快速有效的摄像机标定方法

提出了一种快速有效的基于径向约束的两步算法.该算法综合了线性模型和非线性模型的优点,在求解摄像机参数的过程中,采用线性模型标定摄像机中的一部分参数,进一步考虑非线性畸变,通过条件化简将非线性方程转化成线性方程求解其余摄像机参数,有效避免了直接求解非线性方程带来的计算繁琐和结果不稳定的缺陷.最后对标定结果采取最优化算法求精.实验结果表明,优化后的两步算法提高了摄像机标定的效率和准确性.     

双目视觉的立体标定方法

为实现双目视觉系统的立体标定,分析了摄像机成像模型,并充分考虑了透镜的径向畸变和切向畸变,提出了一种新的立体标定算法。该算法利用张正友的灵活标定算法,初步求取摄像机的内参数,结合Brown算法并提取图像中角点的子像素级坐标,精确求取摄像机内参数和畸变向量。为方便后续的图像校正,基于前面的单个摄像机标定,通过计算空间中的景物点在左右摄像机成像平面上的位置关系,计算出双目视觉系统中两个摄像机之间的旋转矩阵R和平移向量T,从而实现了立体标定。实验结果表明,该算法能取得较高的精度,可以应用于双目视觉系统。     

考虑径向畸变的机器人视觉系统的参数标定

针对GRB-400机器人视觉系统,建立完整的摄像机一阶径向畸变模型。用GRB系统的Matrox图像采集卡采集标定点坐标,利用径向排列约束方法 (简称RAC)建立线性方程组求解摄像机的径向畸变参数。基于此畸变参数实现图像校正,以提高对目标物体的定位精度,从而提高机器人视觉系统对摄像机标定参数变化的鲁棒性。实验结果表明,该方法有较高的标定精度。     

基于一阶径向畸变算法的双目摄像机多位姿标定方法

双目立体视觉中在对物体进行三维测量或精准定位时,需要对摄像机进行标定以获得其内外参数。研究径向畸变摄像机模型,构造了基于一阶径向畸变（RAC）算法的双目摄像机内外参数线性求解公式。考虑侧倾角、旋转角、俯仰角以及透镜的主要畸变因素,修正了传统RAC标定法中只考虑径向畸变、部分参数需要先验值的缺陷。利用标定所得内、外参数进行了多位姿双目摄像机三维重构实验。实验结果表明,该标定方法重投影误差分布在[-0.3,0.3],动态识别结果与实际运行轨迹重合率为96%,对降低双目立体视觉三维测量误差率有积极性影响。     

双目视觉测量系统的标定及3维测量

针对结构光形位公差视觉测量系统,提出了一阶径向畸变的摄像机成像模型和标定方法,并对传统的直接线性变换(DLT变换)标定算法做了些改进。即先针对没有畸变的线性模型,利用传统的标定算法,解线性超定方程组求解摄像机的全部参数,再针对引入一阶径向畸变的成像模型,以线性模型的参数为初值,通过非线性迭代优化摄像机的图像中心,等效焦距,倾斜因子,畸变系数等内部参数。实验结果表明,该方法无需预标定,精度适中,是相对简单实用的标定方法。     

摄像机自标定算法在虚拟演播室中的应用

本文就摄像机自标定算法进行了分析,在虚拟演播环境中,提出了基于平面点的标定算法,采用自己设计的实验方法,计算出了摄像机的内参数,结果证明能满足虚拟演播室中摄像机标定的精度。     

基于Matlab工具箱的摄像机标定

研究了摄像机成像的基本原理,论述了摄像机内部参数和外部参数的推导过程,借助Matlab标定工具箱,采用平面靶标的标定方法,快速准确地完成了标定实验,得出内部参数和外部参数,并对所得外部参数进行数据处理,得出摄像机运动前后两坐标系之间的相互位置关系.     

基于粒子群遗传算法的BP神经网络摄像机标定

摄像机标定是从二维图像提取三维空间信息的关键步骤,标定的精度直接关系到三维重构结果的逼真程度。为了有效解决传统摄像机标定算法中的多参数、计算费时费力等问题,提高摄像机标定的精度和速度,将粒子群遗传算法（particle swarm optimization genetic algorithm,PSO-GA）应用于摄像机标定中。对参数进行粒子群算法优化后,再使用遗传算法中的选择、交叉和变异等操作进行参数优化,以实现粒子群算法与遗传算法的融合。结合后的算法全局搜索能力较强,收敛速度更快,优化能力与鲁棒性得以提高。同时,基于神经网络的摄像机标定方法所能覆盖的标定空间十分有限,提出了一种采用粒子群遗传算法优化BP神经网络的摄像机标定方法,以解决传统摄像机标定方法难以解决的问题。实验数据表明,基于粒子群遗传算法的BP神经网络标定是一种可行的方法,标定精度高,收敛速度快,泛化能力强。     

