大型结构形变的组网摄像测量方法研究
|
摘要
随着科技的发展，越来越多的大型结构出现在人们的生产生活中，诸如桥梁、隧道、飞机、飞艇、风电叶片、天线等。全场、高精度、动态测量大型结构的形貌、变形，即形变参数，是其动态性能实验和质量监测的基本要求。
摄像测量具有非接触、高精度、可测点多、实时动态测量等特点，在大型结构的形变测量方面有独特的优势。但是，由于大型结构的形变测量同时要求大尺度和高精度，大尺度要求像机具有大视场，高精度需要有高空间分辨率，双像机摄像测量方法在这两者之间存在矛盾。为此，本文采用组网摄像测量方法，研究了该方法在大型结构形变测量应用中亟需解决的四个关键问题：像机组网优化、高精度像机标定、实时三维交会和快速平差优化。
本文取得的主要成果如下：
1.提出了基于改进遗传算法的摄像测量网络设计方法。
待测大型结构复杂，组网摄像测量中像机数目和类型多，环境约束多，仅凭经验布置像机，不能保证像机数量已确定情形下的测量精度最优。因此，组网摄像测量首要解决的问题是在给定待测模型、像机类型和数量、测量环境约束等条件下，优化设计满足精度要求的布置形式，即摄像测量网络设计。
由于摄像测量网络设计具有多参数、多约束、运算量大等特点，本文使用改进的遗传算法随机寻找全局最优解。本文算法中考虑了可见性和可测性判断、分块的反向误差精度估算等因素，能够得到满足约束条件和测量精度的组网配置结果，为实施现场组网测量提供依据。
2.提出了组网摄像测量中的一系列像机标定方法。
由于多像机组网提供了足够的多视图几何约束，组网摄像测量系统可用两视图自标定结果作为初值，统一平差优化所有像机参数，实现系统的高精度标定。此外，为解决像机标定中各参数耦合的问题，研究了逐级独立的像机标定；为解决待测区域难以布置大量控制点的问题，研究了较为相应的大视场像机标定；作为固定像机的有益补充，经纬像机能够全场高精度测量，本文研究了中远场测量中经纬像机的标定。
2.1提出了从焦距简便解析解自标定变焦两视图的方法。
该方法仅需要已知像机的长宽比和零扭曲这两种内在约束，即可由同名像点得到等效焦距的更简单的解析表达式，进而标定像机，并对目标进行欧氏重构。
2.2提出了基于位姿估计变化量逐级标定像机的方法。
该方法首先基于位姿估计变化量标定等效焦距，然后逐级标定其他参数，解决了像机内、外参数之间耦合的问题，各参数值与其物理意义相符，可独立使用。
2.3提出了基于无穷单应的大视场像机标定方法。
该方法最少只需要四个非共线控制点和像机粗略的位置即可求解无穷单应，并且提出了一种坐标变换方法以保证线性求解和优化无穷单应时的稳定性。
2.4提出了准同心广义经纬像机的成像模型及高精度标定方法。
对于广义经纬像机的标定，在近场及单幅图像内控制点数目比较多时，可使用手眼标定等方法，但在中远场标定与测量时这些方法不适用。为方便中远场测量的经纬像机的标定，本文假设像机与旋转平台准同心，提出了准同心广义经纬像机的成像模型及高精度标定方法。
3.提出了最小化空间距离的线性交会方法。
交会测量时平差物点的迭代方法虽然能够保证精度，但是运算量大，不能保证实时。本文所提方法为线性方法，交会方程的物理意义为最小化物点到各像点反投影射线的距离平方和，只需一步解算得到物点坐标，精度与平差物点的迭代方法相当。
4.提出了两种平差方法：共内参多视图物点形式的稀疏LM（Levenberg-Marquardt）光束法间接平差；附有限制条件的稀疏LM光束法间接平差。
光束法平差是提高测量精度的必要步骤，稀疏分块平差能显著提高平差速度，且考虑既有约束的平差模型才更加符合真实物理意义。本文提出的第一种平差方法利用了多视图共内参的约束，能够同时优化物点，适用于在控制点精度不高情形下的像机标定。第二种方法能够灵活设定参数是否需要优化，能够添加各种内在约束、场景约束和运动约束，但仍然保留了稀疏分块结构，适用于存在各种约束的大型结构形变测量。
利用上述研究成果，设计和研制了大型结构形变组网摄像测量系统，成功应用于风电叶片、机翼和飞艇等的形变测量。该系统能够以较优的摄像测量组网配置，测量大型结构的整体变形和各个局部区域变形，适用于场景比较单一的区域中大视场像机的标定和测量，也适用于待测区域内无任何控制信息的测量。组网摄像测量方法为大型结构形变参数的实时/准实时、动态、高精度测量提供了有力的理论和技术支持，有效拓展了传统摄像测量的应用范围，增强了摄像测量在宏观测量方面的工程实用性，有广泛应用前景。
With the development of science and technology, more and more large-scalestructures come into our lives, such as bridge, tunnel, airplane, airship, wind turbineblade and antenna, etc. It’s a basic requirement of corresponding dynamic performanceexperiment and quality monitoring to measure the shape and deformation of theselarge-scale structures universally and dynamically with high precision.
Compared with other measuring means, videometrics has a lot of advantages in thedeformation measurement of large-scale structure, it can conduct non-contact dynamicmeasurement with high precision, and can handle large set of points simultaneously. Butdeformation measurement of large-scale structure has requirement on measuring scaleand precision at the same time, that is the large field of view and high spatial resolutionrespectively, which brings contradiction for dual-camera videometrics. To cope with thisproblem, networked videometrics is proposed in this dissertation, and fourcorresponding critical problems are studied: camera network optimization, cameracalibration with high precision, real-time3D intersection and rapid bundle adjustment.
Main contributions of this dissertation are as follows:
1. Videometric network design based on improved genetic algorithm is proposed.
The structure to be measured is quite complex, besides, cameras to be used innetworked videometrics are many and versatile, and there are a lot of environmentalrestrictions, so the optimal measuring accuracy given the amount of camera cannot beguaranteed only by experience. Thus, the first problem that has to be solved is todetermine the layout of cameras, given the model to be measured, along with the typeand the amount of camera and environmental restrictions, in order to achieve therequired measuring accuracy, and this procedure is so called videometric networkdesign.
For videometric network design is a computationally exhausting nonlinearprocedure with multiple parameters and constraints, improved genetic algorithm isadopted in this dissertation to search for global optimum randomly. Kinds of factors,such as visibility, measurability and block reverse error precision estimation, etc. areconsidered in the algorithm, design results satisfying the constraining conditions andmeasuring accuracy are acquired, which can provide reliable reference for conductingfield networked measurement.
2. A series of camera calibration methods is proposed for networked videometrics.
Since multiple cameras are used in networked videometrics, there are adequatemultiple view geometric constraints. Self calibration result based on two views can beadopted as initial values for networked videometrics system, followed by bundleadjustment to obtain calibration result with high precision. Besides, to cope withcoupling among each parameter in camera calibration, stepwise camera calibrationmethod is studied. Sometimes, it is difficult to locate large set of control points in the area to be measured, to cope with this, flexible calibration method for large field ofview camera is studied. As a beneficial supplement to fixed camera, Theodolite-cameracan conduct full field of view measurement with high precision, correspondingcalibration method when using theodolite-camera for middle-field and far-fieldmeasurement is studied.
2.1A camera self calibration method for two-view with varying focal length, whichis originated from simplified analytical solution of equivalent focal length, is proposed.
Given only aspect ratio and zero-skew, this method can get the simplifiedanalytical solution of equivalent focal length from corresponding points of two images,and then cameras are calibrated and Euclidean structure is reconstructed further.
2.2A stepwise camera calibration method based on pose estimation variation isproposed.
In this method, first equivalent focal length is determined based on pose estimationvariation, and then other parameters are determined step by step. This method caneffectively avoid coupling among each camera parameter, in which way the value ofeach parameter can match its own physical meaning pretty well, and can be usedindependently.
2.3A calibration method for large field of view camera based on infinitehomography is proposed.
This method only needs approximate position of the camera and at least fournon-collinear control points to compute the infinite homography. A coordinatetransformation method is also proposed to ensure stability of the linear solution andoptimization process of the infinite homography.
2.4Imaging model and high precision calibration method for quasi-concentricgeneral theodolite-camera is proposed.
As for general theodolite-camera, it can be calibrated by hand-eye calibrationmethod when used for near-field measurement, if there are adequate control points in asingle image, which is not suitable for middle-field and far-field use. In order toexpediently calibrate the theodolite-camera used for middle-field and far-fieldmeasurement, we suppose the optical center of camera is quasi-concentric with therotation center of rotation frame, and its corresponding imaging model and highprecision calibration method is proposed.
3. A linear intersection method, which minimizes the spatial distance, is proposed.
As for the intersection, although the iterative method can guarantee accuracy, it iscomputationally expensive and cannot guarantee efficiency. Proposed method is linear,in which the geometric meanings of intersection equation is to minimize the sum ofsquares of the spatial distances from the object point to the back-projected radial of itseach corresponding image points. Only one-step resolvent is demanded, and themeasuring accuracy is very approximate to the ones of iterative method.
4. Two bundle adjustment methods are proposed. One is the sparse LM indirect bundle adjustment with the form as mutual internal parameter, multiple view and objectpoints. Another is the sparse LM indirect bundle adjustment with restricted condition.
Bundle adjustment is an imperative procedure to refine measuring results, in whichthe sparse block mechanism can speed the adjustment procedure up significantly, andconsidering existing restricted condition should be in accordance with the true physicalmeanings. The first method is applicable for the calibration of cameras with the samefixed internal parameters and can optimize the positions of the reference pointssimultaneously, it is suitable for camera calibration under situations in which theaccuracy of reference points is not good enough. The second method can easily set theparameters whether be adjusted or not, and can add various constraints such as intrinsicconstraint, scene constraint and motion constraint. Besides, it still preserves sparseblock structure, so it is applicable for the deformation measurement of large-scalestructure which has various constraints.
Utilizing above research achievements, we have designed and developed apractical networked videometrics system for the deformation measurement oflarge-scale structure, and applied it to practical deformation measurement of windturbine blade, airplane wing and airship, etc. This system can measure the integraldeformation and all of the local deformation. The system is applicable for thecalibration and measurement of large field of view camera in simplex scene, and for themeasurement of regions without any control information for calibration.
