  One of the most essential prerequisites behind a successful task execution of
a team of agents is to accurately estimate and track their poses. We consider a
cooperative multi-agent positioning problem where each agent performs
single-agent positioning until it encounters some other agent. Upon the
encounter, the two agents measure their relative pose, and exchange particle
clouds representing their poses. We propose a cooperative positioning algorithm
which fuses the received information with the locally available measurements
and infers an agent's pose within Bayesian framework. The algorithm is scalable
to multiple agents, has relatively low computational complexity, admits
decentralized implementation across agents, and imposes relatively mild
requirements on communication coverage and bandwidth. The experiments indicate
that the proposed algorithm considerably improves single-agent positioning
accuracy, reduces the convergence time of a particle cloud and, unlike its
single-agent positioning counterpart, exhibits immunity to an impeding
feature-scarce and symmetric environment layout.