基于数据拟合的摄像机标定和图像的数值校正

采用的摄像机成像模型考虑了透镜的径向畸变和切向畸变。标定过程包括对摄像机的内外部参数的直接线性变换估算和基于内部映射牛顿法的子空间置信域法的非线性优化算法优化。同时提出了对畸变图像校正的数值计算方法。     

基于OpenCV的改进两步法摄像机标定

采用改进两步法实现了摄像机的标定,并给出基于OpenCV算法,充分发挥了OpenCV的函数库功能。对两步标定法的改进是在原两步法得到摄像机参数值的基础上,用非线性优化算法对所有参数进一步优化,得到最终参数值,既提高了摄像机标定精度和计算效率,又无需对摄像机像面中心预标定,简化了标定过程。     

Context calibration

The basic starting point of this paper is that context constitutes most of the user interface when doing VR-related experiments, but even so one bases performance measures on only a few active tasks. Thus, in order to meaningfully compare results obtained in vastly different experiments one needs to somehow subtract the contribution to observables that are due to the context. For the case where one is investigating whether changes in one observable causes changes in another, a method, context calibration, is proposed that does just that. This method is expected to, to a large extent, factor out the part of one's results that are due to factors that are not explicitly considered when evaluating the experiment, factors that the experimenter might not even suspect influences the experiment. A procedure for systematically investigating the theoretical assumptions underlying context calibration is also discussed as is an initial experiment adhering to the proposed methodology.     

基于MATLAB中calibration toolbox的相机标定应用研究

相机标定的目的是确定相机的几何和光学参数以及相机相对于世界坐标系的方位。cal-ibration toolbox作为一个标定工具,容纳了如Tsai、Faugeras等多种经典的标定方法,从自主标定的使用方面详细介绍了calibration toolbox的使用方法。     

Camera calibration issues in robot calibration with eye-on-hand configuration

One of the possible methods for accurate, fast, low-cost and automated robot calibration is to employ a single camera rigidly mounted to the robot end-effector together with a single camera calibration board. The end-effector pose is measured by calibration of the camera at every robot measurement configuration. This paper contends that, with several modifications, Tsai's radial alignment constraint (RAC) camera calibration method can be made a fast and sufficiently accurate pose measurement technique. This paper focuses on speed, accuracy and cost enhancement of RAC-based camera calibration. A fast RAC-based algorithm is proposed, which cuts the computation time of Tsai's original algorithm by about a 5: 1 ratio while keeping its accuracy within the tolerances required for a successful robot calibration. A low-cost method for estimation of the ratio of scale factors of the camera/vision system is also proposed. This method does not require a precision vertical micrometer stage to provide non-coplanar calibration points data for camera calibration. Finally, the phenomenon of perspective projection distortion of circular camera calibration points is fully analyzed and error compensation methods are proposed.     

Paracatadioptric camera calibration

Catadioptric sensors refer to the combination of lens-based devices and reflective surfaces. These systems are useful because they may have a field of view which is greater than hemispherical, providing the ability to simultaneously view in any direction. Configurations which have a unique effective viewpoint are of primary interest, among these is the case where the reflective surface is a parabolic mirror and the camera is such that it induces an orthographic projection and which we call paracatadioptric. We present an algorithm for the calibration of such a device using only the images of lines in space. In fact, we show that we may obtain all of the intrinsic parameters from the images of only three lines and that this is possible without any metric information. We propose a closed-form solution for focal length, image center, and aspect ratio for skewless cameras and a polynomial root solution in the presence of skew. We also give a method for determining the orientation of a plane containing two sets of parallel lines from one uncalibrated view. Such an orientation recovery enables a rectification which is impossible to achieve in the case of a single uncalibrated view taken by a conventional camera. We study the performance of the algorithm in simulated setups and compare results on real images with an approach based on the image of the mirror's bounding circle     

CCD摄像机标定

在基于单目视觉的农业轮式移动机器人自主导航系统中,CCD摄像机标定是农业轮式移动机器人正确和安全导航的前提和关键。摄像机标定确立了地面某点的三维空间坐标与计算机图像二维坐标之间的对应关系,机器人根据该关系计算出车体位姿值自主导航。因此,根据CCD摄像机针孔成像模型,利用大地坐标系中平面模板上已知的各点坐标,建立与计算机图像空间中各对应像素值之间的关系方程组,在Matlab环境下拟合出摄像机各内外参数。实验结果表明：该方法可以正确完成CCD摄像机标定。     

