Lost Shopping! Monocular Localization in Large Indoor Spaces

In this paper we propose a novel approach to localization in very large indoor spaces (i.e., 200+ store shopping malls) that takes a single image and a floor plan of the environment as input. We formulate the localization problem as inference in a Markov random field, which jointly reasons about text detection (localizing shop's names in the image with precise bounding boxes), shop facade segmentation, as well as camera's rotation and translation within the entire shopping mall... (read more)

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