An EM algorithm for Lidar deconvolution
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yuan-matthewy-sm-orc-2021-thesis.pdf
Description
Thesis PDF
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5.36 MB
Format
Adobe PDF
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d0013221ab67e8a8d183dbd0356f4330
Author(s)
Yuan, Matthew
Advisor(s)
Jaillet, Patrick
Skelly, Luke
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
Abstract
Airborne Lidar is a range sensing method which is effective in determining ground terrain from a distance. However, the return signal we observe is a noisy, convolved distortion of the ground return. Deconvolution is one approach to restore the original ground return from the observed return signal. The expectation-maximization (EM) algorithm has been used in signal deconvolution, to produce a maximum-likelihood estimate (MLE) for the original signal. We explain the benefits of the EM algorithm over other benchmark algorithms in Lidar deconvolution, then propose a modified EM algorithm with smoothing and denoising parameters to address some issues with the standard EM algorithm. We then derive a quality metric to test the proposed EM algorithm on simulated and actual data and evaluate its performance. Using our quality metric on simulated data, the proposed algorithm recovers 95% of signal compared to 79% by the benchmark Richardson-Lucy (RL) algorithm, and we show improved image quality and reduced noise on real-life Lidar scenarios.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
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