一种基于平面模板的单应矩阵求解方法

在视觉测量、摄像机标定和三维重建等领域中,单应矩阵的估计都起着至关重要的作用。用平面模板代替传统的传感器进行视觉测量和摄像机标定,由于其成本低,操作快速简单等优点,已经得到了越来越广泛的应用。文中主要讨论了二维成像平面的透视投影模型,给出了一种最大似然估计法获取平面模板与成像平面的单应矩阵的求解过程,并用Levenberg-Marqurdt算法对结果进行非线性优化。该方法简单、快速、易实现,在计算机视觉应用中具有一定的理论意义和应用价值。     

一种基于平面模板的单应矩阵求解方法

在视觉测量、摄像机标定和三维重建等领域中,单应矩阵的估计都起着至关重要的作用.用平面模板代替传统的传感器进行视觉测量和摄像机标定,由于其成本低,操作快速简单等优点,已经得到了越来越广泛的应用.文中主要讨论了二维成像平面的透视投影模型,给出了一种最大似然估计法获取平面模板与成像平面的单应矩阵的求解过程,并用Levenberg-Marquardt算法对结果进行非线性优化.该方法简单、快速、易实现,在计算机视觉应用中具有一定的理论意义和应用价值.     

矿用输送带双目视觉检测自适应标定方法

针对煤矿输送带纵向撕裂视觉检测中,传统的摄像机标定方法复杂、精度低等问题,提出了一种高效的双目视觉检测自适应标定方法。分析了摄像机数学模型与双目视觉基本原理,在线性模型的基础上引入非线性畸变参数,将输送带图像与7×7矩阵模型融合提取出的关键特征角点坐标值代入矩阵约束方程,求解出摄像机内外参数及结构参数、畸变参数,并对其进行非线性优化,得到精确值,最后采用贝叶斯误差估计方法对计算出的参数与Faugeras自标定方法所得结果进行对比分析。实验结果表明该方法精度高,可靠性好。     

双目视觉技术在结构件尺寸测量中的应用

对结构件视觉测量的工程应用、检测模型进行讨论,提出一种双目视觉检测结构件空间尺寸的方法。应用改进的求解摄像机内外参数的方法对单摄像机有效焦距进行标定,应用以像素坐标到对应的极线距离为目标函数的优化方法对基本矩阵进行了估算,通过对本质矩阵的奇异值分解得到两个摄像机之间的旋转矩阵和平移向量。实验室条件下对轻型车凸轮轴凸轮间距空间尺寸进行了实际测量,结果表明,测量方法能够达到监测组合式凸轮轴凸轮间距的要求。     

基于共面靶标双目立体视觉传感器标定

根据双目立体视觉传感器的特点,提出了一种基于共面靶标的双目立体视觉传感器的标定方法.在不需要外部测量设备的情况下,通过共面靶标在平面上自由移动,获得靶标的标定点图像坐标,利用类似于Dr.Janne Heikkil(a)和Dr.Olli Silven所提出来的摄像机内部参数标定模型,采用线性和非线性结合的方法对双目摄像机进行摄像机内部参数的标定.试验结果表明,标定参数与出厂参数有一定的差异,但和实际情况还是吻合的,并且标定方法操作简便,切实可行.     

双目视觉测量传感器研究

研究了按照激光三角测量原理由 1个线激光器及 2个CCD摄像机组成的双目视觉测量传感器。探讨了 2个CCD摄像机的摆放位置及其对测量精度的影响,确定了其几何参数。在此基础上,论述了其工作过程,包括其标定方法、标定过程、扫描图像采集、特征提取、特征匹配及三维数据生成。该双目视觉测量传感器配合扫描机构(如三坐标测量机 )即可对物体进行非接触式三维激光扫描测量,其测量速度快,精度比较高,可以应用于逆向工程领域。     

基于本质矩阵的摄像机自标定方法

本质矩阵描述了在摄像机内参数矩阵已知的条件下的对极几何关系,是归一化图像坐标下的基础矩阵。鉴于本质矩阵具有两相等的非零奇异值,提出了一种基于本质矩阵的自标定方法,该方法首先利用本质矩阵这个特性来构造目标函数,考虑到传统非线性优化算法的诸多不足,最后用粒子群优化算法来求解。实验结果表明,该方法精度较高、鲁棒性较强,是一种简单而有效的自标定方法。     

基于主动视觉的手眼矩阵和光平面标定方法

为解决现有基于主动视觉方法标定手眼矩阵和结构光平面操作较复杂的问题,提出一种基于主动视觉的同时标定手眼矩阵和光平面的方法。通过精确控制机器人做两次相互正交的平移运动,求解手眼矩阵的旋转部分;而后通过两次及以上带旋转运动,求解手眼矩阵的平移部分和光平面方程。该方法简单,无需使用特定靶标,标定过程只需3个特征点,即可实现机器人手眼矩阵和光平面方程的标定;实验结果证明了该方法的有效性。     

