## 4 Experiments
### Mapping Results  
For the qualitative mapping results, Figure 3 illustrates the differences between the proposed method, NeRF-LOAM, and LiV-GS in removing dynamic objects from outdoor street scenes in the KITTI dataset. The results show that the proposed method excels in reconstructing scenes with dynamic objects, offering a clear advantage in mitigating the interference caused by moving pedestrians and vehicles. In contrast, both NeRF-LOAM and LiV-GS demonstrate reduced performance when handling dynamic objects within street scenes. NeRF-LOAM particularly struggles with motion blur, while LiV-GS is capable of general scene reconstruction but fails to accurately remove the dynamic elements without distorting the static scene. On the other hand, our method is not only effective in removing dynamic objects but also restores the integrity of the street environment, maintaining a high level of detail and accuracy in the reconstruction. This is evident in the cleaner and more consistent representations of buildings, roads, and other static objects in the results, highlighting the superior dynamic object handling and scene fidelity of our approach. Figure 4 presents the reconstruction results of the proposed method on industrial manufacturing scenarios from the ROI dataset. In complex industrial environments, our method excels at handling both static and dynamic objects. The proposed system effectively reconstructs the geometric structures of mechanical equipment and other industrial elements while maintaining accuracy, despite the presence of moving components such as robotic arms, forklifts, and human operators. Notably, our method maintains high-quality reconstructions even in highly cluttered environments with large machinery objects and varying levels of occlusion, which can often be problematic for conventional SLAM approaches. This adaptability to dynamic objects coupled with precise 3D reconstruction makes our approach particularly suitable for applications where robust and reliable scene reconstruction is essential for system operation, including automated inspection, production line monitoring, and real-time tracking of equipment. Its ability to differentiate and suppress dynamic interference ensures that critical structures are accurately mapped, enabling improved operational efficiency and safety in industrial settings. Figure 5 presents the reconstruction results of the proposed method on outdoor scenes from the ROI dataset. Our method demonstrates remarkable adaptability in complex outdoor environments, accurately reconstructing both geometric structures and texture details despite the presence of various environmental challenges. These challenges include lighting variations, vegetation occlusions, and irregular terrain, all of which can negatively impact other SLAM systems. Our method effectively mitigates these challenges, ensuring that both the structure and appearance of the environment are captured with high fidelity. Notably, the system handles dynamic objects such as pedestrians and vehicles with remarkable accuracy. This ability to maintain high reconstruction accuracy even in the presence of dynamic elements is critical for applications such as outdoor navigation, environmental modeling, and autonomous driving technologies. The resilience of our method to dynamic  
Figure 4: Comparison of mapping results in the outdoor scenarios of the ROI dataset (ROI-OD). Figure 3: Comparison of mapping results in the industrial scenarios of the ROI dataset (ROI-IND). interference ensures that it can be reliably used in real-world outdoor environments where unpredictable object movement is a frequent occurrence. For the quantitative mapping results, we compared the proposed method with LDAR-NeRF [31], SHINE [32], NeRF-LOAM, and LiV-GS using the MaiCity and Newer College datasets. Tables 1 and 2 present the comparative results on these datasets, with the highest values for each metric highlighted in bold.