直线导轨滚转角测量误差自校正方法

提出一种具有滚转角测量自动校正功能的五自由度运动误差测量系统,解决了平行双光束法在测量滚转角误差时受光束平行度影响较大的问题。介绍了平行双光束法测量五自由度运动误差的原理,建立了关于双光束平行度误差与滚转角测量误差间的模型,提出了基于双自准直单元的双光束平行度误差测量方法。实验结果表明:双光束之间存在15.4″的平行度误差时,可以将滚转角测量误差的残差由44″减小到2.7″。该校正方法有效提高了双光束测量滚转角的准确性,可用于直线导轨运动误差的在线监测。     

合成孔径声纳中拖体偏航角的估计方法分析

单发射阵多接收阵合成孔径声纳成像中,拖体的偏航角是主要的运动误差之一,为得到高质量的成像结果必须对其准确估计并补偿。主要分析了偏航角的两种估计方法,即偏置相位中心算法和基于相邻两帧图像互相关算法。通过仿真和湖试试验结果表明,在强点目标场景下偏置相位中心算法估计偏航角的精度较低,且随拖体的速度的增加估计精度大幅度下降,当拖体速度过快导致无重叠相位中心时该算法失效,而基于相邻两帧图像互相关算法估计的偏航角相对于偏置相位中心算法不仅精度高,且具有很好的鲁棒性。     

一种基于矩特征和特征光流的运动目标跟踪方法

提出了一种基于矩特征和特征光流的运动目标跟踪方法.首先进行角点特征提取,按照提出的基于矩特征的局部范围内匹配角点的策略,完成了序列图像的角点匹配;然后,按照本文提出的光流聚类准则完成了两个图像目标的聚类.仿真实验表明,本文算法在减少计算量的同时可提高跟踪精度,且可克服目标做小角度旋转时的失跟问题.     

旋转载体锥形运动的两轴旋转表征方法研究

针对旋转载体因旋转表征方式不同出现姿态解算诱导误差问题,通过分析旋转特点,提出两轴旋转表征方法。定义锥形运动的姿态角,建立角运动方程及姿态解算方程,并给出与常规姿态角的关系式,仿真分析不同表征方式的角速度和姿态解算的差异,并通过试验设计进行对比验证。综合仿真和实验结果表明:在相同的锥形运动条件下,两轴旋转表征方法与三轴旋转表征方法的差异随着锥形运动条件的恶劣而变大,但两轴旋转表征方法解算姿态误差较小,因此采用两轴旋转表征方法更为有效。该表征方法不但为旋转载体的抑制姿态误差提供理论基础,还能为攻角和侧滑角测量提供新思路。     

地磁旋转弹滚转角及角速率的估计方法

针对高转速的旋转弹等制导弹药滚转角及滚转角速率实时获取的问题,提出一种基于地磁信息的滚转角及滚转角速率的实时快速估计方法。首先根据旋转弹的轴向滚转运动特性,利用卡尔曼滤波算法实时估计弹丸在轴向高速旋转的状态下的滚转角以及角速率,考虑弹载实时应用需求,在卡尔曼滤波的基础上进一步采用α-β-γ滤波来提高估计算法的实时性。通过仿真数据以及半物理验证,结果表明,相比于直接用卡尔曼滤波估计,采用α-β-γ滤波估计的时间缩短一个数量级,明显提高算法的实时性和快速性。同时该方法估计的滚转角误差在3°以内,比系统直接解算的滚转角准确度提高1倍;滚转角速率的估计准确度在5°/s以内,比直接求导准确度提高6倍以上,满足常规旋转弹的需求。     

四轴人造卫星跟踪摄影机定位误差和跟踪误差估计

对四轴人造卫星跟踪摄影机提出了定位误差和跟踪误差的估计方法。考虑了轴系误差、予报误差、目标的视角加速度、小圆弧与轨道差、目视跟踪误差、机械传动误差、电调速误差和底片测量误差等8种误差对定位和跟踪精确度的影响。在三种跟踪拍摄条件下的定量计算表明,各种误差因素的综合结果为:定位误差小于4角秒。跟踪精度可达到1％。机械传动误差和电调速稳定度是决定跟踪误差和定位误差的主要因素,轴系误差影响较小。目标视运动加速度的影响不可忽视,应在底片处理时加以修正。为了提高低速卫星的跟踪精确度,在采用自动跟踪方案时,必须改善电气调速的平滑度。     

