Active multi-view object recognition: A unifying view on online feature selection and view planning.

Many robots are limited in their operating capabilities, both computational and energy-wise. A strong desire exists to keep computation cost and energy consumption to a minimum when executing tasks like object recognition with a mobile robot. Adaptive action selection is a paradigm, offering great flexibility in trading off the cost of acquiring information against making robust and reliable inference under uncertainty. In this paper, we study active multi-view object recognition and describe an information-theoretic framework that combines and unifies two common techniques: online feature selection for reducing computational costs and view planning for resolving ambiguities and occlusions. Our algorithm adaptively chooses between the two strategies of either selecting only the features that are most informative to the recognition, or moving to a new viewpoint that optimally reduces the expected uncertainty on the identity of the object. This two step process allows us to keep overall computation cost minimal but simultaneously increase recognition accuracy. Extensive empirical studies on a large RGB-D dataset, and with two different feature sets, have validated the effectiveness of the proposed framework. Our experiments show that dynamic feature selection alone reduces the computation time at runtime 2.5–6 times and, when combining it with viewpoint selection, we significantly increase the recognition accuracy on average by 8%–18% absolute compared to systems that do not use these two strategies. By establishing a link between active object recognition and change detection, we were further able to use our framework for the follow-up task of actively detecting object change. Furthermore, we have successfully demonstrated the framework’s applicability to a low-powered quadcopter platform with limited operating time. [ABSTRACT FROM AUTHOR]