• DOI:10.48550/arXiv.2208.08865
• Corpus ID: 251643715

Lessons from a Space Lab - An Image Acquisition Perspective

@article{Pauly2022LessonsFA,
  title={Lessons from a Space Lab - An Image Acquisition Perspective},
  author={Leo Pauly and Michele L. Jamrozik and Miguel Ortiz del Castillo and Olivia Borgue and Inderpreet Singh and Mohatashem Reyaz Makhdoomi and Olga-Orsalia Christidi-Loumpasefski and Vincent Gaudilli{\`e}re and Carol Mart{\'i}nez and Arunkumar Rathinam and Andreas M. Hein and Miguel Angel Olivares-M{\'e}ndez and Djamila Aouada},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.08865}
}

• Leo Pauly, M. Jamrozik, +10 authors Djamila Aouada
• Published 18 August 2022
• Computer Science
• ArXiv

The use of Deep Learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically generated images can be used, the DL models trained on synthetic data are often susceptible to performance degradation, when tested in real-world environments. In this context, the Interdisciplinary Center of Security, Reliability and Trust (SnT) at the University of Luxembourg has developed the 'SnT Zero-G Lab', for training and validating vision-based space algorithms in conditions emulating real-world space environments. An important aspect of the SnT Zero-G Lab development was the equipment selection. From the lessons learned during the lab development, this article presents a systematic approach combining market survey and experimental analyses for equipment selection. In particular, the article focus on the image acquisition equipment in a space lab: background materials, cameras and illumination lamps. The results from the experiment analyses show that the market survey complimented by experimental analyses is required for effective equipment selection in a space lab development project.