基于2D高斯泼溅的全局一致RGB-D SLAM
Globally Consistent RGB-D SLAM with 2D Gaussian Splatting
AI总结：
本文提出2DGS-SLAM系统，利用2D高斯泼溅的深度一致渲染特性实现精确位姿优化与几何重建，并结合MASt3R实现高效闭环检测和全局一致建图。
摘要：
近年来，基于3D高斯泼溅的RGB-D SLAM在高保真3D重建方面表现出卓越性能。然而，深度渲染一致性的缺失和低效的闭环检测限制了其几何重建质量以及在线执行全局一致建图的能力。本文提出了2DGS-SLAM，一个使用2D高斯泼溅作为地图表示的RGB-D SLAM系统。通过利用2D变体具有深度一致性的渲染特性，我们提出了一种精确的相机位姿优化方法，并实现了几何精确的3D重建。此外，我们利用3D基础模型MASt3R实现了高效的闭环检测和相机重定位，并通过维护局部活动地图实现高效的地图更新。实验表明，与现有基于渲染的SLAM方法相比，我们的2DGS-SLAM方法在跟踪精度、表面重建质量和全局地图一致性方面均表现更优，同时保持了高保真图像渲染并提升了计算效率。
英文摘要：
Recently, 3D Gaussian splatting-based RGB-D SLAM displays remarkable performance of high-fidelity 3D reconstruction. However, the lack of depth rendering consistency and efficient loop closure limits the quality of its geometric reconstructions and its ability to perform globally consistent mapping online. In this paper, we present 2DGS-SLAM, an RGB-D SLAM system using 2D Gaussian splatting as the map representation. By leveraging the depth-consistent rendering property of the 2D variant, we propose an accurate camera pose optimization method and achieve geometrically accurate 3D reconstruction. In addition, we implement efficient loop detection and camera relocalization by leveraging MASt3R, a 3D foundation model, and achieve efficient map updates by maintaining a local active map. Experiments show that our 2DGS-SLAM approach achieves superior tracking accuracy, higher surface reconstruction quality, and more consistent global map reconstruction compared to existing rendering-based SLAM methods, while maintaining high-fidelity image rendering and improved computational efficiency.
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