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车载三维激光雷达外参数的分步自动标定算法

Step-By-Step Automatic Calibration Algorithm for Exterior Parameters of 3D Lidar Mounted on Vehicle

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摘要

三维激光雷达外参数标定是智能车通过激光雷达感知环境的基础, 针对常见标定方法实施繁琐、精度低, 以及依赖其他传感器的问题, 提出了一种分步自动标定算法。第1步对地面点云进行拟合得到地面方程, 构造水平度函数, 通过粒子群优化(PSO)算法优化水平度函数完成对激光雷达俯仰角、横滚角和纵向位移的标定; 第2步标定以第1步标定的完成为基础, 在车辆沿直线行驶过程中采集多帧含有同一标定杆的激光点云, 通过聚类得到标定杆聚类中心, 然后在二维平面内对多帧同一标定杆的聚类中心进行直线拟合, 根据直线斜率计算航向角。结果表明, 所提算法的精度可达10-5数量级, 耗时0.5 s, 极大地提高了标定精度和效率, 能满足实际工程的使用需求。上述两步自动标定算法由程序自动完成, 并且不依赖于其他传感器即可得到高精度的标定结果。

Abstract

The calibration of exterior parameters of a 3D lidar is the basis of an intelligent vehicle perceiving environment through the lidar. Aiming at the major problems of common calibration methods including cumbersome implementation, low precision, and relying on other sensors, we propose a step-by-step automatic calibration algorithm. The first step is to get the ground equation by fitting the ground point cloud, construct the levelness function, and complete the calibration of the pitch angle, roll angle, and longitudinal displacement of the lidar by optimizing the levelness function with the particle swarm optimization (PSO) algorithm. Based on the completion of the first step calibration, the second step calibration needs to collect multi-frame point clouds containing the same calibration pole during the vehicle moving along a straight line. The calibration pole is clustered to get the clustering centers. The linear equation with multi-frame clustering centers is fitted in the two-dimensional plane, and the yaw angle is calculated according to the slope of the fitted straight line. The results show that the precision of the proposed algorithm can reach the magnitude order of 10-5, and the algorithm takes 0.5 s. The calibration precision and efficiency are greatly improved, and actual engineering demands can be satisfied. The two steps are done automatically by the program, and the calibration results with high-precision can be acquired without other sensors.

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中图分类号:TN958.98

DOI:10.3788/cjl201744.1010004

所属栏目:遥感与传感器

收稿日期:2017-05-22

修改稿日期:2017-06-13

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作者单位    点击查看

陈贵宾:吉林大学汽车仿真与控制国家重点实验室, 吉林 长春 130022
高振海:吉林大学汽车仿真与控制国家重点实验室, 吉林 长春 130022
何 磊:吉林大学汽车仿真与控制国家重点实验室, 吉林 长春 130022

联系人作者:陈贵宾(cgbcgb22@qq.com)

备注:陈贵宾(1993-), 男, 硕士研究生, 主要从事智能驾驶车辆环境感知方面的研究。

【1】Yang Fei, Zhu Zhu, Gong Xiaojin, et al. Real-time dynamic obstacle detection and tracking using 3D lidar[J]. Journal of Zhejiang University (Engineering Science), 2012, 46(9): 1565-1571.
杨飞, 朱株, 龚小谨, 等. 基于三维激光雷达的动态障碍实时检测与跟踪[J]. 浙江大学学报(工学版), 2012, 46(9): 1565-1571.

【2】Wang Xinzhu, Li Jun, Li Hongjian, et al. Obstacle detection based on 3D laser scanner and range image for intelligent vehicle[J]. Journal of Jilin University (Engineering and Technology Edition), 2016, 46(2): 360-365.
王新竹, 李骏, 李红建, 等. 基于三维激光雷达和深度图像的自动驾驶汽车障碍物检测方法[J]. 吉林大学学报(工学版), 2016, 46(2): 360-365.

【3】Qian C, Liu H, Tang J, et al. An integrated GNSS/INS/LiDAR-SLAM positioning method for highly accurate forest stem mapping[J]. Remote Sensing, 2017, 9(1): 1-16.

【4】Thuy M, León F P. Lane detection and tracking based on lidar data[J]. Metrology and Measurement Systems, 2010, 17(3): 311-321.

【5】Tan Jun. Research on road-scene perception technologies based on information fusion in structural environments[D]. Changsha: National University of Defense Technology, 2014.
谭筠. 结构化环境下基于信息融合的道路场景感知技术研究[D]. 长沙: 国防科学技术大学, 2014.

