Cooperative Multi-Robot Map Merging with Task Distribution

抄録

This paper presents cooperative multi-robot map merging process for mobile robots operating in indoor environments. Recent methods in single robot SLAM has been very successful in mapping small to medium sized areas but suffers from optimizing maps for larger areas due to drift. Multi-robot systems can simplify the process of mapping and exploring large environments by distributing tasks between the robot teams. By merging the local maps from the robots, a global representation of the area can be used for different applications. We present an online multi-robot mapping based on multiple pose graphs. The main contributions of this paper is a an online map merging process that formulates relationship between multiple pose graphs using visual and scan features to give a consistent global multi-robot mapping solution. The pose graph information are shared to all robots and a central workstation is used where the local pose graphs are optimized globally. The process allows our system for data association and efficient loop closure even for large areas. A task distribution method is used for complete autonomous exploration in unknown environments. The proposed system is tested with real robots and the results are discussed.