Networked videometrics method provides a powerful theoretical and technicalsupport for real-time/quasi real-time, dynamic and high precision measurement oflarge-scale structure deformation. It effectively expands the application range oftraditional videometrics, and enhances the engineering practicality in the aspect ofmacroscopic measurement of videometrics, and has a wide and important applicationalprospect.
摄像测量具有非接触、高精度、可测点多、实时动态测量等特点，在大型结构的形变测量方面有独特的优势。但是，由于大型结构的形变测量同时要求大尺度和高精度，大尺度要求像机具有大视场，高精度需要有高空间分辨率，双像机摄像测量方法在这两者之间存在矛盾。为此，本文采用组网摄像测量方法，研究了该方法在大型结构形变测量应用中亟需解决的四个关键问题：像机组网优化、高精度像机标定、实时三维交会和快速平差优化。
本文取得的主要成果如下：
1.提出了基于改进遗传算法的摄像测量网络设计方法。
待测大型结构复杂，组网摄像测量中像机数目和类型多，环境约束多，仅凭经验布置像机，不能保证像机数量已确定情形下的测量精度最优。因此，组网摄像测量首要解决的问题是在给定待测模型、像机类型和数量、测量环境约束等条件下，优化设计满足精度要求的布置形式，即摄像测量网络设计。
由于摄像测量网络设计具有多参数、多约束、运算量大等特点，本文使用改进的遗传算法随机寻找全局最优解。本文算法中考虑了可见性和可测性判断、分块的反向误差精度估算等因素，能够得到满足约束条件和测量精度的组网配置结果，为实施现场组网测量提供依据。
2.提出了组网摄像测量中的一系列像机标定方法。
由于多像机组网提供了足够的多视图几何约束，组网摄像测量系统可用两视图自标定结果作为初值，统一平差优化所有像机参数，实现系统的高精度标定。此外，为解决像机标定中各参数耦合的问题，研究了逐级独立的像机标定；为解决待测区域难以布置大量控制点的问题，研究了较为相应的大视场像机标定；作为固定像机的有益补充，经纬像机能够全场高精度测量，本文研究了中远场测量中经纬像机的标定。
2.1提出了从焦距简便解析解自标定变焦两视图的方法。
该方法仅需要已知像机的长宽比和零扭曲这两种内在约束，即可由同名像点得到等效焦距的更简单的解析表达式，进而标定像机，并对目标进行欧氏重构。
2.2提出了基于位姿估计变化量逐级标定像机的方法。
该方法首先基于位姿估计变化量标定等效焦距，然后逐级标定其他参数，解决了像机内、外参数之间耦合的问题，各参数值与其物理意义相符，可独立使用。
2.3提出了基于无穷单应的大视场像机标定方法。
该方法最少只需要四个非共线控制点和像机粗略的位置即可求解无穷单应，并且提出了一种坐标变换方法以保证线性求解和优化无穷单应时的稳定性。
2.4提出了准同心广义经纬像机的成像模型及高精度标定方法。
对于广义经纬像机的标定，在近场及单幅图像内控制点数目比较多时，可使用手眼标定等方法，但在中远场标定与测量时这些方法不适用。为方便中远场测量的经纬像机的标定，本文假设像机与旋转平台准同心，提出了准同心广义经纬像机的成像模型及高精度标定方法。
3.提出了最小化空间距离的线性交会方法。
交会测量时平差物点的迭代方法虽然能够保证精度，但是运算量大，不能保证实时。本文所提方法为线性方法，交会方程的物理意义为最小化物点到各像点反投影射线的距离平方和，只需一步解算得到物点坐标，精度与平差物点的迭代方法相当。
4.提出了两种平差方法：共内参多视图物点形式的稀疏LM（Levenberg-Marquardt）光束法间接平差；附有限制条件的稀疏LM光束法间接平差。
光束法平差是提高测量精度的必要步骤，稀疏分块平差能显著提高平差速度，且考虑既有约束的平差模型才更加符合真实物理意义。本文提出的第一种平差方法利用了多视图共内参的约束，能够同时优化物点，适用于在控制点精度不高情形下的像机标定。第二种方法能够灵活设定参数是否需要优化，能够添加各种内在约束、场景约束和运动约束，但仍然保留了稀疏分块结构，适用于存在各种约束的大型结构形变测量。
利用上述研究成果，设计和研制了大型结构形变组网摄像测量系统，成功应用于风电叶片、机翼和飞艇等的形变测量。该系统能够以较优的摄像测量组网配置，测量大型结构的整体变形和各个局部区域变形，适用于场景比较单一的区域中大视场像机的标定和测量，也适用于待测区域内无任何控制信息的测量。组网摄像测量方法为大型结构形变参数的实时/准实时、动态、高精度测量提供了有力的理论和技术支持，有效拓展了传统摄像测量的应用范围，增强了摄像测量在宏观测量方面的工程实用性，有广泛应用前景。
With the development of science and technology, more and more large-scalestructures come into our lives, such as bridge, tunnel, airplane, airship, wind turbineblade and antenna, etc. It’s a basic requirement of corresponding dynamic performanceexperiment and quality monitoring to measure the shape and deformation of theselarge-scale structures universally and dynamically with high precision.
Compared with other measuring means, videometrics has a lot of advantages in thedeformation measurement of large-scale structure, it can conduct non-contact dynamicmeasurement with high precision, and can handle large set of points simultaneously. Butdeformation measurement of large-scale structure has requirement on measuring scaleand precision at the same time, that is the large field of view and high spatial resolutionrespectively, which brings contradiction for dual-camera videometrics. To cope with thisproblem, networked videometrics is proposed in this dissertation, and fourcorresponding critical problems are studied: camera network optimization, cameracalibration with high precision, real-time3D intersection and rapid bundle adjustment.
Main contributions of this dissertation are as follows:
1. Videometric network design based on improved genetic algorithm is proposed.
The structure to be measured is quite complex, besides, cameras to be used innetworked videometrics are many and versatile, and there are a lot of environmentalrestrictions, so the optimal measuring accuracy given the amount of camera cannot beguaranteed only by experience. Thus, the first problem that has to be solved is todetermine the layout of cameras, given the model to be measured, along with the typeand the amount of camera and environmental restrictions, in order to achieve therequired measuring accuracy, and this procedure is so called videometric networkdesign.
For videometric network design is a computationally exhausting nonlinearprocedure with multiple parameters and constraints, improved genetic algorithm isadopted in this dissertation to search for global optimum randomly. Kinds of factors,such as visibility, measurability and block reverse error precision estimation, etc. areconsidered in the algorithm, design results satisfying the constraining conditions andmeasuring accuracy are acquired, which can provide reliable reference for conductingfield networked measurement.
2. A series of camera calibration methods is proposed for networked videometrics.
Since multiple cameras are used in networked videometrics, there are adequatemultiple view geometric constraints. Self calibration result based on two views can beadopted as initial values for networked videometrics system, followed by bundleadjustment to obtain calibration result with high precision. Besides, to cope withcoupling among each parameter in camera calibration, stepwise camera calibrationmethod is studied. Sometimes, it is difficult to locate large set of control points in the area to be measured, to cope with this, flexible calibration method for large field ofview camera is studied. As a beneficial supplement to fixed camera, Theodolite-cameracan conduct full field of view measurement with high precision, correspondingcalibration method when using theodolite-camera for middle-field and far-fieldmeasurement is studied.
2.1A camera self calibration method for two-view with varying focal length, whichis originated from simplified analytical solution of equivalent focal length, is proposed.
Given only aspect ratio and zero-skew, this method can get the simplifiedanalytical solution of equivalent focal length from corresponding points of two images,and then cameras are calibrated and Euclidean structure is reconstructed further.
2.2A stepwise camera calibration method based on pose estimation variation isproposed.
In this method, first equivalent focal length is determined based on pose estimationvariation, and then other parameters are determined step by step. This method caneffectively avoid coupling among each camera parameter, in which way the value ofeach parameter can match its own physical meaning pretty well, and can be usedindependently.
2.3A calibration method for large field of view camera based on infinitehomography is proposed.
This method only needs approximate position of the camera and at least fournon-collinear control points to compute the infinite homography. A coordinatetransformation method is also proposed to ensure stability of the linear solution andoptimization process of the infinite homography.
2.4Imaging model and high precision calibration method for quasi-concentricgeneral theodolite-camera is proposed.
As for general theodolite-camera, it can be calibrated by hand-eye calibrationmethod when used for near-field measurement, if there are adequate control points in asingle image, which is not suitable for middle-field and far-field use. In order toexpediently calibrate the theodolite-camera used for middle-field and far-fieldmeasurement, we suppose the optical center of camera is quasi-concentric with therotation center of rotation frame, and its corresponding imaging model and highprecision calibration method is proposed.
3. A linear intersection method, which minimizes the spatial distance, is proposed.
As for the intersection, although the iterative method can guarantee accuracy, it iscomputationally expensive and cannot guarantee efficiency. Proposed method is linear,in which the geometric meanings of intersection equation is to minimize the sum ofsquares of the spatial distances from the object point to the back-projected radial of itseach corresponding image points. Only one-step resolvent is demanded, and themeasuring accuracy is very approximate to the ones of iterative method.
4. Two bundle adjustment methods are proposed. One is the sparse LM indirect bundle adjustment with the form as mutual internal parameter, multiple view and objectpoints. Another is the sparse LM indirect bundle adjustment with restricted condition.
Bundle adjustment is an imperative procedure to refine measuring results, in whichthe sparse block mechanism can speed the adjustment procedure up significantly, andconsidering existing restricted condition should be in accordance with the true physicalmeanings. The first method is applicable for the calibration of cameras with the samefixed internal parameters and can optimize the positions of the reference pointssimultaneously, it is suitable for camera calibration under situations in which theaccuracy of reference points is not good enough. The second method can easily set theparameters whether be adjusted or not, and can add various constraints such as intrinsicconstraint, scene constraint and motion constraint. Besides, it still preserves sparseblock structure, so it is applicable for the deformation measurement of large-scalestructure which has various constraints.
Utilizing above research achievements, we have designed and developed apractical networked videometrics system for the deformation measurement oflarge-scale structure, and applied it to practical deformation measurement of windturbine blade, airplane wing and airship, etc. This system can measure the integraldeformation and all of the local deformation. The system is applicable for thecalibration and measurement of large field of view camera in simplex scene, and for themeasurement of regions without any control information for calibration.
