应用神经网络隐式视觉模型进行立体视觉的三维重建

针对传统的基于精确数学模型的立体视觉方法过程繁琐的不足，提出一种应用BP神经网络隐式视觉模型进行三维重建的算法。该算法将多个标定平面放置在有效视场内，用神经网络模拟立体视觉由两个二维图像重建三维几何的过程，经过网络训练建模后，无须摄像机标定即可进行三维重建。仿真实验结果证明，该算法比较简单，且能保持较高的精度。     

基于立体视觉的实时三维地图构建

提出了由视差图实时构建三维地图的算法.首先给出从视差图到车体坐标系下的三维地图的重建方法.其次建立车体坐标系与全局坐标系的转换关系,得到全局坐标系下的三维地图.最后建立全局坐标系下的全局地图,并实时地把当前时刻的三维地图融入全局地图.实验结果表明,该算法计算量小,鲁棒性强,能实时正确地构建地图.     

平行双目视觉系统的三维重建研究

随着图像处理,模式识别的快速发展,人们对双目视觉系统和三维创建越来越重视.本文研究和设计一套双目视觉系统目的是获得生动的立体图像.从二维图像恢复到三维图像实现了三维物体的可视化.     

仿生眼运动视觉与立体视觉3维感知

为使机器人同时具备双目立体视觉和单目运动视觉的仿人化环境感知能力,克服双目视场狭窄、单目深度感知精度低的缺陷,本文基于人眼结构特点,设计了一个具有4个旋转自由度的双目仿生眼平台,并分别基于视觉对准策略和手眼标定技术实现了该平台的初始定位和参数标定.给出了基于外部参数动态变化的双目立体感知方法和单目运动立体感知方法,前者通过两架摄像机实时获取的图像信息以及摄像机相对位姿信息进行3维感知,后者综合利用单个摄像机在多个相邻时刻获取的多个图像及其对应姿态进行3维感知.实验结果中的双目视觉相对感知精度为0.38%,单目运动视觉相对感知精度为0.82%.本文方法不但能够有效拓宽传统双目视觉的感知视野,而且能够保证双目感知和单目运动感知的准确性.     

基于立体视觉的水下三维测量系统研究

随着我国海洋战略的提出,对于海洋观测技术和装备的需求日趋迫切。针对现有水下成像系统无法实现精确三维测量这一难题,该文提出了一种基于双目立体视觉原理的水下三维测量系统研究方法,并对其可行性进行了验证。针对水下成像过程存在的水体界面折射问题,该文提出了相应的相机成像模型及系统参数标定方法,建立了防水深度达 30 m 的双目水下测量及照明装置,并在水池、近海条件下进行了实地测试。实验结果显示,在水体条件较好的情况下,系统观测距离可达 8 m 以上,有效测量距离为 0.5～4.5 m,在 0.5 m 和 4.5 m 距离处的测量误差分别为 2 mm 和 20 mm。实验验证了 水下双目成像模型、立体标定、测量模型等方法的有效性和精确性,可为水下检修作业等海洋工程行业提供一种有效的三维测量技术手段。     

基于双目立体视觉的鞋楦三维建模

利用双目立体视觉的三维重建作为实现鞋楦数字化方法,避免了传统测量方法效率低、难以精确的困难.根据双目视觉原理,通过双CCD摄像机构成的双目立体视觉系统,论述了该系统的原理、结构,详细讨论了图像特征点提取、特征点立体匹配以及特征点三维重构等三维重构建模核心技术问题,并结合鞋楦特点,提出了相应的解决方法,提高了三维重构的准确性和质量,达到了实用的要求.     

基于立体视觉的三维人脸识别

1.引言 近年来生物特征在安全领域的应用越来越受到重视,人脸识别即为其中一个重要分支.迄今为止,人们已经提出了多种人脸识别的算法[1,2],大多数算法是针对二维图像设计的,具有识别速度快、方法直观的特点.存在的问题是很难克服光照、背景及表情等因素的干扰.本文尝试通过人脸的三维数据匹配进行人脸识别,以提高识别的可靠性.由于实验设备、速度及特征提取等多方面因素的影响,关于人脸三维识别的研究工作相对较少.Gordon[3]利用人脸深度图计算出人脸表面曲线的曲率分布并进行分析,得到人脸各个器官的位置及相关几何特征;Beumier和Acheroy[4]通过对人脸投影结构光提取人脸的三维信息,将人脸侧面轮廓曲线与人脸中线附近区域的灰度分布相结合作为识别特征.我们利用人脸的三维稠密数据同人脸三维模型进行匹配来实现人脸的识别,取得了满意的效果.     