基于固定扫描点的建筑物三维测量方法

提出一种基于固定激光扫描点的三角投影法测量建筑物大目标三维尺寸的方法。利用线结 构光旋转扫描形面和对投影图像进行处理分析,实现三维重建和测量。导出了三维几何尺寸的计算公式,进行了仿真实验,获得满意的数据。在进行建筑物等大目标三维测量时,不需激光器和传感器平行移动,减少误差来源,提高精度,方便可行。     

基于UKF的简化交互多模型视频图像机动目标跟踪算法

在视频图像运动目标的状态估计与跟踪问题中,常用的扩展卡尔曼(EKF)算法简单、计算量小,但仅适用于弱非线性和弱高斯环境下.本文提出一种基于无迹卡尔曼滤波(UKF)与简化交互多模型(IMM算法相结合的视频图像运动目标跟踪算法,有效地克服了EKF算法在强非线性状态下或对小运动目标跟踪时精度低,容易发散的问题.仿真结果表明,该算法估计和跟踪非线性目标的性能明显优于基于EKF算法,其跟踪精度可达到三阶(泰勒级数展开)精度.     

工业机器人视觉与平面运动参数的估计

本文提出了用二维Hotelling变换对平面运动参数进行估计的一种方法,并进行了模拟实验。首先提取出图象序列中各图象目标的轮廓特征,然后利用Hotelling变换使各轮廓的方位标准化,从而可求得目标的位置和旋转角等运动参数。实验得到了较为满意的结果。     

一种水下被动目标运动分析与仿真

文章讨论了一种水下运动的目标和观测平台在不同平面的三维情形下,被动目标运动分析与仿真问题,文中从声呐测量的方位角、爷角和频率的数据入手,采用伪线性估计、最大似然估计两种不同的方法,对水下被动TMA问题进行了研究,并分析了两种不同方法的状态参数估计的误差特性。仿真结果表明：由于增加了观测的信息量,上述方法均能得到较好的参数估计结果,且伪线性估计方差向CRLB界接近。上述方法可推广到目标和观测平台处于同一平面的二维情形。     

Research on Multi-View Image Reconstruction Technology Based on Auto-Encoding Learning

Traditional three-dimensional (3D) image reconstruction method, which highly dependent on the environment and has poor reconstruction effect, is easy to lead to mismatch and poor real-time performance. The accuracy of feature extraction from multiple images affects the reliability and real-time performance of 3D reconstruction technology. To solve the problem, a multi-view image 3D reconstruction algorithm based on self-encoding convolutional neural network is proposed in this paper. The algorithm first extracts the feature information of multiple two-dimensional (2D) images based on scale and rotation invariance parameters of Scale-invariant feature transform (SIFT) operator. Secondly, self-encoding learning neural network is introduced into the feature refinement process to take full advantage of its feature extraction ability. Then, Fish-Net is used to replace the U-Net structure inside the self-encoding network to improve gradient propagation between U-Net structures, and Generative Adversarial Networks (GAN) loss function is used to replace mean square error (MSE) to better express image features, discarding useless features to obtain effective image features. Finally, an incremental structure from motion (SFM) algorithm is performed to calculate rotation matrix and translation vector of the camera, and the feature points are triangulated to obtain a sparse spatial point cloud, and meshlab software is used to display the results. Simulation experiments show that compared with the traditional method, the image feature extraction method proposed in this paper can significantly improve the rendering effect of 3D point cloud, with an accuracy rate of 92.5% and a reconstruction complete rate of 83.6%.     

基于视差空间运动估计的高精度立体视觉定位

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.     

