We present SGS-SLAM, the first semantic visual SLAM system based on Gaussian Splatting. It incorporates appearance, geometry, and semantic features through multi-channel optimization, addressing the oversmoothing limitations of neural implicit SLAM systems in high-quality rendering, scene understanding, and object-level geometry. We introduce a unique semantic feature loss that effectively compensates for the shortcomings of traditional depth and color losses in object optimization. Through a semantic-guided keyframe selection strategy, we prevent erroneous reconstructions caused by cumulative errors. Extensive experiments demonstrate that SGS-SLAM delivers state-of-the-art performance in camera pose estimation, map reconstruction, precise semantic segmentation, and object-level geometric accuracy, while ensuring real-time rendering capabilities.

翻译：我们提出SGS-SLAM，首个基于高斯泼溅的语义视觉SLAM系统。该系统通过多通道优化融合外观、几何和语义特征，有效克服了神经隐式SLAM系统在高保真渲染、场景理解和物体级几何建模中的过平滑局限。我们创新性地引入语义特征损失函数，弥补了传统深度损失与颜色损失在物体优化中的不足。通过语义引导的关键帧选择策略，有效防止了累积误差导致的错误重建。大量实验表明，SGS-SLAM在相机位姿估计、地图重建、精确语义分割及物体级几何精度方面均达到领先水平，同时保持实时渲染能力。