双目立体视觉和GSOM相结合的机器人自主地图构建方法

双目立体视觉和自组织可增长特征映射图GSOM(Growing Self-organizing Map)相结合的机器人地图构建方法首先利用双目立体摄像机采集图像,借助双目立体视觉处理技术,将采集到的图像信息转化成神经网络的训练样本;然后利用GSOM的地图绘制算法,通过不断增加新的神经元实现网络规模的增长,用441个SOM神经元便表示了2000个样本点的环境特征信息的拓扑地图,体现了对输入样本分布的逼近特性;实验结果表明双目立体视觉和GSOM相结合的机器人自主地图构建方法可行,并表现出类似生物的自主智能行为。     

3D Object Recognition in Cluttered Environments by Segment-Based Stereo Vision

We propose a new method for 3D object recognition which uses segment-based stereo vision. An object is identified in a cluttered environment and its position and orientation (6 dof) are determined accurately enabling a robot to pick up the object and manipulate it. The object can be of any shape (planar figures, polyhedra, free-form objects) and partially occluded by other objects. Segment-based stereo vision is employed for 3D sensing. Both CAD-based and sensor-based object modeling subsystems are available. Matching is performed by calculating candidates for the object position and orientation using local features, verifying each candidate, and improving the accuracy of the position and orientation by an iteration method. Several experimental results are presented to demonstrate the usefulness of the proposed method.     

一种新的基于OpenCV的立体视觉三维测量方法

主要探讨一种实现双目视觉三维测量建模的新方法;即在对已获得的两幅标定图像特征提取后,采用一种融合立体匹配方法得到两两对应的像素点,进而提出一种考虑方程组所代表几何意义的束调整算法求取物体三维空间点坐标;利用异面直线公垂线中点逼近物体空间点,并进一步优化获得更精确结果,同时给出基于Opencv算法,充分发挥了OpenCV的函数库功能;通过实验、数值分析与计算,最终验证了该方法理论分析的正确性及实践可行性。     

基于格雷码和多步相移法的双目立体视觉三维测量技术研究

传统的被动式双目立体视觉三维测量技术,具有操作简单,使用灵活方便,相机标定技术成熟的优点,但是对于特征点稀疏图像,寻找匹配点困难,匹配精度低。编码结构光测量方式通过向待测物体投射特定的编码图案,获取编码图像进行解码求解物体的三维信息,具有着测量精度高,速度快的优点,但是存在着投影仪标定精度低,实现难度大的缺点。提出了将双目立体视觉和编码结构光相结合的三维测量方法,在完成双目校正的基础上,向待测物体投射格雷码图案和多步相移图案,给予被测物体容易识别和可控制的特征信息,最后求取物体的三维信息。而且通过实验论证了投射多步相移图案比起4步相移图案,测量精度更高,能够更好的体现物体细节。     

Active Appearance-Based Robot Localization Using Stereo Vision

A vision-based robot localization system must be robust: able to keep track of the position of the robot at any time even if illumination conditions change and, in the extreme case of a failure, able to efficiently recover the correct position of the robot. With this objective in mind, we enhance the existing appearance-based robot localization framework in two directions by exploiting the use of a stereo camera mounted on a pan-and-tilt device. First, we move from the classical passive appearance-based localization framework to an active one where the robot sometimes executes actions with the only purpose of gaining information about its location in the environment. Along this line, we introduce an entropy-based criterion for action selection that can be efficiently evaluated in our probabilistic localization system. The execution of the actions selected using this criterion allows the robot to quickly find out its position in case it gets lost. Secondly, we introduce the use of depth maps obtained with the stereo cameras. The information provided by depth maps is less sensitive to changes of illumination than that provided by plain images. The main drawback of depth maps is that they include missing values: points for which it is not possible to reliably determine depth information. The presence of missing values makes Principal Component Analysis (the standard method used to compress images in the appearance-based framework) unfeasible. We describe a novel Expectation-Maximization algorithm to determine the principal components of a data set including missing values and we apply it to depth maps. The experiments we present show that the combination of the active localization with the use of depth maps gives an efficient and robust appearance-based robot localization system.     

