基于3D基础模型的分布式协作SLAM中的回环闭合
3D Foundation Model-Based Loop Closing for Decentralized Collaborative SLAM
AI总结：
本文提出基于3D基础模型的分布式协作SLAM回环闭合方法，通过整合基础模型提升多机器人建图的鲁棒性和效率。
摘要：
分布式协作同时定位与建图（C-SLAM）技术常常因机器人之间显著的视角变化而难以识别地图重叠。受近期3D基础模型的进展启发，这些模型能够在较大的视角差异下对齐图像，我们提出了一种稳健的回环闭合方法，利用这些模型来建立机器人间的测量。与需要在集中式地图中进行完整3D重建的资源密集型方法不同，我们的方法将基础模型整合到现有的SLAM流程中，实现了可扩展且稳健的多机器人建图。我们的贡献包括：（1）将3D基础模型整合到分布式C-SLAM中，以可靠地从单目图像对中估计相对姿态；（2）引入对这些相对姿态至关重要的稳健异常值抑制技术；（3）开发专门的位姿图优化公式，以高效解决尺度模糊性问题。我们评估了我们的方法与最先进的方法相比，证明了在定位和建图准确性上的改进，以及在计算和内存效率上的显著提升。这些结果突显了我们的方法在大规模多机器人场景中的应用潜力。
英文摘要：
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.
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