# Stereo Gaussian Splatting with Adaptive Scene Depth Estimation for Semantic Mapping

## TL;DR

This paper introduces StereoGS-SLAM, a new system for mapping environments using stereo cameras and 3D Gaussian Splatting, improving accuracy and efficiency in robotics and AR.

## Contribution

The novel contribution is StereoGS-SLAM, which uses stereo inputs and adaptive depth estimation for real-time semantic mapping without active depth sensors.

## Key findings

- StereoGS-SLAM achieves robust and scale-consistent reconstruction using passive RGB stereo inputs.
- The hybrid keyframe selection strategy improves keyframe diversity and maintains real-time optimization.
- Experiments show competitive performance in localization, rendering, and semantic reconstruction compared to existing systems.

## Abstract

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.