Semantic Mapping Using Mobile Robots

Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.     

基于标记点图案的三维脚型建模与测量方法

面向个性化制鞋,提出并实现了一种新颖的基于立体视觉原理的三维脚型建模与测量方法.该方法把标记点图案设置在袜子上,通过10台普通摄像机来采集用户穿着这种袜子的脚型图像;然后从脚型图像中提取出标记点并进行匹配,根据立体视觉原理重建出袜子上各个标记点的三维几何位置,并利用三角化处理以及曲面细分等手段获取用户的三维脚型;最后根据制鞋工艺要求测量出制鞋相关的参数.实验结果表明,该方法可瞬间完成无盲区图像的采集,有效地减少了生物体抖动所带来的误差,使误差在±1mm之内,可应用于个性化制鞋服务.     

Using Real-Time Stereo Vision for Mobile Robot Navigation

This paper describes a working vision-based mobile robot that navigates and autonomously explores its environment while building occupancy grid maps of the environment. We present a method for reducing stereo vision disparity images to two-dimensional map information. Stereo vision has several attributes that set it apart from other sensors more commonly used for occupancy grid mapping. We discuss these attributes, the errors that some of them create, and how to overcome them. We reduce errors by segmenting disparity images based on continuous disparity surfaces to reject spikes caused by stereo mismatches. Stereo vision processing and map updates are done at 5 Hz and the robot moves at speeds of 300 cm/s.     

基于镜面成像技术的三维立体视觉测量与重构综述

利用镜面成像技术获取被测物体或场景的三维信息得到研究者越来越多的关注。光线与平面镜或曲面镜交互时产生镜面成像。平面镜的反射属性可以改善视觉效果,基于光路射线展开过程可应用于不同的平面镜成像系统,采用光路展开替代镜面交互应用于三维场景,得到虚拟三维空间,平面镜成像使得复杂的射线交互可以用一种虚拟的方式可视化,且坐标系统的变化容易跟踪。曲面镜成像通常不具有透视投影属性,根据曲面的曲率来改变空间显示。曲面镜常常导致折反射,故针对不同的三维立体视觉测量及重构需设计相应的几何恢复算法。从计算机图形学和计算机视觉的角度,分析了镜面成像的基本原理,对近年来较典型的基于镜面成像技术的三维测量与重构方法及最新研究进展进行综述。     

基于立体视觉的大型工件尺寸测量的研究

大型工件在加工过程中的尺寸测量是保证加工精度和提高生产效率的重要手段.本文介绍了双目立体视觉的基本原理,深入探讨了其中的关键技术和主要算法,提出基于双目立体视觉实现精确测量工件尺寸的非接触测量方法.     

Outdoor Localization Using Stereo Vision Under Various Illumination Conditions

We present a mobile robot localization method using only a stereo camera. Vision-based localization in outdoor environments is a challenging issue because of extreme changes in illumination. To cope with varying illumination conditions, we use two-dimensional occupancy grid maps generated from three-dimensional point clouds obtained by a stereo camera. Furthermore, we incorporate salient line segments extracted from the ground into the grid maps. The grid maps are not significantly affected by illumination conditions because occupancy information and salient line segments can be robustly obtained. On the grid maps, a robot's poses are estimated using a particle filter that combines visual odometry and map matching. We use edge-point-based stereo simultaneous localization and mapping to obtain simultaneously occupancy information and robot ego-motion estimation. We tested our method under various illumination and weather conditions, including sunny and rainy days. The experimental results showed the effectiveness and robustness of the proposed method. Our method enables localization under extremely poor illumination conditions, which are challenging for even existing state-of-the-art methods.