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

Earth and Space Science

Thomas J. Fuchs²  David R. Thompson¹  Brian D. Bue²  Julie Castillo-Rogez²  Steve A. Chien²  Dero Gharibian² 

[1] ORCID:orcid.org/0000-0003-1100-7550;Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA

关键词: small bodies;    comets;    asteroids;    flyby;    machine learning;    computer vision;   

DOI  :  10.1002/2014EA000042

来源: Wiley

【 摘 要 】

Abstract

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.

【 授权许可】

CC BY-NC-ND   
©2015. The Authors.

Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.