A Probabilistic On-Line Mapping Algorithm for Teams of Mobile Robots

Abstract

An efficient probabilistic algorithm for the concurrent mapping and localization problem that arises in mobile robotics is presented. The algorithm addresses the problem in which a team of robots builds a map on-line while simultaneously accommodating errors in the robots’ odometry. At the core of the algorithm is a technique that combines fast maximum likelihood map growing with a Monte Carlo localizer that uses particle representations. The combination of both yields an on-line algorithm that can cope with large odometric errors typically found when mapping environments with cycles. The algorithm can be implemented in a distributed manner on multiple robot platforms, enabling a team of robots to cooperatively generate a single map of their environment. Finally, an extension is described for acquiring three-dimensional maps, which capture the structure and visual appearance of indoor environments in three dimensions.