Issue 4 (190), article 3
Kibern. vyčisl. teh., 2017, Issue 4 (190), pp.
Melnichuk S.V., Dr (Engineering),
Researcher of Dynamic Systems Control Ddepartment
Gubarev V.F., Professor,
Dr (Engineering), Professor,
Corresponding Member of NAS of Ukraine,
Head of Dynamic Systems Control Department
Salnikov N.N., (Engineering),
Senior Researcher of Dynamic Systems Control Department
Space Research Institute National Academy of Sciences of Ukraine
and State Space Agency of Ukraine,
Acad. Glushkov av. 40, 4/1, 03680, Kyiv 187, Ukraine
USING INFORMATION FEATURES IN COMPUTER VISION FOR 3D POSE ESTIMATION IN SPACE
Introduction. Autonomous rendezvous and docking is an important technological capability that enables various spacecraft missions. It requires the real-time relative pose estimation i.e. determination of the position and attitude of a target object relative to a chaser. The usage of techniques based on optical measurement has certain advantages at close range phases of docking.
The purpose of the paper is to create a computer vision system, that estimates position and attitude of the target relative to the chaser. To develop the design of a computer vision system and suited mathematical methods. To use a new learning-based method, which can be implemented for the real-time execution with limited computing power.
Methods. A non-standard approach to solving the problem was used. A combination of image processing techniques, machine learning, decision trees and piecewise linear
approximation of functions were used. The tool of informative features computed by images was essentially used.
Results. A two-stage algorithm, which involves training the computer vision system to recognize the attitude and position of the target in a changing lighting environment was developed. The calculation of the camera parameters was carried out to ensure a given accuracy of the solution of the problem.
Conclusion. It was shown that the informative features can be used to create a high-performance on-board system for estimating relative attitude and position. Implementation of the proposed algorithm allows to create a competitive device for docking in space.
Keywords: autonomous rendezvous, uncooperative pose estimation, model-based pose estimation, vision-based pose estimation, computer vision, decision tree, linear approximation, informative features, image processing, machine learning, identification, relative position and attitude estimation.
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