An Infrastructure-Less, Control-Independent Solution to Relative Localisation of a Team of Mobile Robots Using Ranging Measurements
Paolo Golinelli, Tommaso Faraci, Daniele Fontanelli
AI summary
Problem
Existing cooperative localization methods rely on fixed anchor points or strict motion control to guarantee observability, making them impractical for fast, flexible deployments in unstructured or dynamic environments.
Approach
The method maintains all feasible relative pose configurations as clusters of particles and updates them collaboratively using only local odometry and sparse inter-agent distance measurements.
Key results
- Proposes the MHDCL algorithm for anchor-less, decentralized cooperative localization
- Maintains multiple feasible pose hypotheses to handle transient unobservability
- Enables cooperative localization in partially connected networks via cluster sharing
- Demonstrates robust performance in realistic multi-robot case studies
Why it matters
Provides a scalable, low-deployment alternative for multi-robot fleets operating in dynamic or infrastructure-free environments.
Abstract
The ability to localise teams of robots is essential for applications ranging from robotic fleets in unstructured en- vironments 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.