火箭特征段三维测量系统的研究
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摘要
xx-x运载火箭作为全新研制的火箭型号，采用了不少新的设计和材料。例如，xx-x火箭整流罩首次采用复合材料，故需通过地面分离试验验证其分离可靠性。火箭包带连接解锁装置（简称包带）用于实现星箭间的连接与分离，是重要的单机产品，要求解锁高可靠，且解锁后必须在限定的包络内完成分离。为了在型号设计阶段，掌握和考核新型火箭的设计性能，需要对这些重要部件的真实运动状态进行记录和分析。以此为研究背景，本文设计了一套火箭特征段三维测量系统，用于记录重要机构运动过程，分析其三维位置信息并完成被测目标的三维重建。本论文以提高三维测量系统的测量精度为主要研究内容，重点研究了基于多约束融合算法的视觉测量方法及相关问题，给出了一种利用测量数据进行三维重建的方法。
高精度内外方位元素检测一直是三维测量系统的研究重点，本文采用距离约束算法实现了多摄像机的内外方位元素在线检测。根据中心投影成像的特点，建立了像点坐标与空间距离之间的关系模型，高精度地检测了摄像机的内、外参数和图像畸变系数。在计算过程中，方位元素检测和三维坐标测量工作是分步进行的，实验结果表明，该方法稳定性好，具有较高的检测精度。
提出了一种基于多约束融合算法的视觉测量算法，融合视场中的距离约束、共线约束和共面约束条件，将其引入到描述中心投影成像模型的共线方程，建立了附有约束条件的目标函数。论文中分别列出了靶标点像点、具有约束条件的像点和普通待测像点的法方程，通过牛顿迭代法得到待测点的三维坐标、各相机的方位参数和图像畸变系数。多约束融合算法降低了图像畸变对测量的影响，提高了视觉测量系统的精度。与传统的光线束平差法和基于单一约束的算法相比，本算法使三维测量系统的测量精度有了很大提高。
在三维测量过程中，像点坐标中心定位的精度直接影响最终的测量精度，因此选择合适的像点中心计算方法，并保证较高的处理速度也是本文研究的一个重点工作。本文结合边界搜索和灰度加权质心法，实现了对目标点中心的高精度定位。首先，利用边界搜索算法搜索目标点成像的边界，从而确定待测点的成像区域，然后利用灰度加权质心法确定像点的中心位置。
像点匹配结果的优劣直接影响最终空间点坐标计算程序的稳定性和精度，因此，选择的匹配算法应具有高的匹配正确率和匹配效率。像点匹配是一个复杂的问题，对于具体场景需要具体的匹配方法。本文通过编码元信息获取图像对间同名点的对应匹配，计算变换矩阵F，利用相似性和相容性度量，得到图像对间同名非编码元的初始匹配结果，最后通过模糊度准则、相似性准则及距离约束误差准则去除误匹配。
融合八叉树结构、Delaunay三角剖分法和基于μ GA的RBF神经网络，提出了基于测量点云数据的三维重建算法。首先，采用八叉树方法对点云数据空间分割，然后结合微种群遗传算法和RBF网络对各叶结点进行三角剖分，最终获得高质量的点云数据三维重建结果。
基于上述的理论研究，设计了适用于火箭特征段三维测量的多摄像机动态三维测量系统。测量系统包括：控制靶及约束控制、高速摄像机组、计算机、拍摄控制设备、照明光源、海量数据存储器、标志点以及三维测量软件。利用拍摄控制设备通过外触发方式使多摄像机同步拍摄，最终利用多约束融合算法完成对待测目标的三维位置和面型的测量。
As a new development of the rocket models, many new designs and materialsare applied to the xx-x launch vehicle. xx-x rocket fairing, for example, has adoptedcomposite materials for the first time, so it needs the ground separate test to verifythe separation reliability. Rocket package-connection-unlocking device (packet forshort) is used to realize the connection and separation between the satellite and therocket. The device is an important stand-alone product, which requires a highunlocking reliability, and the packet must be separated within a limited envelopeafter unlocking. In order to learn and evaluate the mechanism of the new rocket atthe stage of design, recording and analyzing the real motion state of these importantparts is necessary. A rocket three-dimensional feature measurement system designedin this thesis is used to record the kinematic process of the important parts, toanalyze three-dimensional position information and complete the three-dimensionalreconstruction of the measured target. In this thesis, with the main topic ofimproving the measurement accuracy of three-dimensional measurement system, thispassage focuses on the vision measurement methods and related issues based onmulti-constraint fusion algorithm, and proposed a method of three-dimensionalreconstruction using the measurement data.
The camera calibration has been the key point of the vision measurementsystem. The distance constraint method is used to realize multi-camera calibration online in this thesis. According to the characteristics of the central projection, arelational model between the pixel coordinate and spatial distance is built, and theinternal and external parameters and image distortion coefficient of the camera arecalibrated in high-precision. In the calibration process, the camera parametercalibration and three-dimensional coordinate measurement are completed step bystep. Experiment results show that the calibration method is of good stability and ahigh calibrationaccuracy.
