  This paper addresses the problem of vehicle-mounted camera localization by
matching a ground-level image with an overhead-view satellite map. Existing
methods often treat this problem as cross-view image retrieval, and use learned
deep features to match the ground-level query image to a partition (eg, a small
patch) of the satellite map. By these methods, the localization accuracy is
limited by the partitioning density of the satellite map (often in the order of
tens meters). Departing from the conventional wisdom of image retrieval, this
paper presents a novel solution that can achieve highly-accurate localization.
The key idea is to formulate the task as pose estimation and solve it by
neural-net based optimization. Specifically, we design a two-branch {CNN} to
extract robust features from the ground and satellite images, respectively. To
bridge the vast cross-view domain gap, we resort to a Geometry Projection
module that projects features from the satellite map to the ground-view, based
on a relative camera pose. Aiming to minimize the differences between the
projected features and the observed features, we employ a differentiable
Levenberg-Marquardt ({LM}) module to search for the optimal camera pose
iteratively. The entire pipeline is differentiable and runs end-to-end.
Extensive experiments on standard autonomous vehicle localization datasets have
confirmed the superiority of the proposed method. Notably, e.g., starting from
a coarse estimate of camera location within a wide region of 40m x 40m, with an
80% likelihood our method quickly reduces the lateral location error to be
within 5m on a new KITTI cross-view dataset.
