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Missions to small celestial bodies rely heavily on optical feature trackingfor characterization of and relative navigation around the target body. Whiletechniques for feature tracking based on deep learning are a promisingalternative to current human-in-the-loop processes, designing deeparchitectures that can operate onboard spacecraft is challenging due to onboardcomputational and memory constraints. This paper introduces a novel deep localfeature description architecture that leverages binary convolutional neuralnetwork layers to significantly reduce computational and memory requirements.We train and test our models on real images of small bodies from legacy andongoing missions and demonstrate increased performance relative to traditionalhandcrafted methods. Moreover, we implement our models onboard a surrogate forthe next-generation spacecraft processor and demonstrate feasible runtimes foronline feature tracking.

Efficient Feature Description for Small Body Relative Navigation using Binary Convolutional Neural Networks