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双目立体视觉是一种商业化较成熟的三维测量技术,左右摄像机内外参数的精确标定是实现三维重构的基础和关键。针对棋盘格和圆点两种标定板图案研究了相应的图像处理技术,实现了标定点的亚像素精度定位及其有效排序。采用基于单映性约束和非线性优化的多视角平面标定算法实现了摄像机光学及空间位置参数求解。用极线约束残差法衡量标定结果的准确度。基于桥式三坐标标准实现标定点三维重构平面度以及多平面空间夹角测量结果的校准,基于光学三坐标标准实现了标定点三维重构空间距离的校准,并分析了校准结果的不确定度。圆标志点三维重构空间距离示值误差为0.029 mm,不确定度U=24 μm(k=2) 相似文献
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Chia-Hung Chen 《中国工程学刊》2013,36(5):638-646
A robust and accurate method for estimating the 3-D pose of a planar rigid object is presented. This article demonstrates that 3-D pose estimation becomes feasible by using the 2-D tracking points on an object of scale-invariant feature transform (SIFT) and 3-D point cloud detected by stereo vision on an object, assuming that a 3-D geometric model of an object is known a priori. The roll and pitch angles of an object are estimated by the normal vector of approximate plane of 3-D point cloud on an object and the yaw angle is estimated by 2-D tracking point on an object of SIFT. Accurate object detection and localization in the camera coordinate system is crucial for grasping. In the motion planning, the bidirectional rapidly exploring random tree algorithm is used to search for a valid path for efficient grasping. Our robot arm can robustly and autonomously grasp a randomly rotative rigid object detected by SIFT in 3-D space. We have realized a grasping scenario with a dexterous arm (ADAM) such that an object in front of ADAM can be grasped. This demonstration shows how the proposed components build a dexterous and robust system integrating object detection, pose estimation, and motion planning. 相似文献
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为解决相机位姿估计的问题,提出了一种基于四元数最少特征点的相机位姿估计算法。在相机拍摄的二维图像中检测并匹配特征点,根据特征点坐标与约束条件建立多项式系统,通过求解对应的矩阵方程来求解多项式系统。用四元数表示相机的旋转,避免了求解中相机旋转与平移相互纠缠的问题,当2个摄像机视图之间的平移为零时能够很好地进行位姿求解。求解中结合5点算法对求解原理进行了详细推导,并进行抗噪声测试。测试中随着匹配的特征点数增加,算法平均估计误差范围不超过2%。利用KITTI数据集测试算法的实用性,并与其他几种方法进行结果比较。结果显示,相同条件下算法将估计精度提高了24.5%以上,体现出良好的估计精度。 相似文献
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Face tracking and pose estimation with automatic three-dimensional model construction 总被引:1,自引:0,他引:1
A method for robustly tracking and estimating the face pose of a person using stereo vision is presented. The method is invariant to identity and does not require previous training. A face model is automatically initialised and constructed online: a fixed point distribution is superposed over the face when it is frontal to the cameras, and several appropriate points close to those locations are chosen for tracking. Using the stereo correspondence of the cameras, the three-dimensional (3D) coordinates of these points are extracted, and the 3D model is created. The 2D projections of the model points are tracked separately on the left and right images using SMAT. RANSAC and POSIT are used for 3D pose estimation. Head rotations up to plusmn45deg are correctly estimated. The approach runs in real time. The purpose of this method is to serve as the basis of a driver monitoring system, and has been tested on sequences recorded in a moving car. 相似文献
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High accuracy of locating the sphere center imaging points is crucial for the vision measurement system. This paper investigates the effect of projection projective model on extraction of the sphere center, upon which the sub-pixel edge-location algorithm based on the sphere projection model is proposed. The sphere center projection model is established by analysing the process of sphere imaging, and the sub-pixel edge-location algorithm consisted of the novel edge model and the improved Zernike moment computing is studied. The novel edge model based on the Erf (error function) is adopted for modelling the practical edge part, which obtains the edge distribution points with high precision. Then a closed-form solution of edge locating error compensation is calculated based on Zernike moments. Finally, the proposed method is verified via simulations and experiments. The relevant results show that the proposed method can accurately locate the sphere center imaging point, which further improves the precision and robustness of the vision measurement system. 相似文献
