This paper reports a partially decentralized implementation of an ExtendedKalman filter for the cooperative localization of a team of mobile robots withlimited onboard resources. Unlike a fully centralized scheme that requires, ateach timestep, information from the entire team to be gathered together and beprocessed by a single device, our algorithm only requires that the robotscommunicate with a central command unit at the time of a measurement update.Every robot only needs to propagate and update its own state estimate, whilethe central command unit maintains track of cross-covariances. Therefore, thecomputational and storage cost per robot in terms of the size of the team is oforder O(1). Moreover, when the system model is linear the algorithm is robustto occasional in-network communication link failures while the updatedestimates of the robots receiving the update message are of minimum variance atthat given timestep. For problems with nonlinear robot models, our algorithmunder message drop-out provides a suboptimal solutions because of thelinearization approximation similar to the Extended Kalman filter model. Wedemonstrate the performance of the algorithm in simulation.
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