Novelty Detection and Online Learning for Vibration-based Terrain Classification
Weiss Christian, Andreas Zell · IOS Press eBooks · 2008
In outdoor environments, a great variety of ground surfaces exists. To ensure safe navigation, a mobile robot should be able to identify the current terrain so that it can adapt its driving style. If the robot navigates in known environments, a terrain classification method can be trained on the expected terrain classes in advance. However, if the robot is to explore previously unseen areas, it may face terrain types that it has not been trained to recognize. In this paper, we present a vibration-based terrain classification system that uses novelty detection based on Gaussian mixture models to detect if the robot traverses an unknown terrain class. If the robot has collected a sufficient number of examples of the unknown class, the new terrain class is added to the classification model online. Our experiments show that the classification performance of the automatically learned model is only slightly worse than the performance of a classifier that knows all classes beforehand.