A distributed maximum likelihood algorithm for multi-robot mapping

In the last decade, several algorithms, usually based on information filtering techniques, have been proposed to address multi-robot mapping problem. Less interest has been devoted to investigate a parallel or distributed organization of such algorithms in the perspective of multi-robot exploration. In this paper, we propose a distributed algorithm for map estimation based on Gauss-Seidel relaxation. The complete map is shared among independent tasks running on each robot, which integrate the independent robot measurements in local submaps, and a server, which stores contour nodes separating the submaps. Each task updates its local submap and periodically checks for inter-robot data associations. Gauss-Seidel relaxation is performed independently on each robot and afterwards on the contour nodes set on the server. Results illustrate the potential and flexibility of the new approach.