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This paper presents a stereo vision inspection process which derives precise 3D measurements. Two artificial neural networks are used to facilitate the whole measurement process. At first, a simple camera calibration process is developed to derive the focal lengths and the relative information. A Hopfield neural network is used to solve the stereo matching problem, which has been constructed as an energy function. By means of a recursive process, the disparities of extracted feature points are obtained. In addition, a backpropagation neural network-based measurement error correction model for 3D measurement is proposed. It reduces the errors of 3D measurement associated with a part's orientation, position, magnitude and distance between the object and cameras. Four procedural processes are designed to implement this model. Our laboratory experiments demonstrate that the proposed measurement process has a satisfactory measurement result. 相似文献
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Mun K. Leung Thomas S. Huang 《International journal of imaging systems and technology》1992,4(2):80-97
We propose a motion estimation system that uses stereo image pairs as the input data. To perform experimental work, we also obtain a sequence of outdoor stereo images taken by two metric cameras. The system consists of four main stages, which are (1) determination of point correspondences on the stereo images, (2) correction of distortions in image coordinates, (3) derivation of 3D point coordinates from 2D correspondences, and (4) estimation of motion parameters based on 3D point correspondences. For the first stage of the system, we use a four-way matching algorithm to obtain matched point on two stereo image pairs at two consecutive time instants (ti and ti + 1). Since the input data are stereo images taken by cameras, it has two types of distortions, which are (i) film distortion and (ii) lens distortion. These two distortions must be corrected before any process can be applied on the matched points. To accomplish this goal, we use (i) bilinear transform for film distortion correction and (ii) lens formulas for lens distortion correction. After correcting the distortions, the results are 2D coordinates of each matched point that can be used to derive 3D coordinates. However, due to data noise, the calculated 3D coordinates to not usually represent a consistent rigid structure that is suitable for motion estimation; therefore, we suggest a procedure to select good 3D point sets as the input for motion estimation. The procedure exploits two constraints, rigidity between different time instants and uniform point distribution across the object on the image. For the last stage, we use an algorithm to estimate the motion parameters. We also wish to know what is the effect of quantization error on the estimated results; therefore an error analysis based on quantization error is performed on the estimated motion parameters. In order to test our system, eight sets of stereo image pairs are extracted from an outdoor stereo image sequence and used as the input data. The experimental results indicate that the proposed system does provide reasonable estimated motion parameters. 相似文献
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立体视觉定位算法的运动估计通常在3D欧式空间中进行,但由于特征点3D坐标的噪声各向异性且分布不均匀,3D重建在深度方向上比另两个方向上的准确性差,从而导致3D欧式空间运动估计精确不高.本文提出了一种新的基于视差空间运动估计的高精度立体视觉定位算法.算法首先采用视差空间4点闭环线性解法和RANSAC算法得到初始鲁棒运动估计和匹配内点.接着,利用新的视差空间再投影误差函数提出了基于LM算法的视差空间运动参数非线性优化方法,对初始运动参数进一步优化.视差空间噪声分布均匀且各向同性,本文的初始运动参数线性估计和非线性优化都在视差空间中进行且能达到全局最小.仿真实验和真实实验结果表明,本文算法能得到高精度的立体视觉定位结果,优于传统的3D欧式空间运动估计方法. 相似文献