Networked videometrics method provides a powerful theoretical and technicalsupport for real-time/quasi real-time, dynamic and high precision measurement oflarge-scale structure deformation. It effectively expands the application range oftraditional videometrics, and enhances the engineering practicality in the aspect ofmacroscopic measurement of videometrics, and has a wide and important applicationalprospect.
引文
[1]任朴舟.机翼动态三维变形影像测量技术[C]. Proceedings of the2011年航空试验测试技术学术交流会论文集,2011.
[2]赵金涛.用于机翼后缘变形的智能蒙皮及变形测量方法研究[D],2010.
[3]于起峰,尚洋,伏思华,杨夏.大型结构变形及形貌摄像测量技术研究进展[J].实验力学,2011,26(5).
[4] Jensen FM, Falzon B, Ankersen J, Stang H. Structural testing and numericalsimulation of a34m composite wind turbine blade[J]. Composite Structures,2006,76(1):52-61.
[5] J rgensen ER, Borum KK, McGugan M, Thomsen C, Jensen FM. Full scaletesting of wind turbine blade to failure-flapwise loading[J]. Report Ris,2004.
[6] Larsen GC, Hansen MH, Baumgart A, Carlén I. Modal analysis of wind turbineblades[M]. Ris National Laboratory,2002.
[7] Larsen JW, Nielsen SRK. Nonlinear parametric instability of wind turbinewings[J]. Journal of sound and vibration,2007,299(1):64-82.
[8] Robson S, Setan H. The dynamic digital photogrammetric measurement andvisualisation of a21m wind turbine rotor blade undergoing structural analysis[J].Proceedings, ISPRS Commission,1996,5:493-498.
[9]苑云,张小虎,朱肇昆,孙祥一,尚洋.大型风力发电叶片变形的摄像测量方法研究[J].计算机应用,2012,32(S1):114-117.
[10]邸蓉.大型结构应变测量技术研究[J].机械工程与自动化,2005(5):2.
[11]陈军,周立强.大跨度桥梁挠度测量新方法的应用研究[J].科技创新与应用,2011(20).
[12]姚海敏.大型桥梁结构变形监测应用研究[D],2009.
[13]张瑞云.大型桥梁结构健康监测与评估技术的研究与应用[D],2008.
[14] Forsyth DA, Ponce J. Computer vision: A modern approach[M]. London:Prentce Hall,2003.
[15] Jensen SC, Rudin L. Measure: An interactive tool for accurate forensicphoto/videogrammetry[J]. Proceeding of SPIE,1995,2567:73-83.
[16] Legac A. Videogrammetry or digital photogrammetry: General remarks,methodology, applications[J]. Proceeding of SPIE,1994,2350:16-21.
[17] McGlone J. Manual of photogrammetry[M]. Annapolis: American Society forPhotogrammetry and Remote Sensing,2004.
[18]张剑清,潘励,王树根.摄影测量学[M].武汉:武汉大学出版社,2003.
[19]张祖勋.数字摄影测量30年[M].武汉:武汉大学出版社,2007.
[20] Hartley R, Zisserman A. Multiple View Geometry in Computer Vision,2edition[M]. Cambridge: Cambridge University Press,2003.
[21]刘波,刘娟丽,邓军.变形监测技术研究进展综述[J].山西建筑,2011,37(23).
[22]卫建东,徐忠阳,张良琚,包欢,樊刚.测量机器人监测网络系统在地铁结构监测中的应用[C],精密与大型工程测量技术研讨交流会论文集,
[23]陈顺超,黄平明,孙胜江.测量机器人用于悬索桥静载试验的精度分析[J].广西大学学报（自然科学版）,2011,36(1).
[24]郑俊,赵红旺,朵兴茂.应力应变测试方法综述[J].汽车科技,2009(1):5-8.
[25]肖鸾,胡友健,王晓华. GPS技术在变形监测中的应用综述[J].工程地球物理学报,2005,2(2):160-165.
[26] GHARRA F.常规测量和GPS结合监测结构变形的研究[D],2009.
[27]胡应东,孙永荣,于捷杰,熊智.基于GPS多天线的高层建筑变形监测系统研究[J].传感器与微系统,2008,27(11):11-14.
[28]胡友健,梁新美,许成功.论GPS变形监测技术的现状与发展趋势[J].测绘科学,2006,31(5):155-157.
[29]阿帕尔,沙塔尔.浅谈GPS在工程测绘中的应用[J].商品与质量：学术观察,2012(3).
[30]蒋睿嵩,张定华,汪文虎,卜昆,程云勇.基于逆向工程技术的涡轮叶片型面精铸位移场测评[J].特种铸造及有色合金,2009,29(1):3.
[31]宋得军,卜昆,王红霞,刘杰,董一巍.精铸涡轮叶片收缩变形的测量与分析[J].现代制造工程,2008(3):4.
[32]王建军,王颖.光栅法在船体横扭角测量中的应用[J].光学精密工程,2005,13(3):5.
[33] Jiang R, Jauregui D. A novel network control method for photogrammetricbridge measurement[J]. Experimental Techniques,2007,31(3):48-53.
[34] Jáuregui DV, White KR, Woodward CB, Leitch KR. Noncontactphotogrammetric measurement of vertical bridge deflection[J]. Journal of BridgeEngineering,2003,8(4):212-222.
[35] Xiao Z, Liang J, Yu D, Asundi A. Large field-of-view deformation measurementfor transmission tower based on close-range photogrammetry[J]. Measurement,2011,44:1705-1712.
[36] Moon BW, Park JH, Lee SK, Kim J, Kim T. Performance evaluation of atransmission tower by substructure test[J]. Journal of Constructional SteelResearch,2009,65(1):1-11.
[37] B W Moon JHP. Performance evaluation of a transmission tower bysubstructure test[J]. J Constr Steel Res2009,65:11.
[38] Maas HG. Photogrammetric techniques for deformation measurements on thereservoir walls[C].1998.
[39] YANG J, PENG C, XIAO J, ZENG J, YUAN Y. Application of videometrictechnique to deformation measurement for large-scale composite wind turbineblade[J]. Applied Energy,2012,98(0):292-300.
[40] SUN X, YUAN Y, ZHU Z, ZHANG X, YU Q. Videometric research ondeformation measurement of large-scale wind turbine blades[J]. Theoretical andapplied mechanics letters,2011,1(1):011005.
[41]邾继贵,郭磊,叶声华.现场条件下大空间三维精密定位原理与方法[J].光学学报,2009,29(7):1872-1876.
[42]田胜利.隧道及地下空间结构变形的数字化摄影测量与监测数据处理新技术研究[D],2005.
[43]姜广文.像机链位姿传递摄像测量方法及船体变形测量研究[D].长沙;国防科学技术大学,2010.
[44]张小虎,周翔,周剑,尚洋,于起峰.结构位移摄像测量系统的设计与实现[J].应用光学,2009,30(4):622-625.
[45] Brown JD. V-STARS—a system for digital industrial photogrammetry[C].Proceedings of the In: Gruen, A, Kahmen, H Optical3-D MeasurementTechniques III Wichmann Verlag,Heidelberg,1995.
[46] Zhang DH, Liang J, Guo C, Liu JW, Zhang XQ. Exploitation ofphotogrammetry measurement system[J]. Optical Engineering,2010,49(3).
[47] Hartley R, Zisserman A. Multiple view geometry in computer vision[M].Cambridge: Cambridge University Press,2000.
[48] Lourakis MIA. Sparse non-linear least squares optimization for geometricvision[J]. ECCV'10: Proceedings of the11th European conference on Computervision: Part II,2010,2:43-56.
[49] Lourakis MIA, Argyros AA. SBA: A software package for generic sparse bundleadjustment[J]. ACM Trans Math Softw,2009,36(1).
[50] Xue J, Su X. A new approach for the bundle adjustment problem with fixedconstraints in stereo vision[J]. Optik-International Journal for Light andElectron Optics,2011(0).
[51] Wu C, Agarwal S, Curless B, Seitz SM. Multicore bundle adjustment[J]. CVPR'11: Proceedings of the2011IEEE Conference on Computer Vision and PatternRecognition,2011,30:57-3064.
[52] Konolige K, Garage W. Sparse Bundle Adjustment[C]. Proceedings of theBMVC,2010.
[53] Jeong, Yekeun, Nister, David, Steedly. Pushing the Envelope of ModernMethods for Bundle Adjustment[J]. CVPR,2010:1474-1481.
[54] Byr d M, str m K. Conjugate gradient bundle adjustment[J]. ECCV'10:Proceedings of the11th European conference on Computer vision: Part II,2010:114-127.
[55] Kai N, Steedly D, Dellaert F. Out-of-Core Bundle Adjustment for Large-Scale3D Reconstruction[C]. Proceedings of the Computer Vision,2007ICCV2007IEEE11th International Conference on,14-21Oct.2007,2007.
[56] Mouragnon E, Lhuillier M, Dhome M, Dekeyser F, Sayd P. Real TimeLocalization and3D Reconstruction[J]. CVPR '06: Proceedings of the2006IEEE Computer Society Conference on Computer Vision and PatternRecognition-Volume1,2006,1(1):363-370.
[57] Engels C, Stewenius H, Nister D. Bundle Adjustment Rules[J].Photogrammetric Computer Vision,2006.
[58] Lourakis MIA, Argyros AA. Is Levenberg-Marquardt the Most EfficientOptimization Algorithm for Implementing Bundle Adjustment?[J]. ICCV '05:Proceedings of the Tenth IEEE International Conference on Computer Vision-Volume2,2005,2:1526-1531.
[59] Lourakis M, Argyros A, The design and implementation of a generic sparsebundle adjustment software package based on the Levenberg-Marquardtalgorithm,2004.
[60] Wang X, Clarke TA. Separate adjustment in close-range photogrammetry[J].ISPRS Journal of Photogrammetry&Remote Sensing,2001,55:289–298.
[61] Triggs B, McLauchlan P, Hartley R, Fitzgibbon A. Bundle Adjustment—AModern Synthesis Vision Algorithms: Theory and Practice[M]. Springer Berlin,Heidelberg.2000:153-177.
[62] Madsen K, Nielsen HB, Tingleff O. Methods for non-linear least squaresproblems[J].1999(2):43-56.
[63] Heung-Yeung S, Qifa K, Zhengyou Z. Efficient bundle adjustment with virtualkey frames: a hierarchical approach to multi-frame structure from motion[C].Proceedings of the Computer Vision and Pattern Recognition,1999IEEEComputer Society Conference on,1999,1999.
