Multi-robot Simultaneous Localization and Mapping using Particle Filters

This paper describes an on-line algorithm for multirobot simultaneous localization and mapping (SLAM). We take as our starting point the single-robot Rao-Blackwellized particle filter described in [1] and make two key generalizations. First, we extend the particle filter to handle multi-robot SLAM problems in which the initial pose of the robots is known (such as occurs when all robots start from the same location). Second, we introduce an approximation to solve the more general problem in which the initial pose of robots is not known a priori (such as occurs when the robots start from widely separated locations). In this latter case, we assume that pairs of robots will eventually ‘bump into’ one another, thereby determining their relative pose. We use this relative pose to initialize the filter, and combine the subsequent (and prior) observations from both robots into a common map. This algorithm has been experimentally validated using data from a team of four robots equipped with odometry and scanning laser range-finders.