Vision measurement algorithm based on multi-constraint fusion is proposed,which is the integration of distance constraint in the field of view, the collinearconstraint and coplanarity constraint. This algorithm is introduced to the collinearequations which describes the central projection imaging model, and the objectivefunction with constraints is built. The paper gives the normal equations of targetpixels, the pixels with constraints and the pixels to test, and obtain three-dimensionalcoordinates of the test points, the orientation parameters of each camera and imagedistortion coefficient with the Newton iterative method. Multi-constraint fusionalgorithm reduces the impact of image distortion, and improves the accuracy ofvisual measurement system. Compared with the traditional bundle adjustmentalgorithm and the algorithm based on a single constraint, the algorithm proposedimproves the vision measurement system accuracy greatly.
The accuracy of the pixel coordinate center location affects the finalmeasurement result directly. Therefore, to select the appropriate pixel centercalculation method and ensure a high processing speed are also a focus of this thesis.In this paper, combining the border search and gray-scale weighted-centroid, a highprecision locating of the target point center is achieved. First, search the boundary ofthe target point using border search algorithm to determine the imaging area, andthen determine the position of the pixel by using gray-scale weigthed-centroidmethod.
The pros and cons of the point matching results directly affect the stability andaccuracy of the calculation procedure of the final space coordinates, so, the matching algorithm chosen should match with high accuracy and matching efficiency. Pixelpoint matching is a complex issue, and the matching method should vary accordingto specific scenes. This article introduces how to obtain the corresponding match ofthe same name point between the image pairs by encoding meta information, andcalculate the transformation matrix F, to make use of the measurement of similarityand compatibility, and get the initial match results of the same-name but non-codingmeta between the image pares, and finally through the fuzzy criterion, similarguidelines and distance constraint error criterion to remove the mismatch.
In the process of the3D reconstruction, by means of using RBF network basedon micro genetic algorithm, the three-dimensional reconstruction of the3D pointcloud data sets is accomplished, and the three-dimensional reconstruction of theparallax image proves the superior processing power of this algorithm on largedatasets. The learning of the micro genetic algorithm based on radial basis networkavoids the premature phenomenon during the network evolution, while theconvergence speed and accuracy have also been improved.
Based on the theoretical studies above, a multi-camera vision measurementsystem for the rocket characteristic three-dimensional measurement is designed. Inthis paper, the design of the vision measurement system includes: the control targetand the constraint control, the high-speed camera groups, the shoot controlequipment, computer, lighting, massive data memorizer, landmarks, as well asthree-dimensional measurement software. Use shoot control equipment via externaltrigger mode to make several cameras shoot in sync, and finally multi-constrainedintegration algorithm is applied to complete the measurement of thethree-dimensional position and surface of the target.
高精度内外方位元素检测一直是三维测量系统的研究重点，本文采用距离约束算法实现了多摄像机的内外方位元素在线检测。根据中心投影成像的特点，建立了像点坐标与空间距离之间的关系模型，高精度地检测了摄像机的内、外参数和图像畸变系数。在计算过程中，方位元素检测和三维坐标测量工作是分步进行的，实验结果表明，该方法稳定性好，具有较高的检测精度。
提出了一种基于多约束融合算法的视觉测量算法，融合视场中的距离约束、共线约束和共面约束条件，将其引入到描述中心投影成像模型的共线方程，建立了附有约束条件的目标函数。论文中分别列出了靶标点像点、具有约束条件的像点和普通待测像点的法方程，通过牛顿迭代法得到待测点的三维坐标、各相机的方位参数和图像畸变系数。多约束融合算法降低了图像畸变对测量的影响，提高了视觉测量系统的精度。与传统的光线束平差法和基于单一约束的算法相比，本算法使三维测量系统的测量精度有了很大提高。
在三维测量过程中，像点坐标中心定位的精度直接影响最终的测量精度，因此选择合适的像点中心计算方法，并保证较高的处理速度也是本文研究的一个重点工作。本文结合边界搜索和灰度加权质心法，实现了对目标点中心的高精度定位。首先，利用边界搜索算法搜索目标点成像的边界，从而确定待测点的成像区域，然后利用灰度加权质心法确定像点的中心位置。
像点匹配结果的优劣直接影响最终空间点坐标计算程序的稳定性和精度，因此，选择的匹配算法应具有高的匹配正确率和匹配效率。像点匹配是一个复杂的问题，对于具体场景需要具体的匹配方法。本文通过编码元信息获取图像对间同名点的对应匹配，计算变换矩阵F，利用相似性和相容性度量，得到图像对间同名非编码元的初始匹配结果，最后通过模糊度准则、相似性准则及距离约束误差准则去除误匹配。
融合八叉树结构、Delaunay三角剖分法和基于μ GA的RBF神经网络，提出了基于测量点云数据的三维重建算法。首先，采用八叉树方法对点云数据空间分割，然后结合微种群遗传算法和RBF网络对各叶结点进行三角剖分，最终获得高质量的点云数据三维重建结果。
基于上述的理论研究，设计了适用于火箭特征段三维测量的多摄像机动态三维测量系统。测量系统包括：控制靶及约束控制、高速摄像机组、计算机、拍摄控制设备、照明光源、海量数据存储器、标志点以及三维测量软件。利用拍摄控制设备通过外触发方式使多摄像机同步拍摄，最终利用多约束融合算法完成对待测目标的三维位置和面型的测量。
