  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 rendering. Specifically, we
propose an adaptive expansion strategy that adds new or deletes noisy 3D
Gaussians 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. Project page: https://gs-slam.github.io/.
