基于平面格网的非量测CCD相机自标定

传统的相机标定方法通常需要建立复杂3维标定块或高精度3维控制场,在实际应用中受到了一定的限制。本文采用平面控制格网作为标定块,根据相机的理想模型确定内方位元素,利用2维直接线性变换和共线方程分解出相机的外方位元素初值,采用改进的Hough变换算法检测标定图像中的格网直线并利用最小二乘法拟合出最佳直线,通过求直线的交点得到标定格网点的像坐标。最后利用自检校光线束法平差进行相机的精确标定。实际图像数据实验结果表明,主点和焦距的标定精度分别达到了0.2像素和0.3像素左右。可以满足高精度近景3维量测的要求。     

Camera self-calibration with varying intrinsic parameters by an unknown three-dimensional scene

This work proposes a method of camera self-calibration having varying intrinsic parameters from a sequence of images of an unknown 3D object. The projection of two points of the 3D scene in the image planes is used with fundamental matrices to determine the projection matrices. The present approach is based on the formulation of a nonlinear cost function from the determination of a relationship between two points of the scene and their projections in the image planes. The resolution of this function enables us to estimate the intrinsic parameters of different cameras. The strong point of the present approach is clearly seen in the minimization of the three constraints of a self-calibration system (a pair of images, 3D scene, any camera): The use of a single pair of images provides fewer equations, which minimizes the execution time of the program, the use of a 3D scene reduces the planarity constraints, and the use of any camera eliminates the constraints of cameras having constant parameters. The experiment results on synthetic and real data are presented to demonstrate the performance of the present approach in terms of accuracy, simplicity, stability, and convergence.     

摄像机内参数自标定——理论与算法

讨论如何通过摄像机的旋转运动标定其内参数.当摄像机绕其坐标轴旋转时,运用 代数方法给出了计算内参数的公式.该公式在2D投影变换接近理论值P时是非常实用的. 在摄像机绕未知轴旋转时,根据相应的2D投影变换,运用矩阵特征向量理论给出了内参数的 通解公式.通过摄像机绕两个不同未知轴的旋转,摄像机内参数能被唯一地确定.这些结果为 摄像机自标定算法提供了理论基础,同时也给出了实用性算法.模拟实验和真实图像实验的 结果表明本文所给的算法具有一定实用价值.     

摄像机内参数自标定——理论与算法1)

讨论如何通过摄像机的旋转运动标定其内参数.当摄像机绕其坐标轴旋转时,运用代数方法给出了计算内参数的公式.该公式在2D投影变换接近理论值P时是非常实用的.在摄像机绕未知轴旋转时,根据相应的2D投影变换,运用矩阵特征向量理论给出了内参数的通解公式.通过摄像机绕两个不同未知轴的旋转,摄像机内参数能被唯一地确定.这些结果为摄像机自标定算法提供了理论基础,同时也给出了实用性算法。模拟实验和真实图像实验的结果表明本文所给的算法具有一定实用价值.     

带有倾斜因子的摄像机自标定方法

摄像机自标定是三维重建技术的基本问题 ,得到许多学者的大力研究 .为了简化摄像机自标定过程 ,一般假设摄像机内参数中的倾斜因子为零 ,然后对主点和焦距进行自标定 .但在摄像机模型为完全的射影模型时 ,即当倾斜因子 (Skew Factor)值较大时 ,则使用上述假设得到的自标定参数误差较大 ,有时甚至无法得到结果 .为了对倾斜因子值较大的摄像机进行准确标定 ,提出了一种当摄像机的倾斜因子已知但不为零时的摄像机自标定方法 ,试验结果证明该方法可以得到比较准确的摄像机内参数 ,并可使得后续的三维重建得到较好的结果 .     