As a new development of the rocket models, many new designs and materialsare applied to the xx-x launch vehicle. xx-x rocket fairing, for example, has adoptedcomposite materials for the first time, so it needs the ground separate test to verifythe separation reliability. Rocket package-connection-unlocking device (packet forshort) is used to realize the connection and separation between the satellite and therocket. The device is an important stand-alone product, which requires a highunlocking reliability, and the packet must be separated within a limited envelopeafter unlocking. In order to learn and evaluate the mechanism of the new rocket atthe stage of design, recording and analyzing the real motion state of these importantparts is necessary. A rocket three-dimensional feature measurement system designedin this thesis is used to record the kinematic process of the important parts, toanalyze three-dimensional position information and complete the three-dimensionalreconstruction of the measured target. In this thesis, with the main topic ofimproving the measurement accuracy of three-dimensional measurement system, thispassage focuses on the vision measurement methods and related issues based onmulti-constraint fusion algorithm, and proposed a method of three-dimensionalreconstruction using the measurement data.
The camera calibration has been the key point of the vision measurementsystem. The distance constraint method is used to realize multi-camera calibration online in this thesis. According to the characteristics of the central projection, arelational model between the pixel coordinate and spatial distance is built, and theinternal and external parameters and image distortion coefficient of the camera arecalibrated in high-precision. In the calibration process, the camera parametercalibration and three-dimensional coordinate measurement are completed step bystep. Experiment results show that the calibration method is of good stability and ahigh calibrationaccuracy.
Vision measurement algorithm based on multi-constraint fusion is proposed,which is the integration of distance constraint in the field of view, the collinearconstraint and coplanarity constraint. This algorithm is introduced to the collinearequations which describes the central projection imaging model, and the objectivefunction with constraints is built. The paper gives the normal equations of targetpixels, the pixels with constraints and the pixels to test, and obtain three-dimensionalcoordinates of the test points, the orientation parameters of each camera and imagedistortion coefficient with the Newton iterative method. Multi-constraint fusionalgorithm reduces the impact of image distortion, and improves the accuracy ofvisual measurement system. Compared with the traditional bundle adjustmentalgorithm and the algorithm based on a single constraint, the algorithm proposedimproves the vision measurement system accuracy greatly.
The accuracy of the pixel coordinate center location affects the finalmeasurement result directly. Therefore, to select the appropriate pixel centercalculation method and ensure a high processing speed are also a focus of this thesis.In this paper, combining the border search and gray-scale weighted-centroid, a highprecision locating of the target point center is achieved. First, search the boundary ofthe target point using border search algorithm to determine the imaging area, andthen determine the position of the pixel by using gray-scale weigthed-centroidmethod.
The pros and cons of the point matching results directly affect the stability andaccuracy of the calculation procedure of the final space coordinates, so, the matching algorithm chosen should match with high accuracy and matching efficiency. Pixelpoint matching is a complex issue, and the matching method should vary accordingto specific scenes. This article introduces how to obtain the corresponding match ofthe same name point between the image pairs by encoding meta information, andcalculate the transformation matrix F, to make use of the measurement of similarityand compatibility, and get the initial match results of the same-name but non-codingmeta between the image pares, and finally through the fuzzy criterion, similarguidelines and distance constraint error criterion to remove the mismatch.
In the process of the3D reconstruction, by means of using RBF network basedon micro genetic algorithm, the three-dimensional reconstruction of the3D pointcloud data sets is accomplished, and the three-dimensional reconstruction of theparallax image proves the superior processing power of this algorithm on largedatasets. The learning of the micro genetic algorithm based on radial basis networkavoids the premature phenomenon during the network evolution, while theconvergence speed and accuracy have also been improved.
Based on the theoretical studies above, a multi-camera vision measurementsystem for the rocket characteristic three-dimensional measurement is designed. Inthis paper, the design of the vision measurement system includes: the control targetand the constraint control, the high-speed camera groups, the shoot controlequipment, computer, lighting, massive data memorizer, landmarks, as well asthree-dimensional measurement software. Use shoot control equipment via externaltrigger mode to make several cameras shoot in sync, and finally multi-constrainedintegration algorithm is applied to complete the measurement of thethree-dimensional position and surface of the target.
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