Distributed Cooperative SLAM using an Information Consensus Filter

This paper aims to improve the performance of cooperative simultaneous localization and mapping (SLAM) by fusing multiple vehicles’ local maps in a distributed manner. The information consensus filter is used to communicate and fuse the agents’ local maps. We show that when the information consensus filter is used, the team of vehicles reaches consensus on map estimates, assuming a connected and balanced communication topology. Furthermore, the vehicles’ local map estimates obtained using the information consensus filter are equivalent to the estimated map produced in a centralized filter; this fact is shown analytically and verified by simulation. Furthermore, we compare the simulation results of 1) the information consensus filter based SLAM with 2) centralized SLAM and 3) covariance intersection based SLAM to demonstrate both the strengths and drawbacks of cooperative SLAM based on the information consensus filter.

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