基于序列图像的摄像机自标定方法

提出了一种新的结合摄影测量和计算机视觉相关理论的摄像机自标定方法。首先通过序列图像的匹配点对,利用计算机视觉理论中的8点法求得摄像机基础矩阵F,通过矩阵F利用Kruppa方程求得矩阵C,对矩阵C进行Cholesky分解得到摄像机的内参数矩阵K,然后将求出的内参数作为初始值,利用摄影测量理论进行相对定向和绝对定向,最小二乘前方交会计算得到匹配点对的三维空间坐标,最后由匹配点对的三维空间坐标及其图像坐标,采用三维直接线性变换和光束法平差方法解算出摄像机内、外参数及畸变系数。该方法不依赖于特定的场景几何约束条件,只要序列图像之间有匹配点对,就可以进行自标定工作,具有广泛的适用性。模拟数据和真实图像的实验结果表明：该方法计算过程简单,标定精度高,是一种值得借鉴的摄像机自标定方法。     

Camera calibration using one-dimensional information and its applications in both controlled and uncontrolled environments

Camera model and its calibration are required in many applications for coordinate conversions between the two-dimensional image and the real three-dimensional world. Self-calibration method is usually chosen for camera calibration in uncontrolled environments because the scene geometry could be unknown. However when no reliable feature correspondences can be established or when the camera is static in relation to the majority of the scene, self-calibration method fails to work. On the other hand, object-based calibration methods are more reliable than self-calibration methods due to the existence of the object with known geometry. However, most object-based calibration methods are unable to work in uncontrolled environments because they require the geometric knowledge on calibration objects. Though in the past few years the simplest geometry required for a calibration object has been reduced to a 1D object with at least three points, it is still not easy to find such an object in an uncontrolled environment, not to mention the additional metric/motion requirement in the existing methods. Meanwhile, it is very easy to find a 1D object with two end points in most scenes. Thus, it would be very worthwhile to investigate an object-based method based on such a simple object so that it would still be possible to calibrate a camera when both self-calibration and existing object-based calibration fail to work. We propose a new camera calibration method which requires only an object with two end points, the simplest geometry that can be extracted from many real-life objects. Through observations of such a 1D object at different positions/orientations on a plane which is fixed in relation to the camera, both intrinsic (focal length) and extrinsic (rotation angles and translations) camera parameters can be calibrated using the proposed method. The proposed method has been tested on simulated data and real data from both controlled and uncontrolled environments, including situations where no explicit 1D calibration objects are available, e.g. from a human walking sequence. Very accurate camera calibration results have been achieved using the proposed method.     

基于遗传算法的摄像机自标定方法

摄像机标定是计算机视觉领域的关键技术，其中的自标定是只根据图像计算摄像机的内参数，其标定过程简单，适用性强。由于传统的用于摄像机自标定的Kruppa方程不仅需要计算基础矩阵，还要计算图像的极点，而图像的极点又不是固定不变的，且会导致计算结果的不稳定，为此，针对传统摄像机自标定方法的上述不足，利用遗传算法完成了Hartley新的Kruppa方程的摄像机自标定过程，以便将这个过程完全转化为通过代价函数最小化来求得摄像机的内参数，这就排除了极点的不稳定因素。实验结果表明，该方法是简单、有效的，可以作为一种通用的标定工具。     

Self-calibration of a 1D projective camera and its application to the self-calibration of a 2D projective camera

We introduce the concept of self-calibration of a 1D projective camera from point correspondences, and describe a method for uniquely determining the two internal parameters of a 1D camera, based on the trifocal tensor of three 1D images. The method requires the estimation of the trifocal tensor which can be achieved linearly with no approximation unlike the trifocal tensor of 2D images and solving for the roots of a cubic polynomial in one variable. Interestingly enough, we prove that a 2D camera undergoing planar motion reduces to a 1D camera. From this observation, we deduce a new method for self-calibrating a 2D camera using planar motions. Both the self-calibration method for a 1D camera and its applications for 2D camera calibration are demonstrated on real image sequences.     

X射线图像三维重建的自标定优化算法

根据X射线图像成像特点,提出一种适用于X射线图像三维重建的自标定算法。首先基于SIFT算法得到相邻2幅X射线图像对应轮廓特征匹配关系;然后根据匹配关系计算得到基础矩阵;接着根据基础矩阵估计X射线无损检测设备内参数初值;最后基于改进Kruppa方程优化内参数,得到X射线图像三维重建自标定内参数。根据优化前后的内参数建立的电力金具三维模型,从形状和关键尺寸误差2方面进行对比,结果表明优化后的内参数具有较高的精度和可靠性。     

