Pose Estimation for Spacecraft Relative Navigation Using Model-Based Algorithms
This paper presents the performance assessment of innovative model-based algorithms developed for pose estimation of uncooperative targets by processing sparse three-dimensional point clouds. This topic is of interest in the framework of advanced space applications, e.g., on-orbit servicing and acti...
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| Published in: | IEEE transactions on aerospace and electronic systems Vol. 53; no. 1; pp. 431 - 447 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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IEEE
01.02.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9251, 1557-9603 |
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| Abstract | This paper presents the performance assessment of innovative model-based algorithms developed for pose estimation of uncooperative targets by processing sparse three-dimensional point clouds. This topic is of interest in the framework of advanced space applications, e.g., on-orbit servicing and active debris removal, which require a chaser spacecraft to execute autonomous relative navigation maneuvers in close-proximity of a space target. Both the problems of pose acquisition and tracking are addressed. The former one is carried out by combining the concepts of principal component analysis and template matching in order to limit both the computational effort and the amount of on-board data storage compared with traditional approaches. The latter is entrusted to a customized implementation of the iterative closest point algorithm, which adopts multiple model-measurement matching strategies as well as a refinement step, in order to respectively increase robustness and accelerate algorithm convergence. Also, safe transition from acquisition to tracking is implemented by means of autonomous detection of failures of the pose acquisition algorithm. The performance of the proposed techniques is investigated by means of numerical simulations in which the operation of an active LIDAR system as well as the target-chaser relative dynamics are realistically reproduced. Results demonstrate algorithms' effectiveness over a wide range of relative pose conditions and dealing with targets of variable size and shape, in spite of considerable sparseness of the measured datasets. |
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| AbstractList | This paper presents the performance assessment of innovative model-based algorithms developed for pose estimation of uncooperative targets by processing sparse three-dimensional point clouds. This topic is of interest in the framework of advanced space applications, e.g., on-orbit servicing and active debris removal, which require a chaser spacecraft to execute autonomous relative navigation maneuvers in close-proximity of a space target. Both the problems of pose acquisition and tracking are addressed. The former one is carried out by combining the concepts of principal component analysis and template matching in order to limit both the computational effort and the amount of on-board data storage compared with traditional approaches. The latter is entrusted to a customized implementation of the iterative closest point algorithm, which adopts multiple model-measurement matching strategies as well as a refinement step, in order to respectively increase robustness and accelerate algorithm convergence. Also, safe transition from acquisition to tracking is implemented by means of autonomous detection of failures of the pose acquisition algorithm. The performance of the proposed techniques is investigated by means of numerical simulations in which the operation of an active LIDAR system as well as the target-chaser relative dynamics are realistically reproduced. Results demonstrate algorithms' effectiveness over a wide range of relative pose conditions and dealing with targets of variable size and shape, in spite of considerable sparseness of the measured datasets. |
| Author | Grassi, Michele Opromolla, Roberto Fasano, Giancarmine Rufino, Giancarlo |
| Author_xml | – sequence: 1 givenname: Roberto surname: Opromolla fullname: Opromolla, Roberto email: roberto.opromolla@unina.it organization: Dept. of Ind. Eng., Univ. of Naples "Federico II", Naples, Italy – sequence: 2 givenname: Giancarmine surname: Fasano fullname: Fasano, Giancarmine email: giancarmine.fasano@unina.it organization: Dept. of Ind. Eng., Univ. of Naples "Federico II", Naples, Italy – sequence: 3 givenname: Giancarlo surname: Rufino fullname: Rufino, Giancarlo email: giancarlo.rufino@unina.it organization: Dept. of Ind. Eng., Univ. of Naples "Federico II", Naples, Italy – sequence: 4 givenname: Michele surname: Grassi fullname: Grassi, Michele email: michele.grassi@unina.it organization: Dept. of Ind. Eng., Univ. of Naples "Federico II", Naples, Italy |
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| SubjectTerms | Autonomous navigation Data storage Feature extraction Iterative algorithms Iterative closest point algorithm Laser radar LIDAR Maneuvers Mathematical models Model matching Orbital servicing Performance assessment Pose estimation principal component analysis (PCA) Principal components analysis Robustness (mathematics) Space applications Space debris mitigation Spacecraft spacecraft relative navigation Target tracking Template matching Three-dimensional displays Tracking uncooperative target |
| Title | Pose Estimation for Spacecraft Relative Navigation Using Model-Based Algorithms |
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