Merging appearance-based spatial knowledge in multirobot systems

Abstract

This paper considers the merging of appearance-based spatial knowledge among robots having compatible visual sensing. Each robot is assumed to retain its knowledge in its individual long-term spatial memory where i) the place knowledge and their spatial relations are retained in an organized manner in place and map memories respectively; and ii) a ‘place’ refers to a spatial region as designated by a collection of associated appearances. In the proposed approach, each robot communicates with another robot, receives its memory and then merges the received knowledge with its own. The novelty of the merging process is that it is done in two stages: merging of place knowledge followed by the merging of map knowledge. As each robot's place memory is processed as a whole or in portions, the merging process scales easily with respect to the amount and overlap of the appearance data. Furthermore, the merging can be done in decentralized manner. Our experimental results with a team of three robots demonstrate that the resulting merged knowledge enables the robots to reason about learned places.