Analysis of Tsai calibration method using two- and three-dimensional calibration objects

Camera calibration is a fundamental process for both photogrammetric and computer vision. Since the arrival of the direct linear transformation method and its later revisions, new methods have been developed by several authors, such as: Tsai, Heikkilä and Zhang. Most of these have been based on the pinhole model, including distortion correction. Some of these methods, such as Tsai method, allow the use of two different techniques for determining calibration parameters: a non-coplanar calibration technique using three-dimensional (3D) calibration objects, and a coplanar technique that uses two-dimensional (2D) calibration objects. The calibration performed by observing a 3D calibration object has good accuracy, and produces very efficient results; however, the calibration object must be accurate enough and requires an elaborate configuration. In contrast, the use of 2D calibration objects yields less accurate results, is much more flexible, and does not require complex calibration objects that are costly to produce. This article compares these two different calibration procedures from the perspective of stereo measurement. Particular attention was focused on the accuracy of the calculated camera parameters, the reconstruction error in the computer image coordinates and in the world coordinate system and advanced image-processing techniques for subpixel detection during the comparison. The purpose of this work is to establish a basis and selection criteria for choosing one of these techniques for camera calibration, according to the accuracy required in each of the many applications using photogrammetric vision: robot calibration methods, trajectory generation algorithms, articulated measuring arm calibration, and photogrammetric systems.     

红外目标模拟器校准装置构建和校准技术研究

在对传统红外目标模拟器校准装置分析的基础上，结合辐射照度校准理论、方法和误差来源三方面，构建了红外目标模拟器校准装置，开展了辐照度测量系统内部杂散辐射建模仿真和抑制的研究、红外大气传输修正方法的研究和测试结果数据拟合算法的研究，分别提出了基于复合蒙特卡洛法的内部杂散辐射数学模型、基于编码器-解码器结构的卷积神经网络红外辐射大气传输校准算法和基于粒子群优化的极限学习机算法，并在实验室条件下进行了辐照度计量校准实验。分别验证了在追迹光线数相等的情况下复合蒙特卡洛法的精度更高，复合蒙特卡洛法标准偏差为6.1940×10-8，平均误差为0.19293，且仿真结果体现了红外辐射计各部分杂散辐射对探测器入瞳面接收到的杂散辐射造成的影响；基于编码器-解码器结构的卷积神经网络算法能够较好的预测大气透过率和大气程辐射，在三个波段下的平均误差为3.0783%，3.8186%，5.3452%，低于传统方法，降低了大气透过率和大气程辐射的影响；通过与GA-ELM模型、ELM模型进行对，验证了与传统数据拟合方法相比，基于PSO-ELM的方法在1μm~3μm、3μm~5μm、8μm~14μm三个波段下的拟合精度都有所提高，决定系数分别为0.9925、0.9913、0.9814，平均相对误差分别为0.1242%、0.7157%、0.7471%有效提高了红外辐射测量准确度。     

环境卫星CCD传感器场地辐射定标与交叉定标的比较

利用2009年8月敦煌定标场实测光谱数据和大气参数数据,采取反射率定标法,获得环境卫星CCD传感器的定标系数.同时,利用LANDSAT-5的TM影像对环境卫星CCD传感器进行交叉定标.通过对比两组定标结果,并比较其与中国资源卫星应用中心公布的定标系数之间的差异,验证定标系数的可靠性.结果表明,交叉定标与场地定标的差异在...     

On-orbit relative radiometric calibration of optical video satellites without uniform calibration sites

The rapid development of optical video satellite constellations provides a new data model for resource and environmental monitoring, and for commercial applications of satellite remote sensing. High-frequency on-orbit relative radiometric calibration is an indispensable means of improving the quality of raw frames. However, the conventional on-orbit calibration method relies on a uniform field (e.g. desert, snow, clouds, etc.), which renders it unable to perform high-frequency radiometric calibration. Here, a method of on-orbit relative radiometric calibration without uniform calibration sites for optical video satellites is proposed taking into consideration the high frame-rate characteristics of video satellites. The method can be applied using any multi-frame data, it does not require a uniform field, it is not affected by the type of field, and it can achieve near-real-time, high-frequency calibration of video satellites. Moreover, this approach is expected to increase the frequency of on-orbit radiometric calibrations while reducing the associated costs. Our results suggest that the proposed method can effectively calibrate the non-uniformity of each sensor detector, and the compensation effect is equivalent to the on-orbit calibration method based on uniform sites.     

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.