arXiv 2606.244892026-06-24cs.RO

基于对象的分布式位姿图黎曼优化用于多机器人SLAM

Decentralized Pose Graph Riemannian Optimization for Object-based Multi-Robot SLAM

College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海智能自主系统研究院）；Division of Decision and Control Systems, School of EECS, KTH Royal Institute of Technology（瑞典皇家理工学院电气工程与计算机科学学院决策与控制系统系）

Yixian Zhao, Yan Huang, Yang Xu, Liang Li, Jinming Xu

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AI总结：

提出一种完全分布式的黎曼优化框架，通过共识机制解耦对象级多机器人SLAM中的位姿图优化问题，并利用近似牛顿法加速收敛，在SE(d)流形上保持几何一致性，显著降低通信开销并提升精度。

摘要：

位姿图优化（PGO）是网络化多机器人同时定位与地图构建（SLAM）中状态估计的关键后端组件。在基于对象的多机器人SLAM中，问题变得更加紧密耦合，因为机器人必须联合估计其轨迹以及由多个智能体观测到的持久对象的位姿。现有的分布式解决方案通常假设通信图与物理交互拓扑紧密匹配，这在通信稀疏、间歇或时变的实际部署中具有限制性。本文提出了一种完全分布式的黎曼优化框架，用于基于对象的多机器人PGO，该框架通过共识机制解耦耦合的估计问题，从而支持灵活的通信拓扑。为了在有限的通信预算下提高收敛性，我们进一步开发了一种分布式近似牛顿方案，该方案利用局部二阶信息，同时在SE(d)流形上直接操作以保持几何一致性，并建立了收敛到黎曼一阶驻点的理论保证，提供了局部条件数分析以解释近似二阶信息相对于一阶黎曼下降的优势。所提出的方法在不牺牲估计精度的情况下减少了迭代次数和通信开销。在公共基准、大规模仿真和真实多机器人实验上的广泛评估表明，该方法在精度、运行时效率、跨网络拓扑的可扩展性以及对通信故障的鲁棒性方面均有提升。

英文摘要：

Pose graph optimization (PGO) is a key back-end component for state estimation in networked multi-robot simultaneous localization and mapping (SLAM). In object-based multi-robot SLAM, the problem becomes more tightly coupled because robots must jointly estimate both their trajectories and the poses of persistent objects observed by multiple agents. Existing decentralized solutions often assume that the communication graph closely matches the physical interaction topology, which is restrictive in realistic deployments where communication is sparse, intermittent, or time-varying. This paper presents a fully decentralized Riemannian optimization framework for object-based multi-robot PGO that decouples the coupled estimation problem via a consensus mechanism, enabling flexible communication topologies. To improve convergence under limited communication budgets, we further develop a distributed approximate-Newton scheme that exploits local second-order information while operating directly on the SE(d) manifold to preserve geometric consistency, and we establish the convergence to Riemannian first-order stationary points and provide a local condition-number analysis explaining the benefit of approximate second-order information over first-order Riemannian descent. The resulting method reduces iteration count and communication overhead without sacrificing estimation accuracy. Extensive evaluations on public benchmarks, large-scale simulations, and real-world multi-robot experiments demonstrate improved accuracy, runtime efficiency, scalability across network topologies, and robustness to communication failures.

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