  We propose a novel visual localization network when outside environment has
changed such as different illumination, weather and season. The visual
localization network is composed of a feature extraction network and pose
regression network. The feature extraction network is made up of an encoder
network based on the Generative Adversarial Network CycleGAN, which can capture
intrinsic appearance-invariant feature maps from unpaired samples of different
weathers and seasons. With such an invariant feature, we use a 6-DoF pose
regression network to tackle long-term visual localization in the presence of
outdoor illumination, weather and season changes. A variety of challenging
datasets for place recognition and localization are used to prove our visual
localization network, and the results show that our method outperforms
state-of-the-art methods in the scenarios with various environment changes.
