Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT
<|reference_start|>Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT: 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 camera control board and a Graphics Processing Unit (GPU) system-on-module. Loris enables the execution of AI models with on-orbit fine-tuning as well as a next-generation image compression algorithm, including progressive coding. This innovative approach not only enhances the data processing capabilities of nanosatellites but also lays the groundwork for broader applications to remote sensing from space.<|reference_end|>
@article{del castillo2024mitigating,
    title={Mitigating Challenges of the Space Environment for Onboard Artificial
  Intelligence: Design Overview of the Imaging Payload on SpIRIT},
    author={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},
    journal={arXiv preprint arXiv:2404.08399},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08399},
    primaryClass={cs.CV}
}