We propose GSO-SLAM, a real-time monocular dense SLAM system that leverages Gaussian scene representation. Unlike existing methods that couple tracking and mapping with a unified scene, incurring computational costs, or loosely integrate them with well-structured tracking frameworks, introducing redundancies, our method bidirectionally couples Visual Odometry (VO) and Gaussian Splatting (GS). Specifically, our approach formulates joint optimization within an Expectation-Maximization (EM) framework, enabling the simultaneous refinement of VO-derived semi-dense depth estimates and the GS representation without additional computational overhead. Moreover, we present Gaussian Splat Initialization, which utilizes image information, keyframe poses, and pixel associations from VO to produce close approximations to the final Gaussian scene, thereby eliminating the need for heuristic methods. Through extensive experiments, we validate the effectiveness of our method, showing that it not only operates in real time but also achieves state-of-the-art geometric/photometric fidelity of the reconstructed scene and tracking accuracy.

翻译：我们提出了GSO-SLAM，一种利用高斯场景表示的实时单目稠密SLAM系统。现有方法要么将跟踪与建图耦合在一个统一的场景中，导致计算成本高昂；要么将其与结构良好的跟踪框架松散集成，引入冗余。与之不同，我们的方法将视觉里程计（VO）与高斯泼溅（GS）进行双向耦合。具体而言，我们的方法在期望最大化（EM）框架内构建联合优化，使得无需额外计算开销即可同时优化VO导出的半稠密深度估计与GS表示。此外，我们提出了高斯泼溅初始化方法，该方法利用来自VO的图像信息、关键帧位姿以及像素关联，生成与最终高斯场景高度近似的初始表示，从而消除了对启发式方法的需求。通过大量实验，我们验证了所提方法的有效性，结果表明它不仅能够实时运行，而且在重建场景的几何/光度保真度以及跟踪精度方面均达到了最先进的水平。