Cooperative localization for mobile agents: a recursive decentralized algorithm based on Kalman filter decoupling
This addresses localization challenges for mobile robot teams, offering a decentralized solution that reduces communication overhead, though it is incremental as it builds on existing Kalman filter methods.
The paper tackles cooperative localization for mobile agents by developing a decentralized algorithm that avoids propagating cross-covariance terms, using intermediate variables to match centralized Extended Kalman Filter performance with reduced communication.
We consider cooperative localization technique for mobile agents with communication and computation capabilities. We start by provide and overview of different decentralization strategies in the literature, with special focus on how these algorithms maintain an account of intrinsic correlations between state estimate of team members. Then, we present a novel decentralized cooperative localization algorithm that is a decentralized implementation of a centralized Extended Kalman Filter for cooperative localization. In this algorithm, instead of propagating cross-covariance terms, each agent 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 agent making this measurement as the interim master. By acquiring information from the interim landmark, the agent 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.