We propose a method for accurately localizing ground vehicles with the aid of
satellite imagery. Our approach takes a ground image as input, and outputs the
location from which it was taken on a georeferenced satellite image. We perform
visual localization by estimating the co-occurrence probabilities between the
ground and satellite images based on a ground-satellite feature dictionary. The
method is able to estimate likelihoods over arbitrary locations without the
need for a dense ground image database. We present a ranking-loss based
algorithm that learns location-discriminative feature projection matrices that
result in further improvements in accuracy. We evaluate our method on the
Malaga and KITTI public datasets and demonstrate significant improvements over
a baseline that performs exhaustive search.

该研究提出了一种利用卫星图像对地面车辆进行精确定位的方法，其采用基于地面 - 卫星特征字典的视觉定位方法，可以在不需要密集的地面图像数据库的情况下估计任意位置的可能性，并通过学习定位判别特征投影矩阵进一步提高精确度，在公共数据集上得到了显著的改进。