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Vision-based Estimation of Dynamics for Space Debris Without Inertial Moments Known

机译:基于视觉的空间碎片动力学估计,没有惯性时刻

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Stereovisions were often adopted to determine position and attitude of target through measuring four non-collinear feature points. For rotation objects, Kalman filtering and plenty of its extended methods were primary used to estimate the rotation states and could achieve excellent outcomes only if enough priori information obtained. But for the noncooperative objects such as space debris, the state estimation is a challenge problem since lack of priori knowledge. Some researchers developed extended methods to deal with estimation problem for noncooperative objects, and these methods typically required the target's inertia moments being known, But such requirement could rarely be met in the real world. A Vision-based Estimation approach which does not need inertia moments of the target as known parameters was put forward in this paper. This method avoids the problem of inaccurate attitude dynamics modeling caused by the unknown or unobservability of the target's inertia by modeling rotation motion through Euler equation in Principal Axis Coordinate System(PACS),and transform the dedicated feature points to PACS. In such case, the maximum inertia axis which orientation is invariable in inertial space, estimation of the target attitude state could be achieved through estimation of orientation of rotation axis and the finite rotation rate. Under above scenary, A Vision-based Estimation approach for relative states of the target object, including rotation rate and orientation, relative position and velocity was established applying Unscented Kalman Filter (UKF) estimation scheme. Some simulation cases and results were induced to ascertain the excellent performance of proposed tracking schemes. Employing one CCD camera, at least three non-collinear feature points are required, If the target in the camera's FOV ,the ratio of sample frequency great than rotation rate, The scheme shows stable convergence performance. The feasibility of this method was verified by simulation
机译:通常采用立体舟通过测量四个非共线特征点来确定目标的位置和态度。对于旋转对象,Kalman滤波和充分的其扩展方法主要用于估计旋转状态,并且只有在获得的足够先验信息时才可以实现出色的结果。但对于空间碎片等非自由度物体,由于缺乏先验知识以来,国家估计是一个挑战问题。一些研究人员开发了处理非支持物体的估计问题的扩展方法,并且这些方法通常需要目标的惯性矩,但是在现实世界中可能很少见到这种要求。本文提出了一种基于视觉的估计方法,其不需要目标的惯性矩。该方法通过在主轴坐标系(PACS)中通过欧拉方程模拟旋转运动来避免由目标惯性的未知或不可观察的姿态动力学建模的问题,并在主轴坐标系(PACS)中,并将专用特征点转换为PACS。在这种情况下,在惯性空间中取向在惯性空间中不变的最大惯性轴,可以通过估计旋转轴的取向和有限旋转速率来实现目标姿态状态的估计。在上面的情况下,建立了基于视觉的目标对象的估计方法,包括旋转速率和方向,相对位置和速度,应用无创的卡尔曼滤波器(UKF)估计方案。诱导了一些模拟案例和结果,以确定所提出的跟踪方案的优异性能。采用一个CCD摄像头,需要至少三个非共线特征点,如果相机的FOV中的目标,则样本频率比旋转速率高的比率,该方案显示出稳定的收敛性能。通过模拟验证了该方法的可行性

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