Cooperative localization under message dropouts via a partially decentralized EKF scheme

For a team of mobile robots with limited onboard resources, we propose a partially decentralized implementation of an extended Kalman filter for cooperative localization. In the proposed algorithm, unlike a fully centralized scheme that requires, at each timestep, information from the entire team to be gathered together and be processed by a single device, we only require that the robots communicate with a central command unit at the time of a measurement update. In addition, the computational and storage cost per robot in terms of the size of the team is reduced to O(1). Moreover, the algorithm is robust to occasional in-network communication link failures while the estimation update of the robots receiving the update message is of minimum variance. We demonstrate the performance of the algorithm in simulations.