A 2D cooperative Range-Only SLAM problem is considered in this paper. In addition to odometry, available through noisy encoder readings on the actuated wheels, the robots measure the distances to a set of landmarks in unknown positions within the environment, as well as to other robots. Inter-landmark distances are not assumed to be available. The robots start at unknown locations, with their relative positions also assumed unknown. A Multi-Hypotheses Extended Kalman Filter, endowed with a Federated Information Sharing mechanism, is proposed to solve the problem in a computationally efficient way, without any delay in the initialization of landmark and robot position estimates. Simulation and experimental results are reported in the paper to demonstrate the effectiveness of the proposed approach, showing significant improvements in both steady-state and transient performance compared to the single-robot scenario
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