Range Synthesis for 3D Environment Modeling

Abstract

This paper examines a novel method we have developed for computing range
data in the context of mobile robotics.  Our objective is to compute dense 
range maps of locations in the environment, but to do this using intensity 
images and very limited range data as input.
We develop a statistical learning method for inferring and extrapolating
range data from a combination of a single video intensity image and a limited
amount of input range data. Our methodology is to compute the relationship 
between the observed range data and the variations in the intensity image,
and use this to extrapolate new range values. These variations can be 
efficiently captured by the neighborhood system of a Markov Random Field (MRF)
without making any strong assumptions about the kind of surfaces in the
world. Experimental results show the feasibility of our method.