GLC-SLAM：基于高效循环闭合的高斯点云SLAM
GLC-SLAM: Gaussian Splatting SLAM with Efficient Loop Closure
AI总结：
GLC-SLAM通过全局优化和高效循环闭合解决大规模环境中的SLAM漂移问题，提升密集建图性能。
摘要：
三维高斯点云（3DGS）因其在密集同时定位与建图（SLAM）中的应用而受到广泛关注，能够实现实时渲染和高保真建图。然而，现有的基于3DGS的SLAM方法往往存在累积跟踪误差和地图漂移问题，特别是在大规模环境中。为了解决这些问题，我们引入了GLC-SLAM，一种集成全局优化相机姿态和场景模型的高斯点云SLAM系统。我们的方法采用帧到模型的跟踪，并通过全局到局部的策略触发分层循环闭合以最小化漂移积累。通过将场景划分为3D高斯子地图，我们实现了在大规模场景中通过循环修正后的高效地图更新。此外，我们的不确定性最小化关键帧选择策略优先选择观察更多有价值3D高斯点的关键帧以增强子地图优化。在各种数据集上的实验结果表明，GLC-SLAM在跟踪和建图性能上优于或与最先进的密集RGB-D SLAM系统相比具有优越或竞争性的表现。
英文摘要：
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.
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