基于投影矩阵的摄像机参数标定

摄像机标定的关键是第一步计算出来的外部参数和与畸变无关的内部参数的初值。由于初值的计算不是纯粹的线性问题,计算起来比较复杂。本文对此进行了改进,采用一种在计算投影矩阵的基础上,利用旋转矩阵的正交性进行算法改进的方法计算初值。实验结果表明,本文算法运算量较小,标定的结果精确度较高。     

摄像机内参数自标定——理论与算法

讨论如何通过摄像机的旋转运动标定其内参数.当摄像机绕其坐标轴旋转时,运用 代数方法给出了计算内参数的公式.该公式在2D投影变换接近理论值P时是非常实用的. 在摄像机绕未知轴旋转时,根据相应的2D投影变换,运用矩阵特征向量理论给出了内参数的 通解公式.通过摄像机绕两个不同未知轴的旋转,摄像机内参数能被唯一地确定.这些结果为 摄像机自标定算法提供了理论基础,同时也给出了实用性算法.模拟实验和真实图像实验的 结果表明本文所给的算法具有一定实用价值.     

摄像机内参数自标定——理论与算法1)

讨论如何通过摄像机的旋转运动标定其内参数.当摄像机绕其坐标轴旋转时,运用代数方法给出了计算内参数的公式.该公式在2D投影变换接近理论值P时是非常实用的.在摄像机绕未知轴旋转时,根据相应的2D投影变换,运用矩阵特征向量理论给出了内参数的通解公式.通过摄像机绕两个不同未知轴的旋转,摄像机内参数能被唯一地确定.这些结果为摄像机自标定算法提供了理论基础,同时也给出了实用性算法。模拟实验和真实图像实验的结果表明本文所给的算法具有一定实用价值.     

近场声源定位的改进最大似然方法研究

研究近场声源定位性能优化问题,针对常规最大似然方法在空间非均匀高斯噪声背景下定位准确率下降的问题,基于平面阵建立了近场声源信号模型,推导了空间非均匀噪声条件下求解声源方位和距离信息的最大似然定位方法,提出使用引力搜索算法解决了以上最大似然方法在多维参数空间搜索的高运算复杂度问题,通过仿真验证了改进方法的可行性和有效性,并说明估计精度较高,在低信噪比下方位和距离的均方误差都小于常规最大似然方法,并且在高信噪比条件下方位和距离的均方误差都逼近克拉美-罗界.     

基于模糊遗传算法的极大似然参数估计

提出用模糊遗传算法和极大似然估计法结合的方法,通过模糊推理调节遗传算法的交叉和变异概率,使参数估计不受变量初值影响,提高求解精度和收敛速度,并以三参数威布尔分布为例进行参数估计.结果表明,改进的遗传算法在求解效率和收敛性能上达到了较好的平衡,能更好地将优化方法和极大似然估计法相结合,优于一般遗传算法,从而使模糊推理方法更好地应用于数理统计中.     

基于度量误差模型的摄像机参数求解算法

给出了基于度量误差模型的摄像机定标方案，即由最小二乘法计算投影矩阵，从投影矩阵中分解出参数作为进一步计算的初始值，然后考虑投影矩阵中约束条件及图像坐标的误差结构，由度量误差模型优化参数。结果表明，该方案不仅保证了旋转矩阵的正交性，同时提高了定标精度。     

基于移动信标和DOA的声阵列网络自定位机制

研究了面向声阵列网络的自定位问题,提出了三种基于移动信标和波达方向(DOA)的最大似然自定位机制:基本最大似然估计(BML)、具有声信号传播延时补偿功能的最大似然估计(PCML)和基于二次计算的最大似然估计(RML).研究了声信号传播延时效应,并采用PCML和RML对此进行了补偿.详细分析了信标移动速度、DOA误差和信...     

一种稳健的GPS多径估计算法

为研究抵制多径误差的信号处理,提出了一种在低信噪比、多经和干扰条件下GPS码延时估计算法.首先,使用一种基于块平均的预处理技术把零均值的干扰转换成有色高斯噪声,然后一种结合白化滤波的基于最大似然估计方法用来估计相关的参数.在实现最大似然估计时,使用了数据压缩技术和脉冲内插技术来降低优化计算的计算复杂度.计算仿真表明所提出的方法在低信噪比和干扰条件下优于传统的最大似然估计方法.为高精度定位提出了有效的方法.     

不同精度冗余数据的融合

针对融合误差的最大值和数学期望,提出了一个评判数据融合方法优劣的标准.随后,提出了一种新的数据融合方法,扩展加权平均法.当待融合数据为两个时,通过理论分析得到了计算融合参数的公式.当有更多的数据参与融合时,通过数值仿真得到了该方法的各个融合参数.该方法能解决最大似然估计法所难以解决的均匀分布数据的融合问题,且具有比包括最大似然估计法在内的其它三种有代表性的数据融合方法更高的精度.     

