Adaptive Range Sampling Using a Stochastic Model

Abstract

We consider the task of sequential point sampling for three-dimensional structure reconstruction and focus on terrestrial topographic mapping using a laser range scanner. Both the sampling and the reconstruction rely on a stochastic model of the sampled object. We describe several algorithms for sequential point sampling including a new adaptive algorithm that is specifically designed for mechanical devices and produces grid-like sampling patterns. Experimental results verify that relying on the stochastic model indeed leads to efficient sampling associated with accurate surface reconstruction.