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Map Merging of Oriented Topological Semantic Maps

In this paper we propose a solution for the problem of merging together partial spatial models relying on our recently introduced Oriented Topological Semantic Maps (OTSM). This problem arises when a group of robots cooperatively explore an environment, and each one independently builds a partial map that must be combined with the others into a full map. Our methodology is inspired by the Warrington's Object Recognition Model, a cognitive model hypothesizing two post-sensory categorical stages working together for object recognition. Accordingly, we use two stages to compare different maps and match them together based on their mutual resemblance. Our method is complemented by a scoring system to measure the likelihood that two vertices in different OTSMs correspond to the same vertex, despite possible errors in labeling, orientation, or topological structure. Our methodology is validated in a simulation informed by an ongoing real robot implementation, thus allowing us to perform various experiments with carefully controlled error sources.