Semantic-aware 3D scene reconstruction is essential for autonomous robots to perform complex interactions. Semantic SLAM, an online approach, integrates pose tracking, geometric reconstruction, and semantic mapping into a unified framework, shows significant potential. However, existing systems, which rely on 2D ground truth priors for supervision, are often limited by the sparsity and noise of these signals in real-world environments. To address this challenge, we propose GSFF-SLAM, a novel dense semantic SLAM system based on 3D Gaussian Splatting that leverages feature fields to achieve joint rendering of appearance, geometry, and N-dimensional semantic features. By independently optimizing feature gradients, our method supports semantic reconstruction using various forms of 2D priors, particularly sparse and noisy signals. Experimental results demonstrate that our approach outperforms previous methods in both tracking accuracy and photorealistic rendering quality. When utilizing 2D ground truth priors, GSFF-SLAM achieves state-of-the-art semantic segmentation performance with 95.03\% mIoU, while achieving up to 2.9$\times$ speedup with only marginal performance degradation.

本文研究了一个重要问题，即现有语义SLAM系统在实际环境中面临的稀疏和噪声信号的限制。为此，提出了GSFF-SLAM，一种基于3D高斯点云的新型密集语义SLAM系统，它利用特征场实现外观、几何体和N维语义特征的联合渲染。实验结果表明，该方法在跟踪精度和照片级真实感渲染质量上均优于此前的方法，同时在使用2D真实值先验时，实现了95.03\%的mIoU语义分割性能，速度提升达到2.9倍。 
  

GSFF-SLAM：基于特征场的3D语义高斯点云SLAM