Random impulsive scan for lidar sampling.

In this paper, we address the problem of sampling the LIDAR range map. The significance of this problem is based upon the fact that large datasets generated by sampling inefficiently impose storage, processing and transmission limitations. Current compression approaches addressing these issues rely on collecting large amounts of data to analyze and throw away the redundancies. Unfortunately, the sampling performed by these approaches is still inefficient. Our approach to compression consists instead, on using a random uniform impulsive scan sampling scheme with sampling densities depending on the surface complexity. Turns out that the number of scanned time-resolved waveforms required to achieve “good” approximations, needs to satisfy the bound equation, where equation is an estimator of the surface numerical rank. Such a bound allows one to sub-sample surfaces which are close to a low-rank space and update, on the fly, the sampling densities required to improve the quality of the approximation. [ABSTRACT FROM PUBLISHER]