Measurement of relative poses between two non-cooperative spacecrafts

XU Wen-fu; LIU Yu; LIANG Bin; LI Cheng; QIANG Wen-yi

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Measurement of relative poses between two non-cooperative spacecrafts

Article | 更新时间：2020-08-12

- Measurement of relative poses between two non-cooperative spacecrafts
- Optics and Precision Engineering Vol. 17, Issue 7, Pages: 1570-1581(2009)
- 作者机构：
  
  哈尔滨工业大学空间智能系统研究所,黑龙江哈尔滨,150001
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.4;V448.22
- Received：25 July 2008，
  
  Revised：20 October 2008，
  
  Published Online：25 July 2009，
  
  Published：25 July 2009
- 稿件说明：
移动端阅览
徐文福, 刘宇, 梁斌, 李成, 强文义. 非合作航天器的相对位姿测量[J]. 光学精密工程, 2009,17(7): 1570-1581

XU Wen-fu, LIU Yu, LIANG Bin, LI Cheng, QIANG Wen-yi. Measurement of relative poses between two non-cooperative spacecrafts[J]. Editorial Office of Optics and Precision Engineering, 2009,17(7): 1570-1581
徐文福, 刘宇, 梁斌, 李成, 强文义. 非合作航天器的相对位姿测量[J]. 光学精密工程, 2009,17(7): 1570-1581 DOI：

XU Wen-fu, LIU Yu, LIANG Bin, LI Cheng, QIANG Wen-yi. Measurement of relative poses between two non-cooperative spacecrafts[J]. Editorial Office of Optics and Precision Engineering, 2009,17(7): 1570-1581 DOI：

摘要

为了解决在轨维护和轨道垃圾清除中非合作目标的识别问题

提出了基于立体视觉的位姿(位置和姿态)测量方法。采用中值滤波器对原始图像进行平滑

去除星空背景干扰和其它噪声;将Canny边沿检测器用于对平滑后的图像进行检测

得到包含边沿信息的二值图像。然后

对该二值图像进行Hough变换

提取待识别对象的直线特征

并计算直线间的交点。最后

对所提取的左、右相机图像的点特征进行3D重构

得到各点在世界坐标系中的坐标

并据此建立目标坐标系

求出其相对于世界坐标系的位置和姿态。仿真结果表明

对于较远距离(>2.5 m)

位置测量精度优于40 mm

而近距离内(<2.0 m)优于10 mm

相对姿态精度优于2

基本满足对非合作目标进行跟踪、接近、绕飞等位姿测量要求。

Abstract

A stereo vision-based method was proposed to measure the relative poses (position and attitude) between the two non-cooperative spacecrafts to solve the problem of the non-cooperative target recognition for the on-orbital maintenance and space debris removal. A median filter was used to smooth the original images by removing the disturbance of orbital background and other noises. The edges in the smoothed images were detected using the Canny edge detector to obtain the corresponding binary images. Then

the straight lines of the object to be recognized were extracted from the binary images through Hough transform

and the intersection points among these lines were determined. Finally

the feature points in the left and right images were reconstructed in 3D to obtain the coordinates of the object points in the world frame. Based on these points

the target frame was defined and the pose of the target with respect to the world frame was determined. Simulation results indicate that the position errors are lower than 40 mm in the farther distance (>2.5 m) and 10 mm in the nearer distance (<2.0 m)

and the orientation errors are less than 2

which satisfies the requirements of pose measurements during the tracking

approaching and flying round the non-cooperative target.

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references

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