Marking-level high-definition maps (HD maps) are of great significance for autonomous vehicles (AVs), especially in large-scale, appearance-changing scenarios where AVs rely on markings for localization and lanes for safe driving. In this paper, we propose a pose-guided optimization framework for automatically building a marking-level HD map with accurate markings positions using a simple sensor setup (one or more monocular cameras). We optimize the position of the marking corners to fit the result of marking segmentation and simultaneously optimize the inverse perspective mapping (IPM) matrix of the corresponding camera to obtain an accurate transformation from the front view image to the bird's-eye view (BEV). In the quantitative evaluation, the built HD map almost attains centimeter-level accuracy. The accuracy of the optimized IPM matrix is similar to that of the manual calibration. The method can also be generalized to build HD maps in a broader sense by increasing the types of recognizable markings. The supplementary materials and videos are available at http://liuhongji.site/V2HDM-Mono/.

翻译：标记级高清地图（HD maps）对于自动驾驶车辆（AVs）至关重要，尤其是在大规模、外观变化场景中，车辆依赖标记进行定位和车道保持安全行驶。本文提出一种位姿引导优化框架，利用简单传感器配置（一个或多个单目相机）自动构建具有精确标记位置的标记级高清地图。我们通过优化标记角点位置以适应标记分割结果，同时优化对应相机的逆透视映射（IPM）矩阵，从而获得从正面视图到鸟瞰视图（BEV）的精确变换。在定量评估中，所构建的高清地图几乎达到厘米级精度。优化后的IPM矩阵精度与手动标定结果相当。通过增加可识别标记类型，该方法还可推广至更广义的高清地图构建。补充材料与视频见http://liuhongji.site/V2HDM-Mono/。