自动驾驶环境下基于语义分割的激光雷达与相机外参标定方法  

External Parameter Calibration Method of LiDAR and Camera Based on Semantic Segmentation in Automatic Driving Environment

作　　者：史鹏涛[1,2] 危康乐 吴昊 李杰 Shi Pengtao;Wei Kangle;Wu Hao;LiJie(College of Mechanical and Electrical Engineering,Shaanxi University of Science and Technology,Xi’an 710021,Shaanxi,China;Sichuan Digital Economy Industry Development Research Institute,Chengdu 611336,Sichuan,China)

机构地区：[1]陕西科技大学机电工程学院,陕西西安710021 [2]四川数字经济产业发展研究院,四川成都611336

出　　处：《激光与光电子学进展》2024年第24期296-301,共6页Laser & Optoelectronics Progress

基　　金：国家重点研发计划(2022YFB3205801);四川省科技计划资助项目(2023NSFSC1432);陕西省一般项目(2024JCYBQN-0455);安徽省重点项目(2022e03020002)。

摘　　要：感知是自动驾驶领域的一项重要技术,主要依赖于雷达、相机,为准确感知环境信息要对传感器外参进行准确标定。为了解决标定无法及时更新和汽车颠簸致标定失效的问题,提出一种适用于城市街道的无目标外参标定的方法,选择建筑物、车辆和道路标识线作为特征物体,同时提取点云和图像的特征点,基于初始外参采用随机搜索算法对点云和图像进行匹配,通过最佳匹配结果得到最优外参,以KITTI数据集为例通过大量实验验证所提方法的可行性和有效性。实验结果表明,当旋转扰动量为3°以下的情况时,平移量误差均值保持在0.095 m之内,旋转误差均值保持在0.32°之内,精度较高。在只扰动旋转量的情况下,相比与基于线特征的CRLF方法,所提方法的平移量误差降低了0.1 m,旋转量误差降低了0.55°。这证明该方法适用于大部分城市街道自动驾驶场景,并且具有较好的精准性。Perception is an important technology in the field of autonomous driving,primarily relying on radar and cameras.To perceive accurate environmental information,it is necessary to accurately calibrate the external parameters of sensors.To solve the problem of calibration failure due to the inability in updating the calibration in a timely manner and car bumps,this study proposes an untargeted extrinsic calibration method suitable for urban streets.Buildings,vehicles,and road markings were selected as feature objects,and point clouds and image feature points were extracted.Based on the initial extrinsic,a random search algorithm was used to match the point cloud and image,and the optimal extrinsic was obtained based on the best matching result.Considering the KITTI dataset as an example,the feasibility and effectiveness of the method were verified through various experiments.The experimental results indicate that for rotational disturbances under 3°,the mean translation error remains under 0.095 m and the mean rotation error remains under 0.32°,indicating high accuracy.Compared with the CRLF method based on line features,the proposed method reduces the translation and rotation errors by 0.1 m and 0.55°,respectively,when only perturbing the rotation amount.Thus,the method is applicable to most urban street autonomous driving scenarios and exhibits good accuracy.

关 键 词：自动驾驶 雷达 相机 标定 

分 类 号：V323.19[航空宇航科学与技术—人机与环境工程]