Localization using ambiguous bearings from radio signal strength

In this paper, we consider the problem of localizing a mobile robot team capable of measuring ambiguous bearing estimates using received signal strength indicators (RSSI) from radio transceivers (e.g., ZigBee). More precisely, we formulate a robust bearing estimator that leverages anisotropic but symmetric radiation profiles to identify π-periodic bearing estimates between pairs of communicating agents. Utilizing these ambiguous bearing estimates along with compass and odometric measurements, we present a Multi-hypothesis Extended Kalman Filter-based framework that exploits agent motion to resolve the resulting state ambiguity and achieve localization up to translation. Despite the combinatoric nature of our problem, for teams exhibiting certain topological properties, we show that only two initial hypotheses need consideration to recover state. Experimental results from a small team of differential drive robots are presented to demonstrate the utility of our approach. Simulation results are also presented that explore our framework's convergence properties for larger team sizes.