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

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

王硕^{1, 2,},
祝海江^1,,
李和平^2,,
吴毅红^2,

1.
北京化工大学信息科学与技术学院北京 100029
2.
中国科学院自动化研究所模式识别国家重点实验室北京 100190

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

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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

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

WANG Shuo^{1, 2
,},
ZHU Hai-Jiang^1
,,
LI He-Ping^2
,,
WU Yi-Hong^2
,

1.
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029
2.
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190

Funds: Supported by National Natural Science Foundation of China (61672084, 61572499, 61421004) and Fundamental Research Funds for the Central Universities (XK1802-4)

摘要

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.
- 多传感器标定 /
- 距离传感器与相机标定 /
- 深度相机与相机标定 /
- 激光与相机标定
Abstract: Relative pose calibration between a range sensor and a camera has been widely studied and applied in the environment perception of unmanned vehicles, of which the methods based on the planar features are greatly easy to be implemented. However, most of the current methods are based on point matching, which is easy to have errors and low robustness. In this paper, a relative pose calibration method between a range sensor and a camera from two coplanar circles is proposed. Using such a calibration object including two coplanar circles, the pose between the camera and the calibration object can be determined as well as the pose between the range sensor and the calibration object. Moreover, moving the calibration object to obtain more data, the center coordinates of two circles in the range sensor and camera coordinate system can be computed to refine the reprojection errors and 3D-3D correspondence point errors. Then, the pose between a range sensor and a camera can be estimated. The advantages of this method are as follows: matching among multiple points are not needed by using projective invariance. The simulation and real data experiments proved that this method has high accuracy and robustness.
- Multi-sensors calibration /
- ranger sensor and camera calibration /
- depth camera and camera calibration /
- laser and camera calibration

HTML全文

图 1 共平面圆及两条关联直线^[15]

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

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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

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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

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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)

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图 5 文献[11]标定结果误差

Fig. 5 Calibration errors by [11]

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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)

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图 7 文献[13]标定结果误差

Fig. 7 Calibration errors by [13]

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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)

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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)

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图 10 不同角度偏差下旋转误差

Fig. 10 Rotation errors at different angel deviations

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图 11 不同角度偏差下平移误差

Fig. 11 Translation errors at different angel deviations

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图 12 采集到的彩色图与深度图

Fig. 12 RGB image and depth image

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图 13 深度图与彩色图融合结果

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

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图 14 二维激光与图像融合结果

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

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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	0.4	0	0	0
2	0	0	0	0.5	0	0
3	0	0	0	0.3	0.1	0.2
4	−0.3	0.2	−0.2	0.3	−0.1	0.2
5	0	0	0	0	0.1	0
6	0	0	0	0	0	0.4
7	0	0	0	0	0	0

下载: 导出CSV

参考文献(20)

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