
Proceedings Paper
A new approach to apply compressive sensing to LIDAR sensingFormat | Member Price | Non-Member Price |
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Paper Abstract
Recently, Compressive Sensing (CS) has been successfully applied to multiple branches of science. However, most CS
methods require sequential capture of a large number of random data projections, which is not advantageous to LIDAR
systems, wherein reduction of 3D data sampling is desirable. In this paper, we introduce a new method called Resampling
Compressive Sensing (RCS) that can be applied to a single capture of a LIDAR point cloud to reconstruct a 3-
dimensional representation of the scene with a significant reduction in the required amount of data. Examples of 50 to
80% reduction in point count are shown for sample point cloud data. The proposed new CS method leads to a new data
collection paradigm that is general and different from traditional CS sensing such as the single-pixel camera architecture.
Paper Details
Date Published: 23 May 2014
PDF: 10 pages
Proc. SPIE 9109, Compressive Sensing III, 91090U (23 May 2014); doi: 10.1117/12.2058777
Published in SPIE Proceedings Vol. 9109:
Compressive Sensing III
Fauzia Ahmad, Editor(s)
PDF: 10 pages
Proc. SPIE 9109, Compressive Sensing III, 91090U (23 May 2014); doi: 10.1117/12.2058777
Show Author Affiliations
Richard C. Lau, Applied Communication Sciences (United States)
T. K. Woodward, Applied Communication Sciences (United States)
Published in SPIE Proceedings Vol. 9109:
Compressive Sensing III
Fauzia Ahmad, Editor(s)
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