Cooperative localization for mobile agents: a recursive decentralized algorithm based on Kalman filter decoupling

TL;DR

This paper introduces a novel decentralized cooperative localization algorithm for mobile agents that efficiently maintains state estimate correlations using intermediate variables, closely approximating centralized Kalman filter performance with minimal communication.

Contribution

It proposes a decentralized EKF-based algorithm that propagates intermediate variables instead of cross-covariances, enabling accurate localization with reduced communication overhead.

Findings

Algorithm closely matches centralized EKF accuracy

Reduces communication by only exchanging data during relative measurements

Demonstrates effective localization in simulated multi-agent scenarios

Abstract

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.