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° in highland areas.
Highlights
A landmark is a perceptually distinctive geographical feature of interest at a particular location and date [1]
We propose a landmark selection method based on deep learning
For the highland areas near the poles, the altitude of the sun is too low to enable the extraction of navigational information from a dark image, because the angle of the lunar obliquity to the ecliptic plane is less than 1.543◦
Summary
A landmark is a perceptually distinctive geographical feature of interest at a particular location and date [1]. Regions of permanent shadow created by rough terrain and low solar altitude around the poles, where cold traps with water ice are present, have been located and are considered to be suitable places for a lunar base with human habitation [13]–[16] Such areas can adversely affect onboard self-localization functions that require visual landmarks. The selection of highly rated landmarks among many, according to a flight plan, will make possible the use of optical navigation systems under the worst of conditions To obtain such landmarks, a massive dataset based on real lunar-surface data is used to train a DNN to maximize the discrimination between local areas of the moon.
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