  Visual localization plays an important role in many applications. However,
due to the large appearance variations such as season and illumination changes,
as well as weather and day-night variations, it's still a big challenge for
robust long-term visual localization algorithms. In this paper, we present a
novel visual localization method using hybrid handcrafted and learned features
with dense semantic 3D map. Hybrid features help us to make full use of their
strengths in different imaging conditions, and the dense semantic map provide
us reliable and complete geometric and semantic information for constructing
sufficient 2D-3D matching pairs with semantic consistency scores. In our
pipeline, we retrieve and score each candidate database image through the
semantic consistency between the dense model and the query image. Then the
semantic consistency score is used as a soft constraint in the weighted
RANSAC-based PnP pose solver. Experimental results on long-term visual
localization benchmarks demonstrate the effectiveness of our method compared
with state-of-the-arts.