[64] GRAFAREND EW. Optimisation of geodetic networks[J]. Bollettino diGeodesia a Scienze Affini,1974,33(4):351-406.
[65] Fraser CS. Network design considerations for non-topographicphotogrammetry[J]. Photogrammetric Engineering and Remote Sensing,1984,50(8):1115-1126.
[66] Fraser CS. Network design optimization in non-topographic photogrammetry[J].IA PRS,1984,25:296-308.
[67] Fraser CS. Limiting error propagation in network design[J]. PhotogrammetricEngineering and Remote Sensing,1987,53(5):487-493.
[68] Brown DC. STARS, A turnkey system for close-range photogrammetry[J].International Archives of Photogrammetry,1982,24(V/1):68-89.
[69] Gustafson PC, Brown JD. Interactive photogrammetric network design on amicrocomputer[C].1985.
[70] Shortis MR, Hall CJ. Network design methods for close-rangephotogrammetry[J]. Australian Journal of Geodesy, Photogrammetry andSurveying,1989,50:51-72.
[71] Hastedt H. Monte-carlo-simulation in close-range photogrammetry[C].2004.
[72] Mason S. Conceptual model of the sensor station placement task in configuringmulti-station convergent nefworks[J]. i Beitr ge zu den Symposien der ISPRS1994,1996.
[73] Mason S. Heuristic reasoning strategy for automated sensor placement[J].Photogrammetric Engineering and Remote Sensing,1997,63(9):1093-1102.
[74] Dunn E, Olague G. Evolutionary computation for sensor planning: the taskdistribution plan[J]. EURASIP Journal on Applied Signal Processing,2003:748-756.
[75] Dunn E, Olague G. Multi-objective sensor planning for efficient and accurateobject reconstruction[J]. Applications of Evolutionary Computing,2004:312-321.
[76] Olague G. Autonomous photogrammetric network design using geneticalgorithms[J]. Applications of Evolutionary Computing,2001:353-363.
[77] Olague G. Automated photogrammetric network design using geneticalgorithms[J]. Photogrammetric Engineering and Remote Sensing,2002,68(5):423-431.
[78] Olague G, Dunn E. Development of a practical photogrammetric network designusing evolutionary computing[J]. Photogrammetric Record,2007,22(117):22-38.
[79] Olague G, Dunn E. Evolutionary photogrammetric network design[M].2007.
[80] Olague G, Mohr R. Optimal camera placement to obtain accurate3d pointpositions[C]. Proceedings of the Proceedings Fourteenth InternationalConference on Pattern Recognition,1998. IEEE.
[81] Olague G, Mohr R. Optimal camera placement for accurate reconstruction[J].Technical Report3338,1998.
[82] Olague G, Mohr R. Optimal camera placement for accurate reconstruction[J].Pattern Recognition,2002,35(4):927-944.
[83] YETKI M, Cevat I, YIGIT CO. Optimal design of deformation monitoringnetworks using pso algorithm[J].2008.
[84] Saadatseresht M, Fraser CS, Samadzadegan F, Azizi A. Visibility analysis invision metrology network design[J]. Photogrammetric Record,2004,19(107):219-236.
[85] SaadatSeresht M, Samdzadegan F, Azizi A, Hahn M. Camera placement fornetwork design in vision metrology based on fuzzy inference system[C].2004.
[86] Saadatseresht M, Samadzadegan F, Azizi A. Automatic camera placement invision metrology based on a fuzzy inference system[J]. PhotogrammetricEngineering and Remote Sensing,2005,71(12):1375-1385.
[87] Saadatseresht M, Varshosaz M. Visibility prediction based on artificial neuralnetworks used in automatic network design[J]. The Photogrammetric Record,2007,22(120):336-355.
[88]王君,吕乃光,邓文怡,王学娟.视觉测量仿真系统及其网络布局优化研究[J].光学技术,2008,34(1):36-40.
[89] Tarabanis KA, Allen PK, Tsai RY. A survey of sensor planning in computervision[J]. Ieee Transactions on Robotics and Automation,1995,11(1):86-104.
[90] Akyildiz IF, Su WL, Sankarasubramaniam Y, Cayirci E. A survey on sensornetworks[J]. IEEE Commun Mag,2002,40(8):102-114.
[91] David P, Idasiak V, Kratz F. A sensor placement approach for the monitoring ofindoor scenes[J]. Smart Sensing and Context,2007:110-125.
[92] Van Graan SJ. Network Configuration Improvement and Design Aid UsingArtificial Intelligence[D]; University of Pretoria,2007.
[93] Abidi BR, Aragam NR, Yao Y, Abidi MA. Survey and Analysis of MultimodalSensor Planning and Integration for Wide Area Surveillance[J]. Acm ComputingSurveys,2008,41(1).
[94] Chen S, Li Y, Zhang J, Wang W. Active Sensor Planning for Multiview VisionTasks[M]. Springer,2008.
[95]汪定伟,王俊伟,王洪峰,张瑞友,郭哲.智能优化方法[M].北京;高等教育出版社.2007.
[96] Boers EJW. Applications of Evolutionary Computing: EvoWorkshops2001:EvoCOP, EvoFlight, EvoIASP, EvoLearn, and EvoSTIM[C]. Como, Italy,2001.Springer Verlag.
[97]龚纯,王正林.精通Matlab最优化计算[M].北京:电子工业出版社,2009.
[98]冯文灏.近景摄影测量:物体外形与运动状态的摄影法测定[M].武汉大学出版社,2002.
[99]李德仁,袁修孝.误差处理与可靠性理论[M].武汉;武汉大学出版社.2002.
[100]武汉大学测绘学院测量平差学科组.误差理论与测量平差基础[M].武汉;武汉大学出版社.2003.
[101]王穗辉.误差理论与测量平差[M].上海;同济大学出版社.2010.
[102] Fraser CS. Optimization of Precision in Close-Range Photogrammetry[J].Photogrammetric Engineering&Remote Sensing,1982,48(4):561-570.
[103]张祖勋,张剑清.数字摄影测量学[M].武汉;武汉大学出版社.1997.
[104]张祖勋.数字摄影测量与计算机视觉[J].2004,12(29):1035-1039.
[105]于起峰,尚洋.摄像测量学原理与应用研究[M].北京;科学出版社.2009.
[106]于起峰,测量学,陆宏伟,刘肖琳.基于图像的精密测量与运动测量[M].北京:科学出版社,2002.
[107]马颂德,张正友.计算机视觉——计算理论与算法基础[M].北京;科学出版社.1998.
[108]贾云得.机器视觉[M].科学出版社,2000.
[109]冯文灏.近景摄影测量——物体外形与运动状态的摄影法测定[M].武汉;武汉大学出版社.2002.
[110]陈鹰.遥感影像的数字摄影测量[M].同济大学出版社,2003.
[111]王之卓.摄影测量原理续篇[M].武汉大学出版社,2007.
[112]王之卓.摄影测量原理[M].武汉大学出版社,2007.
[113]胡占义,吴福朝.基于主动视觉摄像机标定方法[J].计算机学报,2002,25(11):1149-1156.
[114]朱嘉,李醒飞,徐颖欣.摄像机的一种主动视觉标定方法[J].光学学报,2010(5):1297-1303.
[115] I.Hartley R. Self-Calibration of Stationary Cameras[J]. International Journal ofComputer Vision,1997,22(1).
[116]霍炬,杨卫,杨明.基于消隐点几何特性的摄像机自标定方法[J].光学学报,2010(2):465-472.
[117] Brown DC. Close-range camera calibration[J]. Photogrammetric Engineering,1971,37(8):855-866.
[118] Zhang Z. A flexible new technique for camera calibration[J]. Ieee Transactionson Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334.
[119]张琼,王志良,迟健男,史雪飞.基于平面镜面向双摄像机视线追踪系统的标定方法[J].光学学报,2011,31(4):192-198.
[120] Zhang Z. Camera calibration with one-dimensional objects[J]. Pattern Analysisand Machine Intelligence, IEEE Transactions on,2004,26(7):892-899.
[121] Tsai RY. A versatile camera calibration technique for high-accuracy3dmachine vision metrology using off-the-shelf tv cameras and lenses[J]. IeeeJournal of Robotics and Automation,1987,3(4):323-344.
[122] Weng JY, Cohen P, Herniou M. Camera calibration with distortion models andaccuracy evaluation[J]. Ieee Transactions on Pattern Analysis and MachineIntelligence,1992,14(10):965-980.
[123] Shang Y, Yu QF, Zhang XH. Analytical method for camera calibration from asingle image with four coplanar control lines[J]. Appl Optics,2004,43(28):5364-5369.
[124] Changjiang Y, Fengmei S, Zhanyi H. Planar conic based camera calibration[C].Proceedings of the Pattern Recognition,2000Proceedings15th InternationalConference on,2000,2000.
[125] Chen Q, Wu H, Wada T. Camera Calibration with Two Arbitrary CoplanarCircles[M].2004.
[126] Wu Y, Zhu H, Hu Z, Wu F. Camera Calibration from the Quasi-affineInvariance of Two Parallel Circles[C]. Proceedings of the ECCV,2004. SpringerBerlin, Heidelberg.
[127]王年,范益政,梁栋,韦穗.一种基于平面模板的摄像机自定标方法[J].机器人,2004(06):538-542.
[128] Jun-Sik K, Gurdjos P, In-So K. Geometric and algebraic constraints ofprojected concentric circles and their applications to camera calibration[J].Pattern Analysis and Machine Intelligence, IEEE Transactions on,2005,27(4):637-642.
[129]张广军,周富强.基于双圆特征的无人机着陆位置姿态视觉测量方法[J].航空学报,2005,26(03):344-348.
[130] Kannala J, Salo M, Heikkil J. Algorithms for Computing a PlanarHomography from Conics in Correspondence[C]. Proceedings of the BritishMachine Vision Conference,2006.
[131] Morde A, Bouzit M, Rabiner L. A novel approach to planar cameracalibration[J]. Computer Vision Theory and Applications,2006:87--92.
[132]胡钊政,谈正.利用二次曲线拟合和圆环点进行摄像机标定[J].西安交通大学学报,2006(10):1065-1068.
[133] Ying X, Zha H. Camera Calibration Using Principal-Axes Aligned Conics[C].Proceedings of the ACCV,2007. Springer Berlin, Heidelberg.
[134] Ma W, Zheng Y, Liu Y. Camera calibration using two concentric circles: linearapproach[J]. Optical Engineering,2009,48(5):53602.