混合遗传算法和隐马尔可夫模型的Web信息抽取

传统Web信息抽取的隐马尔可夫模型对初值十分敏感和在实际训练中极易得到局部最优模型参数。提出了一种使用遗传算法优化HMM模型参数的Web信息抽取混合算法。该算法使用实数矩阵编码表示染色体,似然概率值为适应度取值,将GA与Baum-Welch算法相结合对HMM模型参数进行全局优化,并且调整GA-HMM的Baum-Welch算法参数实现Web信息抽取。实验结果表明,新的算法在精确度和召回率指标上比传统HMM具有更好的性能。     

基于半镜面靶标的无重叠视域相机全局标定

针对智能无人车、监控等多相机全局标定问题,往往存在无重叠视域或无法单向观测的情况,因此本文提出了一种基于半镜面平面靶标的多相机全局标定方法。该方法首先完成了单相机的内参数标定,然后固定点激光器并投射光线到第一个相机c1视场内并进行散斑点的观测,接着利用半镜面平面靶将光路反射到第二个相机c2视场内并观测散斑点像,由于单相机自身的内外参数已经标定,因此可计算得到反射光路的直线方程,最后根据直线的方向向量可计算全局坐标系的转换矩阵 和平移向量 。仿真和实验的结果表明,所提方法在500mm-1200mm距离,相机视域 范围内,其标定误差为0.36毫米。相对于现有方法,本文所提方法具有更广泛的适应性,可针对多相机复杂视场结构完成标定,同时具有设备简单、操作方便、计算简便等优点。     

Camera calibration using identical objects

This paper describes a method for camera calibration using identical products. In this paper, we postulate an imaginative rigid motion between any two identical products, and the imaginative rigid motion could offer a pair of circular points. As is known, three pairs of projections of the circular points are needed to result in the closed-form solution for calibration. In our method, we obtain three pairs of projections of the circular points from only two images of three identical products, or three images of two identical products. When only two identical products are utilized, our method is almost the dual of the stereo calibration from rigid motions. A direct approach is taken here instead of the two-step process in stereo calibration. Furthermore, a better projective reconstruction could be performed from the estimation of the camera parameters to avoid the dominant projective-to-affine error in the stereo calibration. Finally, we conduct a nonlinear refinement based on the maximum likelihood estimation. The experimental results from synthetic data and real data prove our method convenient and robust to noise.     

Orientation in manhattan: equiprojective classes and sequential estimation

The problem of inferring 3D orientation of a camera from video sequences has been mostly addressed by first computing correspondences of image features. This intermediate step is now seen as the main bottleneck of those approaches. In this paper, we propose a new 3D orientation estimation method for urban (indoor and outdoor) environments, which avoids correspondences between frames. The scene property exploited by our method is that many edges are oriented along three orthogonal directions; this is the recently introduced Manhattan world (MW) assumption. The main contributions of this paper are: the definition of equivalence classes of equiprojective orientations, the introduction of a new small rotation model, formalizing the fact that the camera moves smoothly, and the decoupling of elevation and twist angle estimation from that of the compass angle. We build a probabilistic sequential orientation estimation method, based on an MW likelihood model, with the above-listed contributions allowing a drastic reduction of the search space for each orientation estimate. We demonstrate the performance of our method using real video sequences.     

基于单平面模板的摄像机定标研究

提出了一种摄像机定标方法，只需要摄像机从不同方向拍摄平面模板的多幅图像，摄像机与平面模板间可以自由地移动，运动的参数无需已知。对于每个视点获得图像，提取图像上的网格角点；平面模板与图像间的网格角点对应关系，确定了单应性矩阵；对每幅图像，就可确定一个单应性矩阵，这样就能够进行摄像机定标。该算法先有一个线性解法，然后基于极大似然准则对线性结果进行非线性优化求精。该方法同时也考虑了镜头畸变的影响。实验结果表明该算法简单易用。     

一种雷达和摄像机的空间标定方法

多传感器融合技术已经广泛应用在智能汽车环境感知领域中;雷达和摄像机的空间标定是伴随信息实时融合的道路检测技术的基础;结合智能汽车的实际应用,提出了针对激光雷达和摄像机的空间标定方法;通过特制的标定板来获得雷达数据和图像数据,选取激光雷达坐标系作为世界坐标系,通过参数拟合的方法来求取图像坐标系与雷达坐标系的变换关系,进而实现两种传感器的空间配准;该方法只需要标定板就能够完成激光雷达和摄像机的空间标定,标定精度高,实现了多个传感器世界坐标系的统一,避免了后续处理中数据解释的二义性;实验结果表明这种方法简单准确,满足系统要求。     

