This work addresses visual cross-view metric localization for outdoor
robotics. Given a ground-level color image and a satellite patch that contains
the local surroundings, the task is to identify the location of the ground
camera within the satellite patch. Related work addressed this task for
range-sensors (LiDAR, Radar), but for vision, only as a secondary regression
step after an initial cross-view image retrieval step. Since the local
satellite patch could also be retrieved through any rough localization prior
(e.g. from GPS/GNSS, temporal filtering), we drop the image retrieval objective
and focus on the metric localization only. We devise a novel network
architecture with denser satellite descriptors, similarity matching at the
bottleneck (rather than at the output as in image retrieval), and a dense
spatial distribution as output to capture multi-modal localization ambiguities.
We compare against a state-of-the-art regression baseline that uses global
image descriptors. Quantitative and qualitative experimental results on the
recently proposed VIGOR and the Oxford RobotCar datasets validate our design.
The produced probabilities are correlated with localization accuracy, and can
even be used to roughly estimate the ground camera's heading when its
orientation is unknown. Overall, our method reduces the median metric
localization error by 51%, 37%, and 28% compared to the state-of-the-art when
generalizing respectively in the same area, across areas, and across time.

本文使用新型网络结构和更密集的卫星图像描述符进行多模式区域定位，可将度量定位误差中位数相对于同一区域、跨区域和跨时间的现有技术分别降低 51％、37％和 28％。