Abstract
Deep Learning methods can deploy a fast, robust and lightweight model to solve the problem of 6-DOF camera relocalization in large-scale outdoor environments. However, two significant characteristics of captured images in a large-scale outdoor environment are moving objects, which should not include in the representation of an environment, and also motion blur which widely exists in the images captured with moving cameras. None of the existing approaches study and investigate these two problems in their method. This paper, for the first time, proposes a deep network architecture that is trained based on the knowledge achieved by combining deblurring and semantic segmentation modules and examines the effect of this combination on a challenging dataset. Results show approximately 20 and 50% improvement in camera position and orientation re-localization error respectively.
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