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


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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Received 14.06.2017

Issue 185, article 4


KVT, 2016, Issue 185, pp.35-47

UDC 629.7.05


Simakov V.A., Gubarev V.F., Salnikov N.N., Melnichuk S.V.

Space Research Institute of the National Academy of Science of Ukraine and State Space Agency of Ukraine, Kyiv, Ukraine , , ,

Introduction. Automatic orbital berthing systems require permanent availability of relative position and attitude of a target spacecraft. In the most general case the only source of information is video filming. Extracting mutual disposition parameters from a video frame is based upon special techniques which can be divided into two large groups: feature-based and model-based. Major difference between them is defined by data structure used for the target description (individual points for feature-based approach vs. rigorous visual model for model-based one). This article is devoted to the research of mathematical problem that appears in considering pose estimation for two orbital spacecraft in the presence of wireframe model of the target when only video filming is available.

The purpose of the article is to construct a model-based method that provides fast and accurate estimation of relative position and attitude of the target spacecraft. We discuss possible drawbacks of direct procedures based on straightforward (pixel-wise) image fitting and propose a subtle algorithm which satisfies formulated conditions.

Results. The algorithm composed of three independent parts (initialization, pose refinement and pose tracking) has been developed and tested on simple initial datum. Initialization stage, responding for rough estimation in the absence of preliminary information, has given relatively poor but quite enough accuracy for the aims of initial approximation. Pose refinement stage which is implemented as iterative procedure based on closeness of neighboring frames demonstrated almost total matching with actual values. Pose tracking (state estimation based on equations of motion) was redundant for our simple example as it could not improve the result provided by pose refinement.

Conclusions. Constructed algorithm has been tested on simplified situation and demonstrated very high precision. More realistic conditions including noises and occlusions can bring to corrupted result that should be recovered. This requires introducing additional steps into the algorithm which are reflected in the text. The notable feature of the algorithm is its high modularity which allows each stage to be implemented and configured independently according to available resources and mission requirements.

Keywords: orbital rendezvous, pose estimation, orbital video filming, computer vision.

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Received 10.06.16