As space missions aim to explore increasingly hazardous terrain, accurate and timely position estimates are required to ensure safe navigation. Vision-based navigation achieves this goal through correlating impact craters visible through onboard imagery with a known database to estimate a craft's pose. However, existing literature has not sufficiently evaluated crater-detection algorithm (CDA) performance from imagery containing off-nadir view angles. In this work, we evaluate the performance of Mask R-CNN for crater detection, comparing models pretrained on simulated data containing off-nadir view angles and to pretraining on real-lunar images. We demonstrate pretraining on real-lunar images is superior despite the lack of images containing off-nadir view angles, achieving detection performance of 63.1 F1-score and ellipse-regression performance of 0.701 intersection over union. This work provides the first quantitative analysis of performance of CDAs on images containing off-nadir view angles. Towards the development of increasingly robust CDAs, we additionally provide the first annotated CDA dataset with off-nadir view angles from the Chang'e 5 Landing Camera.

翻译：随着太空任务旨在探索日益危险的地形，需要准确及时的位置估计以确保安全导航。基于视觉的导航通过将机载图像中可见的撞击坑与已知数据库相关联来估计航天器的姿态，从而实现这一目标。然而，现有文献尚未充分评估陨石坑检测算法在包含离天底点视角图像中的性能。在本工作中，我们评估了Mask R-CNN在陨石坑检测中的性能，比较了在包含离天底点视角的模拟数据上预训练的模型与在真实月球图像上预训练的模型。我们证明，尽管缺乏包含离天底点视角的图像，在真实月球图像上进行预训练仍具有优越性，其检测性能达到63.1的F1分数，椭圆回归性能达到0.701的交并比。本工作首次对陨石坑检测算法在包含离天底点视角图像上的性能进行了定量分析。为了开发日益鲁棒的陨石坑检测算法，我们还首次提供了来自嫦娥五号着陆相机、包含离天底点视角的带标注陨石坑检测数据集。