Consistent batch fusion for decentralized multi-robot cooperative localization

This paper investigates the problem of decentralized multi-robot cooperative localization. This problem involves collaboratively estimating the poses of a group of robots with respect to a common reference coordinate system using robot-to-robot relative measurements and intermittent absolute measurements in a distributed manner. To address this problem, we present a decentralized fusion method that enables batch updating to handle relative measurements from multiple robots simultaneously. This method can improve both the accuracy and computational efficiency of cooperative localization. To reduce communication costs and reliance on connectivity, we do not maintain the inter-robot state correlations. Instead, we adopt a covariance intersection (CI) technique to design an upper bound that replaces unknown joint correlations. We propose an optimization method to determine a tight upper bound for the correlations in the joint update. The consistency and convergence of our proposed algorithm is theoretically analyzed. Furthermore, we conduct Monte Carlo numerical simulations and real-world experiments to demonstrate that the proposed method outperforms existing approaches in terms of both accuracy and consistency.