高速公路云台相机的自动标定

目的 云台相机因监控视野广、灵活度高,在高速公路监控系统中发挥出重要的作用,但因云台相机焦距与角度不定时地随监控需求变化,对利用云台相机的图像信息获取真实世界准确的物理信息造成一定困难,因此进行云台相机非现场自动标定方法的研究对高速公路监控系统的应用具有重要价值。方法 本文提出了一种基于消失点约束与车道线模型约束的云台相机自动标定方法,以建立高速公路监控系统的图像信息与真实世界物理信息之间准确描述关系。首先,利用车辆目标运动轨迹的级联霍夫变换投票实现纵向消失点的准确估计,其次以车道线模型物理度量为约束,并采用枚举策略获取横向消失点的准确估计,最终在已知相机高度的条件下实现高速公路云台相机标定参数的准确计算。结果 将本文方法在不同的场景下进行实验,得到在不同的距离下的平均误差分别为4.63%、4.74%、4.81%、4.65%,均小于5%。结论 对多组高速公路监控场景的测试实验结果表明,本文提出的云台相机自动标定方法对高速公路监控场景的物理测量误差能够满足应用需求,与参考方法相比较而言具有较大的优势和一定的应用价值,得到的相机内外参数可用于计算车辆速度与空间位置等。     

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.     

Bayesian self-calibration of a moving camera

In this paper, a Bayesian self-calibration approach using sequential importance sampling (SIS) is proposed. Given a set of feature correspondences tracked through an image sequence, the joint posterior distributions of both camera extrinsic and intrinsic parameters as well as the scene structure are approximated by a set of samples and their corresponding weights. The critical motion sequences are explicitly considered in the design of the algorithm. The probability of the existence of the critical motion sequence is inferred from the sample and weight set obtained from the SIS procedure. No initial guess for the calibration parameters is required. The proposed approach has been extensively tested on both synthetic and real image sequences and satisfactory performance has been observed.     

基于Adept机器人的视觉伺服控制系统

机器人视觉伺服控制在理论和应用等方面还有许多问题需要研究,例如特征选择、系统标定和伺服控制算法等.针对Adept机器人,提出了一种简单快速的不需要精确标定摄像机内外部参数的摄像机标定方法,完成了从被观测物体表面所在的视觉平面坐标系到机器人基坐标系的坐标变换.使用图像的全局特征,即图像矩特征进行伺服跟踪;利用所推导的图像雅可比矩阵,设计了由图像反馈与目标运动自适应补偿组成的视觉伺服控制器.将算法对静态目标的定位实验进行了验证,然后又将其应用到移动目标的跟踪上,通过调节和优选控制参数,实现了稳定的伺服跟踪和抓取.实验结果表明采用图像矩作为图像特征能够避免复杂的特征匹配过程,并且能够获得较好的跟踪精度.     

Self-identifying patterns for plane-based camera calibration

Determining camera calibration parameters is a time-consuming task despite the availability of calibration algorithms and software. A set of correspondences between points on the calibration target and the camera image(s) must be found, usually a manual or manually guided process. Most calibration tools assume that the correspondences are already found. We present a system which allows a camera to be calibrated merely by passing it in front of a panel of self-identifying patterns. This calibration scheme uses an array of fiducial markers which are detected with a high degree of confidence, each detected marker provides one or four correspondence points. Experiments were performed calibrating several cameras in a short period of time with no manual intervention. This marker-based calibration system was compared to one using the OpenCV chessboard grid finder which also finds correspondences automatically. We show how our new marker-based system more robustly finds the calibration pattern and how it provides more accurate intrinsic camera parameters.     

Plane-Based Calibration for Linear Cameras

Linear or 1D cameras are used in several areas such as industrial inspection and satellite imagery. Since 1D cameras consist of a linear sensor, a motion (usually perpendicular to the sensor orientation) is performed in order to acquire a full image. In this paper, we present a novel linear method to estimate the intrinsic and extrinsic parameters of a 1D camera using a planar object. As opposed to traditional calibration scheme based on 3D-2D correspondences of landmarks, our method uses homographies induced by the images of a planar object. The proposed algorithm is linear, simple and produces good results as shown by our experiments.     

