We describe an approach to the automated construction of visual maps of an unknown environment. These maps take the form of image-based “walk-throughs” rather than 2D or 3D models. Our approach is based on the selection of informative viewpoints within the environment. These viewpoints are locations in the environment associated with views containing maximal visual interest. This approach to environment representation is analogous to image compression. Our goal is to obtain a set of representative views resembling those that would be selected by a human observer given the same task. Our computational procedure is inspired by models of human visual attention appearing in the literature on human psychophysics. We make use of the underlying edge structure of a scene, as it is largely unaffected by variations in illumination. Our implementation uses a mobile robot to traverse the environment, and then builds an image-based virtual representation of the environment, only keeping the views whose responses were highest. We demonstrate the effectiveness of our attention operator on both single images, and in viewpoint selection within an unknown environment.
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