Improved Pose Estimation for Mobile Robots by Fusion of Odometry Data and Environment Map

This paper proposes a new approach for calibration of dead reckoning process. Using the well-known UMBmark (University of Michigan Benchmark) is not sufficient for a desirable calibration of dead reckoning. Besides, existing calibration methods, usually require explicit measurement of actual motion of the robot. Some recent methods, use the smart encoder trailer or long range finder sensors such as ultrasonic or laser range finders for automatic calibration. Manual measurement is necessary in the case of the robots that are not equipped with long range detectors or such smart encoder trailer. Our proposed approach, uses an environment map that is created by fusion of proximity data, in order to calibrate the odometry error automatically. In the new approach, the systematic part of the error is adaptively estimated and compensated by an efficient and incremental maximum likelihood algorithm. Actually, environment map data are fused with the odometry and current sensory data in order to acquire the maximum likelihood estimation. The advantages of the proposed approach are demonstrated in some experiments with Khepera robot. It is shown that the amount of pose estimation error is reduced by a percentage of more than 80%.     

Quaternion‐based visual servo control in the presence of camera calibration error

Visual servo control systems use information from images along with knowledge of the optic parameters (i.e. camera calibration) to position the camera relative to some viewed object. If there are inaccuracies in the camera calibration, then performance degradation and potentially unpredictable response from the visual servo control system may occur. Motivated by the desire to incorporate robustness to the camera calibration, different control methods have been developed. Previous adaptive/robust controllers (especially for six degree‐of‐freedom camera motion) rely heavily on properties of the rotation parameterization to formulate state estimates and a measurable closed‐loop error system. All of these results are based on the singular axis–angle parameterization. Motivated by the desire to express the rotation by a non‐singular parameterization, efforts in this paper address the question: Can state estimates and a measurable closed‐loop error system be crafted in terms of the quaternion parameterization when the camera calibration parameters are unknown? To answer this question, a contribution of this paper is the development of a robust controller and closed‐loop error system based on a new quaternion‐based estimate of the rotation error. A Lyapunov‐based analysis is provided which indicates that the controller yields asymptotic regulation of the rotation and translation error signals given a sufficient approximate of the camera calibration parameters. Simulation results are provided that illustrate the performance of the controller for a range of calibration uncertainty. Copyright © 2009 John Wiley & Sons, Ltd.     

Using vanishing points for camera calibration

In this article a new method for the calibration of a vision system which consists of two (or more) cameras is presented. The proposed method, which uses simple properties of vanishing points, is divided into two steps. In the first step, the intrinsic parameters of each camera, that is, the focal length and the location of the intersection between the optical axis and the image plane, are recovered from a single image of a cube. In the second step, the extrinsic parameters of a pair of cameras, that is, the rotation matrix and the translation vector which describe the rigid motion between the coordinate systems fixed in the two cameras are estimated from an image stereo pair of a suitable planar pattern. Firstly, by matching the corresponding vanishing points in the two images the rotation matrix can be computed, then the translation vector is estimated by means of a simple triangulation. The robustness of the method against noise is discussed, and the conditions for optimal estimation of the rotation matrix are derived. Extensive experimentation shows that the precision that can be achieved with the proposed method is sufficient to efficiently perform machine vision tasks that require camera calibration, like depth from stereo and motion from image sequence.     

Calibration procedures when both measurements are subject to error: A comparative simulation study of the unreplicated case

The problem of statistical calibration, when both standard and non-standard measurements are subject to error, is formulated as a predictive errors-in-variables model. Under the assumptions of unreplicated observations, a simulation study was conducted to compare relative performances of the ordinary least squares and the maximum likelihood estimation method, each combined with classical and inverse prediction. The Fuller method is also included for comparison. The mean squared error of prediction and the probability of concentration are adopted as criteria, based upon which guidelines are provided for selecting appropriate calibration procedures for the given situation.     

Robot vision modelling-camera modelling and camera calibration

Camera modelling, especially parameter calibration, is a crucial problem for further progress in robot vision applications. Simply stated, the problem of calibration is how to evaluate the position and orientation of the camera easily, rapidly, accurately, and reliably. After the importance of camera modelling is explained, this paper will review the state of the art of calibration techniques. Categorized calibration techniques are described and characterized in detail. A common set of symbols is adopted and applied to the various techniques, and complementary derivations are partly made to clarify the peculiarities and problems of each technique. Detailed considerations on the preservation of the orthogonality constraints for the rotation transformation matrix and on the instability of the calibrated intrinsic parameter values using real experimental set-ups are expected to pave the way for future research on robot vision applications.