Extrinsic calibration of a camera and laser range finder using a new calibration structure of a plane with a triangular hole

Sensor fusion of a camera and laser range finder is important for the autonomous navigation of mobile robots. Finding the transformation between the camera and laser range finder is the first necessary step for the fusion of information. Many algorithms have been proposed, but these tend to require many different steps in order to achieve reliable and accurate results. A calibration structure that has triangular hole on its plane is proposed for the extrinsic calibration of a camera and laser range finder. Locations of laser scan data that are invisible on the calibration plane can be determined using property on the proposed calibration structure. First, we classify the laser scan data into two groups where one is on the plane and the other is off the plane. Then, we determine the absolute location of the laser scan data on the plane through a search of the parameters of the line. Finally, we can establish 3D-3D correspondences between the camera and laser range finder. Extrinsic calibration between a camera and laser range finder is found using a conventional 3D-3D transformation computing algorithm. Keywords: Calibration k]camera k]extrinsic calibration k]laser range finder     

A multimedia stereo calibration algorithm based on rectangular pyramidal method used to aid visual navigation of ALVs under low illumination

In order to measure and reconstruct accurate three-dimension (3D) data for visual aided navigation of autonomous land vehicles (ALVs), a multimedia stereo calibration algorithm which is suitable for normal scene and especially for low illumination scene is proposed. Firstly, an expression of object-point re-projection errors is derived by the collinear equation model, and the non-linear least square algorithm (NLS) is introduced to iteratively optimize external parameters for individual camera. A rectangular pyramidal method enforcing the rectangular geometric constraint is presented, to produce more stable initial parameter values. Then, according to imaging-point correspondences between the left and right camera, a re-projection error model is constructed for this stereo calibration system, of which all parameters are further optimized and calculated through the calibrated results of two separate cameras. Experimental results show that the proposed algorithm can achieve re-projection errors of no more than 0.5 pixels and converge fast usually with less than 10 interation times, whether under normal illumination or low illumination, so it can get better performance and realize a rapid re-calibration.

     

一种基于TOF相机与CCD相机的联合标定算法研究

针对基于Time-of-Flight(TOF)相机的彩色目标三维重建需标定CCD相机与TOF相机联合系统的几何参数,在研究现有的基于彩色图像和TOF深度图像标定算法的基础上,提出了一种基于平面棋盘模板的标定方法。拍摄了固定在平面标定模板上的彩色棋盘图案在不同角度下的彩色图像和振幅图像,改进了Harris角点提取,根据棋盘格上角点与虚拟像点的共轭关系,建立了相机标定系统模型,利用Levenberg-Marquardt算法求解,进行了标定实验。获取了TOF与CCD相机内参数,并利用像平面之间的位姿关系估计两相机坐标系的相对姿态,最后进行联合优化,获取了相机之间的旋转矩阵与平移向量。实验结果表明,提出的算法优化了求解过程,提高了标定效率,能够获得较高的精度。     

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.     

一种基于主动视觉的三维结构恢复和直接欧氏重建算法

利用三正交平移运动, 提出了一种三维结构恢复和直接欧氏重建新算法. 算法仅需利用主动视觉平台控制相机作一组三正交平移运动, 然后通过图像对应点和平移运动的距离就可以恢复平面结构信息和进行欧氏重建. 并且无需假定相机畸变因子为零. 算法计算过程中无需求解相机的内参数, 也无需进行分层重构, 它是一种直接的欧氏重建算法, 避免了传统算法中的相机标定、仿射重建等两大难题, 并且计算过程完全线性化, 简单实用. 最后用模拟实验和真实图像实验对算法进行验证, 实验结果表明了算法的有效性和准确性.     

