Autonomous relative navigation around uncooperative spacecraft based on a single camera

Pesce, Vincenzo; Opromolla, Roberto; Sarno, S.; Lavagna, Michèle; Grassi, Michele

doi:10.1016/j.ast.2018.11.042

Cited by 62 publications

(39 citation statements)

References 46 publications

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“…Referring to Pesce et al. 20 the sets of measurements in the simulations are given as the summations of the theoretical value and measurement noise. The theoretical value is generated by projecting the 3D features on the 2D image according to the true relative attitude and position parameters.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In the past years, the monocular vision-based method has been widely used for the pose estimation problem. 18–26 Among these works, Segal and Gurfil 19 and D’Amico et al. 22 investigated the vision-based relative state estimation method of non-cooperative spacecraft for on-orbit servicing and formation flying applications.…”

Section: Introductionmentioning

confidence: 99%

“…20 present a loosely coupled architecture for relative navigation toward a non-cooperative, known target, and the initial acquisition feature matching and a robust filter are also considered. 20 Kim et al. 22 and Xing et al.…”

Section: Introductionmentioning

confidence: 99%

“…[18][19][20][21][22][23][24][25][26] Among these works, Segal and Gurfil 19 and D'Amico et al 22 investigated the vision-based relative state estimation method of non-cooperative spacecraft for on-orbit servicing and formation flying applications. Pesce et al 20 present a loosely coupled architecture for relative navigation toward a non-cooperative, known target, and the initial acquisition feature matching and a robust filter are also considered. 20 Kim et al 22 and Xing et al 23 employed the line-of-sight measurements and extended Kalman filter to estimate the relative position and attitude for spacecraft.…”

Section: Introductionmentioning

confidence: 99%

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Monocular-vision-based spacecraft relative state estimation under dual number algebra

Liang

Wang

Huo³

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper develops a relative state estimation method for two spacecraft based on monocular vision measurement, where the leader spacecraft of three-dimensional shape is observed by a calibrated camera fixed on the follower spacecraft. The two-dimensional image of the leader spacecraft with multiple geometric features is obtained by the camera. Multiple geometric features including points, lines and circles are described under dual number algebra, and the observation models are proposed based on the geometric projection relationships of these features. By the proposed method, the geometric features appeared on the leader spacecraft can be employed to estimate the relative state between the two spacecraft. Another contribution of this paper is to develop a six-degree-of-freedom relative motion dynamics using dual number. The dynamics model describes the relative motion between arbitrary points on the spacecraft, and both the kinematically and dynamically coupling effects are considered. Based on the derived process model and observation model, an extended Kalman filter is presented to estimate the relative state between the two spacecraft. In addition, an unscented Kalman filter is also developed to avoid complicated calculation of derivations. Finally, numerical simulations are performed to evaluate the proposed EKF and UKF approaches based on multiple geometric feature measurement. Simulation results verify the effectiveness of the proposed method, and show that by using EKF or UKF technique, the relative translation and rotation estimation errors converge rapidly with the desired high estimation accuracy. Comparison simulations are made with the situations using less feature measurement, which demonstrate that employing multiple feature measurement provides superior estimation performance than methods with less feature measurement.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monocular-vision-based spacecraft relative state estimation under dual number algebra

Liang

Wang

Huo³

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…However, due to the small number of image samples used, it is impossible to be sure that this method is robust to any scenario, nor can it be evaluated whether it is suitable for variable illumination conditions in typical space environment. Furthermore, Pesce et al [34] use the GFTT algorithm [35] to extract the feature points of the target 3D model, and adopt a combination of PCA [36] and RANSAC [37] to obtain the correspondences between the target image and the 3D model. Finally, the EPnP solver is used to initialize the pose.…”

Section: Related Workmentioning

confidence: 99%

Fast and Accurate Spacecraft Pose Estimation From Single Shot Space Imagery Using Box Reliability and Keypoints Existence Judgments

Huo

Zhang

2020

IEEE Access

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Real-time 6DOF (6 Degree of Freedom) pose estimation of an uncooperative spacecraft is an important part of proximity operations, e.g., space debris removal, spacecraft rendezvous and docking, on-orbit servicing, etc. In this paper, a novel efficient deep learning based approach is proposed to estimate the 6DOF pose of uncooperative spacecraft using monocular-vision measurement. Firstly, we introduce a new lightweight YOLO-liked CNN to detect spacecraft and predict 2D locations of the projected keypoints of a prior reconstructed 3D model in real-time. Then, we design two novel models for predicting the bounding box (bbox) reliability scores and the probability of keypoints existence. The two models not only significantly reduce the false positive, but also speed up convergence. Finally, the 6DOF pose is estimated and refined using Perspective-n-Point and geometric optimizer. Results demonstrate that the proposed approach achieves 73.2% average precision and 77.6% average recall for spacecraft detection on the SPEED dataset after only 200 training epochs. For the pose estimation task, the mean rotational error is 0.6812 • , and the mean translation error is 0.0320m. The proposed approach achieves competitive pose estimation performance and extreme lightweight (∼ 0.89 million learnable weights in total) on the SPEED dataset while being efficient for real-time applications.

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Close Relative Navigation to a Non-cooperative Maneuvering Target Using Variable Dimension Filters

Hu¹,

Wang

2019

Lecture Notes in Electrical Engineering

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Autonomous relative navigation around uncooperative spacecraft based on a single camera

Cited by 62 publications

References 46 publications

Monocular-vision-based spacecraft relative state estimation under dual number algebra

Monocular-vision-based spacecraft relative state estimation under dual number algebra

Fast and Accurate Spacecraft Pose Estimation From Single Shot Space Imagery Using Box Reliability and Keypoints Existence Judgments

Close Relative Navigation to a Non-cooperative Maneuvering Target Using Variable Dimension Filters

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