Decentralized Collaborative Simultaneous Localization And Mapping (C-SLAM) techniques often struggle to identify map overlaps due to significant viewpoint variations among robots. Motivated by recent advancements in 3D foundation models, which can register images despite large viewpoint differences, we propose a robust loop closing approach that leverages these models to establish inter-robot measurements. In contrast to resource-intensive methods requiring full 3D reconstruction within a centralized map, our approach integrates foundation models into existing SLAM pipelines, yielding scalable and robust multi-robot mapping. Our contributions include: (1) integrating 3D foundation models to reliably estimate relative poses from monocular image pairs within decentralized C-SLAM; (2) introducing robust outlier mitigation techniques critical to the use of these relative poses; and (3) developing specialized pose graph optimization formulations that efficiently resolve scale ambiguities. We evaluate our method against state-of-the-art approaches, demonstrating improvements in localization and mapping accuracy, alongside significant gains in computational and memory efficiency. These results highlight the potential of our approach for deployment in large-scale multi-robot scenarios.

翻译：去中心化协同即时定位与地图构建技术常因机器人间显著的视角差异而难以识别地图重叠区域。受近期三维基础模型进展的启发——这类模型能够在大视角差异下实现图像配准——我们提出一种鲁棒的闭环检测方法，该方法利用此类基础模型建立机器人间的测量关联。相较于需要在集中式地图中进行完整三维重建的资源密集型方法，我们的方案将基础模型集成至现有SLAM流程中，实现了可扩展且鲁棒的多机器人建图。我们的贡献包括：(1) 在去中心化C-SLAM框架中集成三维基础模型，实现单目图像对间相对位姿的可靠估计；(2) 提出对使用此类相对位姿至关重要的鲁棒异常值抑制技术；(3) 开发能高效解决尺度模糊性的专用位姿图优化模型。通过对比现有先进方法进行实验评估，本方法在定位与建图精度上均取得提升，同时在计算与内存效率方面获得显著增益。这些结果凸显了该方法在大规模多机器人场景中部署的应用潜力。