  Accurate and robust visual localization under a wide range of viewing
condition variations including season and illumination changes, as well as
weather and day-night variations, is the key component for many computer vision
and robotics applications. Under these conditions, most traditional methods
would fail to locate the camera. In this paper we present a visual localization
algorithm that combines structure-based method and image-based method with
semantic information. Given semantic information about the query and database
images, the retrieved images are scored according to the semantic consistency
of the 3D model and the query image. Then the semantic matching score is used
as weight for RANSAC's sampling and the pose is solved by a standard PnP
solver. Experiments on the challenging long-term visual localization benchmark
dataset demonstrate that our method has significant improvement compared with
the state-of-the-arts.
