Dynamic Master-Slave Distributed Algorithm for Cooperative Localization with Low Computational Cost

Extended Kalman filter (EKE) is prevailing for cooperative localization, where the cross-covariance (representing the correlation of estimated position) determines the benefit quantity from the local measurement. In this paper, the covariance factor set is adopted for cross-covariance maintaining in distributed architecture. During two exteroceptive measurements, the covariance factor set is propagated independently in each agent. When the updating information from the measuring agent is received by the other agents, a temporary relative master-slave relationship is determined between them. The updated correlation is retained in the receiver (slave) agent as a covariance factor. Meanwhile, the counterpart in the measuring (master) agent is set as identify matrix. The operation of matrix decomposition and the feedback for covariance update from slave to master is saved. Thus, the computational consumption and communication burden are reduced. It is significant for real-time cooperative localization.