Simultaneous Localization and Mapping (SLAM) based on 3D Gaussian Splats (3DGS) has recently shown promise towards more accurate, dense 3D scene maps. However, existing 3DGS-based methods fail to address the global consistency of the scene via loop closure and/or global bundle adjustment. To this end, we propose LoopSplat, which takes RGB-D images as input and performs dense mapping with 3DGS submaps and frame-to-model tracking. LoopSplat triggers loop closure online and computes relative loop edge constraints between submaps directly via 3DGS registration, leading to improvements in efficiency and accuracy over traditional global-to-local point cloud registration. It uses a robust pose graph optimization formulation and rigidly aligns the submaps to achieve global consistency. Evaluation on the synthetic Replica and real-world TUM-RGBD, ScanNet, and ScanNet++ datasets demonstrates competitive or superior tracking, mapping, and rendering compared to existing methods for dense RGB-D SLAM.
基于3D高斯投影（3DGS）的同步定位与地图构建（SLAM）最近显示出实现更精确、密集的3D场景地图的潜力。然而，现有的基于3DGS的方法未能通过回环闭合和/或全局束调整来解决场景的全局一致性问题。为此，我们提出了LoopSplat，它以RGB-D图像作为输入，利用3DGS子地图和帧对模型跟踪进行密集映射。LoopSplat在线触发回环闭合，并直接通过3DGS配准计算子地图之间的相对回环边约束，相较于传统的全局到局部点云配准，提高了效率和准确性。它采用了一种稳健的位姿图优化方法，通过刚性对齐子地图来实现全局一致性。在合成的Replica数据集和真实世界的TUM-RGBD、ScanNet以及ScanNet++数据集上的评估显示，相较于现有的密集RGB-D SLAM方法，LoopSplat在跟踪、映射和渲染方面表现出竞争力或更优的性能。