Modeling lidar scene sparsity using compressive sensing

One of the major problems associated with LIDAR sensing is that significant amounts of data must be collected to obtain detailed topographical information about a region. Current efforts to solve this problem have focused on designing compression algorithms which operate on the collected data. These, however, require the collection of large amounts of data only to discard most of it in some transformed domain. Instead, compressive sensing has demonstrated that highly accurate signal reconstructions are achievable even when sampling below the Nyquist rate. Such sensing is clearly desirable for LIDAR range data compression if it can be achieved. One notes, however, that compressive sensing requires a priori knowledge of the sparsifying basis of the signal which is a major problem for LIDAR since that basis depends not only on the underlying scene complexity but also on the laser spot size and target distance. For these reasons, the goal of this research is to take the first steps in establishing a relationship between typical LIDAR scenes of varying complexity and the sparsity of the scene compressively sampled.