Visual detection of novel terrain via two-class classification

Abstract

Remote sensing of terrain characteristics is an important component for autonomous operation of mobile robots in natural terrain. Often this involves classification of terrain into one of a set of a priori known terrain classes. Situations can frequently arise, however, where an autonomous robot encounters a terrain class that does not belong to one of these known classes. This paper proposes an approach for visual detection of novel terrain based on a two-class support vector machine (SVM) for situations when known terrain classes can be confidently associated with only a subset of the training data. Experimental results from a four-wheeled mobile robot in Mars analog terrain demonstrate the effectiveness of this approach.