## 1 Introduction  
With the advancement of Simultaneous Localization and Mapping (SLAM) [1, 2, 3, 4, 5, 6, 7], SLAM systems oriented towards multi-sensor fusion are gaining prominence in robotic perception, applications, and embodied intelligence as well as in motion control. Dense SLAM, which integrates LiDAR and visual sensors, has attracted significant attention over sparse SLAM due to its superior scene reconstruction capabilities and its potential for semantic segmentation. Real-time dense SLAM systems capable of reconstructing high-fidelity models with rich texture details are crucial for enhancing the comprehensive capabilities of robotic systems, particularly in complex and dynamic environments where accurate 3D reconstructions are essential for effective navigation, interaction, and task execution. The fusion of LiDAR and visual data allows systems to leverage the complementary strengths of both sensor types, where LiDAR provides precise depth measurements and visual sensors offer rich color and texture information. Recently, 3D Gaussian Splatting (3 DGS) has demonstrated significant performance in multi-view high-fidelity reconstruction and rapid rendering, leading to the emergence of 3 DGS-based SLAM systems [8; 9; 10; 11; 12]. 3 DGS has proven to be particularly effective in scenarios that require fine details, such as in environments with complex structures, dynamic scenes, and large-scale maps. However, existing 3 DGS-SLAM systems face two major challenges that significantly affect their practical utility. First, most of these systems focus on single-sensor setups such as LiDAR or RGB/RGB-D cameras, and lack effective sensor fusion techniques that would enable them to fully exploit the strengths of each sensor modality. While individual sensors such as LiDAR or cameras can provide valuable data, combining them in a meaningful way remains an open challenge in real-world environments. Second, these systems struggle with the dynamic objects commonly encountered in real-world environments. Dynamic objects such as moving vehicles, pedestrians, and other obstacles introduce significant errors in pose estimation and cause map reconstruction artifacts. In certain cases, dynamic interference can lead to complete failure in scene reconstruction, making the SLAM system unreliable for critical applications such as autonomous driving, robotics in indoor environments, and augmented reality. To address these challenges, this paper proposed a novel 3 DGS-based SLAM system which effectively integrates the multiple sensor modalities of LiDAR and vision for enhanced robustness against dynamic interference and improved dynamic object removal. Our key innovation lies in the integration of a hybrid uncertainty-based 3D segmentation method, a dynamic rendering loss function, and a sliding window-based keyframe fusion strategy. Dynamic interference, particularly in the case of LiDAR, primarily occurs during point-cloud initialization, where moving objects may be incorrectly incorporated into the 3D map. To mitigate this, we introduce a point cloud segmentation strategy that leverages visual uncertainty estimation and 3D object detection techniques to identify and eliminate dynamic points from the point cloud. Additionally, we apply uncertainty-based segmentation to the Gaussian components, effectively removing dynamic points from the 3D Gaussian scene representation. This dynamic object removal strategy ensures that the system maintains a high level of accuracy in static map reconstruction even in the presence of moving objects. To further improve the proposed system's ability to handle dynamic environments, we introduce a keyframe fusion strategy based on a sliding window optimization approach. This method minimizes the Gaussian rendering loss by selectively fusing keyframes that provide complementary information from both LiDAR and visual inputs. The sliding window optimization ensures that the most relevant frames are considered, thereby reducing computational overhead while maintaining reconstruction accuracy.