Deep Neural Network-Based Landmark Selection Method for Optical Navigation on Lunar Highlands

Abstract

Spacecraft that rely on self-localization based on optical terrain images require suitable landmark information along their flight paths. When navigating within the vicinity of the moon, a lunar crater is an intuitive choice. However, in highland areas or regions having low solar altitudes, craters are less reliable because of heavy shadowing, which results in infrequent and unpredictable crater detections. This paper, therefore, presents a method for suggesting navigation landmarks that are usable, even with unfavorable illumination and rough terrain, and it provides a procedure for applying this method to a lunar flight plan. To determine a good landmark, a convolutional neural network (CNN)-based object detector is trained to distinguish likely landmark candidates under varying lighting geometries and to predict landmark detection probabilities along flight paths attributable to various dates. Dates having more favorable detection probabilities can be determined in advance, providing a useful tool for mission planning. Numerical experiments show that the proposed landmark detector generates usable navigation information at sun elevations of less than 1.8 & x00B0; in highland areas.