## 5 Conclusions  
In this paper, we have proposed a dynamic scene-oriented LiDAR fusion SLAM framework based on 3D Gaussian Splatting. By integrating LiDAR point clouds, 3D semantic segmentation, and 3 DGS rendering, our method addresses dynamic interference and large-scale scene reconstruction challenges. Through its use of a sliding window \begin{table}
\begin{tabular}{l c c c c} \hline
**Window** & **Completion \(\downarrow\)** & **Accuracy \(\downarrow\)** &
\begin{tabular}{c} **Chamfer-L1** \\ **Distance \(\downarrow\)** \\ \end{tabular} & **F-Score \(\uparrow\)** \\ \hline No Window & 3.86 & 3.18 & 3.52 & 91.34 \\ \(w=10\) & 3.25 & 2.79 & 2.71 & 94.12 \\ \(w=20\) & **2.67** & **2.21** & **2.35** & **96.17** \\ \(w=30\) & 3.49 & 2.54 & 2.86 & 93.58 \\ \(w=40\) & 3.74 & 2.99 & 3.67 & 90.81 \\ \hline \end{tabular}
\end{table} 
Table 4: Results of ablation study on the MaiCity dataset with different sliding window lengths. Where lower values are better, and bold represents the best value. \begin{table}
\begin{tabular}{l c c c c} \hline
**Rendering** & **Completion \(\downarrow\)** & **Accuracy \(\downarrow\)** &
\begin{tabular}{c} **Chamfer-L1** \\ **Distance \(\downarrow\)** \\ \end{tabular} & **F-Score \(\uparrow\)** \\ \hline w/o \(\mathcal{L}_{c}\) & 4.27 & 3.45 & 3.79 & 92.36 \\ w/o \(\mathcal{L}_{d}\) & 3.54 & 2.87 & 3.05 & 94.28 \\ w/o \(\mathcal{L}_{edge}\) & 5.13 & 3.96 & 4.76 & 90.89 \\ FULL & **2.67** & **2.21** & **2.35** & **96.17** \\ \hline \end{tabular}
\end{table} 
Table 5: Results of ablation study on the MaiCity dataset with different rendering losses. Where lower values are better, and bold represents the best value. strategy, dynamic point cloud occupancy management, and novel loss functions, our system shows significantly improved accuracy and robustness in dynamic environments. Experiments on public and custom datasets show that our system outperforms state-of-the-art methods, particularly in dynamic outdoor and industrial environments. Our approach effectively reconstructs static backgrounds, removes dynamic artifacts, and maintains scene fidelity. Its ability to handle dynamic objects while preserving scene features demonstrates potential for real-world applications such as autonomous navigation and intelligent manufacturing. While the proposed system achieves optimal performance with a window length of \(w=20\), further improvements could be made for other window lengths. For \(w=10\), the system could benefit from enhanced sensitivity to local scene variations, potentially through adaptive window sizing or multiscale analysis. For larger window lengths such as \(w=30\) and \(w=40\), reducing computational complexity and improving dynamic object removal efficiency are critical. Techniques such as parallel processing, optimized data structures, and advanced dynamic object detection algorithms could be explored to address these challenges. Despite these advancements, there are a number of limitations. The computational complexity of the proposed system can be an issue when processing large-scale or highly dense environments. While it performs well in many cases, delays can occur with large datasets or dense scenes, potentially affecting real-time performance. Additionally, reliance on accurate dynamic object detection and segmentation may cause issues in unstructured or ambiguous environments, where segmentation accuracy may degrade and transient objects may still introduce errors. Future work will focus on improving computational efficiency, developing optimized segmentation models, and enhancing adaptability to challenging environments. We also plan to explore additional sensor modalities such as radar in order to increase robustness in conditions such as low visibility or extreme weather. Conceptualization, Y.Z. and G.F.; Methodology and Software, Y.Z. ; Validation, Y.Z., G.J. and M.L. ; Formal Analysis and Investigation, Y.Z. ; Writing--Original Draft Preparation, Y.Z. ; Writing--Review & Editing, Y.Z.