An Infrastructure-less, Control-Independent Solution to Relative Localisation of a Team of Mobile Robots using Ranging Measurements
This work addresses the practical problem of localising robot teams in unstructured environments where infrastructure is unavailable and motion control is undesirable, offering a fully decentralised and anchor-less solution.
The paper presents a decentralised cooperative localisation algorithm for robot teams that requires no fixed infrastructure, no motion control for observability, and only local odometry, sparse ranging, and short-range communication. The method maintains multiple hypotheses to ensure robustness under unobservable conditions and achieves accurate relative localisation in simulations and real-world experiments.
The ability to localise teams of robots is essential for applications ranging from robotic fleets in unstructured environments to cooperative control and navigation tasks. In such contexts, fixed infrastructure is often unavailable, deployments must be fast and flexible, and system requirements must be minimal. We present a decentralised cooperative localisation algorithm that addresses all these challenges at once. The method is anchor-less, fully decentralised, and, unlike most existing approaches, does not require controlling the robots motion to ensure team observability. It relies only on local odometry, sparse inter-agent ranging measurements, and short-range communication, all of which are widely available in practice. The algorithm adopts a multi-hypothesis Bayesian framework that maintains the entire set of feasible solutions, ensuring robustness under transient unobservable conditions. Moreover, through information sharing, each agent benefits from the estimates of the entire group, even in partially connected conditions.