Semantic understanding plays a crucial role in Dense Simultaneous Localization and Mapping (SLAM). Recent advancements that integrate Gaussian Splatting into SLAM systems have demonstrated its effectiveness in generating high-quality renderings. Building on this progress, we propose SGS-SLAM which provides precise 3D semantic segmentation alongside high-fidelity reconstructions. Specifically, we propose to employ multi-channel optimization during the mapping process, integrating appearance, geometric, and semantic constraints with key-frame optimization to enhance reconstruction quality. Extensive experiments demonstrate that SGS-SLAM delivers state-of-the-art performance in camera pose estimation, map reconstruction, and semantic segmentation. It outperforms existing methods by a large margin meanwhile preserves real-time rendering ability.

翻译：语义理解在稠密同时定位与地图构建（SLAM）中扮演着关键角色。近期将高斯溅射集成至SLAM系统的研究进展已证明其在生成高质量渲染方面的有效性。在此基础上，我们提出SGS-SLAM框架，该框架能够在实现高保真重建的同时提供精确的三维语义分割。具体而言，我们提出在建图过程中采用多通道优化策略，将外观、几何和语义约束与关键帧优化相结合，以提升重建质量。大量实验表明，SGS-SLAM在相机位姿估计、地图重建和语义分割方面均实现了最先进的性能。该方法在显著超越现有方法的同时，保持了实时渲染能力。