We introduce RGS-SLAM, a robust Gaussian-splatting SLAM framework that replaces the residual-driven densification stage of GS-SLAM with a training-free correspondence-to-Gaussian initialization. Instead of progressively adding Gaussians as residuals reveal missing geometry, RGS-SLAM performs a one-shot triangulation of dense multi-view correspondences derived from DINOv3 descriptors refined through a confidence-aware inlier classifier, generating a well-distributed and structure-aware Gaussian seed prior to optimization. This initialization stabilizes early mapping and accelerates convergence by roughly 20\%, yielding higher rendering fidelity in texture-rich and cluttered scenes while remaining fully compatible with existing GS-SLAM pipelines. Evaluated on the TUM RGB-D and Replica datasets, RGS-SLAM achieves competitive or superior localization and reconstruction accuracy compared with state-of-the-art Gaussian and point-based SLAM systems, sustaining real-time mapping performance at up to 925 FPS. Project page:https://breeze1124.github.io/rgs-slam-project-page/

翻译：本文提出RGS-SLAM，一种鲁棒的高斯溅射SLAM框架，它采用免训练的对应点-高斯初始化方法，取代了GS-SLAM中基于残差的致密化阶段。不同于通过残差揭示缺失几何结构而逐步添加高斯体的方法，RGS-SLAM对源自DINOv3描述符的密集多视角对应点进行一次性三角化（这些描述符通过置信度感知的内点分类器进行优化），从而在优化前生成分布均匀且具有结构感知的高斯种子先验。这种初始化方式稳定了早期建图过程，并将收敛速度提升了约20%，在纹理丰富和杂乱场景中实现了更高的渲染保真度，同时与现有GS-SLAM流程保持完全兼容。在TUM RGB-D和Replica数据集上的评估表明，相较于最先进的高斯及基于点的SLAM系统，RGS-SLAM在定位与重建精度上达到竞争性或更优的水平，并维持高达925 FPS的实时建图性能。项目页面：https://breeze1124.github.io/rgs-slam-project-page/