Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT
Miguel Ortiz del Castillo, Jonathan Morgan, Jack McRobbie, Clint, Therakam, Zaher Joukhadar, Robert Mearns, Simon Barraclough, Richard Sinnott,, Andrew Woods, Chris Bayliss, Kris Ehinger, Ben Rubinstein, James Bailey,, Airlie Chapman, Michele Trenti

TL;DR
This paper details the design of an onboard AI imaging payload for nanosatellites, addressing space environment challenges to enable advanced onboard computer vision and data processing capabilities.
Contribution
It introduces the Loris payload system optimized for space conditions, combining multiple cameras, a GPU, and innovative image compression for onboard AI tasks.
Findings
Robust hardware design for space conditions
On-orbit AI model fine-tuning capability
Advanced image compression with progressive coding
Abstract
Artificial intelligence (AI) and autonomous edge computing in space are emerging areas of interest to augment capabilities of nanosatellites, where modern sensors generate orders of magnitude more data than can typically be transmitted to mission control. Here, we present the hardware and software design of an onboard AI subsystem hosted on SpIRIT. The system is optimised for on-board computer vision experiments based on visible light and long wave infrared cameras. This paper highlights the key design choices made to maximise the robustness of the system in harsh space conditions, and their motivation relative to key mission requirements, such as limited compute resources, resilience to cosmic radiation, extreme temperature variations, distribution shifts, and very low transmission bandwidths. The payload, called Loris, consists of six visible light cameras, three infrared cameras, a…
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Taxonomy
TopicsSpacecraft Design and Technology · CCD and CMOS Imaging Sensors · Satellite Communication Systems
