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基于共面圆的距离传感器与相机的相对位姿标定

王硕 祝海江 李和平 吴毅红

王硕, 祝海江, 李和平, 吴毅红. 基于共面圆的距离传感器与相机的相对位姿标定. 自动化学报, 2020, 46(6): 1154−1165 doi: 10.16383/j.aas.c190115
引用本文: 王硕, 祝海江, 李和平, 吴毅红. 基于共面圆的距离传感器与相机的相对位姿标定. 自动化学报, 2020, 46(6): 1154−1165 doi: 10.16383/j.aas.c190115
Wang Shuo, Zhu Hai-Jiang, Li He-Ping, Wu Yi-Hong. Relative pose calibration between a range sensor and a camera using two coplanar circles. Acta Automatica Sinica, 2020, 46(6): 1154−1165 doi: 10.16383/j.aas.c190115
Citation: Wang Shuo, Zhu Hai-Jiang, Li He-Ping, Wu Yi-Hong. Relative pose calibration between a range sensor and a camera using two coplanar circles. Acta Automatica Sinica, 2020, 46(6): 1154−1165 doi: 10.16383/j.aas.c190115

基于共面圆的距离传感器与相机的相对位姿标定

doi: 10.16383/j.aas.c190115
基金项目: 国家自然科学基金(61672084, 61572499, 61421004), 中央高校基本科研业务费专项基金(XK1802-4)资助
详细信息
    作者简介:

    王硕:北京化工大学自动化系硕士研究生. 2017年获得北京化工大学自动化专业学士学位. 主要研究方向为图像处理与计算机视觉. E-mail: 2017200698@mail.buct.edu.cn

    祝海江:北京化工大学信息科学与技术学院教授. 2004年获得中国科学院自动化研究所模式识别国家重点实验室博士学位. 主要研究方向为图像处理与计算机视觉. 本文通信作者. E-mail: zhuhj@mail.buct.edu.cn

    李和平:中国科学院自动化研究所副研究员. 2007年获得中国科学院自动化研究所模式识别与智能系统方向博士学位. 主要研究方向为机器人导航, 实时三维重建与应用. E-mail: heping.li@ia.ac.cn

    吴毅红:中国科学院自动化研究所模式识别国家重点实验室研究员. 2001年获得中国科学院系统科学研究所数学机械化研究中心应用数学博士学位. 主要研究方向为图像匹配, 相机标定, 相机位姿估计, 三维重建, 同时定位与地图构建.E-mail: yhwu@nlpr.ia.ac.cn

Relative Pose Calibration Between a Range Sensor and a Camera Using Two Coplanar Circles

Funds: Supported by National Natural Science Foundation of China (61672084, 61572499, 61421004) and Fundamental Research Funds for the Central Universities (XK1802-4)
  • 摘要: 近年来, 距离传感器与摄像机的组合系统标定在无人车环境感知中得到了广泛的研究与应用, 其中基于平面特征的方法简单易行而被广泛采用. 然而, 目前多数方法基于点匹配进行, 易错且鲁棒性较低. 本文提出了一种基于共面圆的距离传感器与相机的组合系统相对位姿估计方法. 该方法使用含有两个共面圆的标定板, 可以获取相机与标定板间的位姿, 以及距离传感器与标定板间的位姿. 此外, 移动标定板获取多组数据, 根据计算得到两个共面圆的圆心在距离传感器和相机下的坐标, 优化重投影误差与3D对应点之间的误差, 得到距离传感器与相机之间的位姿关系. 该方法不需要进行特征点的匹配, 利用射影不变性来获取相机与三维距离传感器的位姿. 仿真实验与真实数据实验结果表明, 本方法对噪声有较强的鲁棒性, 得到了精确的结果.
  • 图  1  共平面圆及两条关联直线[15]

    Fig.  1  Two coplanar circles and correspondingassociated lines[15]

    图  2  二维激光扫描平面与标定板相交的两段弦

    Fig.  2  Intersecting two chords of a 2D laser scan plane with a calibration board

    图  3  二维激光扫描平面与标定板不垂直的情况

    Fig.  3  Laser scan plane is not perpendicular tocalibration plane

    图  4  采用本文方法标定结果误差(三维)

    Fig.  4  Calibration errors by the proposed method (3D)

    图  5  文献[11]标定结果误差

    Fig.  5  Calibration errors by [11]

    图  6  采用本文方法在不同噪声系数下的标定结果误差(三维)

    Fig.  6  Calibration errors by the proposed method in different noise coefficients (3D)

    图  7  文献[13]标定结果误差

    Fig.  7  Calibration errors by [13]

    图  8  不同噪声水平下旋转误差与平移误差(二维)

    Fig.  8  Rotation errors and translation errors at different noise levels (2D)

    图  9  不同噪声系数下平移误差与旋转误差(二维)

    Fig.  9  Translation errors and rotation errors in different noise coefficients (2D)

    图  10  不同角度偏差下旋转误差

    Fig.  10  Rotation errors at different angel deviations

    图  11  不同角度偏差下平移误差

    Fig.  11  Translation errors at different angel deviations

    图  12  采集到的彩色图与深度图

    Fig.  12  RGB image and depth image

    图  13  深度图与彩色图融合结果

    Fig.  13  The fusion results of RGB images and depth images

    图  14  二维激光与图像融合结果

    Fig.  14  The fusion results of 2D laser points and images

    表  1  仿真实验中所用的激光相机位姿参数

    Table  1  Camera-Lidar transformation parameters in the simulator settings used for the experiments

