CNN-based local features for navigation near an asteroid

Olli Knuuttila, Antti Kestila, Esa Kallio

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

This article addresses the challenge of vision-based proximity navigation in asteroid exploration missions and on-orbit servicing. Traditional feature extraction methods struggle with the significant appearance variations of asteroids due to limited scattered light. To overcome this, we propose a lightweight feature extractor specifically tailored for asteroid proximity navigation, designed to be robust to illumination changes and affine transformations. We compare and evaluate state-of-the-art feature extraction networks and three lightweight network architectures in the asteroid context. Our proposed feature extractors and their evaluation leverage synthetic images and real-world data from missions such as NEAR Shoemaker, Hayabusa, Rosetta, and OSIRIS-REx. Our contributions include a trained feature extractor, incremental improvements over existing methods, and a pipeline for training domain-specific feature extractors. Experimental results demonstrate the effectiveness of our approach in achieving accurate navigation and localization. This work aims to advance the field of asteroid navigation and provides insights for future research in this domain.

Original languageEnglish
Pages (from-to)16652 - 16672
Number of pages21
JournalIEEE Access
Volume12
DOIs
Publication statusPublished - 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • Convolution
  • Convolutional neural networks
  • Detectors
  • Feature extraction
  • feature extraction
  • Head
  • Navigation
  • simultaneous localization and mapping
  • Solar system
  • space exploration
  • Training

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