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.

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)