In this paper, we introduce 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.
在这篇论文中，我们介绍了首次在同时定位与建图（SLAM）系统中利用3D高斯表示的GS-SLAM。它促进了效率和准确性之间的更好平衡。与最近采用神经隐式表示的SLAM方法相比，我们的方法利用了一个实时可微分的溅射渲染管线，为地图优化和RGB-D重渲染提供了显著的加速。具体来说，我们提出了一种自适应扩展策略，以添加新的或删除噪声3D高斯，从而高效地重建新观测到的场景几何，并改进之前观测区域的映射。这一策略对于将3D高斯表示扩展到重建整个场景而不是在现有方法中合成静态对象至关重要。此外，在姿态跟踪过程中，设计了一种有效的粗到细技术，用于选择可靠的3D高斯表示来优化相机姿态，从而减少运行时间并提供鲁棒的估计。我们的方法在Replica、TUM-RGBD数据集上与现有最先进的实时方法相比达到了竞争性能。源代码将很快发布。