SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM

Understand

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.