Pose estimation for spacecraft relative navigation using model-based algorithms
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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Opromolla et al. [49] proposed a model-based pose framework for spacecraft pose estimation, but it was designed to process 3D point clouds and thus its application was constrained to LIDARs or stereovision systems.
“Opromolla et al. [49] proposed a model-based pose framework for spacecraft pose estimation.However, the framework was designed to process 3D point clouds and thus its application was constrained to LIDARs or stereovision systems.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [49]
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“LIDAR systems can produce 3D point clouds [8].”
- Authors
- Opromolla, R., Fasano, G., Rufino, G., & Grassi, M.
- Affiliations
- University of Naples Federico II (IT)
- Venue
- IEEE Transactions on Aerospace and Electronic Systems
- Published
- 2017-02
- Volume
- 53
- Issue
- 1
- Pages
- 431-447
- ISSN
- 0018-9251 (ISSN-L)
- OpenAlex
- W2568139125
- Cited by
- 127
- References
- 41
- Citations by year
- 2026: 15 · 2025: 20 · 2024: 14 · 2023: 11 · 2022: 12 · 2021: 13 · 2020: 9 · 2019: 14 · 2018: 13 · 2017: 6
- FWCI
- 10.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Aerospace and Electronic Systems (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Space Satellite Systems and Control
- Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Dynamics and Control
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- spacecraft relative navigation, pose estimation, non-cooperative targets, on-orbit servicing, active debris removal, chaser spacecraft, principal component analysis, template matching, iterative closest point, fault detection, LiDAR
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