基于形态变换的非刚体运动及变形的新方法及其应用

提出非刚体运动及变性的新方法,与传统的线性方法相比,解决了任意非同拓扑结构（包括有孔及凹多边形）的内插问题,理论上证明此种方法是一种全局优化的内插方法并证明此方法的正确性。实验表明此方法效果自然,质量好、速度快、自动化程度高,为字体合成、计算机动画、模型基编码、虚拟人机通信提供一个强有力的方法和工具。     

Robust estimation of 3D trajectories from a monocular image sequence

This article describes a new method for object trajectory estimation that uses sequences of images taken from a monocular camera. The method integrates a Kalman filter to estimate the three‐dimensional (3D) parameters of the optical system and a lineal projective model to determine 3D point coordinates projected on the retinal plane. It works with at least three distinctive points in the image, and they are updated with correlation methods. The result is an estimation of the rotation and translation parameters between successive images within the sequence and yield to the 3D coordinates of the points selected for correspondence. The scaling problem related to 3D reconstruction is tackled via a priori information of the objects being observed. The method is tested with synthetic images to evaluate its accuracy, and later, an interesting application in autonomous navigation is presented. © 2002 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 128–137, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10020     

Exact analytical approach for six-degree-of-freedom measurement using image-orientation-change method

An exact analytical approach is proposed for measuring the six-degree-of-freedom (6-DOF) motion of an object using the image-orientation-change (IOC) method. The proposed measurement system comprises two reflector systems, where each system consists of two reflectors and one position sensing detector (PSD). The IOCs of the object in the two reflector systems are described using merit functions determined from the respective PSD readings before and after motion occurs, respectively. The three rotation variables are then determined analytically from the eigenvectors of the corresponding merit functions. After determining the three rotation variables, the order of the translation equations is downgraded to a linear form. Consequently, the solution for the three translation variables can also be analytically determined. As a result, the motion transformation matrix describing the 6-DOF motion of the object is fully determined. The validity of the proposed approach is demonstrated by means of an illustrative example.     

基于多视图立体视觉的沙堆三维尺寸测量研究

利用从运动中恢复结构方法(SFM),提出了一种基于多视图立体视觉的沙堆三维重建及三维尺寸测量方法。首先根据SFM方法的求解不稳定特点,结合光束平差法对SFM求解过程进行分析及优化;其次针对SFM重建结果为稀疏点云的问题,利用基于面片的稠密重建算法重新生成稠密的三维点云,再利用泊松算法对密集点云进行三维曲面重建;最后获得模型的三维尺寸信息。对某建筑工地的沙堆进行了三维尺寸的测量实验,实验结果验证了该方法的有效及可行性,提高了重建能力及精度,同时考虑了目标实际测量误差与重建误差,能够满足实际智能测量的应用需求。     

基于四元数最少特征点的相机位姿估计

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。     

Stereo calibration of binocular ultra-wide angle long-wave infrared camera based on an equivalent small field of view camera

ABSTRACT

In this paper, a stereo calibration method for binocular ultra-wide angle long-wave infrared camera is proposed on the basis of an equivalent small field of view camera. Extrinsic parameters are calibrated through the corrected images from the left and right cameras. They can be viewed as images taken by a small field of view camera. The calibration procedure consists of three steps: monocular calibration, distortion correction and extrinsic parameters calibration. In order to evaluate the accuracy of the method, stereo vision of the camera is modelled and a 3D reconstruction approach is presented. A series of experiments, including intrinsic parameters, extrinsic parameters and 3D reconstruction, are conducted to validate the proposed method. The results show that the baseline length error decreases to 0.67%, and the relative error for the 3D reconstruction of corners is smaller than 8.11%. In contrast to the common stereo calibration method, it improves calibration accuracy.     

Measurement of two-dimensional small rotation angles by using orthogonal parallel interference patterns

Based on measuring one-dimensional small rotation angles by using a parallel interference pattern (PIP), a method for measuring two-dimensional (2D) small rotation angles by using two different PIP's that are orthogonal to each other is proposed. We simultaneously measure the 2D small rotation angles Δθ and Δφ by detecting the phases of the orthogonal PIP's reflected by an object at two detection points. A sensitivity of 4.9 mrad/arcsec and a spatial resolution of 1.5 × 1.5 mm(2) are achieved in the measurement. Theoretical analysis and experimental results show that error ε(1) in the measurement of Δφ is almost equal to -0.01Δθ and error ε(2) in the measurement of Δθ is almost equal to -0.01Δφ. For small rotation angles of less than a few tens of arcseconds, the random errors whose standard deviations are 0.6 arcsec are dominant.