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This paper deals with large scale map building by using Multi-Robot in D-SLAM framework. D-SLAM is a decoupled solution to the SLAM problem. Specifically, it is demonstrated that each robot estimates a local map using EKF, and these local maps are merged into a global map. In this paper, we propose a new RLS-based algorithm for map merging in this D-SLAM framework. 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. The convergence of the error covariance matrix in this algorithm can be proven. In experimental results, we confirm the validity of the proposed algorithm and correctness of derived theorems for the convergence.

Multi-robot D-SLAM for Large Scale Map Building and Its Control Theoretical Analysis