Visual detection of novel terrain via two-class classification

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. Categories and Subject Descriptors I.5.2 [Pattern Recognition]: Design Methodology – classifier design and analysis General Terms Algorithms, Experimentation, Theory Keywords Machine vision, robot sensing systems, terrain mapping, image classification.

• Type: Conference
• Year: 2009
• Where: SAC
• Authors: Christopher A. Brooks, Karl Iagnemma
• Keywords: Applied Computing, Priori Known Terrain, SAC 2009, Terrain, Terrain Classes