基于PnP问题求解的摄像机标定

三维人脸识别中,双目成象系统是初始工作中极为关键的一步.本文引入了PnP问题讨论双目成象系统中摄像机的标定算法.本文提出解出P4P问题的唯一解,即确定摄像机的标定,该方法简单快捷.最后本文给出具体标定过程.     

未标定摄像机非共面P4P问题的一种解析解

经典PnP问题是以摄像机内参已知为前提条件的,然而对未标定摄像机PnP问题的研究更具有实际意义.文中针对未标定二参数针孔摄像机,首次研究了非共面P4P问题解的个数,同时提出了一种解析解.不仅可以求出摄像机相对于世界坐标系的位姿,而且还能得到摄像机的内参.首先根据投影方程和旋转矩阵的性质,利用14个变量构造出了14个约束...     

基于矢量差分的未标定摄像机P5P问题的求解

针对PnP问题的方程建立与求解, 提出一种基于矢量差分的未标定摄像机P5P问题的线性求解算法. 将5个控制点组成非共面的4点集合, 按照成像过程, 通过两个集合间的控制点矢量差分逐步构建摄像机姿态及相机矩阵的线性约束方程. 再依据线性理论与旋转阵 R 的正交性化简约束方程, 利用矢量积运算给出未标定P5P问题摄像机姿态及相机矩阵的解析解. 模拟和真实实验都验证了该方法的有效性.     

标定靶面平行成像平面时Tsai算法的改进

针对机器视觉应用中出现的标定靶面平行于摄像机成像平面的情况,提出对基于RAC(radialalignmentconstraint)摄像机标定算法的改进。建立了该种情形下摄像机标定模型,根据RAC约束的方向不变性、RAC约束的等比例性等特性,使用Levenber-Marquardt算法计算出部分参数,以及利用针孔成像原理求出余下的参数。通过实验对算法进行了验证和分析,结果表明,该算法计算量小,并且具有较高的标定精度。     

基于单幅图像的摄像机畸变参数估计方法

摄像机畸变参数估计是摄像机标定的重要步骤.针对已有的摄像机畸变估计方法大多数为首先对无畸变下的摄像机参数进行标定,再进一步估计畸变参数,导致畸变参数估计过程复杂的问题.采用单应矩阵直接估计畸变参数,提出一种基于单幅图像的摄像机畸变参数估计算法.首先利用图像主点附近的图像特征点估计空间平面到图像平面的单应矩阵,然后利用该单应矩阵估计图像畸变误差和畸变参数,最后采用非线性优化算法对单应矩阵与畸变参数进行整体优化.模拟数据与实际图像的实验结果验证了本文算法的有效性;由于该算法仅需要1幅图像即可估计畸变参数,因此有效地提高了摄像机标定方法的灵活性.     

线结构光传感器参数现场标定方法

为了精确、快速、高效地标定线结构光传感器参数,提出了一种线结构光传感器参数现场标定方法。根据摄像机标定方法,并结合L-M非线性优化算法对摄像机内外参数及镜头畸变系数进行标定。拍摄不同姿态下的平面靶标图像,利用靶标图像计算摄像机外参计算靶标上的圆点在摄像机坐标系下的三维坐标,并构建靶标平面方程。将激光线投射到不同姿态的靶标平面上,通过靶标平面方程计算出激光线上点在摄像机坐标系下的3D坐标,由不同位置重构出激光点在摄像机坐标系下的3D坐标来完成光平面参数标定。通过对摄像机参数、畸变系数和光平面参数的标定,重构目标物体进行测试。测量结果表明:该算法能够快速、准确地获取小车车体的三维坐标,并构造出车体的三维模型。该方法适用于大视场的工业现场标定。     

基于神经网络的摄像机平面模板标定方法

根据立体视觉原理,提出了基于神经网络摄像机标定方法,该方法的特点是采用平面模板作为标定物.由于传统的标定算法在考虑各种非线性畸变时计算十分繁琐,利用BP神经网络来直接学习图像信息与三维信息之间的关系,可实现从输入到输出的任意非线性映射.为了提高BP神经网络训练速度和避免陷入局部最小解,采用引入动态因子的学习算法.实验结果表明,该算法具有较高的标定精度,而且可行.     

