A passive camera based determination of a non-cooperative and unknown satellite's pose and shape

Volpe, Renato; Palmerini, Giovanni B.; Sabatini, Marco

doi:10.1016/j.actaastro.2018.06.061

Cited by 27 publications

(14 citation statements)

References 10 publications

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“…The vision system will have to employ template recognition methods derived from CAD files. However, an unscented Kalman filter can estimate shape and relative attitude, position and velocity of a satellite target by building a 3D map of points from features detected by a monocular camera and LIDAR on the chaser [70]. This suggests that template-matching with a priori data may be dispensed with, but a priori model data does yield superior accuracy [71].…”

Section: Space Manipulator Operations-evolution From Teleoperation Tomentioning

confidence: 99%

Tutorial Review on Space Manipulators for Space Debris Mitigation

Ellery

2019

Robotics

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Space-based manipulators have traditionally been tasked with robotic on-orbit servicing or assembly functions, but active debris removal has become a more urgent application. We present a much-needed tutorial review of many of the robotics aspects of active debris removal informed by activities in on-orbit servicing. We begin with a cursory review of on-orbit servicing manipulators followed by a short review on the space debris problem. Following brief consideration of the time delay problems in teleoperation, the meat of the paper explores the field of space robotics regarding the kinematics, dynamics and control of manipulators mounted onto spacecraft. The core of the issue concerns the spacecraft mounting which reacts in response to the motion of the manipulator. We favour the implementation of spacecraft attitude stabilisation to ease some of the computational issues that will become critical as increasing level of autonomy are implemented. We review issues concerned with physical manipulation and the problem of multiple arm operations. We conclude that space robotics is well-developed and sufficiently mature to tackling tasks such as active debris removal.

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Section: Space Manipulator Operations-evolution From Teleoperation Tomentioning

confidence: 99%

Tutorial Review on Space Manipulators for Space Debris Mitigation

Ellery

2019

Robotics

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“…As the target tumbling in orbit, new rectangular features are observed and must be added to the state vector, and some features disappear and can be only propagated in the filter. This strategy has been proven effective in literatures [6,7,19].…”

Section: Is the Attitude Rotation Matrix From The Frame O T X T Y T Zmentioning

confidence: 99%

“…Complex image processing techniques are developed to measure the relative position and relative attitude of noncooperative targets [4,5]. Volpe et al proposed an on-orbit relative pose and shape estimation with the use of a monocular camera and a distance sensor [6,7]. In China, Caixiu proposed a method by using a single camera to measure the relative pose by utilizing a solar panel triangle structure and a circular docking ring [8].…”

Section: Introductionmentioning

confidence: 99%

Pose Determination for Malfunctioned Satellites Based on Depth Information

Feng

Zhao

Zhang

2019

International Journal of Aerospace Engineering

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Autonomous on-orbit servicing is the future space activity which can be utilized to extend the satellite life. Relative pose estimation for a malfunctioned satellite is one of the key technologies to achieve robotic on-orbit servicing. In this paper, a relative pose determination method by using point cloud is presented for the final phase of the rendezvous and docking of malfunctioned satellites. The method consists of three parts: (1) planes are extracted from point cloud by utilizing the random sample consensus algorithm. (2) The eigenvector matrix and the diagonal eigenvalue matrix are calculated by decomposing the point cloud distribution matrix of the extracted plane. The eigenvalues are utilized to recognize rectangular planes, and the eigenvector matrix is the attitude rotation matrix from the sensor to the plane. The solution of multisolution problem is also presented. (3) An extended Kalman filter is designed to estimate the translational states, the rotational states, the location of mass center, and the moment-of-inertia ratios. Because the method only utilizes the local features without observing the whole satellite, it is suitable for the final phase of rendezvous and docking. The algorithm is validated by a series of mathematical simulations.

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“…Besides, the active cameras such as Flash-LIDAR's can be used to detect the unknown object [11]. For the noncooperative system localization, stereo vision and monocular vision [12] can both recognize the unknown objects by edge detection and feature matching. Active vision with structure light obtains the object 3D information when structure light scans the object surface, and it can not only help to recognize the object but also reconstruct the 3D information of unknown object.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Object Recognition and Precise Localization for Space Body Control

Shangguan¹,

Wang²,

Zhang³

et al. 2019

International Journal of Aerospace Engineering

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The space motion control is an important issue on space robot, rendezvous and docking, small satellite formation, and some on-orbit services. The motion control needs robust object detection and high-precision object localization. Among many sensing systems such as laser radar, inertia sensors, and GPS navigation, vision-based navigation is more adaptive to noncontact applications in the close distance and in high-dynamic environment. In this work, a vision-based system serving for a free-floating robot inside the spacecraft is introduced, and the method to measure space body 6-DOF position-attitude is presented. At first, the deep-learning method is applied for robust object detection in the complex background, and after the object is navigated at the close distance, the reference marker is used for more precise matching and edge detection. After the accurate coordinates are gotten in the image sequence, the object space position and attitude are calculated by the geometry method and used for fine control. The experimental results show that the recognition method based on deep-learning at a distance and marker matching in close range effectively eliminates the false target recognition and improves the precision of positioning at the same time. The testing result shows the recognition accuracy rate is 99.8% and the localization precision is far less than 1% in 1.5 meters. The high-speed camera and embedded electronic platform driven by GPU are applied for accelerating the image processing speed so that the system works at best by 70 frames per second. The contribution of this work is to introduce the deep-learning method for precision motion control and in the meanwhile ensure both the robustness and real time of the system. It aims at making such vision-based system more practicable in the real-space applications.

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A passive camera based determination of a non-cooperative and unknown satellite's pose and shape

Cited by 27 publications

References 10 publications

Tutorial Review on Space Manipulators for Space Debris Mitigation

Tutorial Review on Space Manipulators for Space Debris Mitigation

Pose Determination for Malfunctioned Satellites Based on Depth Information

Vision-Based Object Recognition and Precise Localization for Space Body Control

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