基于视觉识别的机动车辆智能驾驶系统

将机器人视觉原理运用于对机动车辆智能驾驶的研究中,通过识别高速公路车道线的变化,使得车辆能够实现自动驾驶。由CMOS图像传感器摄取道路图像,采用小波处理、线性递推估计、波门跟踪滤波和二次拟合抽取车道线信息,从而得到行驶控制误差;控制命令通过串行232口发送到单片机上,驱动步进电机带动游戏杆转动来仿真实际的自动驾驶过程;获得了很好的实验结果。     

New Geometric Methods for Computer Vision: An Application to Structure and Motion Estimation

We discuss a coordinate-free approach to the geometry of computer vision problems. The technique we use to analyse the three-dimensional transformations involved will be that of geometric algebra: a framework based on the algebras of Clifford and Grassmann. This is not a system designed specifically for the task in hand, but rather a framework for all mathematical physics. Central to the power of this approach is the way in which the formalism deals with rotations; for example, if we have two arbitrary sets of vectors, known to be related via a 3D rotation, the rotation is easily recoverable if the vectors are given. Extracting the rotation by conventional means is not as straightforward. The calculus associated with geometric algebra is particularly powerful, enabling one, in a very natural way, to take derivatives with respect to any multivector (general element of the algebra). What this means in practice is that we can minimize with respect to rotors representing rotations, vectors representing translations, or any other relevant geometric quantity. This has important implications for many of the least-squares problems in computer vision where one attempts to find optimal rotations, translations etc., given observed vector quantities. We will illustrate this by analysing the problem of estimating motion from a pair of images, looking particularly at the more difficult case in which we have available only 2D information and no information on range. While this problem has already been much discussed in the literature, we believe the present formulation to be the only one in which least-squares estimates of the motion and structure are derived simultaneously using analytic derivatives.     

A Geometric Approach for the Theory and Applications of 3D Projective Invariants

A central task of computer vision is to automatically recognize objects in real-world scenes. The parameters defining image and object spaces can vary due to lighting conditions, camera calibration and viewing position. It is therefore desirable to look for geometric properties of the object which remain invariant under such changes in the observation parameters. The study of such geometric invariance is a field of active research. This paper presents the theory and computation of projective invariants formed from points and lines using the geometric algebra framework. This work shows that geometric algebra is a very elegant language for expressing projective invariants using n views. The paper compares projective invariants involving two and three cameras using simulated and real images. Illustrations of the application of such projective invariants in visual guided grasping, camera self-localization and reconstruction of shape and motion complement the experimental part.     

Conformal Geometric Algebra for Robotic Vision

In this paper the authors introduce the conformal geometric algebra in the field of visually guided robotics. This mathematical system keeps our intuitions and insight of the geometry of the problem at hand and it helps us to reduce considerably the computational burden of the problems. As opposite to the standard projective geometry, in conformal geometric algebra we can deal simultaneously with incidence algebra operations (meet and join) and conformal transformations represented effectively using spinors. In this regard, this framework appears promising for dealing with kinematics, dynamics and projective geometry problems without the need to resort to different mathematical systems (as most current approaches do). This paper presents real tasks of perception and action, treated in a very elegant and efficient way: body–eye calibration, 3D reconstruction and robot navigation, the computation of 3D kinematics of a robot arm in terms of spheres, visually guided 3D object grasping making use of the directed distance and intersections of lines, planes and spheres both involving conformal transformations. We strongly believe that the framework of conformal geometric algebra can be, in general, of great advantage for applications using stereo vision, range data, laser, omnidirectional and odometry based systems. Eduardo Jose Bayro-Corrochano gained his Ph.D. in Cognitive Computer Science in 1993 from the University of Wales at Cardiff. From 1995 to 1999 he has been Researcher and Lecturer at the Institute for Computer Science, Christian Albrechts University, Kiel, Germany, working on applications of geometric Clifford algebra to cognitive systems.  His current research interest focuses on geometric methods for artificial perception and action systems. It includes geometric neural networks, visually guidevsd robotics, color image processing, Lie bivector algebras for early vision and robot maneuvering. He is editor and author of the following books: Geometric Computing for Perception Action Systems, E. Bayro-Corrochano, Springer Verlag, 2001; Geometric Algebra with Applications in Science and Engineering, E. Bayro-Corrochano and G. Sobczyk (Eds.), Birkahauser 2001; Handbook of Computational Geometry for Pattern Recognition, Computer Vision, Neurocomputing and Robotics, E. Bayro-Corrochano, Springer Verlag, 2005. He authored more than 90 strictly reviewed papers. Leo Hendrick Reyes-Lozano received his degree in Computer Engineering from the University of Guadalajara in 1999. He earned his MSc. and Ph.D. from the Center of Research and Advanced Studies (CINVESTAV) Guadalajara in 2001 and 2004, respectively. His research interests include Computer Vision, Geometric Algebra and Computer Graphics. Julio Zamora-Esquivel received his degree in Electronic Engineering at the Guzman City Institute of Tecnology in 2000. He earned his MSc. at the Center of Research and Advanced Studies (CINVESTAV) in Guadalajara in 2003. He is currently a Ph.D Candidate at CINVESTAV. His research interests include Computer Vision, Geometric Algebra and Robotics.     