[135] Wenjuan M, Yinqiang Z, Yuncai L. A Linear Approach to Camera CalibrationUsing Concentric Circles[C]. Proceedings of the Information Technology andComputer Science,25-26July,2009.
[136]夏仁波,刘伟军,赵吉宾,徐金亭.基于圆形标志点的全自动相机标定方法[J].仪器仪表学报,2009(02):368-373.
[137]赵征,张广军,魏振忠.一种基于共面圆的摄像机自标定算法[J].光电子激光,2009(12):1576-1579.
[138]张虎,达飞鹏,李勤.基于两相同圆的自标定方法[J].仪器仪表学报,2010(03):618-624.
[139] YUAN Y, ZHU Z, GUI Y, XIE T. Camera calibration method based on circularring[C]. Proceedings of the International Symposium on PhotoelectronicDetection and Imaging: Advances in Imaging Detectors and Applications,Beijing, China,2011. SPIE.
[140]孙凤梅,王波.关于PnP问题多解的分布与解的稳定性的讨论[J].自动化学报,2010,36(9).
[141] Lepetit V, Moreno-Noguer F, Fua P. Epnp: An accurate o (n) solution to thepnp problem[J]. International Journal of Computer Vision,2009,81(2):155-166.
[142] Triggs B. Camera pose and calibration from4or5known3d points[C].1999.IEEE.
[143] Bujnak M, Kukelova Z, Pajdla T. New efficient solution to the absolute poseproblem for camera with unknown focal length and radial distortion[J].Computer Vision–ACCV2010,2011:11-24.
[144] Bujnak M, Kukelova Z, Pajdla T. A general solution to the P4P problem forcamera with unknown focal length[C].2008. IEEE.
[145] Lu CP, G. D. Hager, et al. Fast and globally convergent pose estimation fromvideo images[J]. Pattern Analysis and Machine Intelligence, IEEE,2000,22(6):610-622.
[146] Lepetit V, Moreno-Noguer F, Fua P. EPnP: An Accurate O(n) Solution to thePnP Problem[J]. International Journal of Computer Vision,2008,81(2).
[147]杨森,吴福朝.摄像机位姿的加权线性算法[J].软件学报,2011(10):2476-2487.
[148]张政.车载光电经纬仪在准动基座下的测量误差修正研究[D].长沙;国防科学技术大学硕士学位论文,2005.
[149] Ansar A, Daniilidis K. Linear pose estimation from points or lines[J]. PatternAnalysis and Machine Intelligence, IEEE Transactions on,2003,25(5):578-589.
[150] Zhang X, Zhang Z, Li Y, Zhu X, Yu Q. Robust camera pose estimation fromunknown or known line correspondences[J]. Appl Opt,2012,51(7):936-948.
[151] Schweighofer GAP. Robust pose estimation from a planar target[J]. PatternAnalysis and Machine Intelligence, IEEE,2006,28(12):2024~2030.
[152] Abidi MA, Chandra T. A new efficient and direct solution for pose estimationusing quadrangular targets: Algorithm and evaluation[J]. Pattern Analysis andMachine Intelligence, IEEE Transactions on,1995,17(5):534-538.
[153] Fischler MA, Bolles RC. Random Sample Consensus: A Paradigm for ModelFitting with Applications to Image Analysis and Automated Cartography[J].Commun ACM,1981,24.
[154] Haralick RM, Lee C-N, Ottenberg K, N lle M. Review and analysis ofsolutions of the three point perspective pose estimation problem[J]. InternationalJournal of Computer Vision,1994,13.
[155] Gao X, Hou X, Tang J, Cheng H. Complete solution classification for theperspective-three-point problem[J]. Pattern Analysis and Machine Intelligence,IEEE Transactions on,2003,2(8).
[156]吴文俊.几何定理机器证明的基本原理[J].系统科学与数学,1984(03):207-235.
[157] Quan L, Lan Z. Linear N-point camera pose determination[J]. IEEETransactions on Pattern Analysis and Machine Intelligence,1999,2(8).
[158] Fiore PD. Efficient Linear Solution of Exterior Orientation[J]. IEEE TransPattern Anal Mach Intell,2001,23.
[159] Kahl FaDH. Globally optimal estimates for geometric reconstructionproblems[J]. IEEE2005.
[160] Horn BKP, H. M. Hilden, et al. Closed-form solution of absolute orientationusing orthonormal matrices[J][J]. JOSA A,1988,5(7):1127~1135.
[161] Faugeras O. Three Dimensional Computer Vision: A Geometric Viewpoint[M].Cambridge, MA,USA: MIT Press,1993.
[162] Sturm P, Cheng ZL, Chen PCY, Poo AN. Focal length calibration from twoviews: method and analysis of singular cases[J]. Computer Vision and ImageUnderstanding,2005,99(1):58-95.
[163] Sturm P. Critical motion sequences for the self-calibration of cameras andstereo systems with variable focal length[J]. Image and Vision Computing,2002,20(5–6):415-426.
[164] Kahl F, Triggs B, Astrom K. CriticalMotions for Auto-Calibration When SomeIntrinsic Parameters Can Vary[J]. Mathematical Imaging and Vision,2000,13(2):131-146.
[165] Kahl F, Triggs B. Critical Motions in Euclidean Structure from Motion[C].Proceedings of the CVPR,1999.
[166] Sturm P, Svoboda T, Teller S. Special issue: Omnidirectional vision andcamera networks[J]. Computer Vision and Image Understanding,2006,103(3):155.
[167] Sturm P, Ramalingam S. A Generic concept for camera calibration[C].Proceedings of the ECCV,2004.
[168] Sturm P, Maybank S. On plane based camera calibration: a general algorithm,singularities, applications[C]. Proceedings of the CVPR,1999.
[169] Sturm P. On Focal Length Calibration from Two Views[C]. Proceedings of theCVPR, Kauai, Hawaii, USA,2001.
[170] Sturm P. A Case Against Kruppa’s Equations for Camera Self-Calibration[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(10):1199-1204.
[171] Sturm P. Critical motion sequences for the self-calibration of cameras andstereo systems with variable focal length[C]. Proceedings of the BMVC,1999.
[172] Sturm P. Self-calibration of a moving zoom-lens camera by pre-calibration[J].Image and Vision Computing,1997,15(8):583-589.
[173]尚洋.基于视觉的空间目标位置姿态测量方法研究[D].长沙;国防科学技术大学博士学位论文,2006.
[174]姜广文,晁志超,伏思华,于起峰.基于可控旋转的像机高精度标定技术[J].光学学报,2010(5):1308-1314.
[175]韦穗,杨尚俊,章权兵,胡茂林.计算机视觉中的多视图几何[M].合肥;安徽大学出版社.2002.
[176]田震,张玘,熊九龙,王国超.基于神经网络的大范围空间标定技术[J].光学学报,2011,31(4):186-191.
[177]何照才,胡保安.光学测量系统[M].北京;国防工业出版社.2002.
[178]刘利生.外弹道测量数据处理[M].北京;国防工业出版社.2002.
[179]张祖勋,詹总谦,郑顺义,季铮.摄影全站仪系统--数字摄影测量与全站仪的集成[J].测绘通报,2006(11):1-5.
[180]张祖勋.论摄影测量与工程测量的结合——摄影全站仪+数码摄影机[J].地理空间信息,2004,6:1-4.
[181]张峰.基于3S技术的三维数码摄影全站仪系统的研制[D].桂林;桂林工学院,2008.
[182]刘喜,王继承,盛德新.拓普康图像全站仪GPT-7000i与PI-3000软件在3维建模中的应用[J].测绘与空间地理信息,2008,31(2).
[183]靳志光,卫建东,张冠宇.图像全站仪影像测量精度的测试与分析[J].测绘通报,2008.
[184]靳志光,卫建东,汤廷松,余春平,蒲亭汀.摄影全站系统及其精度评定[J].海洋测绘,2008,28(4).
[185]付翔宇. TCA2003测量机器人的开发与应用研究[D].北京;北京建筑工程学院,2008.
[186]王一.测量机器人模型误差及标定方法的研究[D].天津;天津大学,2006.
[187]张小虎,朱肇昆,苑云,周剑,于起峰.经纬像机摄像测量方法及系统,CN101900552A.2010-12-01.
[188]朱肇昆.经纬像机摄像测量方法研究[D].长沙;国防科学技术大学硕士学位论文,2010.
[189] ZHU Z, YUAN Y, ZHANG X. Theodolite-camera videometrics system basedon total station[C]. Proceedings of the International Symposium onPhotoelectronic Detection and Imaging2011: Advances in Imaging Detectorsand Applications, May24,2011-May26,2011, Beijing, China,2011. SPIE.
[190] ZHANG X, ZHU Z, YUAN Y, LI L, SUN X. A universal and flexibletheodolite-camera system for making accurate measurements over largevolumes[J]. Optics and Lasers in Engineering,2012,50(11):1611-1620.
[191]王磊,王守印,周虎,张余彬.平行光管的基本原理及使用方法[J].仪器仪表学报,2006,27(6).
[192]苑云,朱肇昆,张小虎,姜广文.一种逐级标定相机参数的方法[J].激光与光电子学进展,2010,47(11):77-83.
[193]焦春林,高满屯,史仪凯.基于立体视觉的3D地形拼接[J].计算机工程与应用,2008,44(23):206-208.
[194] Luhmann T, Robson S, Kyle S, Harley I. Close range photogrammetry:Principles, methods and applications[M]. Whittles Scotland, UK,2006.
[195] Hartley RI, Sturm P. Triangulation[J]. Computer Vision and ImageUnderstanding,1997,68(2):146-157.
[196]陶本藻.测量数据处理的统计理论和方法[M].北京:测绘出版社,2007.
[197] Blonquist KF, Pack RT. A bundle adjustment approach with inner constraintsfor the scaled orthographic projection[J]. ISPRS Journal of Photogrammetry andRemote Sensing,2011,66(6):919-926.
[198] Furukawa Y, Ponce J. Accurate Camera Calibration from Multi-View Stereoand Bundle Adjustment[J]. International Journal of Computer Vision,2009,84(3):257-268.
[199] Bartoli A, Sturm P. Structure-from-motion using lines: Representation,triangulation, and bundle adjustment[J]. Computer Vision and ImageUnderstanding,2005,100(3):416-441.
[200] S.Granshaw. Bundle Adjustment Methods in Engineering Photogrammetry[J].Photogrammetric Record,1980,10(56):181-207.
