曾占魁, 谷蔷薇, 曹喜滨. 基于正交Procrustes分析的航天器单目视觉相对位姿确定方法[J]. 红外与激光工程, 2015, 44(S1): 113-118.
引用本文: 曾占魁, 谷蔷薇, 曹喜滨. 基于正交Procrustes分析的航天器单目视觉相对位姿确定方法[J]. 红外与激光工程, 2015, 44(S1): 113-118.
Zeng Zhankui, Gu Qiangwei, Cao Xibin. Relative pose monocular vision determination of spacecraft using orthogonal Procrustes analysis[J]. Infrared and Laser Engineering, 2015, 44(S1): 113-118.
Citation: Zeng Zhankui, Gu Qiangwei, Cao Xibin. Relative pose monocular vision determination of spacecraft using orthogonal Procrustes analysis[J]. Infrared and Laser Engineering, 2015, 44(S1): 113-118.

基于正交Procrustes分析的航天器单目视觉相对位姿确定方法

Relative pose monocular vision determination of spacecraft using orthogonal Procrustes analysis

  • 摘要: 相对位姿确定是航天器交会对接、在轨服务等航天任务的关键技术之一,采用单目视觉相机进行相对位姿确定是其有效解决途径。针对基于特征点的空间目标相对位姿单目视觉确定问题,提出了一种基于拟投影线思想和正交Procrustes分析的相对位姿求解迭代方法。该方法在基于逆投影线构建的优化模型基础上,将绝对定向问题转化成正交Procrustes分析模型,利用持续投影算法将姿态矩阵分列优化并进行全局修正求得最优姿态矩阵。最后,以航天器相对位姿确定为背景,对所提算法进行了数学仿真,结果表明该方法能够较快收敛并具有较高的鲁棒性。

     

    Abstract: Pose(relative attitude and position) estimation is one of the key technologies in space missions, such as on-orbit servicing, rendezvous and docking, etc. One of the most efficient way to solve this problem is observation on the target by monocular vision such as single CCD measurement. Aiming at the pose estimation of space target based on feature points, an iteration algorithm using obverse projection and orthogonal Procrustes analysis was introduced. Turning the original pose estimation problem to a standard form of orthogonal Procrustes problem, the orthogonal Procrustes problem was solved in the way of successive projection. The key of successive projection method was optimized the attitude matrix row by row. Each row can be solved as a least square problem constrained by a quadratic equation. This algorithm has a global updating and result will be given when errors are within permission. At last, under the background of pose estimation between non-cooperative spacecraft, the simulation experiment shows that this algorithm has both fast convergence speed and strong robustness.

     

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