Information Theoretic Approaches for Next Best View Planning in Active Computer Vision

This paper describes an information theoretic approach for next best view planning in active state estimation, and its application to three computer vision tasks. In active state estimation, the state estimation process contains sensor actions which affect the state observation, and therefore the final state estimate. We use the information theoretic measure of mutual information to quantify the information content in this estimate. The optimal sensor actions are those that are expected to maximally increase the information content of the estimate. This action selection process is then applied to three seperate computer vision tasks: object recognition, object tracking and object reconstruction. Each task is formulated as an active state estimation problem. In these tasks, a given sensor action describes a camera position, or view. The information theoretic framework allows us to determine the next best view, i.e. the view that best supports the computer vision task. We show the benefits of next best view planning in several experiments, in which we compare the estimation error produced by planned views with the error produced by regularly sampled or unchanging views.

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