Camera array calibration for light field acquisition

Light field cameras are becoming popular in computer vision and graphics, with many research and commercial applications already having been proposed.Various types of cameras have been developed with the camera array being one of the ways of acquiring a 4D light field image usingmultiple cameras. Camera calibration is essential, since each application requires the correct projection and ray geometry of the light field. The calibrated parameters are used in the light field image rectified from the images captured by multiple cameras. Various camera calibration approaches have been proposed for a single camera, multiple cameras, and amoving camera. However, although these approaches can be applied to calibrating camera arrays, they are not effective in terms of accuracy and computational cost. Moreover, less attention has been paid to camera calibration of a light field camera. In this paper, we propose a calibration method for a camera array and a rectification method for generating a light field image from the captured images. We propose a two-step algorithm consisting of closed form initialization and nonlinear refinement, which extends Zhang’swell-known method to the camera array. More importantly, we introduce a rigid camera constraint whereby the array of cameras is rigidly aligned in the camera array and utilize this constraint in our calibration. Using this constraint, we obtained much faster and more accurate calibration results in the experiments.     

Approximate model of fisheye camera based on the optical refraction

This paper proposes an approximate model of fisheye camera based on the optical refraction. The model of fisheye lens is firstly derived from the optical refraction and the structure of fisheye lenses. Secondly, a suitable linearization of the fisheye model is developed in order to obtain an approximate model, and the approximate model including two parameters is constructed from the linearization of the fisheye model. Finally, the estimation algorithm on the model parameters is presented using the epipolar constraint between two fisheye images. Furthermore, we provide lots of experiments with synthetic data and real fisheye images. To start with, the feasibility of the approximate model is tested through fitting the five common designed model of fisheye lens with synthetic data. Two groups of experiments with real fisheye image are then performed to estimate the model parameters. In practical situation, this method can automatically establish image correspondences using an improved random sample consensus algorithm without calibration objects.     

一种简单的基于共面的摄像机参数标定方法

共面摄像机标定就是采用平面式模板来确定摄像机内外参数的过程,在此过程中图像像素和二维特征点是已知的。对于共面标定提出了一种简单及有效的方法去标定摄像机参数。即使用帧缓存中的计算机阵列图像直接来标定参数。首先采用预标定的方法标定出图像中心位置,然后根据帧存图像坐标和世界坐标之间的对应关系使用正交矩阵的约束条件来求解,在此算法中假设尺度因子为1,并且不考虑透镜畸变。所提出的算法用数字仿真图像及真实的图像检验。结果显示,所提出的算法具有较好的精度,是一种简单有效的标定方法。     

点云下地平面检测的RGB-D相机外参自动标定

目的 RGB-D相机的外参数可以被用来将相机坐标系下的点云转换到世界坐标系的点云,可以应用在3维场景重建、3维测量、机器人、目标检测等领域。 一般的标定方法利用标定物（比如棋盘）对RGB-D彩色相机的外参标定,但并未利用深度信息,故很难简化标定过程,因此,若充分利用深度信息,则极大地简化外参标定的流程。基于彩色图的标定方法,其标定的对象是深度传感器,然而,RGB-D相机大部分则应用基于深度传感器,而基于深度信息的标定方法则可以直接标定深度传感器的姿势。方法 首先将深度图转化为相机坐标系下的3维点云,利用MELSAC方法自动检测3维点云中的平面,根据地平面与世界坐标系的约束关系,遍历并筛选平面,直至得到地平面,利用地平面与相机坐标系的空间关系,最终计算出相机的外参数,即相机坐标系内的点与世界坐标系内的点的转换矩阵。结果 实验以棋盘的外参标定方法为基准,处理从PrimeSense相机所采集的RGB-D视频流,结果表明,外参标定平均侧倾角误差为-1.14°,平均俯仰角误差为4.57°,平均相机高度误差为3.96 cm。结论 该方法通过自动检测地平面,准确估计出相机的外参数,具有很强的自动化,此外,算法具有较高地并行性,进行并行优化后,具有实时性,可应用于自动估计机器人姿势。     

Affine Approximation for Direct Batch Recovery of Euclidian Structure and Motion from Sparse Data

