Abstract
LEGO-SLAM is the first framework to achieve real-time, open-vocabulary mapping within a 3DGS-based SLAM system. By distilling high-dimensional language embeddings into a compact, scene-adaptive 16-dimensional feature space, we drastically reduce memory usage and enable 15 FPS performance. Our approach includes a language-guided pruning strategy that significantly reduces Gaussian counts without quality loss, along with an efficient loop detection method that reuses mapping features for robust tracking in novel environments.
Method
System Overview
