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Vision-based localization on robots and vehicles remains unsolved when extreme appearance change and viewpoint change are present simultaneously. The current state of the art approaches to this challenge either deal with only one of these two problems; for example FAB-MAP (viewpoint invariance) or SeqSLAM (appearanceinvariance), or use extensive training within the test environment, an impractical requirement in many application scenarios. In this paper we significantly improve the viewpointdoi:10.1109/cvprw.2015.7301395 dblp:conf/cvpr/MilfordLSSPUSLL15 fatcat:jf4u3qwsxjfgvgqd75yoagvtvi