双目立体视觉传感器的现场标定技术

双目立体视觉传感器在机器视觉和三维测量中有着广泛的应用。其中测量精度是一个很重要的因素,尤其是在三维测量中,高测量精度的要求使得传感器的标定显得尤为重要。该文提出了一种双目立体视觉传感器的现场标定方法,不仅提高了标定精度,还避免了离线标定中大量的重复性劳动,同时也研究了摄像机镜头畸变的问题。实验结果表明,此方法能够得到满意的标定精度。     

自由双目立体视觉摄像机动态外参数的获取

针对自由双目立体视觉中由于摄像机旋转导致的摄像机外参数变化的问题,提出一种基于旋转轴标定的动态外参数获取方法。在多个不同位置,立体标定得到多组旋转平移矩阵,利用最小二乘法求解旋转轴参数;结合初始位置左右摄像机的内、外参数及旋转角度,实时获取左右摄像机的外参数。利用所提方法获取动态外参数,并对棋盘角点进行三维重建,平均误差为0.241mm,标准差为0.156mm;与基于多平面标靶的标定方法相比,精度高且操作简单。所提方法无需实时标定,可完成摄像机旋转情况下动态外参数的获取。     

Catadioptric Stereo Using Planar Mirrors

By using mirror reflections of a scene, stereo images can be captured with a single camera (catadioptric stereo). In addition to simplifying data acquisition single camera stereo provides both geometric and radiometric advantages over traditional two camera stereo. In this paper, we discuss the geometry and calibration of catadioptric stereo with two planar mirrors. In particular, we will show that the relative orientation of a catadioptric stereo rig is restricted to the class of planar motions thus reducing the number of external calibration parameters from 6 to 5. Next we derive the epipolar geometry for catadioptric stereo and show that it has 6 degrees of freedom rather than 7 for traditional stereo. Furthermore, we show how focal length can be recovered from a single catadioptric image solely from a set of stereo correspondences. To test the accuracy of the calibration we present a comparison to Tsai camera calibration and we measure the quality of Euclidean reconstruction. In addition, we will describe a real-time system which demonstrates the viability of stereo with mirrors as an alternative to traditional two camera stereo.     

大视场双目立体视觉的摄像机标定

针对大视场视觉测量应用,在分析摄像机成像模型的基础上,设计制作了可自由转动的十字靶标,实现了大视场双目视觉摄像机的精确标定。将十字靶标在测量空间内多次均匀摆放,两摄像机同步拍摄多幅靶标图像。由本质矩阵得到摄像机参数的初始值,采用自检校光束法平差得到摄像机参数的最优解。该方法不要求特征点共面,仅需要知道特征点之间的物理距离,降低了靶标制作难度。采用TN3DOMS．S进行了实测,在1500mm×1500mm的测量范围内测试标准标杆,误差均方值为0．06mm。     

双目视觉测量系统摄像机外部参数标定研究

针对双目立体视觉测量系统中摄像机标定问题,讨论了基于标准长度的外部参数标定方法,选定了摄像机透视投影模型,采用双摄像机同时对放置于视场内的十字靶标拍摄多幅图像,得出了基于LabVIEW开发的摄像机标定方法.该方法利用了LabVIEW的开发环境,使用了数学工具包,将遗传算法与LM算法相结合,优化迭代获得摄像机外部参数,运算速度和精度大大提高.开发的模块可用于基于LabVIEW开发的工程软件进行高精度尺寸现场测量.在双目立体视觉测量系统标定结果基础上对标准靶进行测量,测量结果标准差达到0.1.     

Essential Matrix Estimation Using Gauss-Newton Iterations on a Manifold

A novel approach for essential matrix estimation is presented, this being a key task in stereo vision processing. We estimate the essential matrix from point correspondences between a stereo image pair, assuming that the internal camera parameters are known. The set of essential matrices forms a smooth manifold, and a suitable cost function can be defined on this manifold such that its minimum is the desired essential matrix. We seek a computationally efficient optimization scheme towards meeting the demands of on-line processing of video images. Our work extends and improves the earlier research by Ma et al., who proposed an intrinsic Riemannian Newton method for essential matrix computations. In contrast to Ma et al., we propose three Gauss-Newton type algorithms that have improved convergence properties and reduced computational cost. The first one is based on a novel intrinsic Newton method, using the normal Riemannian metric on the manifold consisting of all essential matrices. The other two methods are Newton-like methods, that are more efficient from a numerical point of view. Local quadratic convergence of the algorithms is shown, based on a careful analysis of the underlying geometry of the problem.     

