2026-09-19T12:56:24Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1270652021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Jonathan P. How and Theodore J. Steiner.
Downes, Lena(Lena Marie)
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics.
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2020-09-03T17:45:13Z
2020-09-03T17:45:13Z
2020
2020
https://hdl.handle.net/1721.1/127065
1191819083
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 91-95).
Terrain relative navigation can improve the precision of a spacecraft's state estimate by providing supplementary measurements to correct for drift in the inertial navigation system. This thesis presents a crater detector, LunaNet, that uses a convolutional neural network and image processing methods to detect craters from imagery taken by a spacecraft's on-board camera. These detections are matched with known lunar craters, and these matches can be used as features that are input to a extended Kalman filter. Our results show that, on average, LunaNet detects approximately twice the number of craters in an intensity image as two other successful intensity image-based crater detectors, and detects more accurate crater centers and diameters than the other two detectors as well. One of the challenges of using cameras for this task is that they can generate imagery with differences in image qualities and noise levels. These differences can occur for reasons such as changes in irradiance of the lunar surface, heating of camera electronic elements, or the inherent fluctuation of discrete photons. These image noise effects are difficult to compensate for, making it important for a crater detector to be robust to noise. When trained on diverse data, convolutional neural networks are able to generalize over varied imagery. Similarly, LunaNet is shown to be robust to four types of image manipulation that result in changes to image qualities and noise levels of the input imagery. LunaNet also produces more repeatable crater detections from frame to frame throughout a trajectory, and that enables more reliable state estimation over a trajectory. A LunaNet-based EKF experiences fewer spikes in estimation error and has lower average estimation error than EKFs using other successful crater detectors.
by Lena Downes.
S.M.
S.M. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics
95 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Aeronautics and Astronautics.
Lunar orbiter state estimation using neural network-based crater detection
Thesis
application/pdf
2020-09-03T17:45:13Z
Publication
Master
Aero
unknown
unknown
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<Abstract>Terrain relative navigation can improve the precision of a spacecraft's state estimate by providing supplementary measurements to correct for drift in the inertial navigation system. This thesis presents a crater detector, LunaNet, that uses a convolutional neural network and image processing methods to detect craters from imagery taken by a spacecraft's on-board camera. These detections are matched with known lunar craters, and these matches can be used as features that are input to a extended Kalman filter. Our results show that, on average, LunaNet detects approximately twice the number of craters in an intensity image as two other successful intensity image-based crater detectors, and detects more accurate crater centers and diameters than the other two detectors as well. One of the challenges of using cameras for this task is that they can generate imagery with differences in image qualities and noise levels. These differences can occur for reasons such as changes in irradiance of the lunar surface, heating of camera electronic elements, or the inherent fluctuation of discrete photons. These image noise effects are difficult to compensate for, making it important for a crater detector to be robust to noise. When trained on diverse data, convolutional neural networks are able to generalize over varied imagery. Similarly, LunaNet is shown to be robust to four types of image manipulation that result in changes to image qualities and noise levels of the input imagery. LunaNet also produces more repeatable crater detections from frame to frame throughout a trajectory, and that enables more reliable state estimation over a trajectory. A LunaNet-based EKF experiences fewer spikes in estimation error and has lower average estimation error than EKFs using other successful crater detectors.</Abstract>
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