[201] http://www.ics.forth.gr/~lourakis/sba
[202] Camera Calibration Toolbox for Matlab. http://www.ee.oulu.fi/~jth/calibr/
[2]赵金涛.用于机翼后缘变形的智能蒙皮及变形测量方法研究[D],2010.
[3]于起峰,尚洋,伏思华,杨夏.大型结构变形及形貌摄像测量技术研究进展[J].实验力学,2011,26(5).
[4] Jensen FM, Falzon B, Ankersen J, Stang H. Structural testing and numericalsimulation of a34m composite wind turbine blade[J]. Composite Structures,2006,76(1):52-61.
[5] J rgensen ER, Borum KK, McGugan M, Thomsen C, Jensen FM. Full scaletesting of wind turbine blade to failure-flapwise loading[J]. Report Ris,2004.
[6] Larsen GC, Hansen MH, Baumgart A, Carlén I. Modal analysis of wind turbineblades[M]. Ris National Laboratory,2002.
[7] Larsen JW, Nielsen SRK. Nonlinear parametric instability of wind turbinewings[J]. Journal of sound and vibration,2007,299(1):64-82.
[8] Robson S, Setan H. The dynamic digital photogrammetric measurement andvisualisation of a21m wind turbine rotor blade undergoing structural analysis[J].Proceedings, ISPRS Commission,1996,5:493-498.
[9]苑云,张小虎,朱肇昆,孙祥一,尚洋.大型风力发电叶片变形的摄像测量方法研究[J].计算机应用,2012,32(S1):114-117.
[10]邸蓉.大型结构应变测量技术研究[J].机械工程与自动化,2005(5):2.
[11]陈军,周立强.大跨度桥梁挠度测量新方法的应用研究[J].科技创新与应用,2011(20).
[12]姚海敏.大型桥梁结构变形监测应用研究[D],2009.
[13]张瑞云.大型桥梁结构健康监测与评估技术的研究与应用[D],2008.
[14] Forsyth DA, Ponce J. Computer vision: A modern approach[M]. London:Prentce Hall,2003.
[15] Jensen SC, Rudin L. Measure: An interactive tool for accurate forensicphoto/videogrammetry[J]. Proceeding of SPIE,1995,2567:73-83.
[16] Legac A. Videogrammetry or digital photogrammetry: General remarks,methodology, applications[J]. Proceeding of SPIE,1994,2350:16-21.
[17] McGlone J. Manual of photogrammetry[M]. Annapolis: American Society forPhotogrammetry and Remote Sensing,2004.
[18]张剑清,潘励,王树根.摄影测量学[M].武汉:武汉大学出版社,2003.
[19]张祖勋.数字摄影测量30年[M].武汉:武汉大学出版社,2007.
[20] Hartley R, Zisserman A. Multiple View Geometry in Computer Vision,2edition[M]. Cambridge: Cambridge University Press,2003.
[21]刘波,刘娟丽,邓军.变形监测技术研究进展综述[J].山西建筑,2011,37(23).
[22]卫建东,徐忠阳,张良琚,包欢,樊刚.测量机器人监测网络系统在地铁结构监测中的应用[C],精密与大型工程测量技术研讨交流会论文集,
[23]陈顺超,黄平明,孙胜江.测量机器人用于悬索桥静载试验的精度分析[J].广西大学学报（自然科学版）,2011,36(1).
[24]郑俊,赵红旺,朵兴茂.应力应变测试方法综述[J].汽车科技,2009(1):5-8.
[25]肖鸾,胡友健,王晓华. GPS技术在变形监测中的应用综述[J].工程地球物理学报,2005,2(2):160-165.
[26] GHARRA F.常规测量和GPS结合监测结构变形的研究[D],2009.
[27]胡应东,孙永荣,于捷杰,熊智.基于GPS多天线的高层建筑变形监测系统研究[J].传感器与微系统,2008,27(11):11-14.
[28]胡友健,梁新美,许成功.论GPS变形监测技术的现状与发展趋势[J].测绘科学,2006,31(5):155-157.
[29]阿帕尔,沙塔尔.浅谈GPS在工程测绘中的应用[J].商品与质量：学术观察,2012(3).
[30]蒋睿嵩,张定华,汪文虎,卜昆,程云勇.基于逆向工程技术的涡轮叶片型面精铸位移场测评[J].特种铸造及有色合金,2009,29(1):3.
[31]宋得军,卜昆,王红霞,刘杰,董一巍.精铸涡轮叶片收缩变形的测量与分析[J].现代制造工程,2008(3):4.
[32]王建军,王颖.光栅法在船体横扭角测量中的应用[J].光学精密工程,2005,13(3):5.
[33] Jiang R, Jauregui D. A novel network control method for photogrammetricbridge measurement[J]. Experimental Techniques,2007,31(3):48-53.
[34] Jáuregui DV, White KR, Woodward CB, Leitch KR. Noncontactphotogrammetric measurement of vertical bridge deflection[J]. Journal of BridgeEngineering,2003,8(4):212-222.
[35] Xiao Z, Liang J, Yu D, Asundi A. Large field-of-view deformation measurementfor transmission tower based on close-range photogrammetry[J]. Measurement,2011,44:1705-1712.
[36] Moon BW, Park JH, Lee SK, Kim J, Kim T. Performance evaluation of atransmission tower by substructure test[J]. Journal of Constructional SteelResearch,2009,65(1):1-11.
[37] B W Moon JHP. Performance evaluation of a transmission tower bysubstructure test[J]. J Constr Steel Res2009,65:11.
[38] Maas HG. Photogrammetric techniques for deformation measurements on thereservoir walls[C].1998.
[39] YANG J, PENG C, XIAO J, ZENG J, YUAN Y. Application of videometrictechnique to deformation measurement for large-scale composite wind turbineblade[J]. Applied Energy,2012,98(0):292-300.
[40] SUN X, YUAN Y, ZHU Z, ZHANG X, YU Q. Videometric research ondeformation measurement of large-scale wind turbine blades[J]. Theoretical andapplied mechanics letters,2011,1(1):011005.
[41]邾继贵,郭磊,叶声华.现场条件下大空间三维精密定位原理与方法[J].光学学报,2009,29(7):1872-1876.
[42]田胜利.隧道及地下空间结构变形的数字化摄影测量与监测数据处理新技术研究[D],2005.
[43]姜广文.像机链位姿传递摄像测量方法及船体变形测量研究[D].长沙;国防科学技术大学,2010.
[44]张小虎,周翔,周剑,尚洋,于起峰.结构位移摄像测量系统的设计与实现[J].应用光学,2009,30(4):622-625.
[45] Brown JD. V-STARS—a system for digital industrial photogrammetry[C].Proceedings of the In: Gruen, A, Kahmen, H Optical3-D MeasurementTechniques III Wichmann Verlag,Heidelberg,1995.
[46] Zhang DH, Liang J, Guo C, Liu JW, Zhang XQ. Exploitation ofphotogrammetry measurement system[J]. Optical Engineering,2010,49(3).
[47] Hartley R, Zisserman A. Multiple view geometry in computer vision[M].Cambridge: Cambridge University Press,2000.
[48] Lourakis MIA. Sparse non-linear least squares optimization for geometricvision[J]. ECCV'10: Proceedings of the11th European conference on Computervision: Part II,2010,2:43-56.
[49] Lourakis MIA, Argyros AA. SBA: A software package for generic sparse bundleadjustment[J]. ACM Trans Math Softw,2009,36(1).
[50] Xue J, Su X. A new approach for the bundle adjustment problem with fixedconstraints in stereo vision[J]. Optik-International Journal for Light andElectron Optics,2011(0).
[51] Wu C, Agarwal S, Curless B, Seitz SM. Multicore bundle adjustment[J]. CVPR'11: Proceedings of the2011IEEE Conference on Computer Vision and PatternRecognition,2011,30:57-3064.
[52] Konolige K, Garage W. Sparse Bundle Adjustment[C]. Proceedings of theBMVC,2010.
[53] Jeong, Yekeun, Nister, David, Steedly. Pushing the Envelope of ModernMethods for Bundle Adjustment[J]. CVPR,2010:1474-1481.
[54] Byr d M, str m K. Conjugate gradient bundle adjustment[J]. ECCV'10:Proceedings of the11th European conference on Computer vision: Part II,2010:114-127.
[55] Kai N, Steedly D, Dellaert F. Out-of-Core Bundle Adjustment for Large-Scale3D Reconstruction[C]. Proceedings of the Computer Vision,2007ICCV2007IEEE11th International Conference on,14-21Oct.2007,2007.
[56] Mouragnon E, Lhuillier M, Dhome M, Dekeyser F, Sayd P. Real TimeLocalization and3D Reconstruction[J]. CVPR '06: Proceedings of the2006IEEE Computer Society Conference on Computer Vision and PatternRecognition-Volume1,2006,1(1):363-370.
[57] Engels C, Stewenius H, Nister D. Bundle Adjustment Rules[J].Photogrammetric Computer Vision,2006.
[58] Lourakis MIA, Argyros AA. Is Levenberg-Marquardt the Most EfficientOptimization Algorithm for Implementing Bundle Adjustment?[J]. ICCV '05:Proceedings of the Tenth IEEE International Conference on Computer Vision-Volume2,2005,2:1526-1531.
[59] Lourakis M, Argyros A, The design and implementation of a generic sparsebundle adjustment software package based on the Levenberg-Marquardtalgorithm,2004.
[60] Wang X, Clarke TA. Separate adjustment in close-range photogrammetry[J].ISPRS Journal of Photogrammetry&Remote Sensing,2001,55:289–298.
[61] Triggs B, McLauchlan P, Hartley R, Fitzgibbon A. Bundle Adjustment—AModern Synthesis Vision Algorithms: Theory and Practice[M]. Springer Berlin,Heidelberg.2000:153-177.
[62] Madsen K, Nielsen HB, Tingleff O. Methods for non-linear least squaresproblems[J].1999(2):43-56.
[63] Heung-Yeung S, Qifa K, Zhengyou Z. Efficient bundle adjustment with virtualkey frames: a hierarchical approach to multi-frame structure from motion[C].Proceedings of the Computer Vision and Pattern Recognition,1999IEEEComputer Society Conference on,1999,1999.
[64] GRAFAREND EW. Optimisation of geodetic networks[J]. Bollettino diGeodesia a Scienze Affini,1974,33(4):351-406.
