Accurate position estimation is crucial for the successful implementation of future lunar landings using autonomous vehicles, especially in dangerous environments with sparse terrain features. In this paper, we propose a terrain relative navigation (TRN) algorithm combining our deep-learning crater detector, which was designed specifically for the NASA Crater Detection Challenge problem, and an Extended Kalman Filter (EKF). Our detector analyzes crater features from the monocular images acquired from orbit, and their matches with craters from a global database are identified via a Hungarian assignment approach followed by the consensus-based outliers removal method. The estimated measurements are then used to refine an EKF, where spacecraft pose estimation in the Lunar-Centered Lunar-Fixed (LCLF) frame of reference, augmented with altitude aiding information, constrains radial drift. The simulation results indicate that even if the spacecraft is off from its actual location up to 5 km, TRN could recover from this situation, achieving navigation error reduction to a few hundred meters. It should be noted that in order to maintain crater feature correspondences, it is important to match the image resolution and the scales within the scene to the detector training set distribution.

翻译：精确的位置估计对于未来使用自主飞行器实现月球着陆至关重要，尤其是在地形特征稀疏的危险环境中。本文提出了一种结合专门为美国国家航空航天局（NASA）撞击坑检测挑战问题设计的深度学习撞击坑检测器与扩展卡尔曼滤波器（EKF）的地形相对导航（TRN）算法。我们的检测器分析从轨道获取的单目图像中的撞击坑特征，并通过匈牙利分配方法以及基于共识的异常值去除方法，识别这些特征与全球数据库中的撞击坑之间的匹配关系。随后，估计得到的测量值被用于优化EKF，其中航天器在月球中心月固（LCLF）参考系中的位姿估计，结合高度辅助信息，约束了径向漂移。仿真结果表明，即使航天器偏离实际位置高达5公里，TRN也能从这种情况中恢复，将导航误差减小至几百米。需要注意的是，为保持撞击坑特征的对应关系，将图像分辨率和场景中的尺度与检测器训练集分布进行匹配至关重要。