多摄像头结合高斯点云实现高保真实时建图，比单目系统更鲁棒全面。
MCGS-SLAM: A Multi-Camera SLAM Framework Using Gaussian Splatting for High-Fidelity Mapping
- 用多视角RGB图像融合构建可连续优化的高斯地图
- 在合成与真实数据集上轨迹精度和重建质量均优于单目基线
- 适合需要广角覆盖的机器人与自动驾驶场景
近期密集式SLAM研究主要聚焦于单目系统，常以牺牲鲁棒性和几何覆盖为代价。我们提出MCGS-SLAM，首个基于3D高斯点云（3DGS）的纯RGB多摄像头SLAM系统。不同于依赖稀疏地图或惯性数据的已有方法，MCGS-SLAM将多视角密集RGB输入融合为统一、持续优化的高斯地图。通过多摄像头捆绑调整（MCBA）联合优化位姿与深度，利用密集光度与几何残差；尺度一致性模块则借助低秩先验，在不同视图间实现度量对齐。系统仅需RGB输入，支持大规模实时运行。在合成与真实数据集上的实验表明，MCGS-SLAM始终获得准确轨迹与逼真重建，通常优于单目基线。尤其得益于多摄像头带来的宽视场，系统能重建单目系统遗漏的侧向区域，这对安全自主运行至关重要。这些结果凸显了多摄像头高斯点云SLAM在机器人与自动驾驶中高保真建图的巨大潜力。
原文摘要 · Abstract (English)
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.
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