[65] Fraser CS. Network design considerations for non-topographicphotogrammetry[J]. Photogrammetric Engineering and Remote Sensing,1984,50(8):1115-1126.
[66] Fraser CS. Network design optimization in non-topographic photogrammetry[J].IA PRS,1984,25:296-308.
[67] Fraser CS. Limiting error propagation in network design[J]. PhotogrammetricEngineering and Remote Sensing,1987,53(5):487-493.
[68] Brown DC. STARS, A turnkey system for close-range photogrammetry[J].International Archives of Photogrammetry,1982,24(V/1):68-89.
[69] Gustafson PC, Brown JD. Interactive photogrammetric network design on amicrocomputer[C].1985.
[70] Shortis MR, Hall CJ. Network design methods for close-rangephotogrammetry[J]. Australian Journal of Geodesy, Photogrammetry andSurveying,1989,50:51-72.
[71] Hastedt H. Monte-carlo-simulation in close-range photogrammetry[C].2004.
[72] Mason S. Conceptual model of the sensor station placement task in configuringmulti-station convergent nefworks[J]. i Beitr ge zu den Symposien der ISPRS1994,1996.
[73] Mason S. Heuristic reasoning strategy for automated sensor placement[J].Photogrammetric Engineering and Remote Sensing,1997,63(9):1093-1102.
[74] Dunn E, Olague G. Evolutionary computation for sensor planning: the taskdistribution plan[J]. EURASIP Journal on Applied Signal Processing,2003:748-756.
[75] Dunn E, Olague G. Multi-objective sensor planning for efficient and accurateobject reconstruction[J]. Applications of Evolutionary Computing,2004:312-321.
[76] Olague G. Autonomous photogrammetric network design using geneticalgorithms[J]. Applications of Evolutionary Computing,2001:353-363.
[77] Olague G. Automated photogrammetric network design using geneticalgorithms[J]. Photogrammetric Engineering and Remote Sensing,2002,68(5):423-431.
[78] Olague G, Dunn E. Development of a practical photogrammetric network designusing evolutionary computing[J]. Photogrammetric Record,2007,22(117):22-38.
[79] Olague G, Dunn E. Evolutionary photogrammetric network design[M].2007.
[80] Olague G, Mohr R. Optimal camera placement to obtain accurate3d pointpositions[C]. Proceedings of the Proceedings Fourteenth InternationalConference on Pattern Recognition,1998. IEEE.
[81] Olague G, Mohr R. Optimal camera placement for accurate reconstruction[J].Technical Report3338,1998.
[82] Olague G, Mohr R. Optimal camera placement for accurate reconstruction[J].Pattern Recognition,2002,35(4):927-944.
[83] YETKI M, Cevat I, YIGIT CO. Optimal design of deformation monitoringnetworks using pso algorithm[J].2008.
[84] Saadatseresht M, Fraser CS, Samadzadegan F, Azizi A. Visibility analysis invision metrology network design[J]. Photogrammetric Record,2004,19(107):219-236.
[85] SaadatSeresht M, Samdzadegan F, Azizi A, Hahn M. Camera placement fornetwork design in vision metrology based on fuzzy inference system[C].2004.
[86] Saadatseresht M, Samadzadegan F, Azizi A. Automatic camera placement invision metrology based on a fuzzy inference system[J]. PhotogrammetricEngineering and Remote Sensing,2005,71(12):1375-1385.
[87] Saadatseresht M, Varshosaz M. Visibility prediction based on artificial neuralnetworks used in automatic network design[J]. The Photogrammetric Record,2007,22(120):336-355.
[88]王君,吕乃光,邓文怡,王学娟.视觉测量仿真系统及其网络布局优化研究[J].光学技术,2008,34(1):36-40.
[89] Tarabanis KA, Allen PK, Tsai RY. A survey of sensor planning in computervision[J]. Ieee Transactions on Robotics and Automation,1995,11(1):86-104.
[90] Akyildiz IF, Su WL, Sankarasubramaniam Y, Cayirci E. A survey on sensornetworks[J]. IEEE Commun Mag,2002,40(8):102-114.
[91] David P, Idasiak V, Kratz F. A sensor placement approach for the monitoring ofindoor scenes[J]. Smart Sensing and Context,2007:110-125.
[92] Van Graan SJ. Network Configuration Improvement and Design Aid UsingArtificial Intelligence[D]; University of Pretoria,2007.
[93] Abidi BR, Aragam NR, Yao Y, Abidi MA. Survey and Analysis of MultimodalSensor Planning and Integration for Wide Area Surveillance[J]. Acm ComputingSurveys,2008,41(1).
[94] Chen S, Li Y, Zhang J, Wang W. Active Sensor Planning for Multiview VisionTasks[M]. Springer,2008.
[95]汪定伟,王俊伟,王洪峰,张瑞友,郭哲.智能优化方法[M].北京;高等教育出版社.2007.
[96] Boers EJW. Applications of Evolutionary Computing: EvoWorkshops2001:EvoCOP, EvoFlight, EvoIASP, EvoLearn, and EvoSTIM[C]. Como, Italy,2001.Springer Verlag.
[97]龚纯,王正林.精通Matlab最优化计算[M].北京:电子工业出版社,2009.
[98]冯文灏.近景摄影测量:物体外形与运动状态的摄影法测定[M].武汉大学出版社,2002.
[99]李德仁,袁修孝.误差处理与可靠性理论[M].武汉;武汉大学出版社.2002.
[100]武汉大学测绘学院测量平差学科组.误差理论与测量平差基础[M].武汉;武汉大学出版社.2003.
[101]王穗辉.误差理论与测量平差[M].上海;同济大学出版社.2010.
[102] Fraser CS. Optimization of Precision in Close-Range Photogrammetry[J].Photogrammetric Engineering&Remote Sensing,1982,48(4):561-570.
[103]张祖勋,张剑清.数字摄影测量学[M].武汉;武汉大学出版社.1997.
[104]张祖勋.数字摄影测量与计算机视觉[J].2004,12(29):1035-1039.
[105]于起峰,尚洋.摄像测量学原理与应用研究[M].北京;科学出版社.2009.
[106]于起峰,测量学,陆宏伟,刘肖琳.基于图像的精密测量与运动测量[M].北京:科学出版社,2002.
[107]马颂德,张正友.计算机视觉——计算理论与算法基础[M].北京;科学出版社.1998.
[108]贾云得.机器视觉[M].科学出版社,2000.
[109]冯文灏.近景摄影测量——物体外形与运动状态的摄影法测定[M].武汉;武汉大学出版社.2002.
[110]陈鹰.遥感影像的数字摄影测量[M].同济大学出版社,2003.
[111]王之卓.摄影测量原理续篇[M].武汉大学出版社,2007.
[112]王之卓.摄影测量原理[M].武汉大学出版社,2007.
[113]胡占义,吴福朝.基于主动视觉摄像机标定方法[J].计算机学报,2002,25(11):1149-1156.
[114]朱嘉,李醒飞,徐颖欣.摄像机的一种主动视觉标定方法[J].光学学报,2010(5):1297-1303.
[115] I.Hartley R. Self-Calibration of Stationary Cameras[J]. International Journal ofComputer Vision,1997,22(1).
[116]霍炬,杨卫,杨明.基于消隐点几何特性的摄像机自标定方法[J].光学学报,2010(2):465-472.
[117] Brown DC. Close-range camera calibration[J]. Photogrammetric Engineering,1971,37(8):855-866.
[118] Zhang Z. A flexible new technique for camera calibration[J]. Ieee Transactionson Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334.
[119]张琼,王志良,迟健男,史雪飞.基于平面镜面向双摄像机视线追踪系统的标定方法[J].光学学报,2011,31(4):192-198.
[120] Zhang Z. Camera calibration with one-dimensional objects[J]. Pattern Analysisand Machine Intelligence, IEEE Transactions on,2004,26(7):892-899.
[121] Tsai RY. A versatile camera calibration technique for high-accuracy3dmachine vision metrology using off-the-shelf tv cameras and lenses[J]. IeeeJournal of Robotics and Automation,1987,3(4):323-344.
[122] Weng JY, Cohen P, Herniou M. Camera calibration with distortion models andaccuracy evaluation[J]. Ieee Transactions on Pattern Analysis and MachineIntelligence,1992,14(10):965-980.
[123] Shang Y, Yu QF, Zhang XH. Analytical method for camera calibration from asingle image with four coplanar control lines[J]. Appl Optics,2004,43(28):5364-5369.
[124] Changjiang Y, Fengmei S, Zhanyi H. Planar conic based camera calibration[C].Proceedings of the Pattern Recognition,2000Proceedings15th InternationalConference on,2000,2000.
[125] Chen Q, Wu H, Wada T. Camera Calibration with Two Arbitrary CoplanarCircles[M].2004.
[126] Wu Y, Zhu H, Hu Z, Wu F. Camera Calibration from the Quasi-affineInvariance of Two Parallel Circles[C]. Proceedings of the ECCV,2004. SpringerBerlin, Heidelberg.
[127]王年,范益政,梁栋,韦穗.一种基于平面模板的摄像机自定标方法[J].机器人,2004(06):538-542.
[128] Jun-Sik K, Gurdjos P, In-So K. Geometric and algebraic constraints ofprojected concentric circles and their applications to camera calibration[J].Pattern Analysis and Machine Intelligence, IEEE Transactions on,2005,27(4):637-642.
[129]张广军,周富强.基于双圆特征的无人机着陆位置姿态视觉测量方法[J].航空学报,2005,26(03):344-348.
[130] Kannala J, Salo M, Heikkil J. Algorithms for Computing a PlanarHomography from Conics in Correspondence[C]. Proceedings of the BritishMachine Vision Conference,2006.
[131] Morde A, Bouzit M, Rabiner L. A novel approach to planar cameracalibration[J]. Computer Vision Theory and Applications,2006:87--92.
[132]胡钊政,谈正.利用二次曲线拟合和圆环点进行摄像机标定[J].西安交通大学学报,2006(10):1065-1068.
[133] Ying X, Zha H. Camera Calibration Using Principal-Axes Aligned Conics[C].Proceedings of the ACCV,2007. Springer Berlin, Heidelberg.
[134] Ma W, Zheng Y, Liu Y. Camera calibration using two concentric circles: linearapproach[J]. Optical Engineering,2009,48(5):53602.
[135] Wenjuan M, Yinqiang Z, Yuncai L. A Linear Approach to Camera CalibrationUsing Concentric Circles[C]. Proceedings of the Information Technology andComputer Science,25-26July,2009.
