A Statistical Learning Method for Mobile Robot Environment Modeling

This paper presents a statistical learning method for computing range data as an initial solution to the environment modeling problem in the context of mobile robotics. Unlike other methods that are based on a set of geometric primitives, our method computes dense range maps of locations in the environment using only intensity images and very limited amount of range data as an input. This is achieved by exploiting the following assumptions: 1) the observed range and intensity images are correlated and, 2) variations of pixels in the range and intensity images are related to the values elsewhere in the image(s). These variations can be efficiently captured by the neighborhood system of a Markov Random Field (MRF). Experimental results show the feasibility of our method.