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 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.
Abstract (translated)
人工智能（AI）和自主边缘计算在空间是一个正在兴起的兴趣领域，可以增强纳米卫星的性能，其中现代传感器产生的数据比通常发送到地面站的数据要大得多。在这里，我们介绍了在SpIRIT上托管的载有人工智能子系统的硬件和软件设计。该系统针对可见光和长波红外相机进行优化，以进行在轨计算机视觉实验。本文重点介绍了系统在恶劣空间条件下的关键设计选择以及这些选择与关键任务需求（如有限计算资源、宇宙辐射耐受性、极端温度变化、分布偏移和非常低传输带宽）之间的联系。载荷称为Loris，包括六个可见光相机、三个红外相机、一个相机控制板和GPU系统级模块。Loris不仅提高了纳米卫星的数据处理能力，还为从空间遥感的更广泛应用奠定了基础。
URL
https://arxiv.org/abs/2404.08399
