RGS-SLAM：基于单次密集初始化的鲁棒高斯溅射SLAM系统

摘要

我们提出RGS-SLAM——一种基于高斯泼溅的鲁棒性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中基于残差驱动的致密化阶段。与通过残差揭示缺失几何特征而逐步添加高斯元素的方式不同，RGS-SLAM通过对稠密多视角对应关系进行一次性三角测量（这些对应关系源自经过置信感知内点分类器优化的DINOv3描述符），在优化前生成分布均匀且具有结构感知的高斯种子。这种初始化方式稳定了早期建图过程，使收敛速度提升约20%，在纹理丰富和杂乱场景中实现更高的渲染保真度，同时保持与现有GS-SLAM管道的完全兼容。在TUM RGB-D和Replica数据集上的测试表明，相较于最先进的高斯泼溅与基于点的SLAM系统，RGS-SLAM在定位与重建精度方面达到相当或更优水平，并保持最高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：基于单次密集初始化的鲁棒高斯溅射SLAM系统

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

摘要

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