Date of Award:
Master of Science (MS)
Mechanical and Aerospace Engineering
With NASA awarding numerous contracts to build commercial lunar payload spacecraft and human lunar landers, the need for high precision navigation has increased. Traditional inertial navigation alone is not sufficient to autonomously land a vehicle on hazardous lunar terrain. Terrain relative navigation (TRN) systems have been explored in previous research that exploit camera observations of known landmarks. Such approaches require the flight electronics to correctly match features of the observed landmarks to an onboard database, in the drastically varying lighting conditions of moon. This paper explores the performance of a TRN system that does not rely on apriori landmark identification, thus avoiding the challenges associated with landmark identification. The TRN considered in this research consists of a star tracker, terrain camera, and single point Lidar. The terrain camera employs a technique called Visual Odometry (VO), where opportunistic features are tracked across image pairs to produce a direction of motion measurement. The Lidar provides measurements of range to the intersection point of the Lidar on the lunar surface. Proper modeling of the Lidar measurement is challenging and depends on the altitude of the orbit and the underlying variations of the lunar terrain. This research develops a Lidar measurement model based on the line/plane intersection equation, applicable to low lunar orbits where the perceived curvature of the moon is low. The Lidar measurement model is validated in a Monte Carlo framework during a Lunar Descent Orbit with a powered descent at the end. The Monte Carlo simulation also demonstrates the use of an Extended Kalman Filter (EKF) on real lunar terrain. The sensitivity of navigation errors to the availability and timing of the Lidar measurements is assessed.
Hansen, Michael R., "Navigation Performance of Line/Plane Intersection Lidar Model in Conjunction with Opportunistic Feature Tracker" (2021). All Graduate Theses and Dissertations. 8117.
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