This study proposes the use of a split covariance intersection algorithm (Split-CI) for decentralized multirobot cooperative\nlocalization. In the proposed method, each robot maintains a local cubature Kalman filter to estimate its own pose in a predefined\ncoordinate frame.When a robot receives pose information from neighbouring robots, it employs a Split-CI based approach to fuse\nthis received measurement with its local belief. The computational and communicative complexities of the proposed algorithm\nincrease linearly with the number of robots in the multirobot systems (MRS).The proposed method does not require fully connected\nsynchronous communication channels between robots; in fact, it is applicable for MRS with asynchronous and partially connected\ncommunication networks. The pose estimation error of the proposed method is bounded. As the proposed method is capable\nof handling independent and interdependent information of the estimations separately, it does not generate overconfidence state\nestimations.The performance of the proposed method is compared with several multirobot localization approaches.The simulation\nand experiment results demonstrate that the proposed algorithm outperforms the single-robot localization algorithms and achieves\napproximately the same estimation accuracy as the centralized cooperative localization approach, but with reduced computational\nand communicative cost.
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