Existing spatial localization techniques for autonomous vehicles mostly use a pre-built 3D-HD map, often constructed using a survey-grade 3D mapping vehicle, which is not only expensive but also laborious. This paper shows that by using an off-the-shelf high-definition satellite image as a ready-to-use map, we are able to achieve cross-view vehicle localization up to a satisfactory accuracy, providing a cheaper and more practical way for localization. While the utilization of satellite imagery for cross-view localization is an established concept, the conventional methodology focuses primarily on image retrieval. This paper introduces a novel approach to cross-view localization that departs from the conventional image retrieval method. Specifically, our method develops (1) a Geometric-align Feature Extractor (GaFE) that leverages measured 3D points to bridge the geometric gap between ground and overhead views, (2) a Pose Aware Branch (PAB) adopting a triplet loss to encourage pose-aware feature extraction, and (3) a Recursive Pose Refine Branch (RPRB) using the Levenberg-Marquardt (LM) algorithm to align the initial pose towards the true vehicle pose iteratively. Our method is validated on KITTI and Ford Multi-AV Seasonal datasets as ground view and Google Maps as the satellite view. The results demonstrate the superiority of our method in cross-view localization with median spatial and angular errors within $1$ meter and $1^\circ$, respectively.

翻译：现有自动驾驶车辆的空间定位技术大多采用预先构建的三维高清地图，这种地图通常由测绘级三维测绘车辆制作，不仅成本高昂且费时费力。本文证明，通过使用现成的高清卫星图像作为即用型地图，我们能够实现满足精度要求的跨视角车辆定位，提供一种更廉价、更实用的定位方案。尽管利用卫星图像进行跨视角定位已有成熟概念，传统方法主要侧重于图像检索。本文提出了一种突破传统图像检索方法的跨视角定位新方法。具体而言，我们的方法开发了：(1) 几何对齐特征提取器（GaFE），利用实测三维点弥合地面视角与俯视视角之间的几何差异；(2) 姿态感知分支（PAB），采用三元组损失函数促进姿态感知特征提取；(3) 递归姿态优化分支（RPRB），使用莱文贝格-马夸尔特（LM）算法将初始姿态迭代对齐至真实车辆姿态。我们在KITTI和Ford Multi-AV Seasonal数据集（地面视角）与Google Maps（卫星视角）上验证了该方法。结果表明，本方法在跨视角定位中表现优越，中位空间误差和角度误差分别控制在$1$米和$1^\circ$以内。