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Solid model-based 3D pose measurement for ICF target positioning

机译:用于ICF目标定位的基于实体模型的3D姿势测量

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In Inertia Confinement Fusion (ICF) physical experiments, the accuracy of target positioning affects the successful rate of target hitting directly. A 3-CCD camera system is often used for tiny target measurement in ICF target positioning. Most of the current pose measurement methods utilize the well-known digital image processing technology to extract the target features in each image, then calculates the target's spatial coordinate and rotation matrix by integrating the feature values from three CCDs. Therefore, feature extraction errors in each image are superimposed in final result, which reduces the pose measurement precision. In this paper, we propose a solid model-based method which matching the target as a whole by the grey values in each image without utilizing image processing technology. The solid model matching optimistic problem is solved by genetic algorithm (GA). Experiment is performed by using a 3-CCD camera system, the result shows the measurement accuracy and repeatability.
机译:在惯性约束聚变(ICF)物理实验中,目标定位的准确性直接影响目标命中的成功率。在ICF目标定位中,通常使用3-CCD摄像机系统进行微小目标测量。当前大多数姿势测量方法都是利用众所周知的数字图像处理技术来提取每个图像中的目标特征,然后通过整合来自三个CCD的特征值来计算目标的空间坐标和旋转矩阵。因此,将每个图像中的特征提取误差叠加在最终结果中,这降低了姿势测量精度。在本文中,我们提出了一种基于实体模型的方法,该方法通过不使用图像处理技术就可以通过每个图像中的灰度值将目标作为一个整体进行匹配。通过遗传算法(GA)解决了实体模型匹配的乐观问题。通过使用3-CCD相机系统进行实验,结果显示了测量精度和可重复性。

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