Consensus Of Stochastic Maps

In this paper, we consider the agreement problem of estimating the locations of landmarks observed by multiple agents in the absence of a global frame of reference. Maximum likelihood estimators are derived from scenarios of independent and mutual landmark estimation. A triangle-based hypothesis test is proposed for the detection of common landmarks across the coordinate systems of the agents and is followed by a formulation of triangle matching as a linear assignment problem. A landmark agreement model is also proposed with consideration that the agents may observe both common and uncommon landmarks. Simulation examples are provided illustrating the proposed landmark estimation and triangle matching approaches.