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.

翻译：同步定位与建图（SLAM）是机器人学中的关键任务，使系统能够自主导航并理解复杂环境。当前的SLAM方法主要依赖几何线索进行建图与定位，但往往难以保证语义一致性，尤其在动态或高度密集的场景中。为克服这一局限，我们提出了STAMICS，一种将语义信息与三维高斯表示相结合的新方法，以提升定位与建图的精度。STAMICS包含三个核心组件：用于高保真重建的三维高斯场景表示、强制时序语义一致性的基于图结构的聚类技术，以及支持未知物体分类的开放词汇系统。大量实验表明，STAMICS在相机位姿估计与地图质量方面均有显著提升，在降低重建误差的同时超越了现有最优方法。代码将公开提供。