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.

翻译：小行星等小天体探测任务高度依赖光学特征跟踪来实现目标天体的特性描述与相对导航。尽管基于深度学习的特征跟踪技术有望替代当前需要人工交互的流程，但由于星载计算和存储资源的限制，设计能部署在航天器上的深度架构极具挑战性。本文提出一种新型深度局部特征描述架构，通过采用二进制卷积神经网络层显著降低计算与存储需求。我们利用来自历史及在轨任务的小天体真实图像训练并测试模型，证明其性能优于传统人工设计方法。此外，我们还将模型部署至新一代航天器处理器模拟器上，验证了在线特征跟踪的可行运行时间。