Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware
  Homography Estimator
Xiaolong Wang Runsen Xu Zuofan Cui Zeyu Wan Yu Zhang
In this paper, we introduce a novel approach to fine-grained cross-view
geo-localization. Our method aligns a warped ground image with a corresponding
GPS-tagged satellite image covering the same area using homography estimation.
We first employ a differentiable spherical transform, adhering to geometric
principles, to accurately align the perspective of the ground image with the
satellite map. This transformation effectively places ground and aerial images
in the same view and on the same plane, reducing the task to an image alignment
problem. To address challenges such as occlusion, small overlapping range, and
seasonal variations, we propose a robust correlation-aware homography estimator
to align similar parts of the transformed ground image with the satellite
image. Our method achieves sub-pixel resolution and meter-level GPS accuracy by
mapping the center point of the transformed ground image to the satellite image
using a homography matrix and determining the orientation of the ground camera
using a point above the central axis. Operating at a speed of 30 FPS, our
method outperforms state-of-the-art techniques, reducing the mean metric
localization error by 21.3% and 32.4% in same-area and cross-area
generalization tasks on the VIGOR benchmark, respectively, and by 34.4% on the
KITTI benchmark in same-area evaluation.