【6】Xiao Qiang. Multi-elements composed drivable area extraction for unmanned ground vehicles in field terrain[D]. Beijing: Beijing Institute of Technology, 2015.
肖强. 地面无人车辆越野环境多要素合成可通行区域检测[D]. 北京: 北京理工大学, 2015.

【7】Gong Tianan, Wang Yuncai, Kong Lingqin, et al. Chaotic lidar for automotive collision warning system[J]. Chinese J Lasers, 2009, 36(9): 2426-2430.
龚天安, 王云才, 孔令琴, 等. 面向汽车防撞的混沌激光雷达[J]. 中国激光, 2009, 36(9): 2426-2430.

【8】Kou Tian, Wang Haiyan, Wang Fang, et al. Model of moving target trajectory detected based on airborne laser radar imaging[J]. Laser & Optoelectronics Progress, 2015, 52(10): 101002.
寇添, 王海晏, 王芳, 等. 基于机载激光雷达成像的动目标轨迹检测模型[J]. 激光与光电子学进展, 2015, 52(10): 101002.

【9】Ye Gang. Multi-target detection and tracking algorithm for autonomous driving car based on a 3D lidar in urban traffic environment[D]. Beijing: Beijing Institute of Technology, 2016.
叶刚. 城市环境基于三维激光雷达的自动驾驶车辆多目标检测及跟踪算法研究[D]. 北京: 北京理工大学, 2016.

【10】Jiao Hongwei, Qin Shiqiao, Hu Chunsheng, et al. Research on the coordinates calibration of pulse ladar and camera[J]. Chinese J Lasers, 2011, 38(1): 0108006.
焦宏伟, 秦石乔, 胡春生, 等. 一种脉冲激光雷达与摄像机标定方法的研究[J]. 中国激光, 2011, 38(1): 0108006.

【11】Chen Yuan, Zhao Zhimin, Chen Zhen. The improvement of extrinsic calibration of laser rangefinder with CCD and its applications[J]. Applied Laser, 2008, 28(3): 219-222.
陈远, 赵志敏, 陈震. 激光雷达和CCD外部标定方法的改进及其应用研究[J]. 应用激光, 2008, 28(3): 219-222.

【12】Zhu Zhu. 3D data based understanding of unstructured scenes for vehicle navigation[D]. Hangzhou: Zhejiang University, 2014.
朱株. 基于三维数据面向无人车导航的非结构化场景理解[D]. 杭州: 浙江大学, 2014.

【13】Li Wei, Sun Yuanchao, Li Zongchun, et al. An improved least-square plane fitting algorithm[J]. Science of Surveying and Mapping, 2017, 42(1): 15-19, 100.
李伟, 孙元超, 李宗春, 等. 一种改进的最小二乘平面拟合算法[J]. 测绘科学, 2017, 42(1): 15-19, 100.

【14】Zeng Qihong, Mao Jianhua, Li Xianhua, et al. Planar-fitting filtering algorithm for lidar points cloud[J]. Geomatics and Information Science of Wuhan University, 2008, 33(1): 25-28.
曾齐红, 毛建华, 李先华, 等. 激光雷达点云平面拟合过滤算法[J]. 武汉大学学报(信息科学版), 2008, 33(1): 25-28.

【15】Wang Hao, Ouyang Haibin, Gao Liqun. An improved global particle swarm optimization[J]. Control and Decision, 2016, 31(7): 1161-1168.
王皓, 欧阳海滨, 高立群. 一种改进的全局粒子群优化算法[J]. 控制与决策, 2016, 31(7): 1161-1168.

【16】Zhao Wenchong, Cai Jianghui, Zhao Xujun, et al. Fast K-means clustering algorithm based on influence space[J]. Journal of Chinese Computer Systems, 2016, 37(9): 2060-2064.
赵文冲, 蔡江辉, 赵旭俊, 等. 一种影响空间下的快速K-means聚类算法[J]. 小型微型计算机系统, 2016, 37(9): 2060-2064.

【17】Wang Jinliang, Chen Lianjun. Review on filtering algorithm for lidar points cloud data[J]. Remote Sensing Technology and Application, 2010, 25(5): 632-638.
王金亮, 陈联君. 激光雷达点云数据的滤波算法述评[J]. 遥感技术与应用, 2010, 25(5): 632-638.

引用该论文

Chen Guibin,Gao Zhenhai,He Lei. Step-By-Step Automatic Calibration Algorithm for Exterior Parameters of 3D Lidar Mounted on Vehicle[J]. Chinese Journal of Lasers, 2017, 44(10): 1010004

陈贵宾,高振海,何 磊. 车载三维激光雷达外参数的分步自动标定算法[J]. 中国激光, 2017, 44(10): 1010004

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