A monocular pose estimation case study: The Hayabusa2 minerva-II2 deployment

Andrew Price, Kazuya Yoshida

研究成果: Conference contribution

抄録

In an environment of increasing orbital debris and remote operation, visual data acquisition methods are becoming a core competency of the next generation of spacecraft. However, deep space missions often generate limited data and noisy images, necessitating complex data analysis methods. Here, a state-of-the-art convolutional neural network (CNN) pose estimation pipeline is applied to the Hayabusa2 Minerva-II2 rover deployment; a challenging case with noisy images and a symmetric target. To enable training of this CNN, a custom dataset is created. The deployment velocity is estimated as 0.1908 m/s using a projective geometry approach and 0.1934 m/s using a CNN landmark detector approach, as compared to the official JAXA estimation of 0.1924 m/s (relative to the spacecraft). Additionally, the attitude estimation results from the real deployment images are shared and the associated tumble estimation is discussed.

本文言語English
ホスト出版物のタイトルProceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
出版社IEEE Computer Society
ページ1992-2001
ページ数10
ISBN(電子版)9781665448994
DOI
出版ステータスPublished - 2021 6
イベント2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 - Virtual, Online, United States
継続期間: 2021 6 192021 6 25

出版物シリーズ

名前IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN(印刷版)2160-7508
ISSN(電子版)2160-7516

Conference

Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
国/地域United States
CityVirtual, Online
Period21/6/1921/6/25

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学

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