## 4 Experiments
### Tracking Results  
We conducted comprehensive comparative experiments between the proposed method, NeRF-LOAM, and LiV-GS across four distinct datasets to evaluate tracking performance. The results are summarized in Table 3, where the highest values for each metric are highlighted in bold. These experiments demonstrate that our method consistently outperforms both NeRF-LOAM and LiV-GS in terms of pose estimation accuracy and robustness, particularly in dynamic and complex environments. First, our method exhibits significant improvements in pose estimation accuracy compared to NeRF-LOAM and LiV-GS on the KITTI dataset. The KITTI dataset is known for its challenging urban street scenes with moving pedestrians, vehicles, and complex building structures. The results on this dataset highlightssthe strength of our approach in handling dynamic interference. While NeRF-LOAM and LiV-GS show performance degradation when dynamic objects such as moving vehicles and pedestrians are present, our method effectively suppresses the influence of dynamic interference through a hybrid dynamic object removal strategy, allowing it to maintain accurate pose estimates even in environments with high dynamic complexity. On the custom ROI dataset, which consists of both outdoor (ROI-OD) and industrial manufacturing (ROI-IND) scenarios, our method demonstrates significant versatility. In outdoor environments (ROI-OD), which often involve challenging conditions such as varying lighting, weather changes, and occlusions from trees or buildings, our method shows superior tracking accuracy. Its ability to adapt to these variations is highlighted by its lower pose estimation error of 0.32 compared to NeRF-LOAM's 0.84 and LiV-GS's 0.65. This underscores the robustness of our system in outdoor navigation tasks, where environmental dynamics and changing conditions are common. tracking accuracy in industrial manufacturing environments (ROI-IND) is particularly challenging, as dynamic objects such as robotic arms, conveyor belts, and workers frequently move around. Despite these complexities, our system manages to maintain an impressive pose estimation error of 0.19. This performance is significantly better than that of NeRF-LOAM and LiV-GS, which show pose estimation errors of 0.53 and 0.41, respectively. These results illustrate the strength of our method for applications where precise tracking is essential for maintaining workflow continuity and safety, such as automated inspection and production line monitoring. The results shown in Table 3 clearly demonstrate that our method offers significant improvements in pose estimation accuracy across all datasets. Our system achieves the lowest errors on both the MaiCity and Newer College datasets, confirming its generalization capability across different environments. The consistency of our tracking algorithm's performance across various datasets ranging from urban streets to industrial environments underscores its robustness and versatility in diverse real-world scenarios. The results further demonstrate the effectiveness of our dynamic interference removal strategy. In contrast to other methods, which struggle with dynamic humans and produce scanning point clouds that include moving objects, our approach employs a hybrid segmentation strategy based on both visual uncertainty and 3D object detection. This segmentation \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **MaiCity** &
\begin{tabular}{c} **Newer** \\ **College** \\ \end{tabular} & **KITTI** & **ROI-IND** & **ROI-OD** \\ \hline NeRF-LOAM & 0.19 & 0.14 & 9.17 & 0.53 & 0.84 \\ LiV-GS & 0.08 & 0.08 & 8.21 & 0.41 & 0.65 \\ Ours & **0.06** & **0.01** & **6.57** & **0.19** & **0.32** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Tracking results on the Newer College dataset. Where bold represents the best value. strategy allows us to distinguish dynamic objects from the static scene more accurately and remove them from the point cloud.