Integrated cooperative localization of heterogeneous measurement swarm: A unified data-driven method

TL;DR

This paper introduces a unified data-driven cooperative localization method for heterogeneous robotic swarms, enabling accurate localization with minimal measurement requirements and weak connectivity conditions.

Contribution

It develops a pairwise relative localization estimator and a distributed pose-coupling strategy that work under directed, sparse measurement topologies, improving flexibility over existing methods.

Findings

Effective in formation control scenarios

Validated through real-world experiments

Works under weakly connected directed measurement graphs

Abstract

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.