STAMICS: Splat, Track And Map with Integrated Consistency and Semantics for Dense RGB-D SLAM
This addresses the limitation of geometric-only SLAM in dynamic or densely populated scenes for robotics applications, representing an incremental advance by combining existing techniques with semantic integration.
The paper tackles the problem of ensuring semantic consistency in dense RGB-D SLAM for robotics, introducing STAMICS, which integrates semantic information with 3D Gaussian representations to improve camera pose estimation and map quality, outperforming state-of-the-art methods.
Simultaneous Localization and Mapping (SLAM) is a critical task in robotics, enabling systems to autonomously navigate and understand complex environments. Current SLAM approaches predominantly rely on geometric cues for mapping and localization, but they often fail to ensure semantic consistency, particularly in dynamic or densely populated scenes. To address this limitation, we introduce STAMICS, a novel method that integrates semantic information with 3D Gaussian representations to enhance both localization and mapping accuracy. STAMICS consists of three key components: a 3D Gaussian-based scene representation for high-fidelity reconstruction, a graph-based clustering technique that enforces temporal semantic consistency, and an open-vocabulary system that allows for the classification of unseen objects. Extensive experiments show that STAMICS significantly improves camera pose estimation and map quality, outperforming state-of-the-art methods while reducing reconstruction errors. Code will be public available.