  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.
