Multi-robot SLAM for large scale map building using relative information of local maps

This paper deals with Multi-Robot SLAM for large scale map building. Specifically, each robot estimates a local map using EKF, and we merge these local maps into a global map. In this paper, we provide a new RLS based algorithm for map merging. First, we transform local maps into relative information which is considered as measurements for the global map. Then, we update the state estimate by RLS considering the weighting of measurements, which is determined by error propagation from the EKF SLAM. We prove the convergence of the error covariance matrix in this algorithm. In experimental results, we confirm the validity of the proposed algorithm and correctness of derived theorems for the convergence.