A review of cooperative and uncooperative spacecraft pose determination techniques for close-proximity operations

Opromolla, Roberto; Fasano, Giancarmine; Rufino, Giancarlo; Grassi, Michele

doi:10.1016/j.paerosci.2017.07.001

Cited by 217 publications

(107 citation statements)

References 83 publications

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“…On the other hand, if the target is uncooperative then it can be further classified into targets with known and unknown geometry, with the former being especially relevant to O 3 . This grander pose estimation problem for both cooperative and noncooperative spacecraft is widely studied and exhaustively surveyed in [63].…”

Section: A Sensing and Perceptionmentioning

confidence: 99%

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Robotics and AI-Enabled On-Orbit Operations With Future Generation of Small Satellites

et al. 2018

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Section: A Sensing and Perceptionmentioning

confidence: 99%

“…Generally, electro-optical sensors (e.g., monocular and stereo cameras, LiDAR) are "the best option for pose estimation in close proximity" [63] operations. Monocular and stereo cameras are passive sensors that require additional image processing to extract depth information, making their use computationally expensive.…”

Section: A Sensing and Perceptionmentioning

confidence: 99%

Robotics and AI-Enabled On-Orbit Operations With Future Generation of Small Satellites

et al. 2018

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“…In addition, the algorithm cannot operate if the Sun lights the camera or the second spacecraft is in the eclipse. If the second spacecraft has a symmetric form, there is an ambiguity when solving (3). The shooting frequency should be higher than the rotational velocity of the spacecraft.…”

Section: The Second Stage: Relative Pose Determination Algorithmmentioning

confidence: 99%

“…The most reliable sensor type is an optical one that usually comprises a CCD camera. In the paper [2], an overview of the existing visual-based systems is presented, and in the paper [3], the state of the art of the cooperative and uncooperative satellite pose determination techniques are presented. A set of papers are devoted to the developing and flight testing of the algorithms for relative center of mass position determination of the unknown target using anglesonly visual navigation [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Relative Pose and Inertia Determination of Unknown Satellite Using Monocular Vision

Ivanov

Ovchinnikov

Sakovich

2018

International Journal of Aerospace Engineering

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The paper proposes a two-stage algorithm for autonomous relative motion determination of noncooperative and unknown object flying in space. The algorithm is based on image processing and can be applied to motion determination of space debris with unknown geometry and dynamic characteristics. The first stage of the algorithm is aimed at forming a database of possible reference points of the object during continuous observation. Tensor of inertia, initial velocity, and angular velocity of the object are also estimated. Then, these parameters are used in the second stage of the algorithm to determine the relative motion in real time. The algorithm is studied numerically and tested using the video of the Chibis-M microsatellite separation.

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“…For this reason, new technologies must be developed for the real-time estimation of the pose of the uncooperative target, which represents the key challenge during the autonomous close-range approach. Due to the lack of visual markers, the general approach for uncooperative pose estimation is to extract the most important patterns of the target's body from the sensor measurements and fit the known shape of the target with the detected features [10]. In general, extracting meaningful features and computing the pose in real time is a challenging task since the detected pattern may vary frame by frame due to the tumbling motion of the target.…”

Section: Introductionmentioning

confidence: 99%

Close Range Tracking of an Uncooperative Target in a Sequence of Photonic Mixer Device (PMD) Images

et al. 2018

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This paper presents a pose estimation routine for tracking attitude and position of an uncooperative tumbling spacecraft during close range rendezvous. The key innovation is the usage of a Photonic Mixer Device (PMD) sensor for the first time during space proximity for tracking the pose of the uncooperative target. This sensor requires lower power consumption and higher resolution if compared with existing flash Light Identification Detection and Ranging (LiDAR) sensors. In addition, the PMD sensor provides two different measurements at the same time: depth information (point cloud) and amplitude of the reflected signal, which generates a grayscale image. In this paper, a hybrid model-based navigation technique that employs both measurements is proposed. The principal pose estimation technique is the iterative closed point algorithm with reverse calibration, which relies on the depth image. The second technique is an image processing pipeline that generates a set of 2D-to-3D feature correspondences between amplitude image and spacecraft model followed by the Efficient Perspective-n-Points (EPnP) algorithm for pose estimation. In this way, we gain a redundant estimation of the target's current state in real-time without hardware redundancy. The proposed navigation methodology is tested in the German Aerospace Center (DLR)'s European Proximity Operations Simulator. The hybrid navigation technique shows the capability to ensure robust pose estimation of an uncooperative tumbling target under severe illumination conditions. In fact, the EPnP-based technique allows to overcome the limitations of the primary technique when harsh illumination conditions arise.

show abstract

A review of cooperative and uncooperative spacecraft pose determination techniques for close-proximity operations

Cited by 217 publications

References 83 publications

Robotics and AI-Enabled On-Orbit Operations With Future Generation of Small Satellites

Robotics and AI-Enabled On-Orbit Operations With Future Generation of Small Satellites

Relative Pose and Inertia Determination of Unknown Satellite Using Monocular Vision

Close Range Tracking of an Uncooperative Target in a Sequence of Photonic Mixer Device (PMD) Images

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