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

Andrew Price, Kazuya Yoshida

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
PublisherIEEE Computer Society
Pages1992-2001
Number of pages10
ISBN (Electronic)9781665448994
DOIs
Publication statusPublished - 2021 Jun
Event2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 - Virtual, Online, United States
Duration: 2021 Jun 192021 Jun 25

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

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

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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