3D Gaussian Splatting (3DGS) has gained significant attention for its application in dense Simultaneous Localization and Mapping (SLAM), enabling real-time rendering and high-fidelity mapping. However, existing 3DGS-based SLAM methods often suffer from accumulated tracking errors and map drift, particularly in large-scale environments. To address these issues, we introduce GLC-SLAM, a Gaussian Splatting SLAM system that integrates global optimization of camera poses and scene models. Our approach employs frame-to-model tracking and triggers hierarchical loop closure using a global-to-local strategy to minimize drift accumulation. By dividing the scene into 3D Gaussian submaps, we facilitate efficient map updates following loop corrections in large scenes. Additionally, our uncertainty-minimized keyframe selection strategy prioritizes keyframes observing more valuable 3D Gaussians to enhance submap optimization. Experimental results on various datasets demonstrate that GLC-SLAM achieves superior or competitive tracking and mapping performance compared to state-of-the-art dense RGB-D SLAM systems.
3D 高斯投影（3D Gaussian Splatting, 3DGS）因其在密集同步定位与地图构建（SLAM）中的应用，能够实现实时渲染和高保真地图构建，受到了广泛关注。然而，现有基于 3DGS 的 SLAM 方法通常会因累积的跟踪误差和地图漂移问题而受限，特别是在大规模环境中。为了解决这些问题，我们提出了 GLC-SLAM，这是一种整合了相机姿态和场景模型全局优化的高斯投影 SLAM 系统。我们的方法采用帧对模型跟踪，并通过全局到局部策略触发分层回环闭合，以最小化漂移累积。通过将场景划分为 3D 高斯子图，我们在大场景中实现了回环校正后的高效地图更新。此外，我们的最小化不确定性的关键帧选择策略优先选择观测到更多重要 3D 高斯的关键帧，以增强子图优化。多个数据集上的实验结果表明，GLC-SLAM 在跟踪和地图构建性能上优于或与最先进的密集 RGB-D SLAM 系统具有竞争力。