Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection
Name
2007.07702.pdf
Description
Accepted version
Size
915.7 KB
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Unknown
Checksum (MD5)
73973e0ebd91d9402c803bcf9aa0afe3
Author(s)
Downes, Lena(Lena Marie)
Steiner, Ted J
How, Jonathan P
Date Issued
July 2020
Journal
Proceedings of the American Control Conference
Publisher
IEEE
Citation
Downes, Lena M., Steiner, Ted J. and How, Jonathan P. 2020. "Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection." Proceedings of the American Control Conference, 2020-July.
Version
Author's final manuscript
Abstract
© 2020 AACC. Terrain relative navigation can improve the precision of a spacecraft's position estimate by detecting global features that act as supplementary measurements to correct for drift in the inertial navigation system. This paper presents a system that uses a convolutional neural network (CNN) and image processing methods to track the location of a simulated spacecraft with an extended Kalman filter (EKF). The CNN, called LunaNet, visually detects craters in the simulated camera frame and those detections are matched to known lunar craters in the region of the current estimated spacecraft position. These matched craters are treated as features that are tracked using the EKF. LunaNet enables more reliable position tracking over a simulated trajectory due to its greater robustness to changes in image brightness and more repeatable crater detections from frame to frame throughout a trajectory. LunaNet combined with an EKF produces a decrease of 60% in the average final position estimation error and a decrease of 25% in average final velocity estimation error compared to an EKF using an image processing-based crater detection method when tested on trajectories using images of standard brightness.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.23919/acc45564.2020.9147595