未标定摄像机P5P问题的一种解析解

经典PnP问题是以摄像机内参已知为前提条件的,然而对未标定摄像机PnP问题的研究更具有实际意义.文中对未标定四参数针孔摄像机P5P问题的解析解进行了研究,不仅可以求出摄像机相对于世界坐标系的位姿,而且还能得到摄像机的内参.首先根据投影方程和旋转矩阵的性质,利用16个变量构造出了16个约束方程,然后通过消元推导出只含一个未知数的4次多项式方程,分析证实一般情况下未标定摄像机P5P问题最多有4组解.大量的仿真实验表明该算法在确定摄像机位姿上精度很高,且鲁棒性很强.该算法在物体定位、手眼定标、路标导航等领域具有比较重要的实际应用价值.     

基于精密运动的激光平面标定方法

在基于手持光刀法的三维测量中,激光平面标定缺少空间参照.针对该问题,提出基于精密运动的激光平面标定方法引入棋盘格标定靶作为激光平面的空间参照,将激光投射于棋盘格标定靶,通过铣床带动棋盘格标定靶精密运动,解算激光平面上两直线在摄像机坐标系下的位置,利用异面直线修正法确定激光平面的位置.实验结果表明,该方法具有精度高、鲁棒性好的特点.     

未标定摄像机姿态估计的矢量求解算法*

提出一种基于矢量运算未标定摄像机姿态估计的求解算法。重点研究了P5P问题,将五个控制点组成四个矢量,根据成像过程,由控制点矢量运算逐步构建摄像机姿态和相机矩阵的线性约束方程。依据线性理论及旋转阵R的正交性化简约束方程,通过矢量运算给出未标定P5P问题摄像机姿态和相机矩阵的解析解。给出有足够约束条件的PnP问题的求解过程。模拟和真实实验都验证了该方法的有效性。     

Registering real and virtual imagery

Registering a virtual scene with a real scene captured by a video camera has a number of applications including visually guided robotic navigation, surveillance, military training and operation. The fundamental problem involves several challenging research issues including finding corresponding points between the virtual and the real scene and camera calibration. This paper presents our research in defining and mapping a set of reliable image features for registering the two imageries, extracting and selecting reliable control points for the construction of intrinsic and extrinsic camera parameters. A number of innovative algorithms are presented followed by extensive experimental analysis. An application of registering virtual database image with video image is presented. The algorithms we developed for calculating and matching linear structured features and selecting of reliable control points are applicable to image registration beyond virtual and real imageries.     

Projection matrix by orthogonal vanishing points

Calculation of camera projection matrix, also called camera calibration, is an essential task in many computer vision and 3D data processing applications. Calculation of projection matrix using vanishing points and vanishing lines is well suited in the literature; where the intersection of parallel lines (in 3D Euclidean space) when projected on the camera image plane (by a perspective transformation) is called vanishing point and the intersection of two vanishing points (in the image plane) is called vanishing line. The aim of this paper is to propose a new formulation for easily computing the projection matrix based on three orthogonal vanishing points. It can also be used to calculate the intrinsic and extrinsic camera parameters. The proposed method reaches to a closed-form solution by considering only two feasible constraints of zero-skewness in the internal camera matrix and having two corresponding points between the world and the image. A nonlinear optimization procedure is proposed to enhance the computed camera parameters, especially when the measurement error of input parameters or the skew factor are not negligible. The proposed method has been run on real and synthetic data for more precise evaluations. The provided experimental results demonstrate the superiority of the proposed method.     

Real-time and accurate extrinsic camera parameter estimation using feature landmark database for augmented reality

In the field of augmented reality (AR), many kinds of vision-based extrinsic camera parameter estimation methods have been proposed to achieve geometric registration between real and virtual worlds. Previously, a feature landmark-based camera parameter estimation method was proposed. This is an effective method for implementing outdoor AR applications because a feature landmark database can be automatically constructed using the structure-from-motion (SfM) technique. However, the previous method cannot work in real time because it entails a high computational cost or matching landmarks in a database with image features in an input image. In addition, the accuracy of estimated camera parameters is insufficient for applications that need to overlay CG objects at a position close to the user's viewpoint. This is because it is difficult to compensate for visual pattern change of close landmarks when only the sparse depth information obtained by the SfM is available. In this paper, we achieve fast and accurate feature landmark-based camera parameter estimation by adopting the following approaches. First, the number of matching candidates is reduced to achieve fast camera parameter estimation by tentative camera parameter estimation and by assigning priorities to landmarks. Second, image templates of landmarks are adequately compensated for by considering the local 3-D structure of a landmark using the dense depth information obtained by a laser range sensor. To demonstrate the effectiveness of the proposed method, we developed some AR applications using the proposed method.     

