## 4 Experiments
### Mapping Results
The experimental results demonstrate that the proposed method consistently outperforms existing state-of-the-art approaches, especially in dynamic and complex environments. Our method shows significant improvements over the baseline systems in terms of accuracy, completeness, and F-score, which is especially evident in dynamic scenes. These results validate the effectiveness of our dynamic object removal strategy and multi-sensor fusion, as our method significantly reduces errors caused by moving objects, leading to more accurate and robust scene reconstructions. Furthermore, our method exhibits superior performance in terms of scene texture details, where the rendering quality and depth estimation are visibly better compared to other approaches. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Completion \(\downarrow\)** & **Accuracy \(\downarrow\)** &
\begin{tabular}{c} **Chamfer-L1** \\ **Distance \(\downarrow\)** \\ \end{tabular} & **F-Score \(\uparrow\)** \\ \hline LDAR-NeRF & 5.71 & 3.81 & 4.84 & 90.16 \\ SHINE & 4.35 & 4.08 & 4.26 & 89.94 \\ NeRF-LOAM & 4.16 & 3.29 & 3.68 & 93.51 \\ LiV-GS & 3.09 & 2.58 & 2.87 & 94.52 \\ Ours & **2.67** & **2.21** & **2.35** & **96.17** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mapping results on the MaiCity dataset [28]. Where lower values are better, and bold represents the best value. Figure 5: Depth rendering result after removing dynamic objects. We also compared our method with others in terms of rendered depth and radar point cloud reconstruction results, with the results shown in Figures 6 and 7. The color map at the top of each figure represents the rendered depth map, while the grayscale map at the bottom shows the radar point cloud. Our method clearly outperforms the others in handling dynamic interference, particularly in its ability to accurately remove moving humans from radar images, thereby effectively eliminating their impact on the rendered depth. The removal of dynamic objects is consistent across both the tracking and rendering aspects. By removing the corresponding dynamic points in the radar point cloud results, our system achieves a more precise representation of the static scene, which is crucial for reliable SLAM operations in environments with moving objects. The ability to remove dynamic objects from both depth and point cloud data without compromising the integrity of the static scene represents a significant advancement over existing techniques, and ensures that the system can perform robustly even in challenging dynamic environments. Overall, the results demonstrate that our approach offers substantial improvements over existing SLAM methods, particularly in terms of handling dynamic environments; the ability to remove dynamic objects with high accuracy while maintaining detailed and consistent reconstructions of static elements positions our system as a powerful tool for a wide range of applications, from autonomous navigation to industrial robotics. The superior performance of our proposed method in highly dynamic scenes can be attributed to its innovative integration of LiDAR tracking, 3D point cloud segmentation, and 3 DGS rendering, along with our introduction of a sliding window strategy to mitigate the impact of dynamic artifacts. Furthermore, use of the curling loss effectively addresses dynamic objects, enhancing both the accuracy and robustness of dense mapping. In contrast, other methods exhibit suboptimal performance in dynamic scenes, often struggling with reduced mapping accuracy due to interference caused by dynamic objects and artifacts. Figure 6: LiDAR point cloud result after removing dynamic objects. Figure 7: Demonstration of the 3D bounding box setup and the effect of removing laser point clouds. Our method effectively addresses dynamic interference compared to other methods, resulting in a cleaner point cloud with noise points removed.