[136]夏仁波,刘伟军,赵吉宾,徐金亭.基于圆形标志点的全自动相机标定方法[J].仪器仪表学报,2009(02):368-373.
[137]赵征,张广军,魏振忠.一种基于共面圆的摄像机自标定算法[J].光电子激光,2009(12):1576-1579.
[138]张虎,达飞鹏,李勤.基于两相同圆的自标定方法[J].仪器仪表学报,2010(03):618-624.
[139] YUAN Y, ZHU Z, GUI Y, XIE T. Camera calibration method based on circularring[C]. Proceedings of the International Symposium on PhotoelectronicDetection and Imaging: Advances in Imaging Detectors and Applications,Beijing, China,2011. SPIE.
[140]孙凤梅,王波.关于PnP问题多解的分布与解的稳定性的讨论[J].自动化学报,2010,36(9).
[141] Lepetit V, Moreno-Noguer F, Fua P. Epnp: An accurate o (n) solution to thepnp problem[J]. International Journal of Computer Vision,2009,81(2):155-166.
[142] Triggs B. Camera pose and calibration from4or5known3d points[C].1999.IEEE.
[143] Bujnak M, Kukelova Z, Pajdla T. New efficient solution to the absolute poseproblem for camera with unknown focal length and radial distortion[J].Computer Vision–ACCV2010,2011:11-24.
[144] Bujnak M, Kukelova Z, Pajdla T. A general solution to the P4P problem forcamera with unknown focal length[C].2008. IEEE.
[145] Lu CP, G. D. Hager, et al. Fast and globally convergent pose estimation fromvideo images[J]. Pattern Analysis and Machine Intelligence, IEEE,2000,22(6):610-622.
[146] Lepetit V, Moreno-Noguer F, Fua P. EPnP: An Accurate O(n) Solution to thePnP Problem[J]. International Journal of Computer Vision,2008,81(2).
[147]杨森,吴福朝.摄像机位姿的加权线性算法[J].软件学报,2011(10):2476-2487.
[148]张政.车载光电经纬仪在准动基座下的测量误差修正研究[D].长沙;国防科学技术大学硕士学位论文,2005.
[149] Ansar A, Daniilidis K. Linear pose estimation from points or lines[J]. PatternAnalysis and Machine Intelligence, IEEE Transactions on,2003,25(5):578-589.
[150] Zhang X, Zhang Z, Li Y, Zhu X, Yu Q. Robust camera pose estimation fromunknown or known line correspondences[J]. Appl Opt,2012,51(7):936-948.
[151] Schweighofer GAP. Robust pose estimation from a planar target[J]. PatternAnalysis and Machine Intelligence, IEEE,2006,28(12):2024~2030.
[152] Abidi MA, Chandra T. A new efficient and direct solution for pose estimationusing quadrangular targets: Algorithm and evaluation[J]. Pattern Analysis andMachine Intelligence, IEEE Transactions on,1995,17(5):534-538.
[153] Fischler MA, Bolles RC. Random Sample Consensus: A Paradigm for ModelFitting with Applications to Image Analysis and Automated Cartography[J].Commun ACM,1981,24.
[154] Haralick RM, Lee C-N, Ottenberg K, N lle M. Review and analysis ofsolutions of the three point perspective pose estimation problem[J]. InternationalJournal of Computer Vision,1994,13.
[155] Gao X, Hou X, Tang J, Cheng H. Complete solution classification for theperspective-three-point problem[J]. Pattern Analysis and Machine Intelligence,IEEE Transactions on,2003,2(8).
[156]吴文俊.几何定理机器证明的基本原理[J].系统科学与数学,1984(03):207-235.
[157] Quan L, Lan Z. Linear N-point camera pose determination[J]. IEEETransactions on Pattern Analysis and Machine Intelligence,1999,2(8).
[158] Fiore PD. Efficient Linear Solution of Exterior Orientation[J]. IEEE TransPattern Anal Mach Intell,2001,23.
[159] Kahl FaDH. Globally optimal estimates for geometric reconstructionproblems[J]. IEEE2005.
[160] Horn BKP, H. M. Hilden, et al. Closed-form solution of absolute orientationusing orthonormal matrices[J][J]. JOSA A,1988,5(7):1127~1135.
[161] Faugeras O. Three Dimensional Computer Vision: A Geometric Viewpoint[M].Cambridge, MA,USA: MIT Press,1993.
[162] Sturm P, Cheng ZL, Chen PCY, Poo AN. Focal length calibration from twoviews: method and analysis of singular cases[J]. Computer Vision and ImageUnderstanding,2005,99(1):58-95.
[163] Sturm P. Critical motion sequences for the self-calibration of cameras andstereo systems with variable focal length[J]. Image and Vision Computing,2002,20(5–6):415-426.
[164] Kahl F, Triggs B, Astrom K. CriticalMotions for Auto-Calibration When SomeIntrinsic Parameters Can Vary[J]. Mathematical Imaging and Vision,2000,13(2):131-146.
[165] Kahl F, Triggs B. Critical Motions in Euclidean Structure from Motion[C].Proceedings of the CVPR,1999.
[166] Sturm P, Svoboda T, Teller S. Special issue: Omnidirectional vision andcamera networks[J]. Computer Vision and Image Understanding,2006,103(3):155.
[167] Sturm P, Ramalingam S. A Generic concept for camera calibration[C].Proceedings of the ECCV,2004.
[168] Sturm P, Maybank S. On plane based camera calibration: a general algorithm,singularities, applications[C]. Proceedings of the CVPR,1999.
[169] Sturm P. On Focal Length Calibration from Two Views[C]. Proceedings of theCVPR, Kauai, Hawaii, USA,2001.
[170] Sturm P. A Case Against Kruppa’s Equations for Camera Self-Calibration[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(10):1199-1204.
[171] Sturm P. Critical motion sequences for the self-calibration of cameras andstereo systems with variable focal length[C]. Proceedings of the BMVC,1999.
[172] Sturm P. Self-calibration of a moving zoom-lens camera by pre-calibration[J].Image and Vision Computing,1997,15(8):583-589.
[173]尚洋.基于视觉的空间目标位置姿态测量方法研究[D].长沙;国防科学技术大学博士学位论文,2006.
[174]姜广文,晁志超,伏思华,于起峰.基于可控旋转的像机高精度标定技术[J].光学学报,2010(5):1308-1314.
[175]韦穗,杨尚俊,章权兵,胡茂林.计算机视觉中的多视图几何[M].合肥;安徽大学出版社.2002.
[176]田震,张玘,熊九龙,王国超.基于神经网络的大范围空间标定技术[J].光学学报,2011,31(4):186-191.
[177]何照才,胡保安.光学测量系统[M].北京;国防工业出版社.2002.
[178]刘利生.外弹道测量数据处理[M].北京;国防工业出版社.2002.
[179]张祖勋,詹总谦,郑顺义,季铮.摄影全站仪系统--数字摄影测量与全站仪的集成[J].测绘通报,2006(11):1-5.
[180]张祖勋.论摄影测量与工程测量的结合——摄影全站仪+数码摄影机[J].地理空间信息,2004,6:1-4.
[181]张峰.基于3S技术的三维数码摄影全站仪系统的研制[D].桂林;桂林工学院,2008.
[182]刘喜,王继承,盛德新.拓普康图像全站仪GPT-7000i与PI-3000软件在3维建模中的应用[J].测绘与空间地理信息,2008,31(2).
[183]靳志光,卫建东,张冠宇.图像全站仪影像测量精度的测试与分析[J].测绘通报,2008.
[184]靳志光,卫建东,汤廷松,余春平,蒲亭汀.摄影全站系统及其精度评定[J].海洋测绘,2008,28(4).
[185]付翔宇. TCA2003测量机器人的开发与应用研究[D].北京;北京建筑工程学院,2008.
[186]王一.测量机器人模型误差及标定方法的研究[D].天津;天津大学,2006.
[187]张小虎,朱肇昆,苑云,周剑,于起峰.经纬像机摄像测量方法及系统,CN101900552A.2010-12-01.
[188]朱肇昆.经纬像机摄像测量方法研究[D].长沙;国防科学技术大学硕士学位论文,2010.
[189] ZHU Z, YUAN Y, ZHANG X. Theodolite-camera videometrics system basedon total station[C]. Proceedings of the International Symposium onPhotoelectronic Detection and Imaging2011: Advances in Imaging Detectorsand Applications, May24,2011-May26,2011, Beijing, China,2011. SPIE.
[190] ZHANG X, ZHU Z, YUAN Y, LI L, SUN X. A universal and flexibletheodolite-camera system for making accurate measurements over largevolumes[J]. Optics and Lasers in Engineering,2012,50(11):1611-1620.
[191]王磊,王守印,周虎,张余彬.平行光管的基本原理及使用方法[J].仪器仪表学报,2006,27(6).
[192]苑云,朱肇昆,张小虎,姜广文.一种逐级标定相机参数的方法[J].激光与光电子学进展,2010,47(11):77-83.
[193]焦春林,高满屯,史仪凯.基于立体视觉的3D地形拼接[J].计算机工程与应用,2008,44(23):206-208.
[194] Luhmann T, Robson S, Kyle S, Harley I. Close range photogrammetry:Principles, methods and applications[M]. Whittles Scotland, UK,2006.
[195] Hartley RI, Sturm P. Triangulation[J]. Computer Vision and ImageUnderstanding,1997,68(2):146-157.
[196]陶本藻.测量数据处理的统计理论和方法[M].北京:测绘出版社,2007.
[197] Blonquist KF, Pack RT. A bundle adjustment approach with inner constraintsfor the scaled orthographic projection[J]. ISPRS Journal of Photogrammetry andRemote Sensing,2011,66(6):919-926.
[198] Furukawa Y, Ponce J. Accurate Camera Calibration from Multi-View Stereoand Bundle Adjustment[J]. International Journal of Computer Vision,2009,84(3):257-268.
[199] Bartoli A, Sturm P. Structure-from-motion using lines: Representation,triangulation, and bundle adjustment[J]. Computer Vision and ImageUnderstanding,2005,100(3):416-441.
[200] S.Granshaw. Bundle Adjustment Methods in Engineering Photogrammetry[J].Photogrammetric Record,1980,10(56):181-207.
[201] http://www.ics.forth.gr/~lourakis/sba
[202] Camera Calibration Toolbox for Matlab. http://www.ee.oulu.fi/~jth/calibr/
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |