Stereo Gaussian Splatting with Adaptive Scene Depth Estimation for Semantic Mapping

Simultaneous Localization and Mapping (SLAM) is a fundamental capability in robotics and augmented reality. However, achieving accurate geometric reconstruction and consistent semantic understanding in complex environments remains challenging. Although recent neural implicit representations have improved reconstruction quality, they often suffer from high computational cost and the forgetting phenomenon during online mapping. In this paper, we propose StereoGS-SLAM, a stereo semantic SLAM framework based on 3D Gaussian Splatting (3DGS) for explicit scene representation. Unlike existing approaches, StereoGS-SLAM operates on passive RGB stereo inputs without requiring active depth sensors. An adaptive depth estimation strategy is introduced to dynamically refine Gaussian scales based on real-time stereo depth estimates, ensuring robust and scale-consistent reconstruction. In addition, we propose a hybrid keyframe selection strategy that integrates motion-aware selection with lightweight random sampling to improve keyframe diversity and maintain stable, real-time optimization. Experimental evaluations demonstrate that StereoGS-SLAM achieves consistent and competitive localization, rendering, and semantic reconstruction performance compared with recent 3DGS-based SLAM systems.