论文标题
具有姿势引导优化的单眼摄像头映射：增强标记级高清地图精度
Monocular Camera Mapping with Pose-Guided Optimization: Enhancing Marking-Level HD Map Accuracy
论文作者
论文摘要
标记级别的高清地图（HD地图）对自动驾驶汽车（AV）具有重要意义，尤其是在大规模的外观变化场景中，AV依靠标记来定位和车道来安全驾驶。在本文中，我们建议使用简单的传感器设置（一个或多个单眼摄像机）自动构建具有准确标记位置的标记级高清映射的姿势引导的优化框架。我们优化了标记角的位置，以适合标记分割的结果，并同时优化相应摄像机的反视角映射（IPM）矩阵，以获得从前视图映像到鸟类视图（BEV）的准确转换。在定量评估中，构建的高清图几乎达到了百厘厘米级的准确性。优化的IPM矩阵的准确性与手动校准相似。该方法还可以推广以通过增加可识别标记的类型来从更广泛的意义上构建高清图。补充材料和视频可在http://liuuhongji.site/v2hdm-mono/上找到。
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/.
