Acta Optica Sinica, Volume. 41, Issue 6, 0615001(2021)

Trinocular Stereo Visual Measurement Method for Spatial Non-Cooperative Target

An Shu1,2,3, Haodong Pei1,3、*, and Huixian Duan3,4
Author Affiliations
  • 1Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences, Shanghai 200083, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
  • 4Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China
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    Aim

    ing at the problem of how to obtain the relative position and posture of non-cooperative targets in space at close range for on-orbit service mission, a trinocular stereo vision measurement method is proposed. First, three large-field visible light cameras arranged in equilateral triangles are used to acquire images. Then, the proposed method is used to match the feature points. Second, the RANSAC method is used to calculate the pose parameters of the measured target in the world coordinate system. Finally, the static motion, position movement and attitude rotation of the non-cooperative satellite model are tested. Experimental results show that the relative position accuracy is better than 2.2 mm in the static state, and the relative angle measurement accuracy is better than 0.3°; when the model position moves, the absolute position accuracy is better than 3 mm; when the model posture rotates, the relative position accuracy is better than 5.6 mm, the relative angle measurement accuracy is better than 1.7°, indicating that the proposed method can improve the shortcomings of binocular stereo vision technology, such as measurement blind spots, mismatched feature points location and limited measurement field of view.

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    An Shu, Haodong Pei, Huixian Duan. Trinocular Stereo Visual Measurement Method for Spatial Non-Cooperative Target[J]. Acta Optica Sinica, 2021, 41(6): 0615001

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    Paper Information

    Category: Machine Vision

    Received: Sep. 21, 2020

    Accepted: Nov. 5, 2020

    Published Online: Apr. 7, 2021

    The Author Email: Pei Haodong (peihaodong@sina.com)

    DOI:10.3788/AOS202141.0615001

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