Registration of 3D Points Using Geometric Algebra and Tensor Voting

We address the problem of finding the correspondences of two point sets in 3D undergoing a rigid transformation. Using these correspondences the motion between the two sets can be computed to perform registration. Our approach is based on the analysis of the rigid motion equations as expressed in the Geometric Algebra framework. Through this analysis it was apparent that this problem could be cast into a problem of finding a certain 3D plane in a different space that satisfies certain geometric constraints. In order to find this plane in a robust way, the Tensor Voting methodology was used. Unlike other common algorithms for point registration (like the Iterated Closest Points algorithm), ours does not require an initialization, works equally well with small and large transformations, it cannot be trapped in “local minima” and works even in the presence of large amounts of outliers. We also show that this algorithm is easily extended to account for multiple motions and certain non-rigid or elastic transformations.     

Ambiguous Configurations for the 1D Structure and Motion Problem

In this paper we investigate, determine and classify the critical configurations for solving structure and motion problems for 1D retina vision. We give a complete categorization of all ambiguous configurations for a 1D (calibrated or uncalibrated) perspective camera irrespective of the number of points and views. It is well-known that the calibrated and uncalibrated case are linked through the circular points. This link enables us to solve for both cases simultaneously. Another important tool is the duality in exchanging points and cameras and its corresponding Cremona transformation. These concepts are generalized to the 1D case and used for the investigation of ambiguous configurations. Several examples and illustrations are also provided to explain the results and to provide geometrical insight.     

基于直接法的视觉同时定位与地图构建技术综述

视觉同时定位与地图构建（V-SLAM）在机器人、无人机导航、自动驾驶等领域有着广泛的研究。直接法V-SLAM基于环境亮度不变性假设,跟踪相机的位姿并构建环境地图。针对直接法V-SLAM,首先简述其基本原理;然后分析、比较几种具有代表性的直接法V-SLAM系统;最后讨论直接法的优缺点和发展趋势,并进行了总结和展望。     

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.     

计算机视觉的PNP问题的最优解

本文讨论了计算机视觉的PNP(Perspective-N-Point)问题:根据观察到的n个已知特征点的象点求解被观察物体相对于相机的三维运动参数.由于象噪声,该问题本质上是非线性最优化问题.本文导出一个闭式解,并提出若干克服象噪声影响的方法.仿真试验的结果表明本文的方法有很好的应用前景.     

基于直线光流场的三维运动和结构重建

利用直线间运动对应关系,将像素点光流的概念和定义方法应用于直线,提出了直线光流的概念,建立了求解空间物体运动参数的线性方程组,利用三幅图像21条直线的光流场,可以求得物体运动的12个参数以及空间直线坐标．但是在实际应用当中,要找出这21条直线的光流场是很困难的,因此该文提出了运用解非线性方程组的方法,只需要6条直线的光流．就可以分步求出物体的12个运动参数,并根据求得的12个运动参数和一致的图像坐标系中的直线坐标,求得空间直线的坐标,从而实现了三维场景的重建．     

融合3D视觉的机器人自主更换变压器熔断器系统

针对变压器熔断器人工更换工作量大、安全性低的问题,该文设计开发了融合3D视觉的机器人自主更换变压器熔断器系统。该系统以协作机器人为基础,将彩色3D深度相机以eye-in-hand方式与机器人有机结合。采用变压器熔断器作业3D实景还原、自动避障作业技术,实现机器人更换熔断器自主作业。通过在国家电网内部试验场地测试,证明该系统可以替代检修人员进行无人作业,机器人可以安全、高效地完成变压器熔断器更换。     