一种新的基于主动视觉系统的摄像机自标定方法

摄像机标定是从二维图像取获三维信息必不可少的步骤。该文提出了一种新的基于主动视觉系统的摄像机自标定方法,通过控制摄像机平台作4次平移运动（其中任意3次均不在同一平面上）即可线性地标定摄像机的内参数以及摄像机坐标系与平台坐标系之间的旋转矩阵。同时,还分别给出了利用立体视觉方法和纯极点方法唯一求解摄像机坐标系与平台坐标系之间的平移向量的充要条件。     

一种基于校正误差的立体相机标定算法

立体相机的标定是一个精确求解各个相机内参数以及相机之间关系参数的过程.它是三维重建的基础,其标定精度的好坏直接影响立体重建的结果.为此提出了一种使用校正误差作为代价函数的立体相机标定算法.该算法首先使用传统的基于重投影误差的方法对单个相机的内参数进行标定,然后利用校正误差完成对相机之间关系参数的标定求解.由于校正误差的计算只与相机内参数以及关系参数有关,可以避免在标定过程中使用难以精确标定的相机外参数.实验结果表明本算法能够有效的提高立体相机标定的精度.     

Segment Based Camera Calibration

The basic idea of calibrating a camera system in previous approaches is to determine camera parmeters by using a set of known 3D points as calibration reference.In this paper,we present a method of camera calibration in whih camera parameters are determined by a set of 3D lines.A set of constraints is derived on camea parameters in terms of perspective line mapping.Form these constraints,the same perspective transformation matrix as that for point mapping can be computed linearly.The minimum number of calibration lines is 6.This result generalizes that of Liu,Huang and Faugeras^[12] for camera location determination in which at least 8 line correspondences are required for linear computation of camera location.Since line segments in an image can be located easily and more accurately than points,the use of lines as calibration reference tends to ease the computation in inage preprocessing and to improve calibration accuracy.Experimental results on the calibration along with stereo reconstruction are reported.     

Using vanishing points for camera calibration

In this article a new method for the calibration of a vision system which consists of two (or more) cameras is presented. The proposed method, which uses simple properties of vanishing points, is divided into two steps. In the first step, the intrinsic parameters of each camera, that is, the focal length and the location of the intersection between the optical axis and the image plane, are recovered from a single image of a cube. In the second step, the extrinsic parameters of a pair of cameras, that is, the rotation matrix and the translation vector which describe the rigid motion between the coordinate systems fixed in the two cameras are estimated from an image stereo pair of a suitable planar pattern. Firstly, by matching the corresponding vanishing points in the two images the rotation matrix can be computed, then the translation vector is estimated by means of a simple triangulation. The robustness of the method against noise is discussed, and the conditions for optimal estimation of the rotation matrix are derived. Extensive experimentation shows that the precision that can be achieved with the proposed method is sufficient to efficiently perform machine vision tasks that require camera calibration, like depth from stereo and motion from image sequence.     

Camera calibration and three-dimensional world reconstruction of stereo-vision using neural networks

Stereo-pair images obtained from two cameras can be used to compute three-dimensional (3D) world coordinates of a point using triangulation. However, to apply this method, camera calibration parameters for each camera need to be experimentally obtained. Camera calibration is a rigorous experimental procedure in which typically 12 parameters are to be evaluated for each camera. The general camera model is often such that the system becomes nonlinear and requires good initial estimates to converge to a solution. We propose that, for stereo vision applications in which real-world coordinates are to be evaluated, artificial neural networks be used to train the system such that the need for camera calibration is eliminated. The training set for our neural network consists of a variety of stereo-pair images and corresponding 3D world coordinates. We present the results obtained on our prototype mobile robot that employs two cameras as its sole sensors and navigates through simple regular obstacles in a high-contrast environment. We observe that the percentage errors obtained from our set-up are comparable with those obtained through standard camera calibration techniques and that the system is accurate enough for most machine-vision applications.     

改进格雷码结构光双目视觉工件3D测量

机器人喷涂、焊接、研磨等现代制造过程,通常需要获取工件3D几何形状信息以实现路径自动规划。针对此需求,提出了一种改进灰度编码的结构光双目视觉成像与三维测量系统。在离线标定获得相机参数的基础上,采用黑白反向重投影方法解决传统格雷码结构光成像中边界模糊问题;同时,提出快速区域块搜索算法,用于立体匹配和视差图生成;最后,采用Lanczos插值算法填补遮挡造成的视差图空白,从而获得完整的深度信息,实现工件高精度3D重建。实际工件成像实验结果表明：该方法能够实现工件快速、高精度三维重建,尤其对无纹理和弱纹理目标能获得较好的结果,能够满足工业环境测量需求。