Version 1
: Received: 21 January 2018 / Approved: 22 January 2018 / Online: 22 January 2018 (05:11:39 CET)
How to cite:
Post, M.; Li, J. Visual Monocular 3D Reconstruction and Component Identification for Small Spacecraft. Preprints2018, 2018010195. https://doi.org/10.20944/preprints201801.0195.v1
Post, M.; Li, J. Visual Monocular 3D Reconstruction and Component Identification for Small Spacecraft. Preprints 2018, 2018010195. https://doi.org/10.20944/preprints201801.0195.v1
Post, M.; Li, J. Visual Monocular 3D Reconstruction and Component Identification for Small Spacecraft. Preprints2018, 2018010195. https://doi.org/10.20944/preprints201801.0195.v1
APA Style
Post, M., & Li, J. (2018). Visual Monocular 3D Reconstruction and Component Identification for Small Spacecraft. Preprints. https://doi.org/10.20944/preprints201801.0195.v1
Chicago/Turabian Style
Post, M. and Junquan Li. 2018 "Visual Monocular 3D Reconstruction and Component Identification for Small Spacecraft" Preprints. https://doi.org/10.20944/preprints201801.0195.v1
Abstract
A monocular vision pose estimation and identification algorithm used on a small spacecraft for future orbital servicing is studied in this paper. A tracker spacecraft equipped with a short-range vision system is proposed to recover the 3D structural model of a space target in orbit and automatically identify its solar panels and main body using only visual information from an onboard camera. The proposed reconstruction and identification framework is tested using structure-from-motion and point cloud identification methods. The Efficient Perspective-n-Points (EPnP) descriptor is used for pose estimation. Triangulated points are used for component segmentation by means of orientation histogram descriptors. Experimental results based on laboratory images of a spacecraft model show the effectiveness and robustness of our approach.
Keywords
spacecraft; structure from motion; monocular vision; component detection; structure analysis
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
