3D Gaussian Splatting has emerged as a promising technique for high-quality 3D rendering, leading to increasing interest in integrating 3DGS into realism SLAM systems. However, existing methods face challenges such as Gaussian primitives redundancy, forgetting problem during continuous optimization, and difficulty in initializing primitives in monocular case due to lack of depth information. In order to achieve efficient and photorealistic mapping, we propose RP-SLAM, a 3D Gaussian splatting-based vision SLAM method for monocular and RGB-D cameras. RP-SLAM decouples camera poses estimation from Gaussian primitives optimization and consists of three key components. Firstly, we propose an efficient incremental mapping approach to achieve a compact and accurate representation of the scene through adaptive sampling and Gaussian primitives filtering. Secondly, a dynamic window optimization method is proposed to mitigate the forgetting problem and improve map consistency. Finally, for the monocular case, a monocular keyframe initialization method based on sparse point cloud is proposed to improve the initialization accuracy of Gaussian primitives, which provides a geometric basis for subsequent optimization. The results of numerous experiments demonstrate that RP-SLAM achieves state-of-the-art map rendering accuracy while ensuring real-time performance and model compactness.
三维高斯点云技术（3D Gaussian Splatting）已经成为实现高质量三维渲染的一种有前景的方法，这使得将3DGS集成到真实感SLAM系统中的研究兴趣日益增长。然而，现有方法面临着高斯基元冗余、连续优化过程中遗忘问题以及在单目场景中由于缺乏深度信息而难以初始化基元等挑战。为实现高效且光真实感的映射，我们提出了RP-SLAM，这是一种基于三维高斯点云的视觉SLAM方法，适用于单目和RGB-D相机。RP-SLAM通过将相机位姿估计与高斯基元优化解耦，提出了一种高效的增量映射方法，通过自适应采样和高斯基元过滤实现对场景的紧凑且准确表示；引入了一种动态窗口优化方法，以缓解遗忘问题并提高地图一致性；针对单目场景，设计了一种基于稀疏点云的单目关键帧初始化方法，以提高高斯基元初始化的精度，为后续优化提供几何基础。大量实验结果表明，RP-SLAM在确保实时性能和模型紧凑性的同时，实现了业界领先的地图渲染精度。