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.

中文翻译：

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基于最大外轮廓识别的空间非合作目标位姿测量方法

空间非合作目标的相对位姿（位置和姿态）测量对于在轨服务活动非常重要，例如目标跟踪、接近和捕获。传统方法很少考虑目标翻滚引起的特征提取不稳定和图像模糊。本文提出了一种基于最大外轮廓（MOC）识别的目标姿态测量方法。不同的特征提取算法可以同时实现近距离和远距离测量任务。首先，通过图像增强方法恢复拖尾图像。其次，采用“粗+细”组合识别方法对复原图像进行轮廓提取和连通分量标注，目标特征提取时间减少到传统方法的三分之一。此外，MOC上的椭圆表面通过最小二乘法（LSM）拟合，提取椭圆参数（即中心位置、长短轴和偏转角）。提高了目标识别的准确性。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求解非合作目标的位姿；对于远距离测量，目标的轮廓质心由检测到的MOC计算，目标的位置由LSM求解。并通过OpenSceneGraph数值模拟系统验证了该方法的有效性。最后，搭建了由双目相机、UR5机械手和卫星样机组成的实验系统。