您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(工学版)》

山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (6): 41-49.doi: 10.6040/j.issn.1672-3961.0.2022.108

• 交通工程——智慧交通专题 • 上一篇    

基于坐标转换的多路侧激光雷达数据配准方法

周勇1,吕琛2,3,侯福金1,郭鑫铭2,3*,宋修广2   

  1. 1.山东高速建设管理集团有限公司, 山东 济南 250014;2.山东大学齐鲁交通学院, 山东 济南 250002;3.山东大学苏州研究院, 江苏 苏州 215123
  • 发布日期:2022-12-23
  • 作者简介:周勇(1962— ),男,山东胶南人,研究员,博士,主要研究方向为多源数据融合. E-mail: 498589891@qq.com. *通信作者简介:郭鑫铭(1998— ),女,山东青岛人,硕士研究生,主要研究方向为多激光雷达数据融合. E-mail: 202115385@mail.sdu.edu.cn
  • 基金资助:
    国家自然科学基金青年科学基金项目(52002224);江苏省自然科学基金项目(BK20200226);苏州市重点产业技术创新项目(SYG202033);山东省重点研发计划资助项目(2020CXGC010118)

Data fusion method of multi roadside LiDAR based on coordinate transformation

ZHOU Yong1, LÜ Chen2,3, HOU Fujin1, GUO Xinming2,3*, SONG Xiuguang2   

  1. 1. Shandong Hi-Speed Construction Management Group Co. Ltd., Jinan 250014, Shandong, China;
    2. School of Qilu Transportation, Shandong University, Jinan 250002, Shandong, China;
    3. Suzhou Research Institute, Shandong University, Suzhou 215123, Jiangsu, China
  • Published:2022-12-23

摘要: 为准确、高效地获取多个激光雷达配准结果,提出一种基于坐标转换的多激光雷达配准算法。将全球定位系统(global positioning system, GPS)数据转换到地心空间直角坐标系下表示,求解基于空间直角坐标系的激光雷达数据与地心空间直角坐标系数据间的映射关系,根据总体最小二乘法原理进行转换矩关系的优化,基于转换关系,将多个激光雷达数据配准到大地坐标系下,实现多个激光雷达数据在大地坐标下的融合。试验对比4种不同型号、不同线束的激光雷达数据转换效果,结果表明各型号激光雷达经配准后均可以GPS数据形式呈现,通过转换数据与测量数据相比,精度可达到5 cm。本算法可较准确快速地获取多激光雷达配准结果,将多个激光雷达数据统一配准到大地坐标系下,配准后的数据在定位、安全评估等领域具有较高的使用价值。

关键词: 数据配准, 路侧激光雷达, GPS, 坐标转换, 总体最小二乘法

中图分类号: 

  • TN958.98
[1] WILLIAMS K. Synthesis of transportation applications of mobile LiDAR[J]. Remote Sensing, 2013, 5(9): 4652-4692.
[2] WU J Q, XU H, ZHAO J X. Automatic lane identi-fication using the roadside LiDAR sensors[J]. IEEE Intelligent Transportation Systems Magazine, 2020, 12(1): 25-34.
[3] SONG X. Augmented multiple vehicles'trajectories extr-action under occlusions with roadside LiDAR data[J]. IEEE Sensors Journal, 2021, 21(19): 21921-21930.
[4] WU J Q. Automatic ground points filtering of roadside LiDAR data using a channel-based filtering algorithm[J]. Optics and Laser Technology, 2019, 115: 374-383.
[5] 胡远志. 基于激光雷达点云与图像融合的车辆目标检测方法[J]. 汽车安全与节能学报, 2019, 10(4): 451-458. HU Yuanzhi. Vehicle target detection method based on LiDAR point cloud and image fusion[J]. Journal of Automotive Safety and Energy Saving, 2019, 10(4): 451-458.
[6] 李仁忠,杨曼,田瑜,等. 基于iss特征点结合改进icp的点云配准算法[J].激光与光电子学进展, 2017, 54(11): 312-319. LI Renzhong, YANG Man, TIAN Yu, et al. Improved ICP point cloud registration algorithm based on ISS feature point combination[J]. Laser & Optronics Progress, 2017, 54(11): 312-319.
[7] ZHANG X, GLENNIE C, KUSARI A. Change detection from differential airborne LiDAR using a weighted anisotropic iterative closest point algorithm[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(7): 3338-3346.
[8] ZHENG Z Y, LI Y, WU J. LiDAR point cloud registration based on improved ICP method and SIFT feature[C] // In 3rd IEEE International Conference on Progress in Informatcs and Computing(IEEE PIC). Nanjing, China: IEEE: 2015: 588-592.
[9] CAI Z P. Practical optimal registration of terrestrial LiDAR scan pairs[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2019, 147: 118-131.
[10] 左超,鲁敏,谭志国,等. 一种新的点云拼接算法[J].中国激光, 2012, 39(12): 217-224. ZUO Chao, LU Min, TAN Zhiguo, et al. A new point cloud splicing algorithm[J]. China Lasers, 2012, 39(12): 217-224.
[11] 赵明波,何峻,罗小波,等. 基于改进迭代最近点算法的两视角激光雷达数据配准[J]. 光学学报, 2012, 32(11): 305-314. ZHAO Mingbo, HE Jun, LUO Xiaobo, et al. Two-view Lidar data registration based on improved iterative nearest point algorithm[J]. Acta Optica Sinica, 2012, 32(11): 305-314.
[12] 邓嘉,侯晨辉,刁婉,等. 三维点云数据的配准算法综述[J].信息与电脑(理论版), 2017,(23): 51-52. DENG Jia, HOU Chenhui, DIAO Wan, et al. Overview of registration algorithms for 3D point cloud data[J]. Information and Computers(Theory Edition), 2017(23): 51-52.
[13] CHENG L. Registration of laser scanning point clouds: a review[J]. Sensors, 2018, 18(5): 25.
[14] TIAN Y. Projection and integration of connected-infrastructure LiDAR sensing data in a global coordinate[J]. Optics and Laser Technology, 2021, 144: 9.
[15] TIAN Y. A data mapping method for roadside LiDAR sensors[C] // Proceedings of IEEE Intelligent Transportation Systems Conference(IEEE-ITSC). Auckland, New Zealand: IEEE: 2019.
[16] ZONG W P. A survey of laser scan matching methods[J]. Chinese Optics, 2018, 11(6): 914-930.
[17] GOLUB G H, VANLOAN C F. An analysis of total least-squares problem[J]. Siam Journal on Numerical Analysis, 1980, 17(6): 883-893.
[18] 丁克良,欧吉坤,陈义.整体最小二乘法及其在测量数据处理中的应用[C] //中国测绘学会第九次全国会员代表大会论文集. 北京,中国:中国测绘学会,2009:399-405.
[19] 陆珏,陈义,郑波. 总体最小二乘方法在三维坐标转换中的应用[J].大地测量与地球动力学, 2008(5): 77-81. LU Yu, CHEN Yi, ZHENG Bo. Application of total least squares method in 3D coordinate transformation[J]. Geodesy and Geodynamics, 2008(5): 77-81.
[20] 王乐洋,许才军. 总体最小二乘研究进展[J].武汉大学学报(信息科学版), 2013, 38(7): 850-856. WANG Yueyang, XU Caijun. Progress in the study of total least squares[J]. Journal of Wuhan University(Information Science), 2013, 38(7): 850-856.
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!