In an environment of escalating usage of Low Earth Orbit, the active remediation of debris is an increasingly necessary capability. Computer vision pose estimation is a core competency of active debris remediation but state-of-the-art pose estimation methods continue to grow in size and complexity. For bandwidth limited and edge computing cases, smaller networks are more feasibly implemented. A 16,845 synthetic image dataset, applicable to the upcoming JAXA CRD2 project, is rendered and a small pose estimation network is constructed and trained on the dataset. The network is then quantized, reducing the memory requirement by a factor of 8x to a theoretical size of 5.5 MB. The 5.5 MB network demonstrates sufficient accuracy in both single image pose and motion prediction tasks when compared to the full precision 32 bit network.
Render-to-real image dataset and CNN pose estimation for down-link restricted spacecraft missions
2023-03-04
21596203 byte
Conference paper
Electronic Resource
English
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