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In this paper, we present an attention mechanism for mobile robots to face the problem of place categorization. Our approach, which is based on active perception, aims to capture images with characteristic or distinctive details of the environment that can be exploited to improve the efficiency (quickness and accuracy) of the place categorization. To do so, at each time moment, our proposal selects the most informative view by controlling the line-of-sight of the robot's camera through a pan-only unit. We root our proposal on an information maximization scheme, formalized as a next-best-view problem through a Markov Decision Process (MDP) model. The latter exploits the short-time estimated navigation path of the robot to anticipate the next robot's movements and make consistent decisions. We demonstrate over two datasets, with simulated and real data, that our proposal generalizes well for the two main paradigms of place categorization (object-based and image-based), outperforming typical camera-configurations (fixed and continuously-rotating) and a pure-exploratory approach, both in quickness and accuracy. [ABSTRACT FROM AUTHOR]

Abstract Object recognition is a cornerstone task in autonomous and/or assistance systems like robots, autonomous vehicles, or those assisting to visually impaired, aiming to achieve a certain level of understanding of their surroundings. Probabilistic models, such as Conditional Random Fields (CRFs), have been successfully applied to this end given their ability to exploit contextual and situation information, e.g. a bowl is typically found in a cabinet and not in a night-stand. In this work we propose to evolve CRFs into Ontology-based Conditional Random Fields (ob CRFs), which define a multi-level structure where each level assigns a category with different granularity to the same set of objects. For example, a level could assign to an object the category appliance or furniture , while the next one could categorize it into the tv , microwave , cabinet , or table types. In this way, general categorizations can guide the classification into more specialized ones (and vice versa), improving recognition success, and mitigating the CRFs limitations when modeling a high number of object categories (shared, in general, by Machine Learning techniques). To set the categories in each level we propose to mimic the hierarchical structure of ontologies, where categories are naturally codified following a subsumption ordering. This leads us to the second advantage of ob CRFs : the multi-labeling of objects provides a richer understanding of the scene, which can be leveraged for accomplishing high-level tasks (e.g. object search or scheduling). Our approach has been tested with scenes from two state-of-the-art datasets: Robot@Home and Cornell-RGBD , outperforming the results provided by standard CRFs. Highlights • A novel model for object recognition called Ontology-based CRF is proposed. • It uses a multiple-level structure mimicking the subsumption ordering of Ontologies. • Each level jointly categorizes the same set of objects with different granularity. • Granularity ranges from specialized types (oven, fridge) to general ones (appliance). • The proposal has been assessed with the Robot@Home and Cornell-RGBD datasets. [ABSTRACT FROM AUTHOR]