  Missions to small celestial bodies rely heavily on optical feature tracking
for characterization of and relative navigation around the target body. While
techniques for feature tracking based on deep learning are a promising
alternative to current human-in-the-loop processes, designing deep
architectures that can operate onboard spacecraft is challenging due to onboard
computational and memory constraints. This paper introduces a novel deep local
feature description architecture that leverages binary convolutional neural
network layers to significantly reduce computational and memory requirements.
We train and test our models on real images of small bodies from legacy and
ongoing missions and demonstrate increased performance relative to traditional
handcrafted methods. Moreover, we implement our models onboard a surrogate for
the next-generation spacecraft processor and demonstrate feasible runtimes for
online feature tracking.
