Enhanced flyby science with onboard computer vision: Tracking and surface feature detection at small bodies

• Published in: Earth and space science (Hoboken, N.J.) N.J.), 2015-10, Vol.2 (10), p.417-434
• Main Authors: Fuchs, Thomas J., Thompson, David R., Bue, Brian D., Castillo‐Rogez, Julie, Chien, Steve A., Gharibian, Dero, Wagstaff, Kiri L.
• Format: Article
• Language: English
• Subjects: Algorithms; Asteroids; Astrobiology; comets; computer vision; Data collection; Datasets; flyby; machine learning; Physical properties; Remote sensing; Science; small bodies; Solar system; Spacecraft; Spectrum analysis; Vision
• ISSN: 2333-5084; 2333-5084
• DOI: 10.1002/2014EA000042

Spacecraft autonomy is crucial to increase the science return of optical remote sensing observations at distant primitive bodies. To date, most small bodies exploration has involved short timescale flybys that execute prescripted data collection sequences. Light time delay means that the spacecraft must operate completely autonomously without direct control from the ground, but in most cases the physical properties and morphologies of prospective targets are unknown before the flyby. Surface features of interest are highly localized, and successful observations must account for geometry and illumination constraints. Under these circumstances onboard computer vision can improve science yield by responding immediately to collected imagery. It can reacquire bad data or identify features of opportunity for additional targeted measurements. We present a comprehensive framework for onboard computer vision for flyby missions at small bodies. We introduce novel algorithms for target tracking, target segmentation, surface feature detection, and anomaly detection. The performance and generalization power are evaluated in detail using expert annotations on data sets from previous encounters with primitive bodies. Key Points Spacecraft autonomy Onboard computer vision Science return from small bodies