RGS-SLAM：基於單次密集初始化的魯棒高斯濺射SLAM系統

摘要

我们提出RGS-SLAM——一种基于高斯点云的鲁棒性SLAM框架，该框架采用免训练的对应关系高斯初始化方法，取代了GS-SLAM中基于残差驱动的稠密化阶段。与传统方法通过残差揭示缺失几何特征而逐步添加高斯点不同，RGS-SLAM通过对经置信度感知内点分类器优化的DINOv3描述符所生成的稠密多视角对应关系进行一次性三角测量，在优化前即可生成分布均匀且感知结构的高斯点云初始种子。这种初始化策略不仅稳定了早期建图过程，还将收敛速度提升约20%，在纹理丰富和杂乱场景中实现更高渲染保真度，同时保持与现有GS-SLAM流程的完全兼容。在TUM RGB-D和Replica数据集上的测试表明，相较于当前最先进的高斯点云与基于点云的SLAM系统，RGS-SLAM在定位与重建精度方面达到相当或更优水平，并维持最高达925帧/秒的实时建图性能。

English

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.

RGS-SLAM：基於單次密集初始化的魯棒高斯濺射SLAM系統

RGS-SLAM: Robust Gaussian Splatting SLAM with One-Shot Dense Initialization

摘要

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