基于EXIF信息的可变内参数序列图像的三维重构*

基于图像中数码相机嵌入的元数据信息,提出了一种可变内参数的序列图像重构算法,首先从序列中选择两幅图像建立初始结构,然后依次将其他图像加入当前重构结果,进一步通过集束调整来最小化序列中所有图像重投影误差,得到精确的三维重构结果,避免了复杂繁琐的自标定过程。实验结果验证了算法的有效性。     

Autonomous robot calibration using vision technology

Unlike the traditional robot calibration methods, which need external expensive calibration apparatus and elaborate setups to measure the 3D feature points in the reference frame, a vision-based self-calibration method for a serial robot manipulator, which only requires a ground-truth scale in the reference frame, is proposed in this paper. The proposed algorithm assumes that the camera is rigidly attached to the robot end-effector, which makes it possible to obtain the pose of the manipulator with the pose of the camera. By designing a manipulator movement trajectory, the camera poses can be estimated up to a scale factor at each configuration with the factorization method, where a nonlinear least-square algorithm is applied to improve its robustness. An efficient approach is proposed to estimate this scale factor. The great advantage of this self-calibration method is that only image sequences of a calibration object and a ground-truth length are needed, which makes the robot calibration procedure more autonomous in a dynamic manufacturing environment. Simulations and experimental studies on a PUMA 560 robot reveal the convenience and effectiveness of the proposed robot self-calibration approach.     

基于多视图像的摄像机自标定方法

摄像机标定是三维重建时的必要步骤。传统的标定方法对设备要求高、操作繁琐,而自标定方法虽然简便,但精度不高,会严重影响三维重建的效果。因此,越来越需要一种操作简便并且精度高的自标定方法。采用SIFT特征点匹配算法,根据多视序列图像中对应点间的相互关系,利用光束法平差,提出了一种基于局部-全局混合优化的迭代优化方法。针对图像匹配量大的问题,提出了一种邻域内图像互匹配方法来降低时间代价。实验表明,本文提出的多摄像机自标定方法是一种有效的高精度方法,采用的邻域内图像互匹配技术能很好地降低图像匹配的时间消耗。根据多视图像的对应点间相互关系,充分利用局部-全局优化的思想,通过混合优化的方法得到相机参数,对比现有自标定算法,本文给出的方法有较高的精度和鲁棒性。     

Using geometric constraints through parallelepipeds for calibration and 3D modeling

This paper concerns the incorporation of geometric information in camera calibration and 3D modeling. Using geometric constraints enables more stable results and allows us to perform tasks with fewer images. Our approach is motivated and developed within a framework of semi-automatic 3D modeling, where the user defines geometric primitives and constraints between them. In this paper, first a duality that exists between the shape parameters of a parallelepiped and the intrinsic parameters of a camera is described. Then, a factorization-based algorithm exploiting this relation is developed. Using images of parallelepipeds, it allows us to simultaneously calibrate cameras, recover shapes of parallelepipeds, and estimate the relative pose of all entities. Besides geometric constraints expressed via parallelepipeds, our approach simultaneously takes into account the usual self-calibration constraints on cameras. The proposed algorithm is completed by a study of the singular cases of the calibration method. A complete method for the reconstruction of scene primitives that are not modeled by parallelepipeds is also briefly described. The proposed methods are validated by various experiments with real and simulated data, for single-view as well as multiview cases.     

摄像机自标定方法的研究与进展

该文回顾了近几年来摄像机自标定技术的发展,并分类介绍了其中几种主要方法.同 传统标定方法相比,自标定方法不需要使用标定块,仅根据图像间图像点的对应关系就能估计 出摄像机内参数.文中重点介绍了透视模型下的几种重要的自标定方法,包括内参数恒定和内 参数可变两种情形;最后还简要介绍了几种非透视模型下的摄像机自标定方法.     

一种考虑二阶径向畸变的主动视觉自标定算法

基于主动视觉的摄像机自标定是摄像机标定的一个重要分支 ,由于普通的 CCD摄像机拍摄的像片存在着各种类型的几何畸变 ,其中以径向畸变最为严重 ,因此研究考虑径向畸变的自标定技术有着重要的意义 .为了使标定结果更精确 ,提出了一种考虑二阶径向畸变的内参数自标定方法 ,并通过推导考虑二阶径向畸变的极线几何约束 ,得出了如果能控制摄像机做 4次不在同一平面上的平移运动 ,则可以标定摄像机的内参数和二阶径向畸变系数的结论 .仿真实验结果表明 ,该算法精度很高 ,且具有一定的鲁棒性 ,可用于摄像机的标定 .     

