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One of the most essential prerequisites behind a successful task execution ofa team of agents is to accurately estimate and track their poses. We consider acooperative multi-agent positioning problem where each agent performssingle-agent positioning until it encounters some other agent. Upon theencounter, the two agents measure their relative pose, and exchange particleclouds representing their poses. We propose a cooperative positioning algorithmwhich fuses the received information with the locally available measurementsand infers an agent's pose within Bayesian framework. The algorithm is scalableto multiple agents, has relatively low computational complexity, admitsdecentralized implementation across agents, and imposes relatively mildrequirements on communication coverage and bandwidth. The experiments indicatethat the proposed algorithm considerably improves single-agent positioningaccuracy, reduces the convergence time of a particle cloud and, unlike itssingle-agent positioning counterpart, exhibits immunity to an impedingfeature-scarce and symmetric environment layout.

Algorithm for Decentralized Cooperative Positioning of Multiple Autonomous Agents