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.

翻译：本文研究了具有不同测量能力的异构机器人系统中的协同定位问题。在实际应用中，异构传感器会导致有向且稀疏的测量拓扑结构，而现有的大多数协同定位方法依赖于具有严格多邻居几何约束的多边定位。为克服这一限制，我们仅利用相邻机器人之间的相互测量和里程计信息，实现了成对相对定位。首先开发了一种统一的数据驱动自适应相对定位估计器，以处理异构和单向测量。基于收敛的相对定位估计值，进一步设计了一种分布式位姿耦合协同定位策略，该策略保证了在弱连通有向测量拓扑下的协同定位能力，代表了现有结果中约束条件最宽松的情况。所提方法独立于具体控制任务，并通过编队控制应用和真实世界实验进行了验证。