We present a batch method for recovering Euclidian camera motion from sparse image data. The main purpose of the algorithm is to recover the motion parameters using as much of the available information and as few computational steps as possible. The algorithm thus places itself in the gap between factorisation schemes, which make use of all available information in the initial recovery step, and sequential approaches which are able to handle sparseness in the image data. Euclidian camera matrices are approximated via the affine camera model, thus making the recovery direct in the sense that no intermediate projective reconstruction is made. Using a little known closure constraint, the F_A-closure, we are able to formulate the camera coefficients linearly in the entries of the affine fundamental matrices. The novelty of the presented work is twofold: Firstly the presented formulation allows for a particularly good conditioning of the estimation of the initial motion parameters but also for an unprecedented diversity in the choice of possible regularisation terms. Secondly, the new autocalibration scheme presented here is in practice guaranteed to yield a Least Squares Estimate of the calibration parameters. As a bi-product, the affine camera model is rehabilitated as a useful model for most cameras and scene configurations, e.g. wide angle lenses observing a scene at close range. Experiments on real and synthetic data demonstrate the ability to reconstruct scenes which are very problematic for previous structure from motion techniques due to local ambiguities and error accumulation.     

基于仿射点对应的分层重构

提出了一种基于仿射点对应的分层重构方法,所谓仿射点对应是指相差一个仿射变换的两个空间点集的图像对应．该方法主要分为以下三个步骤：首先,从点对应计算准仿射重构;然后,由仿射点对应的准仿射重构建立一个三维射影变换,并利用这个射影变换的特征向量来确定无穷远平面,从而得到仿射重构;最后,从仿射重构所获得的无穷远平面单应矩阵标定摄像机内参数,进而得到度量重构．在上述三个步骤中,第二个步骤是最关键的,即如何确定对应于无穷远平面的特征向量,这也是该文的新思想和主要贡献所在．仿真和真实图像实验均表明,该文的方法是有效的,并且有很好的鲁棒性．     

Estimating parameters of noncentral catadioptric systems using bundle adjustment

This paper describes a new method to calibrate the intrinsic and extrinsic parameters of a generalized catadioptric camera (central or noncentral). The algorithm has two steps. The first one is the estimation of correspondences between incident lines in space and pixels (black box model calibration) in an arbitrary world reference frame. The second step is the calibration of the intrinsic parameters of the pinhole camera, the coefficients of the mirror expressed by a quadric (quadric mirror shape and the pose of the camera in relation to it), the position of the optical center of the camera in the world reference frame and its relative orientation (pose of the camera in world reference frame). A projection model relaxing Snell’s Law is derived. The deviations from Snell’s Law and the image reprojection errors are minimized by means of bundle adjustment. Information about the apparent contour of the mirror can be used to reduce the uncertainty in the estimation by introducing a new term in the cost function of the second step minimization process. Simulations and real experiments show good accuracy and robustness for this framework. However, the convergence is dependent on the initial guess as expected. A well-behaved algorithm to automatically generate the initial estimate to be used in the bundle adjustment is also presented.     

Calibration-free augmented reality in perspective

This paper deals with video-based augmented reality and proposes an algorithm for augmenting a real video sequence with views of graphics objects without metric calibration of the video camera by representing the motion of the video camera in projective space. A virtual camera, by which views of graphics objects are generated, is attached to a real camera by specifying image locations of the world coordinate system of the virtual world. The virtual camera is decomposed into calibration and motion components in order to make full use of graphics tools. The projective motion of the real camera recovered from image matches has the function of transferring the virtual camera and makes the virtual camera move according to the motion of the real camera. The virtual camera also follows the change of the internal parameters of the real camera. This paper shows the theoretical and experimental results of our application of nonmetric vision to augmented reality     

一种基于基本矩阵的相机畸变差自动校正方法

在计算机视觉的应用领域中,为了提高图像量测和3维重建的精度,必须对相机的畸变误差进行修正。为此提出了一种基于基本矩阵的相机径向畸变的自动校正方法,该方法不需要预先获得场景的结构信息和相机的内部参数,仅利用两张影像同名点集之间的内在几何关系,即可求取相机的径向畸变系数,进而可对这两幅图像的畸变误差进行自动校正。试验结果表明,该方法是一种有效的畸变图像校正算法,能够获得到满意的校正结果。