    设定$t_x\;(\rm m)$$t_y\;(\rm m)$$t_z\;(\rm m)$$\psi\;(\rm rad)$$\theta\;(\rm rad)$$\phi\;(\rm rad)$
    1−0.8−0.1 0.4000
    20000.500
    30000.3 0.10.2
    4−0.3 0.2−0.20.3−0.10.2
    50000 0.10
    6000000.4
    7000000
    下载: 导出CSV
  • [1] Ha J H. Extrinsic calibration of a camera and laser range finder using a new calibration structure of a plane with a triangular hole. International Journal of Control, Automation, and Systems, 2012, 10(6): 1240−1244 doi: 10.1007/s12555-012-0619-7
    [2] Hoang V D, Cáceres Hernández D, Jo K H. Simple and efficient method for calibration of a camera and 2D laser rangefinder. In: Proceedings of the 2014 Asian conference on Intelligent Information and Database Systems (ACIIDS 2014). Lecture Notes in Computer Science. Bangkok, Thailand: Springer, Cham, 2014. 561−570
    [3] Zhang Q, Pless R. Extrinsic calibration of a camera and laser range finder (improves camera calibration). In: Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Sendai, Japan: IEEE, 2004. 2301−2306
    [4] Bok Y, Jeong Y, Choi D G, Kweon I S. Capturing village-level heritages with a hand-held camera-laser fusion sensor. International Journal of Computer Vision, 2011, 94(1): 36−53 doi: 10.1007/s11263-010-0397-8
    [5] Bok Y, Choi D G, Vasseur P, Kweon I S. Extrinsic calibration of non-overlapping camera-laser system using structured environment. In: Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). Chicago, IL, USA: IEEE, 2014. 436−443
    [6] Vasconcelos F, Barreto J P, Nunes U. A minimal solution for the extrinsic calibration of a camera and a laser-rangefinder. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2097−2107 doi: 10.1109/TPAMI.2012.18
    [7] 胡钊政, 赵斌, 李娜, 夏克文. 基于虚拟三面体的摄像机与二维激光测距仪外参数最小解标定新算法. 自动化学报, 2015, 41(11): 1951−1960

    Hu Zhao-Zheng, Zhao Bin, Li Na, Xia Ke-Wen. Minimal solution to extrinsic calibration of camera and 2D laser rangefinder based on virtual trihedron. Acta Automatica Sinica, 2015, 41(11): 1951−1960
    [8] Li G H, Liu Y H, Dong L, Cai X P, Zhou D X. An algorithm for extrinsic parameters calibration of a camera and a laser range finder using line features. In: Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. San Diego, CA, USA: IEEE, 2007. 3854−3859
    [9] Lee G M, Lee J H, Park S Y. Calibration of VLP-16 lidar and multi-view cameras using a ball for 360 degree 3D color map acquisition. In: Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017). Daegu, Korea: IEEE, 2017. 64−69
    [10] Dhall A, Chelani K, Radhakrishnan V, Krishna K M. LiDAR-camera calibration using 3D-3D point correspondences [Online], available: https://arxiv.org/pdf/1705.09785.pdf, January 9, 2019
    [11] Geiger A, Moosmann F, Car O, Schuster B. Automatic camera and range sensor calibration using a single shot. In: Proceedings of the 2012 IEEE International Conference on Robotics and Automation. RiverCentre, Saint Paul, Minnesota, USA: IEEE, 2012. 3936−3943
    [12] Velas M, Španěl M, Materna Z, Herout A. Calibration of RGB camera with Velodyne LiDAR. In: Proceedings of the 2014 International Conference on Computer Graphics, Visualization and Computer Vision (WSCG). Plzen, Czech: WSCG, 2014. 135−144
    [13] Guindel C, Beltrán J, Martín D, García F. Automatic extrinsic calibration for lidar-stereo vehicle sensor setups. In: Proceedings of the 20th IEEE International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017.
    [14] Wu Y H, Zhu H J, Hu Z Y, Wu F C. Camera calibration from the quasi-affine invariance of two parallel circles. In: Proceedings of the 2004 European Conference on Computer Vision. Prague, Czech. 2004. 190−202
    [15] Wu Y H, Li X J, Wu F C. Hu Z Y. Coplanar circles, quasi-affine invariance and calibration. Image and Vision Computing, 2006, 24(4): 319−326 doi: 10.1016/j.imavis.2005.11.008
    [16] 范蓉蓉, 唐付林, 刘青山. 基于两个共面圆的无匹配相机位姿计算. 自动化学报, 2019, 45(5): 993−998

    Fan Rong-Rong, Tang Fu-Lin, Liu Qing-Shan. Effective camera localization from two coplanar circles without matching. Acta Automatica Sinica, 2019, 45(5): 993−998
    [17] Wu Y H, Wang H R, Tang F L. Conic fitting: New easy geometric method and revisiting sampson distance. In: Proceedings of the 4th IAPR Asian Conference on Pattern Recognition. Nanjing, China. 2017.
    [18] Arun K S, Huang T S, Blostein S D. Least-squares fitting of two 3-d point sets. IEEE Transactions on Patten Analysis and Machine Intelligence, 1987, 5: 698−700
    [19] Pomerleau F, Colas F, Siegwart R. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends in Robotics (FnTROB), 2015, 4(1): 1−104 doi: 10.1561/2300000035
    [20] Kümmerle R, Grisetti G, Strasdat H, Konolige K, Burgard W. G2o: A general framework for graph optimization. In: Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA). Shanghai, China: IEEE. 2011.
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出版历程
  • 收稿日期:  2019-02-27
  • 录用日期:  2019-08-22
  • 网络出版日期:  2020-07-10
  • 刊出日期:  2020-07-10

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