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.

翻译：三维高斯泼溅已成为高质量三维渲染的一种有前景的技术，这促使人们越来越关注将3DGS集成到逼真SLAM系统中。然而，现有方法面临诸多挑战，例如高斯图元冗余、持续优化过程中的遗忘问题，以及在单目情况下因缺乏深度信息而难以初始化图元。为了实现高效且逼真的建图，我们提出了RP-SLAM，一种基于三维高斯泼溅的视觉SLAM方法，适用于单目和RGB-D相机。RP-SLAM将相机位姿估计与高斯图元优化解耦，并包含三个关键组成部分。首先，我们提出了一种高效的增量式建图方法，通过自适应采样和高斯图元滤波来实现场景的紧凑且准确的表示。其次，提出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。最后，针对单目情况，提出了一种基于稀疏点云的单目关键帧初始化方法，以提高高斯图元的初始化精度，这为后续优化提供了几何基础。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，实现了最先进的地图渲染精度。