A study on three-dimensional vision system for machining setup verification

In computerized numerical control (CNC) machine tools, it is often a time-consuming and error-prone process to verify the Euclidean position accordance between the actual machining setup and its designed three-dimensional (3D) digital model. The model mainly contains the work piece and jigs. The mismatch between them will cause a failure of simulation to precisely detect the collision. The paper presents an on-machine 3D vision system to quickly verify the similarity between the actual setup and its digital model by real and virtual image processing. In this paper, the system is proposed first. Afterwards, a simple on-machine camera calibration process is presented. This calibration process determines all the camera's parameters with respect to the machine tool's coordinate frame. The accurate camera mathematical model (or virtual camera) is derived according to the actual imaging projection. Both camera-captured real images and system-generated virtual images are compensated to make them theoretically and practically identical. The mathematical equations have been derived. Using the virtual image as a reference and then superimposing the real image onto it, the operator can intuitively verify the Euclidean position in accordance to the actual setup and its 3D digital model.     

一种反射折射摄像机的简易标定方法

Central catadioptric cameras are widely used in virtual reality and robot navigation,and the camera calibration is a prerequisite for these applications.In this paper,we propose an easy calibration method for central catadioptric cameras with a 2D calibration pattern.Firstly,the bounding ellipse of the catadioptric image and field of view (FOV) are used to obtain the initial estimation of the intrinsic parameters.Then,the explicit relationship between the central catadioptric and the pinhole model is used to initialize the extrinsic parameters.Finally,the intrinsic and extrinsic parameters are refined by nonlinear optimization.The proposed method does not need any fitting of partial visible conic,and the projected images of 2D calibration pattern can easily cover the whole image,so our method is easy and robust.Experiments with simulated data as well as real images show the satisfactory performance of our proposed calibration method.     

一种反射折射摄像机的简易标定方法

Central catadioptric cameras are widely used in virtual reality and robot navigation, and the camera calibration is a prerequisite for these applications. In this paper, we propose an easy calibration method for central catadioptric cameras with a 2D calibration pattern. Firstly, the bounding ellipse of the catadioptric image and field of view (FOV) are used to obtain the initial estimation of the intrinsic parameters. Then, the explicit relationship between the central catadioptric and the pinhole model is used to initialize the extrinsic parameters. Finally, the intrinsic and extrinsic parameters are refined by nonlinear optimization. The proposed method does not need any fitting of partial visible conic, and the projected images of 2D calibration pattern can easily cover the whole image, so our method is easy and robust. Experiments with simulated data as well as real images show the satisfactory performance of our proposed calibration method.     

基于图像的室内虚拟环境的研究

基于图像的建模和绘制技术,提出了一个构造室内虚拟环境的完整方案,用户只需要输入少数照片,即可重建室内场景的全景图像,方案主要包括以下几点：首先由用户交互确定图像中的匹配象素,通过运动分析算法恢复整个场景的几何结构,然后,将原始图像变换至平面的参数坐标系,抽取纹理图像,并在参数空间对纹理图像进行拼接;最后生成场景的全景图像,算法对拍摄条件和设备没有苛刻要求,运算量较小,有较强的稳定性。     

一种针孔相机与三维激光雷达外参标定方法

针对针孔相机和三维激光雷达的外参标定问题,提出了一种新的方法:利用相机坐标系和激光坐标系下棋盘面的对应性,将外参标定转换为三维空间中旋转、缩放矩阵的求解问题.每帧数据可以提供4个约束,最少只需3帧数据可求解外参矩阵.方法原理简单易懂,直观明了地解释了失效退化原因.仿真实验与真实数据实验表明:提出的方法可以得到高精度的外参矩阵,且在采样帧数较少的情况下依旧可以获得很好的标定效果.