基于环视图象的数字相机内参数自定标方法

描述了一种适用于IBR系统的数字相机内参数自定标方法。该方法基于跟踪机机旋转得到的图象系列的特征匹配点以,而不需要标定物。认定在相机旋转过程中,其光学中心是稳定不变的,也即图象中心是固定的,可以事先定标;但容许相机的焦距在各幅图象间有变化,利用真实图象序列进行了实验验证,表明该方法能鲁棒地估算相机内参数。     

An improved self-calibration approach based on adaptive genetic algorithm for position-based visual servo

An improved self-calibrating algorithm for visual servo based on adaptive genetic algorithm is proposed in this paper. Our approach introduces an extension of Mendonca-Cipolla and G. Chesi＇s self-calibration for the positionbased visual servo technique which exploits the singular value property of the essential matrix. Specifically, a suitable dynamic online cost function is generated according to the property of the three singular values. The visual servo process is carried out simultaneous to the dynamic self-calibration, and then the cost function is minimized using the adaptive genetic algorithm instead of the gradient descent method in G. Chesi＇s approach. Moreover, this method overcomes the limitation that the initial parameters must be selected close to the true value, which is not constant in many cases. It is not necessary to know exactly the camera intrinsic parameters when using our approach, instead, coarse coding bounds of the five parameters are enough for the algorithm, which can be done once and for all off-line. Besides, this algorithm does not require knowledge of the 3D model of the object. Simulation experiments are carried out and the results demonstrate that the proposed approach provides a fast convergence speed and robustness against unpredictable perturbations of camera parameters, and it is an effective and efficient visual servo algorithm.     

基于标记点丢失的多幅自标定图像的3维重建和相机姿态恢复

为了对标记点丢失的多幅自标定图像进行精确重建，提出了一种基于标记点丢失的多幅自标定图像的3维重建和相机姿态恢复的方法。该方法与原来方法的不同之处在于，该方法是利用标记点（编码点和非编码点）的方式，即用编码点进行单CCD相机的自标定和姿态恢复，而用非编码点进行3维点的3维重建。该方法有以下3个主要特点：（1）由于该方法采用了标记点的自动识别匹配，所以避免了手工交互选择图像点对（point correspondences）费工费时的问题；（2）由于标记点匹配精确，提高了3维点的重建精度，故符合工程要求；（3）由于噪音对标记点的像点影响较小，因此该方法比以前的方法具有更好的鲁棒性。实验结果表明，利用该方法产生的3维重建点精确可靠，能够满足逆向工程等应用的要求。     

Self-calibration of hybrid central catadioptric and perspective cameras

Hybrid central catadioptric and perspective cameras are desired in practice, because the hybrid camera system can capture large field of view as well as high-resolution images. However, the calibration of the system is challenging due to heavy distortions in catadioptric cameras. In addition, previous calibration methods are only suitable for the camera system consisting of perspective cameras and catadioptric cameras with only parabolic mirrors, in which priors about the intrinsic parameters of perspective cameras are required. In this work, we provide a new approach to handle the problems. We show that if the hybrid camera system consists of at least two central catadioptric and one perspective cameras, both the intrinsic and extrinsic parameters of the system can be calibrated linearly without priors about intrinsic parameters of the perspective cameras, and the supported central catadioptric cameras of our method can be more generic. In this work, an approximated polynomial model is derived and used for rectification of catadioptric image. Firstly, with the epipolar geometry between the perspective and rectified catadioptric images, the distortion parameters of the polynomial model can be estimated linearly. Then a new method is proposed to estimate the intrinsic parameters of a central catadioptric camera with the parameters in the polynomial model, and hence the catadioptric cameras can be calibrated. Finally, a linear self-calibration method for the hybrid system is given with the calibrated catadioptric cameras. The main advantage of our method is that it cannot only calibrate both the intrinsic and extrinsic parameters of the hybrid camera system, but also simplify a traditional nonlinear self-calibration of perspective cameras to a linear process. Experiments show that our proposed method is robust and reliable.