Swarm-ReID: Decentralized Self-Adaptive Gallery Construction for Multi-Robot Open-World Person Re-Identification

Abstract

Swarm perception enables a robot swarm to collectively sense and understand the environment by integrating sensory inputs from individual robots. We explore its application to person re-identification (re-id), the task of recognizing previously observed individuals. Traditional re-id systems rely on static offline galleries, which restricts their use in open-world scenarios where new identities appear over time. In robotics, most methods address single-robot re-id in person-following tasks, limiting scalability to multi-person settings, while swarm perception studies largely overlook the role of re-id algorithms. To address these gaps, we propose Swarm-ReID, an unsupervised method for decentralized swarm re-identification. Our method introduces mechanisms for robot-to-robot communication and informed movement strategies, enabling the swarm to collaboratively construct adaptive galleries online without centralized control. Simulations across diverse environments, number of people, swarm sizes, communication protocols, and exploration behaviors show that Swarm-ReID consistently outperforms existing swarm perception methods. Our results highlight how communication and informed movement improve recognition performance, establishing Swarm-ReID as a state-of-the-art method for open-world multi-robot person re-identification.