Fault-tolerant pose estimation of space objects

Aghili, Farhad; Kuryllo, Marcin; Okouneva, Galina; English, Chad

doi:10.1109/aim.2010.5695796

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…The pose estimation of uncooperative space objects is a key technology for many space missions such as target recognition and on-orbit service [1,2]. In contrast to other state-of-the-art systems, a monocular camera ensures pose estimation under low power and low hardware complexity [3].…”

Section: Introductionmentioning

confidence: 99%

Pose Estimation of Uncooperative Unknown Space Objects from a Single Image

Ren

Jiang

Wang

2020

International Journal of Aerospace Engineering

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Estimating the 3D pose of the space object from a single image is an important but challenging work. Most of the existing methods estimate the 3D pose of known space objects and assume that the detailed geometry of a specific object is known. These methods are not available for unknown objects without the known geometry of the object. In contrast to previous works, this paper devotes to estimate the 3D pose of the unknown space object from a single image. Our method estimates not only the pose but also the shape of the unknown object from a single image. In this paper, a hierarchical shape model is proposed to represent the prior structure information of typical space objects. On this basis, the parameters of the pose and shape are estimated simultaneously for unknown space objects. Experimental results demonstrate the effectiveness of our method to estimate the 3D pose and infer the geometry of unknown typical space objects from a single image. Moreover, experimental results show the advantage of our approach over the methods relying on the known geometry of the object.

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Section: Introductionmentioning

confidence: 99%

Pose Estimation of Uncooperative Unknown Space Objects from a Single Image

Ren

Jiang

Wang

2020

International Journal of Aerospace Engineering

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“…Because of the merits of non-contact, low power consumption, high accuracy and efficiency, the poseestimation based on vision become the most suitable solution for the near-range phase of ARD.In their paper, NK Philip and MR Ananthasayanamdescribed a scheme of relative position control based on phase-plane control technique and relative attitude control based on relative quaternion feedback and obtained good result [3] .V.Lepetit introduced a non-iterative solution to the PnP problem,which is superior in its rapidity and accuracy to the other iterative techniques [4] .C. P. Lu regarded the relative pose estimation as a minimization problem of the object-space collinear error and proposed a fast orthogonal iterative (OI) algorithm with well global convergence [5] .Thienel and his colleagues devised a non-linear method to estimate the pose of spacecraftand carried out some tests of tracking controls, but its disadvantage lies in that muchpriorknowledge is needed [6] .Khansari-Zadeh proposed a new algorithm of estimation and navigation with vision information based on neural networks, and its accuracy and robustness were verified via a detailed modeling and simulation,and validated through a complete virtual environment based on the six degrees of freedom (6-DOF) nonlinear aircraft dynamical system in an autonomous aerial refueling (AAR) mission [7][8] .Farhad Aghiliand his companionspresented a fault-tolerant estimation method for the pose of space objects using 3-D vision data by integrating Kalman filter (KF) with an algorithm of Iterative Closest Point (ICP) in a closed-loop configuration [9] , in which the Euler-Hill Equations are employed to derive a discrete-time model so as to open out the evolution process of the relative translational motion of a tumbling target satellite with respect to a chaser satellite which is freely falling in a nearby orbit.Daero Lee, Henry Pernicka presented a new approach based on the unscented Kalman filterand got more robust results than the extended Kalman filter [10] . S. S. Mehta and P. Barooah developed a novel method to obtain a refined poseestimationfrom two views captured by a calibrated monocular camera.…”

Section: Introductionmentioning

confidence: 99%

Relative Pose Estimation under Monocular Vision in Rendezvous and Docking

Pan

Jian²,

Shi

2013

AMM

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Autonomous rendezvous and docking (ARD) plays a very important role in planned space programs such as on-orbit construction and assembly, refueling of satellites, repairing or rescuing failed satellites, active removal of space debris, autonomous re-supply and crew exchange of space stations, and so on. However,the success of ARD rests with the estimation accuracy and efficiency of relative pose among various spacecraft in rendezvous and docking. In this paper, a high accuracy and efficiency estimation algorithm of relative pose of cooperative space targets is presented based on monocular vision imaging, in which a modified gravity model approach and multiple targets tracking methods are employed to improve the accuracy of feature extraction and enhance the estimation efficiency, moreover the Levenberg-Marquardt method (LMM) is used to achieve a well global convergence. The comprehensive experiment results demonstrate its outstanding predominance in estimation accuracy and efficiency.

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“…Frame-to-frame object tracking with ICP should be fed from independent pose estimates. Some have used the navigation filter's propagated pose as an initial input to ICP [41], but this is inherently dangerous for two reasons.…”

Section: Uncorrelated Pose Estimates In Navigation Filtersmentioning

confidence: 99%

Utilization of Scale in Keypoint Detection and Feature Description

Rhodes¹

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Ubiquitous availability of inexpensive three dimensional (3D) sensors has led to an abundance of keypoint detection and feature description techniques for point-clouds. While some recent methods utilize color along with geometric information, most only implicitly discuss-if not completely disregard-the information provided by scale. Scale is an inherent characteristic of any keypoint or feature that describes its physical size or region of support. Exploiting information provided by scale facilitates the processes of keypoint detection and feature description. A 3D scale-space, extended from robust and popular 2D methods, is constructed to diffuse a signal on triangulated meshes. Methods of scale-parameter estimation and a novel neighborhood definition are presented that improve the methods' robustness to noise and arbitrary mesh connectivity. Keypoint repeatability experiments show that a novel definition of the Laplace-Beltrami operator (LBO) leads to the most accurate scale-space. A point-cloud feature descriptor, popular in the robotics community and recently proposed for spacecraft relative navigation (RelNav), is modified to explicitly utilize scale in the description and matching processes. The Scaled Oriented Unique Clustered Viewpoint Feature Histogram (SOUR-CVFH) uses a cluster's scale to expedite the histogram matching procedure and increase pose estimation accuracy. SOUR-CVFH is demonstrated in experiments of (1) object recognition in both isolated and cluttered scenes and (2) simulated RelNav of a spacecraft to a asteroid.

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Fault-tolerant pose estimation of space objects

Cited by 8 publications

References 17 publications

Pose Estimation of Uncooperative Unknown Space Objects from a Single Image

Pose Estimation of Uncooperative Unknown Space Objects from a Single Image

Relative Pose Estimation under Monocular Vision in Rendezvous and Docking

Utilization of Scale in Keypoint Detection and Feature Description

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