基于虚拟控制点的像机姿态测量算法

像机姿态测量,包括确定像机的旋转矩阵与平移向量,在机器视觉领域中有着非常广泛的应用。针对控制点异面分布的情况,提出了一种基于虚拟控制点的像机姿态测量算法。算法的主要思想是利用少量虚拟控制点实现像机姿态的短时间迅速求解,然后通过目前非常成熟的正交迭代算法,对求解结果进行精细调节,从而在整体上提高测量算法的精确度与稳定性。实验结果表明,算法在性能上优于目前比较流行的几种像机姿态迭代求解算法,而且具有较强的抗噪声干扰和抗控制点误匹配的能力,可以应用在实际的测量环境当中,是一种有效的像机姿态测量算法。     

点阵测头成像视觉坐标测量系统的研究

柔性三维坐标测量在工业现场测量有较多应用.分析多目标点透视成像原理的基础上,提出了点阵测头成像视觉坐标测量的概念,给出系统完整的三种数学模型及求解方法.设计了一种实用测头,基于单台面阵CCD摄像机实现由三种类型测头组成的视觉测量样机.在距离摄像机1 000 mm,样机进行实际测量,并与三坐标测量机的检测结果进行比对.测试结果表明在摄像机成像面平行的两个方向的测量精度高于摄像机轴向测量精度.     

基于kinect的改进RGB-D视觉里程计

针对RGB-D视觉里程计中kinect相机所捕获的图像深度区域缺失的问题,提出了一种基于PnP(perspective-n-point)和ICP(iterative closest point)的融合优化算法。传统ICP算法迭代相机位姿时由于深度缺失,经常出现特征点丢失导致算法无法收敛或误差过大。本算法通过对特征点的深度值判定,建立BA优化模型,并利用g2o求解器进行特征点与相机位姿的优化。实验证明了该方法的有效性,提高了相机位姿估计的精度及算法的收敛成功率,从而提高了RGB-D视觉里程计的精确性和鲁棒性。     

PnP Problem Revisited

Perspective-n-Point camera pose determination, or the PnP problem, has attracted much attention in the literature. This paper gives a systematic investigation on the PnP problem from both geometric and algebraic standpoints, and has the following contributions: Firstly, we rigorously prove that the PnP problem under distance-based definition is equivalent to the PnP problem under orthogonal-transformation-based definition when n > 3, and equivalent to the PnP problem under rotation-transformation-based definition when n = 3. Secondly, we obtain the upper bounds of the number of solutions for the PnP problem under different definitions. In particular, we show that for any three non-collinear control points, we can always find out a location of optical center such that the P3P problem formed by these three control points and the optical center can have 4 solutions, its upper bound. Additionally a geometric way is provided to construct these 4 solutions. Thirdly, we introduce a depth-ratio based approach to represent the solutions of the whole PnP problem. This approach is shown to be advantageous over the traditional elimination techniques. Lastly, degenerated cases for coplanar or collinear control points are also discussed. Surprisingly enough, it is shown that if all the control points are collinear, the PnP problem under distance-based definition has a unique solution, but the PnP problem under transformation-based definition is only determined up to one free parameter. Yihong Wu received her Bachelor of Science degree in Mathematics from Shanxi Yanbei Normal College in 1995; a Master of Science degree in Computational Algebra from Shaanxi Normal University in 1998; a Doctor of Science degree in Geometric Invariants and Applications from MMRC, Institute of Systems Science, Chinese Academy of Sciences, in 2001. From June 2001 to July 2003, she did her postdoctoral research in NLPR, Institute of Automation, Chinese Academy of Sciences. After then, she joined NLPR as an associate professor. Her research interests include polynomial elimination and applications, geometric invariant and applications, automated geometric theorem proving, camera calibration, camera pose determination, and 3D reconstruction. She has published more than 15 papers on major international journals and major international conferences. Zhanyi Hu was born in Shanxi province, P. R. China in 1961. He received the B.S. Degree in Automation from the North China University of Technology in 1985, the Ph.D. Degree (Docteur d’Etat) in Computer Science from the University of Liege, Belgium, in Jan. 1993. Since 1993, he has been with the Institute of Automation, Chinese Academy of Sciences. From May 1997 to May 1998, he also acted as a visiting scholar of Chinese University of Hong Kong on invitation. Dr. Hu now is a Research Professor of Computer Vision, a member of the Executive Expert Committee of the Chinese National High Technology R&D Program, a deputy editor-in-chief for Chinese Journal of CAD and CG, and an associate editor for Journal of Computer Science and Technology. His current research interests include Camera Calibration, 3D Reconstruction, Feature Extraction, Vision Guided Robot Navigation etc. Dr. Hu has published more than 70 peer-reviewed papers on major national and international journals.     

基于特征点不确定性加权误差的位姿估计新方法

计算机视觉中一般是利用优化技术最小化目标函数来估计位姿,目标函数通常是由图像平面上特征点重投影误差构成,并且假设测量噪声是各向同性且独立同分布的高斯噪声,所得到的位姿是在该假设条件下的极大似然最优估计。然而,在实际应用中这种假设并不总是成立,测量噪声通常是各向异性且非独立同分布,而且常常具有很强的方向性。为此,本文提出了一种新的特征点位姿估计方法,首先对特征点的方向不确定性建模,然后将方向不确定性融入到重投影误差中,构造基于不确定性加权误差的新目标函数,最后利用Levenberg-Marquardt算法优化目标函数求解位姿。大量实验结果表明,本方法可以适应不同程度的方向不确定性,精度优于现有迭代方法。而且随着不确定性的增加,位姿解的精度并没有明显变差。     

激光跟踪测距三维坐标视觉测量系统建模

提出了一种激光跟踪测距视觉坐标测量系统,主要由摄像机、激光测距仪、计算机和光笔组成.测量时摄像机测量光笔上各光反射点的方向,激光测距仪跟踪捕捉并测量出某一光反射点到激光测距仪的距离,由测得的方向和距离计算出光笔笔尖接触点的三维坐标.根据n点透视问题(PnP)原理建立了系统的数学模型,激光测距仪测得的距离参数的引入,使得该数学模型可以线性求解,而且解具有唯一性.依据冗余技术给出了被测点三维坐标的求解方程组及其解法.和单摄像机视觉坐标测量系统的比对实验结果表明:在Z、Y和X轴方向上的测量稳定性精度可分别提高0.366、0.031和0.011 mm.     

基于特征匹配的单目相机姿态解算

本文采用了一种基于AKAZE特征检测和PnP算法的单目视觉测量方法对相机的相对姿态进行解算,用于快速准确地确定空间中两个目标间的位姿关系.采集合作目标的模板图像,提取附加到合作目标上的4个特征点的像素坐标,利用AKAZE关键点对模板图像和待测图像进行匹配并计算映射矩阵,通过映射矩阵得到4个特征点在待测图像中的像素坐标,然后结合合作目标的尺寸信息求解基于4个共面特征点的PnP问题,解算相机与合作目标的相对位置.实验分析表明该方法计算的实时图像相机位姿与真实结果接近,验证了本文方法的有效性.