提出统一数据驱动方法，实现异构机器人稀疏通信下的协同定位。
Integrated cooperative localization of heterogeneous measurement swarm: A unified data-driven method
- 基于互测距与里程计信息，实现邻近机器人的成对相对定位
- 可在弱连通有向拓扑下保证定位收敛，条件比现有方法更宽松
- 无需依赖具体任务，已通过编队控制和真实实验验证
本文研究异构机器人系统中不同测量能力带来的协同定位（CL）问题。实际中，异构传感器导致有向且稀疏的测量拓扑，而多数现有方法依赖多边定位并受限于多邻域几何条件。为此，仅利用相互测量和里程计信息，实现邻近机器人间的成对相对定位（RL）。首先提出统一的数据驱动自适应RL估计器，以处理异构且单向的测量。基于收敛的RL估计，进一步设计分布式位姿耦合式CL策略，在弱连通有向测量拓扑下保证定位成功，这是现有方法中最宽松的条件。该方法不依赖特定控制任务，已在编队控制应用及真实实验中验证。
原文摘要 · Abstract (English)
The cooperative localization (CL) problem in heterogeneous robotic systems with different measurement capabilities is investigated in this work. In practice, heterogeneous sensors lead to directed and sparse measurement topologies, whereas most existing CL approaches rely on multilateral localization with restrictive multi-neighbor geometric requirements. To overcome this limitation, we enable pairwise relative localization (RL) between neighboring robots using only mutual measurement and odometry information. A unified data-driven adaptive RL estimator is first developed to handle heterogeneous and unidirectional measurements. Based on the convergent RL estimates, a distributed pose-coupling CL strategy is then designed, which guarantees CL under a weakly connected directed measurement topology, representing the least restrictive condition among existing results. The proposed method is independent of specific control tasks and is validated through a formation control application and real-world experiments.
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