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1.北京交通大学 机械与电子控制工程学院, 北京 100044
2.北京交通大学 智慧高铁系统前沿科学中心, 北京 100044
3.北京特种机械研究所,北京 100143
[ "王耀东(1982-),男,河北石家庄人,副教授,硕士生导师,2005年于西南交通大学获得学士学位,2008年于北京科技大学获得硕士学位,2011年于日本广岛大学获得博士学位,现为北京交通大学机械与电子控制工程学院副教授,主要从事轨道交通智能检测技术方面的研究。E-mail: ydwang@bjtu.edu.cn" ]
[ "徐金杨(1998-),男,内蒙古赤峰人,硕士研究生,2021年于广西大学获得学士学位,主要从事轨道交通智能检测技术方面的研究。E-mail: 22126064@bjtu.edu.cn" ]
纸质出版日期:2024-03-25,
收稿日期:2023-07-29,
修回日期:2023-09-14,
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王耀东,徐金杨,朱力强等.隧道轮廓激光雷达点云线特征共面约束标定方法[J].光学精密工程,2024,32(06):774-784.
WANG Yaodong,XU Jinyang,ZHU Liqiang,et al.Line feature coplanar constraint calibration method for lidar point cloud of tunnel contour[J].Optics and Precision Engineering,2024,32(06):774-784.
王耀东,徐金杨,朱力强等.隧道轮廓激光雷达点云线特征共面约束标定方法[J].光学精密工程,2024,32(06):774-784. DOI: 10.37188/OPE.20243206.0774.
WANG Yaodong,XU Jinyang,ZHU Liqiang,et al.Line feature coplanar constraint calibration method for lidar point cloud of tunnel contour[J].Optics and Precision Engineering,2024,32(06):774-784. DOI: 10.37188/OPE.20243206.0774.
地铁隧道断面轮廓参数测量过程中,需要对高速激光雷达点云进行高精度系统标定。特别是大场景隧道环境,对标定模板要求高,标定过程复杂,检测精度影响大。针对此问题,本论文提出了一种新颖的适用于地铁隧道轮廓点云的标定方法,并基于双激光雷达测量系统,进行了算法研究。本方法设计了专用的手推行便携式标定系统,利用由点云数据提取的标定板线特征,建立目标函数,通过混合了遗传算法和Levenberg-Marquardt算法的非线性优化方法,寻找全局最优解从而实现对激光雷达的标定。实验结果表明:本方法对于两侧钢轨顶轨的标定误差在±1.5 mm以内;静态测量精度
X
误差在±1 mm内、
Y
误差在±4 mm内;当采集系统以5 km/h的速度进行数据采集时,动态测量精度
X
误差在±4 mm内、
Y
误差在±6 mm内。本方法能够实现激光雷达的高精度标定,算法鲁棒性强,具有易操作、环境适应性强的特点。
In the measurement of subway tunnel profile parameters, it is necessary to calibrate the high-speed Lidar point cloud with high precision. In particular, the tunnel environment in large scenes has high requirements for calibration templates, complicated calibration process, and great influence on detection accuracy. To solve this problem, a novel calibration method for subway tunnel contour point cloud was proposed in this paper, and the algorithm was studied based on dual lidar measurement system. In this method, a special manual portable calibration system was designed. The objective function was established by using the calibration plate line features extracted from point cloud data, and the global optimal solution was found through the nonlinear optimization method combining genetic algorithm and Levenberg-Marquardt algorithm to realize the calibration of lidar. The experimental results show that the calibration error of the top rail is within ±1.5 mm. Static measurement accuracy
X
error within ±1 mm,
Y
error within ±4 mm; When the acquisition system performs data acquisition at a speed of 5 km/h, the dynamic measurement accuracy
X
error is within ±4 mm and
Y
error is within ±6 mm. This method can realize the high-precision calibration of lidar, the algorithm is robust, easy to operate, and has the characteristics of strong environmental adaptability.
激光雷达地铁隧道点云标定共面约束隧道断面
Lidarmetro tunnelspoint cloud calibrationcoplanar constraintstunnel section
宗文鹏, 李广云, 李明磊, 等. 激光扫描匹配方法研究综述[J]. 中国光学, 2018, 11(6): 914-930. doi: 10.3788/co.20181106.0914http://dx.doi.org/10.3788/co.20181106.0914
ZONG W P, LI G Y, LI M L, et al. A survey of laser scan matching methods[J]. Chinese Optics, 2018, 11(6): 914-930.(in Chinese). doi: 10.3788/co.20181106.0914http://dx.doi.org/10.3788/co.20181106.0914
姚连璧, 张邵华, 王子轩. 车载激光扫描系统的动态标定[J]. 测绘科学, 2019, 44(8): 75-81. doi: 10.16251/j.cnki.1009-2307.2019.08.011http://dx.doi.org/10.16251/j.cnki.1009-2307.2019.08.011
YAO L B, ZHANG S H, WANG Z X. Dynamic calibration of vehicle laser scanning system[J]. Science of Surveying and Mapping, 2019, 44(8): 75-81.(in Chinese). doi: 10.16251/j.cnki.1009-2307.2019.08.011http://dx.doi.org/10.16251/j.cnki.1009-2307.2019.08.011
李磊, 严洁, 阮友田. 车载激光测绘系统的标定[J]. 中国光学, 2013, 6(3): 353-358. doi: 10.3788/co.20130603.0353http://dx.doi.org/10.3788/co.20130603.0353
LI L, YAN J, RUAN Y T. Calibration of vehicle-borne laser mapping system[J]. Chinese Optics, 2013, 6(3): 353-358.(in Chinese). doi: 10.3788/co.20130603.0353http://dx.doi.org/10.3788/co.20130603.0353
余祖俊, 杨娅楠, 朱力强. 三维激光扫描测量系统标定方法研究[J]. 电子测量与仪器学报, 2007, 21(6): 31-35.
