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A new approach to apply compressive sensing to LIDAR sensing

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.