A centralized-equivalent decentralized implementation of Extended Kalman Filters for cooperative localization

Abstract

We present a novel decentralized cooperative localization algorithm for mobile robots. The proposed algorithm is a decentralized implementation of a centralized Extended Kalman Filter for cooperative localization. In this algorithm, instead of propagating cross-covariance terms, each robot propagates new intermediate local variables that can be used in an update stage to create the required propagated cross-covariance terms. Whenever there is a relative measurement in the network, the algorithm declares the robot making this measurement as the interim master. By acquiring information from the interim landmark, the robot the relative measurement is taken from, the interim master can calculate and broadcast a set of intermediate variables which each robot can then use to update its estimates to match that of a centralized Extended Kalman Filter for cooperative localization. Once an update is done, no further communication is needed until the next relative measurement. The communication graph can be a time-varying directed graph with the only requirement that it should have a spanning tree rooted at the interim master.