YU Z J, YANG Y N, ZHU L Q. Study on calibration method for 3-D laser scanning systems[J]. Journal of Electronic Measurement and Instrument, 2007, 21(6): 31-35.(in Chinese)
CHEN P X, SHI W Z, BAO S, et al. Low-drift odometry, mapping and ground segmentation using a backpack LiDAR system[J]. IEEE Robotics and Automation Letters, 2021, 6(4): 7285-7292. doi: 10.1109/lra.2021.3097060http://dx.doi.org/10.1109/lra.2021.3097060
ANTONE M, FRIEDMAN Y. Fully automated laser range calibration[C]. Proceedings of the British Machine Vision Conference 2007. Warwick. British Machine Vision Association, 2007: 1-10. doi: 10.5244/c.21.66http://dx.doi.org/10.5244/c.21.66
周勇, 吕琛, 侯福金, 等. 基于坐标转换的多路侧激光雷达数据配准方法[J]. 山东大学学报(工学版), 2022, 52(6): 41-49.
ZHOU Y, LÜ C, HOU F J, et al. Data fusion method of multi roadside LiDAR based on coordinate transformation[J]. Journal of Shandong University (Engineering Science), 2022, 52(6): 41-49.(in Chinese)
张倩. 三维激光扫描测量系统标定算法研究[D]. 北京: 北京交通大学, 2015.
ZHANG Q. Study on Calibration Algorithm for 3-D Laser Scanning System[D]. Beijing: Beijing Jiaotong University, 2015. (in Chinese)
李红帅, 罗笑南, 邓春贵, 等. 基于LM算法的运动相机与激光雷达联合标定方法[J]. 桂林电子科技大学学报, 2022, 42(5): 345-353.
LI H S, LUO X N, DENG C G, et al. Joint calibration of sports camera and lidar based on LM algorithm[J]. Journal of Guilin University of Electronic Technology, 2022, 42(5): 345-353.(in Chinese)
蓝秦隆, 邹进贵, 杨丁亮. 混合优化算法的点云配准[J]. 测绘科学, 2022, 47(7): 119-125.
LAN Q L, ZOU J G, YANG D L. Point cloud registration based on hybrid optimization algorithm[J]. Science of Surveying and Mapping, 2022, 47(7): 119-125.(in Chinese)
张景源, 陈北北, 杨永兴, 等. 融合遗传算法和BP神经网络的光斑定位方法[J]. 中国光学(中英文), 2023, 16(2): 407-414. doi: 10.37188/co.2022-0084http://dx.doi.org/10.37188/co.2022-0084
ZHANG J Y, CHEN B B, YANG Y X, et al. Positioning algorithm for laser spot center based on BP neural network and genetic algorithm[J]. Chinese Optics, 2023, 16(2): 407-414.(in Chinese). doi: 10.37188/co.2022-0084http://dx.doi.org/10.37188/co.2022-0084
唐思圆, 凌翔. 基于遗传算法的多传感器误差配准研究[J]. 电子测量技术, 2021, 44(4): 57-61.
TANG S Y, LING X. Multi-sensor error registration based on genetic algorithm[J]. Electronic Measurement Technology, 2021, 44(4): 57-61.(in Chinese)
李冬毅, 覃方君, 黄春福, 等. 基于LM算法的惯导自主阻尼算法[J]. 传感器与微系统, 2023, 42(6): 112-115.
LI D Y, QIN F J, HUANG C F, et al. Inertial navigation autonomous damping algorithm based on LM algorithm[J]. Transducer and Microsystem Technologies, 2023, 42(6): 112-115.(in Chinese)
杨高朝, 王庆, 蔚保国, 等. 基于抗差LM的视觉惯性里程计与伪卫星混合高精度室内定位[J]. 测绘学报, 2022, 51(1): 18-30. doi: 10.11947/j.AGCS.2022.20200251http://dx.doi.org/10.11947/j.AGCS.2022.20200251
YANG G C, WANG Q, YU B G, et al. High-precision indoor positioning based on robust LM visual inertial odometer and pseudosatellite[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(1): 18-30.(in Chinese). doi: 10.11947/j.AGCS.2022.20200251http://dx.doi.org/10.11947/j.AGCS.2022.20200251
万超. 一种混合遗传LM算法求解非线性最小二乘问题[J]. 长江信息通信, 2021, 34(12): 52-54. doi: 10.3969/j.issn.1673-1131.2021.12.016http://dx.doi.org/10.3969/j.issn.1673-1131.2021.12.016
WAN C. A hybrid genetic LM algorithm for solving nonlinear least squares problems[J]. Changjiang Information & Communications, 2021, 34(12): 52-54.(in Chinese). doi: 10.3969/j.issn.1673-1131.2021.12.016http://dx.doi.org/10.3969/j.issn.1673-1131.2021.12.016
唐洪威, 谢文平, 崔毅, 等. 基于Levenberg-Marquardt算法的航空发动机模型求解混合算法[J]. 航空动力学报, 2023, 38(2): 371-381. doi: 10.13224/j.cnki.jasp.20210367http://dx.doi.org/10.13224/j.cnki.jasp.20210367
TANG H W, XIE W P, CUI Y, et al. Hybrid algorithm for aero-engine model solving based on Levenberg-Marquardt algorithm[J]. Journal of Aerospace Power, 2023, 38(2): 371-381.(in Chinese). doi: 10.13224/j.cnki.jasp.20210367http://dx.doi.org/10.13224/j.cnki.jasp.20210367
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