Recognition of traversable areas for mobile robotic navigation in outdoor environments

In this paper we consider the problem of automatically determining whether regions in an outdoor environment can be traversed by a mobile robot. We propose a two-level classifier that uses data from a single color image to make this determination. At the low level, we have implemented three classifiers based on color histograms, directional filters and local binary patterns. The outputs of these low level classifiers are combined using a voting scheme that weights the results of each classifier using an estimate of its error probability. We present results from a large number of trials using a database of representative images acquired in real outdoor environments.