  Outdoor visual localization is a crucial component to many computer vision
systems. We propose an approach to localization from images that is designed to
explicitly handle the strong variations in appearance happening between daytime
and nighttime. As revealed by recent long-term localization benchmarks, both
traditional feature-based and retrieval-based approaches still struggle to
handle such changes. Our novel localization method combines a state-of-the-art
image retrieval architecture with condition-specific sub-networks allowing the
computation of global image descriptors that are explicitly dependent of the
capturing conditions. We show that our approach improves localization by a
factor of almost 300\% compared to the popular VLAD-based methods on nighttime
localization.
