Robust Features Extraction for On-board Monocular-based Spacecraft Pose Acquisition
Abstract
This paper presents the design, implementation, and validation of a robust feature extraction architecture for real-time on-board monocular vision-based pose initialization of a target spacecraft in application to on-orbit servicing and formation flying. The proposed computer vision algorithm is designed to detect the most significant features of an uncooperative target spacecraft in a sequence of two-dimensional input images that are collected on board the chaser spacecraft. A novel approach based on the fusion of multiple and parallel processing streams is proposed to filter a minimum number of extracted true point features, even in case of unfavourable illumination conditions and in presence of Earth in the background. These are then combined into relevant polyline structures that characterize the true geometrical shape of the target spacecraft.
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The combination of the Fast-Hessian feature detector with the BRISK descriptor has comparable performance in both VIS and TIR spectra, making it a promising option when reduced memory usage is a key requirement.
“the combination of the Fast-Hessian feature detector with the Binary Robust Invariant Scalable Keypoints (BRISK) descriptor proved to have comparable performance in both spectra, resulting in a promising option when reduced memory usage repre-295 sent a key requirement.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [38]
L2 or L1 loss does not represent correctly the actual angular distance for any orientation representation.
“While several works [13, 16, 24] have used L 2 or L 1 loss to regress orientation. This does not represent correctly the actual angular distance for any orientation representation.”
From Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering (2020) · cites this paper as [13]
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- Authors
- Capuano, V., Alimo, S. R., Ho, A. Q., & Chung, S.-J.
- Affiliations
- California Institute of Technology (US)
- Jet Propulsion Laboratory (US)
- Venue
- AIAA Scitech 2019 Forum
- Published
- 2019-01-06
- OpenAlex
- W2910148174
- Cited by
- 39
- References
- 21
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 7 · 2023: 9 · 2022: 2 · 2021: 5 · 2020: 3 · 2019: 4
- FWCI
- 6.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- AIAA SciTech Forum (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Space Satellite Systems and Control
- Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Dynamics and Control
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- spacecraft pose estimation, monocular vision, on-board processing, feature extraction, on-orbit servicing, formation flying, lighting conditions, real-time vision
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