arXiv 2509.141912026-09-09cs.ROcs.CV

MCGS-SLAM：一种基于高斯点划的多相机SLAM框架用于高保真的地图构建

MCGS-SLAM: A Multi-Camera SLAM Framework Using Gaussian Splatting for High-Fidelity Mapping

Department of Mathematics, ETH Zurich（苏黎世联邦理工学院数学系）；Department of Mechanical and Process Engineering, ETH Zurich（苏黎世联邦理工学院机械与工艺工程系）；Department of Informatics, University of Zurich（苏黎世大学信息系）；Institute for Photogrammetry, University of Stuttgart（斯图加特大学摄影测量研究所）；Computer Vision and Geometry Group, ETH Zurich（苏黎世联邦理工学院计算机视觉与几何组）；Microsoft Spatial AI Lab（微软空间AI实验室）；Computer Vision Research Group, University of Amsterdam（阿姆斯特丹大学计算机视觉研究组）

Zhihao Cao, Hanyu Wu, Li Wa Tang, Zizhou Luo, Wei Zhang, Marc Pollefeys, Zihan Zhu, Martin R. Oswald

备注：: Accepted to IEEE International Conference on Robotics and Automation (ICRA) 2026. Code: https://github.com/mcgs-slam/mcgs-slam

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

MCGS-SLAM通过多相机和高斯点划技术实现高保真的SLAM，提供精确轨迹和逼真重建，优于单目方法。

摘要：

近年来，密集SLAM的研究主要集中在单目设置上，往往以鲁棒性和几何覆盖为代价。我们提出了MCGS-SLAM，这是首个基于3D高斯点划（3DGS）的纯RGB多相机SLAM系统。与以往依赖稀疏地图或惯性数据的方法不同，MCGS-SLAM将来自多个视角的密集RGB输入融合到一个统一的、持续优化的高斯地图中。多相机捆绑调整（MCBA）通过密集光度和几何残差共同优化姿态和深度，而一个尺度一致性模块利用低秩先验强制在不同视角之间保持度量一致性。该系统支持RGB输入，并在大规模情况下保持实时性能。在合成和现实数据集上的实验表明，MCGS-SLAM能够持续产生准确的轨迹和逼真重建，通常优于单目基线。值得注意的是，多相机输入的宽视角能够重建单目设置所遗漏的侧视区域，这对于安全的自主操作至关重要。这些结果突显了多相机高斯点划SLAM在机器人和自动驾驶中高保真地图构建的潜力。

英文摘要：

Recent progress in dense SLAM has primarily targeted monocular setups, often at the expense of robustness and geometric coverage. We present MCGS-SLAM, the first purely RGB-based multi-camera SLAM system built on 3D Gaussian Splatting (3DGS). Unlike prior methods relying on sparse maps or inertial data, MCGS-SLAM fuses dense RGB inputs from multiple viewpoints into a unified, continuously optimized Gaussian map. A multi-camera bundle adjustment (MCBA) jointly refines poses and depths via dense photometric and geometric residuals, while a scale consistency module enforces metric alignment across views using low-rank priors. The system supports RGB input and maintains real-time performance at large scale. Experiments on synthetic and real-world datasets show that MCGS-SLAM consistently yields accurate trajectories and photorealistic reconstructions, usually outperforming monocular baselines. Notably, the wide field of view from multi-camera input enables reconstruction of side-view regions that monocular setups miss, critical for safe autonomous operation. These results highlight the promise of multi-camera Gaussian Splatting SLAM for high-fidelity mapping in robotics and autonomous driving.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。