Lessons from a Space Lab: An Image Acquisition Perspective

Abstract

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 focuses 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.

Establishing a Multi-Functional Space Operations Emulation Facility: Insights from the Zero-G Lab

“…Prior experiments conducted in the Zero-G Lab include GNC enabled rendezvous and proximity operations, cooperative docking, free-floating satellite motion, orbital interactions, image acquisition for computer vision, etc. [6,15,[21][22][23][24][25].…”

“…To recreate orbital scenarios within the facility, the Zero-G Lab employs a Godox SL-60 Video Light [15,16]. This LED light source has a 60-watt rating with dimensions of 23 cm × 24 cm × 14 cm.…”