  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.