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为了实现飞机大部件的最佳位姿调整,基于层次分析-误差评定组合方法,研究了一种应用于综合评估数字化装配中关键测量特征点的误差控制权值的计算方法。采用引入权值的最小二乘法求解大部件位姿调整量,提高装配精度。通过层次分析(AHP)方法确定关键测量特征点主观权值,误差评定法确定关键测量特征点客观权值,两者结合综合评定关键测量特征点权值。以最小装配误差为优化目标,利用权值实现多个关键测量特征点的误差分配优化。实例分析中,将超差的对接交点误差由1.23 mm降低到了0.72 mm,满足各个测量点的容差要求。以奇异值分解算法求解目标优化初值,采用牛顿法迭代求解,得到部件的最优位姿,并以中后机身对接为对象分析验证权值分配的合理性。 相似文献
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目的 板料堆垛是现代流程工业生产中常见的生产环节,其堆垛质量影响着包装和运输安全。针对多边形板料自动堆垛中机械手抓取和对齐问题,提出一种基于网格筛选的线特征相机位姿标定方法,给出空间特征点的选择标准,解决因板料纹理不足带来的点特征精度下降的问题。方法 首先通过检测特征点来表示待匹配线,然后利用基于三角网格的线特征构型筛选算法,获得精确的匹配线;再利用单应性矩阵求解获得转换矩阵,最后通过转换在线板料图像,获得实际抓取点和转换角度。结果 通过桌面板料抓取实验和现场验证,重投影误差不超过2像素,1 m板料的堆垛误差为0.5 mm,结论 证明了此方法在板料自动堆垛过程中针对特征检测和位姿估计问题的有效性。 相似文献
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Lu Z Lee S 《Journal of the Optical Society of America. A, Optics, image science, and vision》2011,28(12):2607-2618
This paper presents a probabilistic object recognition and pose estimation method using multiple interpretation generation in cluttered indoor environments. How to handle pose ambiguity and uncertainty is the main challenge in most recognition systems. In order to solve this problem, we approach it in a probabilistic manner. First, given a three-dimensional (3D) polyhedral object model, the parallel and perpendicular line pairs, which are detected from stereo images and 3D point clouds, generate pose hypotheses as multiple interpretations, with ambiguity from partial occlusion and fragmentation of 3D lines especially taken into account. Different from the previous methods, each pose interpretation is represented as a region instead of a point in pose space reflecting the measurement uncertainty. Then, for each pose interpretation, more features around the estimated pose are further utilized as additional evidence for computing the probability using the Bayesian principle in terms of likelihood and unlikelihood. Finally, fusion strategy is applied to the top ranked interpretations with high probabilities, which are further verified and refined to give a more accurate pose estimation in real time. The experimental results show the performance and potential of the proposed approach in real cluttered domestic environments. 相似文献
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为解决空间复杂光照环境下可见光相机获取的图像照度不一、信息失真、特征不完整导致的目标
航天器相对位姿求解困难的问题,提出了一种基于图像增强和弧段特征的双目视觉相对位姿测量方法。首先通
过基于带色彩保留的多尺度Retinex(Multi?scale Retinex with Chromaticity Preservation, MSRCP)自适应图像预处理
方法提升空间暗弱光、局部强曝光环境下的图像质量;然后利用基于边缘弧支撑线段的椭圆检测算法提取目标
航天器表面星箭对接环的弧段特征,并拟合得到椭圆轮廓;最后利用双目相机搭建了空间相对位姿测量物理仿
真实验平台,建立双目空间椭圆锥测量模型解算目标的六自由度相对位姿,实现了近距离正常光照和暗弱光照
场景下目标航天器相对位姿求解。实验结果显示:在正常光照场景下,相对位置平均误差优于20 mm,相对姿
态平均误差优于0. 3°;在暗弱光照场景下,相对位置平均误差优于30 mm,相对姿态平均误差优于1°。研究成
果为近距离空间交会对接等在轨服务任务中的目标识别与测量提供了参考,具有技术借鉴价值。 相似文献
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In order to improve the accuracy and stability of stereo vision calibration, a novel stereo vision calibration approach based on the group method of data handling (GMDH) neural network is presented. Three GMDH neural networks are utilized to build a spatial mapping relationship adaptively in individual dimension. In the process of modeling, the Levenberg-Marquardt optimization algorithm is introduced as an interior criterion to train each partial model, and the corrected Akaike's information criterion is introduced as an exterior criterion to evaluate these models. Experiments demonstrate that the proposed approach is stable and able to calibrate three-dimensional (3D) locations more accurately and learn the stereo mapping models adaptively. It is a convenient way to calibrate the stereo vision without specialized knowledge of stereo vision. 相似文献