Lost in low lunar orbit crater pattern detection and identification
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Recent emphasis by NASA on returning astronauts to the Moon has placed attention on the subject of lunar surface feature tracking. Although many algorithms have been proposed for lunar landmark tracking navigation, much less attention has been paid to the issue of navigational state initialization from lunar craters in a lost in low lunar orbit (LLO) scenario. A new crater detection and identification algorithm is developed in this dissertation that allows for navigation state initialization from as few as one image of the lunar surface with no a priori state knowledge. Craters are detected by a filter that is an extension of the Circular Hough Transform, after which verification is performed by a number of checks on the illuminated portion of the candidate crater interior. Detected craters are identified by matching them to entries in the USGS crater catalog via non-dimensional crater triangle parameters. False identifications are rejected based on a probability check. The algorithm was tested on Apollo 16 LLO images, and shown to perform well.