  Long-Term visual localization under changing environments is a challenging
problem in autonomous driving and mobile robotics due to season, illumination
variance, etc. Image retrieval for localization is an efficient and effective
solution to the problem. In this paper, we propose a novel multi-task
architecture to fuse the geometric and semantic information into the
multi-scale latent embedding representation for visual place recognition. To
use the high-quality ground truths without any human effort, the effective
multi-scale feature discriminator is proposed for adversarial training to
achieve the domain adaptation from synthetic virtual KITTI dataset to
real-world KITTI dataset. The proposed approach is validated on the Extended
CMU-Seasons dataset and Oxford RobotCar dataset through a series of crucial
comparison experiments, where our performance outperforms state-of-the-art
baselines for retrieval-based localization and large-scale place recognition
under the challenging environment.
