In this paper, we introduce $\textbf{GS-SLAM}$ that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping (SLAM) system. It facilitates a better balance between efficiency and accuracy. Compared to recent SLAM methods employing neural implicit representations, our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D re-rendering. Specifically, we propose an adaptive expansion strategy that adds new or deletes noisy 3D Gaussian in order to efficiently reconstruct new observed scene geometry and improve the mapping of previously observed areas. This strategy is essential to extend 3D Gaussian representation to reconstruct the whole scene rather than synthesize a static object in existing methods. Moreover, in the pose tracking process, an effective coarse-to-fine technique is designed to select reliable 3D Gaussian representations to optimize camera pose, resulting in runtime reduction and robust estimation. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica, TUM-RGBD datasets. The source code will be released soon.

翻译：本文提出了$\textbf{GS-SLAM}$，首次将3D高斯表示应用于同步定位与地图构建（SLAM）系统，从而更好地兼顾了效率与精度。与近期采用神经隐式表示的SLAM方法相比，本方法利用实时可微泼溅渲染管线，显著加速了地图优化与RGB-D重渲染过程。具体而言，我们提出了一种自适应扩展策略，通过新增或剔除噪声3D高斯单元，高效重构新观测场景几何结构，并改善先前观测区域的地图构建。该策略是将3D高斯表示从现有方法中仅用于静态物体合成，扩展到完整场景重构的关键所在。此外，在姿态追踪过程中，我们设计了一种有效的由粗到精技术，选取可靠的3D高斯表示来优化相机位姿，从而降低运行时间并实现鲁棒估计。在Replica与TUM-RGBD数据集上的实验表明，本方法达到了与现有实时方法相当的竞争性性能。源代码即将公开。