Active-Semantic Localization with a Single Consumer-Grade Camera

This study addressed the problem of active localization, which requires massive computation. To solve the problem, we developed abstracted measurements that consist of qualitative metrics estimated by a single camera. These are contextual representations consisting of perceived landmarks and their spatial relations, and they can be shared by humans and robots. Next, we enhanced the Markov localization method to support contextual representations with which a robot's location can be sufficiently estimated. In contrast to passive methodologies, our approach actively uses the greedy technique to select a robot's action and improve localization results. The experiment was carried out in an indoor environment, and results indicate that the proposed active-semantic localization yields more efficient localization.