车载LiDAR-IMU外参联合标定算法  

Vehicle-mounted LiDAR-IMU external parameter joint calibration algorithm

作　　者：黄平[1] 胡超 张宁[2] 薛冰 Huang Ping;Hu Chao;Zhang Ning;Xue Bing(College of Intelligent Systems Science and Engineering,Harbin Engineering University,Harbin 150001,China;54th Research Institute of China Electronics Technology Group Corporation,Shijiazhuang 050000,China)

机构地区：[1]哈尔滨工程大学智能科学与工程学院,哈尔滨150001 [2]中国电子科技集团公司第五十四研究所,石家庄050000

出　　处：《仪器仪表学报》2022年第10期128-135,共8页Chinese Journal of Scientific Instrument

基　　金：中央高校基本科研业务费(3072022QBZ0401)项目资助。

摘　　要：为提高LIO-SAM算法的定位精度,本文从LiDAR-IMU外参标定方面开展研究,针对现有的传感器标定算法在车载条件下标定精度低的缺点,提出一种新的车载传感器联合标定算法。针对车载条件下自由度低导致俯仰、横滚方向约束建立不充分的问题,利用车辆的大范围运动轨迹消除平移参数影响,使用正态分布变换(NDT)和迭代最近点(ICP)的点云匹配算法快速得到旋转参数初值,提高俯仰角和横滚角的标定精度。针对粗标定过程中激光里程计存在漂移以及没有标定平移外参的问题,对基于点云优化的全参数标定方案进行改进,利用转弯区域构建对平移外参的约束,结合统计误差平均效应和位移约束构建新的目标函数,迭代优化后得到全参数标定结果。实验结果表明,加入了外参标定模块的LIO-SAM算法的定位精度提升了1.74%~5.92%。To improve the localization accuracy of the LIO-SAM algorithm,the LiDAR-IMU external parameter calibration is studied in this article.To address the low calibration accuracy of existing sensor calibration algorithms in vehicle-mounted conditions,a new joint calibration algorithm is proposed for vehicle sensors.Due to the low degree of freedom under vehicle conditions,the constraints of pitch and roll direction are not established sufficiently.To solve this problem,we first eliminate the influence of translation parameters by using a wide range of vehicle trajectories.Then,the normal distributions transform and iterative closest point algorithm are used to quickly obtain the initial values of rotation parameters.Furthermore,the calibration accuracy of pitch angle and roll angle is improved.In the coarse calibration process,the LiDAR odometer drifts and translation external parameters are not calibrated.Therefore,we further implement the full parameter calibration scheme based on the point cloud optimization method and make some enhancements.In this scheme,the turning region is utilized to construct constraints on the translation external parameters.Then,we combine the statistical error average effect and the displacement constraint to construct a new objective function.Finally,the full parameter calibration results are obtained by iterative optimization.Compared with the original LIO-SAM algorithm,experimental results show that the localization accuracy of LIO-SAM algorithm with external parameter calibration module is improved by 1.74%~5.92%.

关 键 词：LiDAR/IMU 定位 传感器标定 点云优化 

分 类 号：TH86[机械工程—仪器科学与技术]