开放道路中匹配高精度地图的在线相机外参标定  

Online extrinsic camera calibration based on high-definition map matching on public roadway

作　　者：廖文龙[1,2] 赵华卿 严骏驰 Liao Wenlong;Zhao Huaqing;Yan Junchi(Shanghai Jiao Tong University,Shanghai 200240,China;Anhui COWAR0BOT Co.,Ltd.,Wuhu 241010,China)

机构地区：[1]上海交通大学,上海200240 [2]安徽酷哇机器人有限公司,芜湖241010

出　　处：《中国图象图形学报》2021年第1期208-217,共10页Journal of Image and Graphics

基　　金：科技创新2030-重大项目(2020AAA0107600);国家自然科学基金项目(61972250)。

摘　　要：目的相机外参标定是ADAS(advanced driver-assistance systems)等应用领域的关键环节。传统的相机外参标定方法通常依赖特定场景和特定标志物,无法实时实地进行动态标定。部分结合SLAM(simultaneous localization and mapping)或VIO(visual inertia odometry)的外参标定方法依赖于点特征匹配,且精度往往不高。针对ADAS应用,本文提出了一种相机地图匹配的外参自校正方法。方法首先通过深度学习对图像中的车道线进行检测提取,数据筛选及后处理完成后,作为优化问题的输入;其次通过最近邻域解决车道线点关联,并在像平面内定义重投影误差;最后,通过梯度下降方法迭代求解最优的相机外参矩阵,使得像平面内检测车道线与地图车道线真值重投影匹配误差最小。结果在开放道路上的测试车辆显示,本文方法经过多次迭代后收敛至正确的外参,其旋转角精度小于0.2°,平移精度小于0.2 m,对比基于消失点或VIO的标定方法(精度为2.2°及0.3 m),本文方法精度具备明显优势。同时,在相机外参动态改变时,所提出方法可迅速收敛至相机新外参。结论本文方法不依赖于特定场景,支持实时迭代优化进行外参优化,有效提高了相机外参精确度,精度满足ADAS需求。Objective Camera calibration is one of the key factors of the perception in advanced driver-assistance systems (ADAS) and many other applications. Traditional camera calibration methods and even some state-of-the-art calibration algorithms,which are currently widely used in factories,strongly rely on specific scenes and specific markers. Existing methods to calibrate the extrinsic parameters of the camera are inconvenient and inaccurate,and current algorithms have some obvious disadvantages,which might cause serious accidents,damage the vehicle,or threaten the safety of passengers. Theoretically,once calibrated,the extrinsic parameters of the camera,including the position and the posture of camera installation,will be fixed and stable. However,the extrinsic parameters of a camera change throughout the lifetime of a vehicle.Real-time dynamic calibration is useful in cases when vehicles are transported or when cameras are removed for maintenance or replacement. Other extrinsic parameter calibration methods solves the estimation by simultaneous localization and mapping or visual inertia odometry (VIO) technologies. These methods try to extract point features and match points with the same characters,and the spatial transformation of different frames is calculated accordingly from the matched point pairs.However,according to the absence of texture information such as when one is in an indoor environment,the accuracy of extrinsic parameters is not always satisfactory. The common situation is that the algorithm cannot obtain any feature from the existing frames or the features that are obtained are not enough to calculate the position. To solve this problem and achieve the requirement of ADAS,this paper proposes a self-calibrating method that is based on aligning the detected lanes by the camera with a high-definition (HD) map. Method Feature extraction is the first step of calibration. The most common feature extraction method is to acquire features from frames,calculate the gradient or other specific information of e

关 键 词：外参标定 地图匹配 车道线 梯度下降 在线标定 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]