Deep Learning Crater Detection for Lunar Terrain Relative Navigation

Terrain relative navigation can improve the precision of a spacecraft’s position estimate by providing supplementary measurements to correct for drift in the inertial navigation system. This paper presents a system, LunaNet, that uses a convolutional neural network to detect craters from camera imagery taken by an onboard camera. These detections are matched with known lunar craters, and these matches can be used as landmarks for localization. The motivation for generating such landmarks is to provide relative location measurements to a navigation filter, however the details of such a navigation filter are not explored within this work. Our results show that on average LunaNet detects approximately twice the number of craters in an intensity image as two other intensity image-based crater detectors. One of the challenges of cameras is that they can generate imagery with vastly different appearances depending …