During the "13th Five-Year", with the implementation of major special tasks such as the space station and the three phase of navigation, the development of the spacecraft has gradually taken on the characteristics of large quantity, great variety, short cycles and heavy tasks. The traditional method of using theodolite for collimation measurement has long time of measurement, low level of automation and high occupancy rate, which is unable to meet the development requirements of high reliability and high efficiency of the current spacecraft. According to the requirement of equipment assembly accuracy measurement and the characteristics of field implementation in the process of spacecraft assembly integration and test (AIT), a method for carrying theodolite by robot to measure the assembly accuracy of spacecraft equipment is proposed, which takes full advantages of flexible and high automation level of industrial robot. After experiments and field application verification, this method can greatly improve the measurement efficiency and automation level of spacecraft assembly on the basis of ensuring high-accuracy measurement, the corresponding system has been successfully applied to the development process of spacecraft such as China’s space station, BeiDou navigation, remote sensing and so on.
Visual measurement plays an increasingly important role in the field o f aerospace, ship and machinery manufacturing. Camera calibration of large field-of-view is a critical part of visual measurement . For the issue a large scale target is difficult to be produced, and the precision can not to be guaranteed. While a small target has the advantage of produced of high precision, but only local optimal solutions can be obtained . Therefore, studying the most suitable ratio of the target size to the camera field of view to ensure the calibration precision requirement of the wide field-of-view is required. In this paper, the cameras are calibrated by a series of different dimensions of checkerboard calibration target s and round calibration targets, respectively. The ratios of the target size to the camera field-of-view are 9%, 18%, 27%, 36%, 45%, 54%, 63%, 72%, 81% and 90%. The target is placed in different positions in the camera field to obtain the camera parameters of different positions . Then, the distribution curves of the reprojection mean error of the feature points’ restructure in different ratios are analyzed. The experimental data demonstrate that with the ratio of the target size to the camera field-of-view increas ing, the precision of calibration is accordingly improved, and the reprojection mean error changes slightly when the ratio is above 45%.
Because of the close range photogrammetry has wide measuring range, high precision and high efficiency, the precision measurement of large size tasks take more and more important role Among them, the self-calibration measurement model based on adjustment optimization is the important reason to ensure the method to achieve high-precision measurement. However, with commercial grade SLR camera more and more applied to three-dimensional measurement, the measurement accuracy and the professional camera compared to a certain gap A large number of analyses have found that, in addition to the camera itself, the self -calibration model relies too much on the internal parameters of the camera, especially the distortion parameter, which is the important reason leading to the decrease of the measurement accuracy. In order to reduce the influence of the parameterized model on the measurement results, we propose a photogrammetric method that does not rely on the intrinsic parameters of the camera. Firstly, a non-parameterized calibration method for large field of view camera is designed by combining the perpendicular method and Zeiss calibration method. Then, the non-parameterized measurement model based on the angle information can be established after the matching of the same point and the initial value of the difference between different images. Finally, combined with adjustment optimization algorithm, the three-dimensional coordinate of the measured point in space is calculated accurately. Compared with the traditional photogrammetry results, it is proved that this method can effectively improve the photogrammetric accuracy of the large field SLR camera.
Large-size visual shape measurement based on ICP (iterative closest point) mosaicing algorithm generally has a larger
cumulative error; however, this problem can be well solved by precision positioning global control network. Therefore,
this method is widely used in large-size visual shape measurement. Since the positioning accuracy of the global control
network is the key influencing factor of the final measurement accuracy, the method of precision positioning global
control network is researched, which is dependent on the principle of portable close-range photogrammetry. The
precision positioning theory and mathematical model of global control network are investigated in this paper. Bundle
adjustment optimization algorithm is the core of this measurement system, the solution method of this algorithm is
introduced in detail, which can improve the model solution accuracy. As is known, the initial value of the algorithm has a
direct influence on the convergence of the result, so obtaining the initial value is a key part of the measurement system,
including control points matching technology, stations orientation technology and the technology of obtaining the initial
value of the three-dimensional coordinates of global control points. New technological breakthroughs were made based
on the existing researches to get a more precious and stable initial value. Firstly, a nonlinear adjustment model based
control points matching method is proposed, which significantly improves the correct matching rate when the control
points distribute intensively. Secondly, a new station orientation method without using an external orientation device is
studied, which greatly improves the shooting freedom and expands the range of the spatial distribution of the
measurement stations. Finally, a camera calibration method independent with the imaging model is explored, which
converts image coordinate information into image angle information. Thus, the initial value calculation accuracy of the
three-dimensional coordinates of the control points is not affected by the lens distortion and measurement distance. A
large number of experiments were carried out using a high-resolution digital camera, and the experimental results show
that the measurement accuracy of this method can reach 0.02mm (3m * 3m range), and the root mean square is about
0.015mm. Consequently we conclude that this method can achieve the precise positioning of the global control network
and help to improve the accuracy of large-size visual shape measurement.