Real-time vehicle localization (i.e., position and orientation estimation in the world coordinate system) with high accuracy is the fundamental function of an intelligent vehicle (IV) system. In the process of commercialization of IVs, many car manufacturers attempt to avoid high-cost sensor systems (e.g., RTK GNSS and LiDAR) in favor of low-cost optical sensors such as cameras. The same cost-saving strategy also gives rise to an increasing number of vehicles equipped with High Definition (HD) maps. Rooted upon these existing technologies, this article presents the concept of Monocular Localization with Vector HD Map (MLVHM), a novel camera-based map-matching method that efficiently aligns semantic-level geometric features in-camera acquired frames against the vector HD map in order to achieve high-precision vehicle absolute localization with minimal cost. The semantic features are delicately chosen for the ease of map vector alignment as well as for the resiliency against occlusion and fluctuation in illumination. The effective data association method in MLVHM serves as the basis for the camera position estimation by minimizing feature re-projection errors, and the frame-to-frame motion fusion is further introduced for reliable localization results. Experiments have shown that MLVHM can achieve high-precision vehicle localization with an RMSE of 24 cm with no cumulative error. In addition, we use low-cost on-board sensors and light-weight HD maps to achieve or even exceed the accuracy of existing map-matching algorithms.

中文翻译：

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矢量高清地图（MLVHM）的单眼定位：一种用于商业IV的低成本方法。

高精度的实时车辆定位（即，世界坐标系中的位置和方向估计）是智能车辆（IV）系统的基本功能。在IV的商业化过程中，许多汽车制造商试图避免使用高成本的传感器系统（例如RTK GNSS和LiDAR），转而采用低成本的光学传感器（例如相机）。相同的节省成本策略还导致配备高清晰度（HD）地图的车辆数量增加。本文基于这些现有技术，提出了带有矢量高清地图（MLVHM）的单眼定位的概念，一种新颖的基于相机的地图匹配方法，该方法可将矢量图像中的相机采集帧中的语义级几何特征有效地对齐，从而以最小的成本实现高精度的车辆绝对定位。精心选择语义特征，以简化地图矢量对齐方式以及抵抗遮挡和光照波动的弹性。MLVHM中有效的数据关联方法通过最小化特征重投影误差作为相机位置估计的基础，并且进一步引入了帧间运动融合以提供可靠的定位结果。实验表明，MLVHM可以实现24 cm的RMSE的高精度车辆定位，而不会产生累积误差。此外，