Camera-Pose Robust Crater Detection from Chang'e 5
This work addresses the need for robust vision-based navigation in hazardous terrain for space missions, providing the first quantitative analysis and dataset for off-nadir crater detection, though it is incremental in evaluating existing methods on new data.
The paper tackled the problem of crater detection from off-nadir view angles in lunar imagery, evaluating Mask R-CNN models and finding that pretraining on real-lunar images achieved a 63.1 F1-score for detection and 0.701 intersection over union for ellipse regression.
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.