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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >A Pose Measurement Method of a Space Noncooperative Target Based on Maximum Outer Contour Recognition
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A Pose Measurement Method of a Space Noncooperative Target Based on Maximum Outer Contour Recognition

机译：基于最大外部轮廓识别的空间非自由度目标的姿态测量方法

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摘要

The relative pose (position and attitude) measurement of space noncooperative targets is very important for on-orbit servicing activities, such as target tracking, approaching, and capturing. The traditional methods rarely consider the instability of feature extraction and image blurring caused by target tumbling. In this paper, a method based on the maximum outer contour (MOC) recognition is proposed to measure the pose of the target. Different feature extraction algorithms can simultaneously achieve close- and long-range measurement tasks. First, the trailing image is restored by the image enhancement method. Second, the "rough + fine" combination recognition method is used for contour extraction and connected component labeling of the restored image, and the target feature extraction time is reduced to one-third of traditional methods. Furthermore, the elliptical surface on the MOC is fitted by the least squares method (LSM), and the ellipse parameters (i.e., the center position, the long- and short-axes, and the deflection angle) are extracted. The accuracy of the target recognition is improved. Third, for the close-range measurement, based on the detected ellipse parameter, the pose of the noncooperative target is solved by the binocular imaging algorithm of the space circle; for the long-range measurement, the contour centroid of the target is calculated by the detected MOC, and the position of the target is solved by the LSM. Moreover, the effectiveness of the method is verified by the OpenSceneGraph numerical simulation system. Finally, an experimental system consisting of a binocular camera, a UR5 manipulator, and a satellite mockup was built. The experimental results verified the proposed method.
机译：空间非自由度目标的相对姿势（位置和姿态）测量对于轨道服务活动非常重要，例如目标跟踪，接近和捕获。传统方法很少考虑由目标翻滚引起的特征提取和图像模糊的不稳定性。本文提出了一种基于最大外轮廓（MOC）识别的方法来测量目标的姿势。不同的特征提取算法可以同时实现密切，远程测量任务。首先，通过图像增强方法恢复尾部图像。其次，“粗糙+精细”组合识别方法用于恢复图像的轮廓提取和连接部件标记，并且目标特征提取时间减少到传统方法的三分之一。此外，MOC上的椭圆表面由最小二乘法（LSM）和椭圆参数（即中心位置，长轴和偏转角）装配。目标识别的准确性得到改善。第三，对于近距离测量，基于检测到的椭圆参数，通过空间圆的双目成像算法解决了非旋转目标的姿势;对于远程测量，通过检测到的MOC计算目标的轮廓质心，并且LSM求解目标的位置。此外，通过露张编号数值模拟系统验证了该方法的有效性。最后，建立了由双目相机，UR5机械手和卫星样机组成的实验系统。实验结果证实了该方法。

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