Distributed Multi Robot Simultaneous Localization and Mapping with Consensus Particle Filtering

Abstract

These paper present distributed computations of join probabilities SLAM with consensus particle filtering algorithm. We consider groups of robot observe an unknown environment and build the global maps. In this paper, every local map is a global map. Global maps build by using its own information and the information obtained from the other robots. We use particles likelihood as transfer parameters. The information can be transferred to other robot if reach global agreement on particles weight. The global agreement can be obtained by using the Consensus Particle Filtering algorithm, computation is done locally on each robot. After reaching an agreement, then the global map can be built and information of the global map is owned on each robot.