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 upon acceptance.

翻译：本文提出$\textbf{GS-SLAM}$，首次将三维高斯表示应用于同步定位与地图构建系统，在效率与精度之间实现了更优平衡。与近期采用神经隐式表示的SLAM方法相比，本方法利用可实时微分的泼溅渲染管线，显著加速了地图优化与RGB-D重渲染过程。具体而言，我们提出一种自适应扩展策略，通过新增或剔除噪声三维高斯体，高效重建新观测场景几何并改进已观测区域的地图质量——该策略对于将三维高斯表示扩展至全场景重建（而非现有方法中仅合成静态物体）具有关键意义。此外，在姿态追踪过程中，我们设计了一种由粗到精的有效技术以选择可靠的三维高斯表示优化相机位姿，从而降低计算开销并实现鲁棒估计。在Replica与TUM-RGBD数据集上，本方法与现有实时方法相比展现出具有竞争力的性能。源代码将在论文接收后公开。