The problem of localization on a geo-referenced satellite map given a query
ground view image is useful yet remains challenging due to the drastic change
in viewpoint. To this end, in this paper we work on the extension of our
earlier work on the Cross-View Matching Network (CVM-Net) for the
ground-to-aerial image matching task since the traditional image descriptors
fail due to the drastic viewpoint change. In particular, we show more extensive
experimental results and analyses of the network architecture on our CVM-Net.
Furthermore, we propose a Markov localization framework that enforces the
temporal consistency between image frames to enhance the geo-localization
results in the case where a video stream of ground view images is available.
Experimental results show that our proposed Markov localization framework can
continuously localize the vehicle within a small error on our Singapore
dataset.

本文针对在地面视图图像查询给定地理参考卫星地图的定位问题提出了一种基于 CVM-Net 的 Markov 定位框架，通过更广泛的实验结果和分析，它扩展了我们早期关于 CVM-Net 的工作，并提出了一种 Markov 定位框架，该框架可以在可用的地面图像流视频的情况下增强地理定位结果。实验结果表明，我们提出的 Markov 定位框架可以在新加坡数据集上持续实现小误差内的车辆定位。