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Alternative TitleStudy on Camera Calibration
Thesis Advisor胡占义
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword摄像机标定 三维重建 立体视觉 主动视觉 Camera Calibration 3d Reconstruction Stereo Vision Active Vision
Abstract本文作者从一九九八年底进入模式识别国家重点实验室机器人视觉组以来,主要从事三 维立体视觉方面的研究,具体内容包括基于主动视觉的摄像机自标定、三维重建方法的研究, 并初步完成了一个二维重建系统。主要工作归纳如下:1.基于平面信息的主动视觉自标定方法研究。这种方法的主要特点是可以线性求解摄 像机的所有5个内参数。据我们所知,文献中现有的方法仅能线性求解摄像机的4 个内参数。当摄像机为完全的射影模型时,即当有畸变因子(Skew Factor)存在 时,文献中的线性方法均不再适用。这种方法的基本原理是利用图像中平面场景的 信息,通过控制摄像机作多组平面正交平移运动,由平面场景图象的单应性 (HOmography)矩阵建立摄像机内参数线性约束方程组,来求解摄像机的内参数。 2.基于极点信息的主动视觉自标定方法研究。这种方法是基于平面信息的主动视觉自 标定方法的扩展。它的基本思想也是控制摄像机做5组平面正交运动,利用图像中 的极点(epipole)信息米线性标定摄像机。同时,针对摄像机做平移运动时基本 矩阵的特殊形式,我们在实现算法中使用了求基本矩阵(Fundamental matrix)的 2点算法。与8点算法相比较,2点算法大大提高了所求极点的精度利鲁棒性。 3.基于射影重建的摄像机自标定方法研究。这种方法仅需要摄像机作一次纯平移运动 和两次带旋转的任意运动即可。它有以下三个主要特点。1)由于该方法是一种线性 方法,所以避免了人多数非线性方法的局部极小问题。2)该方法是一种基于射影重 建的自标定方法,由于在射影重建过程中利用了所有图像的信息,因此它较以前的 方法具有更好的鲁棒性。3)该方法几乎对硬件设备没有什么特殊的要求,在实际应 用中非常容易实现,如人手持一个摄像机摄取一些图象即可。 4.三维重建系统的实现。搭建了一个针对两幅图像的三维重建系统。重建系统流程描 述如下:首先利用灰度相关性约束和两幅图像之间的极线约束,确定两幅图像中关 键点的对应关系。然后在摄像机内参数已知的前提下,运用 SFM(Structure From Motion)算法计算出离散的空间三维点及摄像机的运动参数。利用图像三角化及图 像点与空间点一一对应的关系将图像纹理映射到三维空间中。最后利用0penGL完 成二维显示工作。本系统的主要特点是:1)可以自动完成整个重建过程:2)采用人 机交互的策略使得
Other AbstractOver the last 3 years I spent in the Robot Vision Group of NLPR, my efforts have been primarily concentrated on Camera Self-Calibration and 3D Reconstruction. The main work can be summarized as follows: 1. A Plane Based Camera Self-Calibration Technique. The novelty of this technique is that it can determine LINEARLY all the FIVE intrinsic parameters of the camera. To our knowledge, techniques reported in the literature up to now can only deal with linearly four of the five ones, in other words, when the camera is of a complete perspective model, i.e., when the skew factor is non-zero, such techniques become invalid. The basic principle of our new calibration technique is to use the planar information in the scene and to control the camera to undergo several sets of orthogonal planar motions. Then, a set of linear constraints on the 5 intrinsic parameters is derived by means of homographies associated with scene planes in images. 2. An Epipole Based Camera Self-Calibration Technique. This technique is an extension of the above plane based technique. In this technique, epipoles, instead of homographies, are used to linearly determine camera's intrinsic parameters. In addition, since the fundamental matrix must be of an anti-symmetric one if the camera's motion between the two images is a pure translation, a 2-point algorithm, rather than the traditional 8-point algorithm, is used to estimate the fundamental matrix. The 2-point algorithm appears to be a great contributor to the substantial increases of the robustness and accuracy of the final calibration results. 3. A Projective Reconstruction Based Camera Self-Calibration Technique. With the camera undergoing a pure translation and two arbitrary motions (a rotation plus a translation), all the five intrinsic parameters can be obtained. This paper's main characteristics are two-fold: Firstly. since it is a linear one, it can avoid the local minima problem plagued in other nonlinear methods in the literature. Secondly, by means of a projective reconstruction, the information of all the available images is used, the method is inherently and fairly robust. 4. Realization of a Prototype of 3D Reconstruction System. At the present stage, the system can reconstruct a 3D scene surface from a pair of images, multiple image based reconstruction will be done later. The main steps of the system are: Firstly, the correspondences of image points are automatically established by using both gray level similarity and consistency of epipolar geometry; Secondly. the camera is calibrated separately; Thirdly, the 3D points are obtained with a standard SFM algorithm. Finally, triangulation and texture mapping are invoked. The system seems to perform nicely.
Other Identifier615
Document Type学位论文
Recommended Citation
GB/T 7714
李华. 摄像机标定问题研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2001.
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