arXiv 2503.031922025-05-13cs.RO

分布式可认证正确的距离辅助SLAM

Distributed Certifiably Correct Range-Aided SLAM

University of California, Los Angeles（加州大学洛杉矶分校）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Sensing and Robotics for Infrastructure Laboratory（基础设施感知与机器人实验室）；Computer Science and AI Lab (CSAIL) at the Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）；MIT-WHOI Joint Program in Oceanography/Applied Ocean Science & Engineering（麻省理工学院-WHOI联合海洋学/应用海洋科学与工程计划）；Robust Autonomy Lab（自主性实验室）

Alexander Thoms, Alan Papalia, Jared Velasquez, David M. Rosen, Sriram Narasimhan

备注：: 8 pages, 3 figures, accepted to the 2025 International Conference on Robotics and Automation (ICRA). This version includes minor clerical edits to the published version in the conference proceedings

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AI总结：

针对通信受限多智能体RA-SLAM缺乏分布式最优性保证的问题，提出DCORA，基于Riemannian Staircase获得可认证全局最优解，并在真实与合成数据上验证其性能。

摘要：

可靠的同步定位与地图构建（SLAM）算法对于安全关键型自主导航必不可少。在通信受限的多智能体场景中，导航系统越来越多地使用点对点距离传感器，因为这类传感器能够提供低带宽需求且数据关联已知的测量。这些系统的状态估计问题表现为距离辅助（RA）SLAM。然而，用于求解RA-SLAM问题的分布式算法缺乏对所返回估计质量的形式化保证。为此，我们提出首个能够高效恢复可认证全局最优解的RA-SLAM分布式算法。我们的算法——分布式可认证正确RA-SLAM（DCORA）——通过Riemannian Staircase方法实现这一点，其中将为分布式可认证正确位姿图优化开发的计算流程推广到RA-SLAM问题。我们在真实世界多智能体数据集上证明了DCORA的有效性，其绝对轨迹误差与最先进的集中式可认证正确RA-SLAM算法相当。此外，我们使用合成数据对RA-SLAM问题的结构进行了参数研究，揭示了常见参数如何影响DCORA的性能。

英文摘要：

Reliable simultaneous localization and mapping (SLAM) algorithms are necessary for safety-critical autonomous navigation. In the communication-constrained multi-agent setting, navigation systems increasingly use point-to-point range sensors as they afford measurements with low bandwidth requirements and known data association. The state estimation problem for these systems takes the form of range-aided (RA) SLAM. However, distributed algorithms for solving the RA-SLAM problem lack formal guarantees on the quality of the returned estimate. To this end, we present the first distributed algorithm for RA-SLAM that can efficiently recover certifiably globally optimal solutions. Our algorithm, distributed certifiably correct RA-SLAM (DCORA), achieves this via the Riemannian Staircase method, where computational procedures developed for distributed certifiably correct pose graph optimization are generalized to the RA-SLAM problem. We demonstrate DCORA's efficacy on real-world multi-agent datasets by achieving absolute trajectory errors comparable to those of a state-of-the-art centralized certifiably correct RA-SLAM algorithm. Additionally, we perform a parametric study on the structure of the RA-SLAM problem using synthetic data, revealing how common parameters affect DCORA's performance.

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