with a specific emphasis on our dynamic object pose estimation method. This figure serves as a comprehensive showcase of the system's capabilities by presenting both velocity and depth information associated with the dynamic objects detected by our PSMS-SLAM system.  
The inclusion of velocity information allows for the observation of the speed and directional movement of each identified dynamic object. This aspect is crucial for applications requiring precise understanding and prediction of object movements, such as in robotics and autonomous navigation. By visually inspecting the velocity information in Figure 8, we gain valuable insights into how well our system can handle the dynamic nature of objects within the environment.  
Moreover, the visualization of depth information in Figure 8 provides a spatial context, depicting the relative distances and positions of the dynamic objects within the scene. This depth information is fundamental for creating accurate 3D maps and understanding the layout of the environment. Our method leverages multi-sensor fusion to enhance the accuracy of depth estimation, ensuring a more faithful representation of the scene's geometry.  
By offering a visual representation of the 6D pose estimation in real time, Figure 8 allows for a qualitative assessment of our system's performance in handling the challenges presented by dynamic entities in the environment. The dynamic object pose estimation module proves to be robust, capturing not only the positional information but also the dynamic behaviors of objects, which is crucial for applications in SLAM.  
Figure 7: The trajectory generated by our PSMS-SLAM method on the M2 DGR dataset, providing a side-by-side comparison with the ground truth trajectory.