智能交通系统中的计算机视觉技术应用

随着经济的发展,如何保障交通的顺畅与安全已成为世界性的热点研究课题之一。文章对智能交通系统进行了分析,并着重讨论了计算机视觉技术在智能交通系统中的应用。提出了一种基于背景差的车辆检测算法,在灰度图像序列中对六条车道同时进行监测,以统计各车道的车流量,并按大中小三种车型对过往车辆进行车型识别。     

The Minimal Structure and Motion Problems with Missing Data for 1D Retina Vision

In this paper we investigate the structure and motion problem for calibrated one-dimensional projections of a two-dimensional environment. The theory of one-dimensional cameras are useful in several areas, e.g. within robotics, autonomous guided vehicles, projection of lines in ordinary vision and vision of vehicles undergoing so called planar motion. In a previous paper the structure and motion problem for all cases with non-missing data was classified and solved. Our aim is here to classify all structure and motion problems, even those with missing data, and to solve them. In the classification we introduce the notion of a prime problem. A prime problem is a minimal problem that does not contain a minimal problem as a sub-problem. We further show that there are infinitely many such prime problems. We give solutions to four prime problems, and using the duality of Carlsson these can be extended to solutions of seven prime problems. Finally we give some experimental results based on synthetic data.     

基于计算机视觉的大型车辆实时定位定向技术

为了解决大型车辆在进行货物装卸时易出现位置和方向误差较大而需多次调整的情况,本文提出一种基于计算机视觉的大型车辆实时定位定向技术.本技术在指示带的辅助下,采用霍夫变换、数字识别等图像处理方法实时测量车辆相对于停车地点的距离、方向和位置信息,最后,系统将利用像素的距离和数量信息来精确计算运动距离,并以图像和数字等多种形式在车辆驾驶室内实时报告、显示.实验证明,该技术可实现大型车辆实时、准确的定位定向.     

计算机视觉中的设备标定和三维图形重构综述

这篇文章回顾了计算机视觉中的设备标定方法和三维重构的多种方法,介绍了各种方法的基本思想和特点,并对它们进行了相应的分析和比较。提出了现有的理论和方法在工程应用中存在的问题以及该学科研究和发展的动态。     

计算机视觉技术在车辆跟踪中的应用

智能交通监控是计算机视觉技术的重要应用之一。对行驶中的车辆进行自动跟踪,能够得到车辆的动态信息,为交通部门提供重要依据。Mean—Shift跟踪算法和基于PPM质心迭代跟踪算法对运动目标的跟踪具有很好的效果,应用两种算法对运动车辆进行跟踪,并对两种算法在车辆跟踪过程中稳定性、精度和实时性上做了对比分析。     

基于计算机视觉高速智能车辆的道路识别

论文研究了基于计算机视觉高速智能车辆的道路识别。通过对JLUIV-4智能高速车辆系统采集的图像进行中值滤波、边缘增强、最优阈值二值化,获得良好的梯度图像。根据道路特征采用Hough变换识别出道路边界。使用感兴趣区域,减少图像处理时间和提高道路识别的可靠性。JLUIV-4的高速导航实验表明,该算法具有良好的实时性、可靠性和鲁棒性。     

计算机视觉中的三维重构建模

三维重构建模是计算机视觉技术的主要内容之一。相机内外参数的标定、图像特征点的提取以及特征点的立体匹配是三维重构建模的技术核心。本文总结了近来三维重构建模的研究成果和计算方法,并提出了一些观点,对三维重构建模的难点和发展趋势作了说明。     

全自主足球机器人视觉系统的方案分析与比较

简单介绍了全自主足球机器人比赛系统。设计了全自主足球机器人视觉系统的典型电路，包括CCD摄像头、SAA7111、AL422B、EPM7128及TMS320VC5402，并详细分析了系统的时序关系。给出了典型电路的变型设计，并进行了比较。     

计算机视觉系统框架的新构思

计算机视觉是人类视觉的模拟。计算机视觉四十多年的发展既取得了成绩也存在不足。在计算机视觉发展过程中,相继提出了三个主要的理论框架：计算视觉理论,基于知识的视觉理论和主动视觉理论。该文对这三个理论框架及其存在的问题进行了讨论,并提出了一个完善和通用的计算机视觉的系统框架。