Autonomous Navigation for On-Orbit Servicing

Benninghoff, Heike; Boge, Toralf; Rems, Florian

doi:10.1007/s13218-014-0297-0

Cited by 13 publications

(9 citation statements)

References 20 publications

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“…/2/.) Two or more references at a time may be put in one set of brackets [3,4]. The references are to be numbered in the order in which they are cited in the text and are to be listed at the end of the contribution under a heading References, see our example below.…”

Section: Methodsmentioning

confidence: 99%

“…Relative pose measurement is of great significance to the near distance rendezvous and docking of the orbit spacecraft and the target acquisition of space robot arm [1][2][3]. Image based vision pose measurement technology has high precision, good adaptability, and it is the main method to realize the relative position and posture measurement [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Monocular pose measurement method based on circle and line features

Cai¹,

Sun²

2016

Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)

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In this paper, a new method based on circle and linear projection is proposed. In a straight line and a circle on the target space as the main object recognition. In the case of knowing the radius of the space circle and coordinate of a point on the space line: First of all, we can use the space circle projection to solve all possible solutions, there are two groups. Secondly, determine the rotation angle of space circle around normal vector in the two groups by using the relative position relation of space straight line and circle. Then, we can get the rotation relation between the object coordinate system and the camera coordinate system and get the coordinate representation of circle and straight line in the camera coordinate system in two groups of solutions. Finally, project the point on the space line onto the image plane in two groups of solutions. In the correct solution, the distance between the projection point and the projection line is small. The experimental results show that the method can find the correct solution in all solutions and determine the rotation angle of the space circle around the normal vector. The method can get the correct result under the condition of noise, so the method has strong robustness.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monocular pose measurement method based on circle and line features

Cai¹,

Sun²

2016

Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)

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“…Scanning LiDAR can be used to determine position and orientations at ranges up to ~ 3 km [65], while conventional monocular cameras have the capability to precisely identify fiducial markers during close approach. Sensor choice for relative navigation is also discussed in [66].…”

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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“…Optical imaging sensors have been widely used as the essential payloads of vision systems in aerospace applications: autonomous rendezvous and docking [1][2][3][4], vision-based landing [5], position and pose estimation [6][7][8][9][10][11][12][13], on-orbit serving [14,15], space robotics [16], satellite recognition [12,[17][18][19], 3D structure reconstruction and component detection [20,21], etc. Vision-based recognition and pose estimation of a target satellite are one of the key technologies to achieve these applications.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression

Zhang

Jiang

et al. 2019

International Journal of Aerospace Engineering

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In this paper, we address the problem of vision-based satellite recognition and pose estimation, which is to recognize the satellite from multiviews and estimate the relative poses using imaging sensors. We propose a vision-based method to solve these two problems using Gaussian process regression (GPR). Assuming that the regression function mapping from the image (or feature) of the target satellite to its category or pose follows a Gaussian process (GP) properly parameterized by a mean function and a covariance function, the predictive equations can be easily obtained by a maximum-likelihood approach when training data are given. These explicit formulations can not only offer the category or estimated pose by the mean value of the predicted output but also give its uncertainty by the variance which makes the predicted result convincing and applicable in practice. Besides, we also introduce a manifold constraint to the output of the GPR model to improve its performance for satellite pose estimation. Extensive experiments are performed on two simulated image datasets containing satellite images of 1D and 2D pose variations, as well as different noises and lighting conditions. Experimental results validate the effectiveness and robustness of our approach.

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Autonomous Navigation for On-Orbit Servicing

Cited by 13 publications

References 20 publications

Monocular pose measurement method based on circle and line features

Monocular pose measurement method based on circle and line features

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

Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression

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