Posterior probability estimation techniques embedded in a bayes filter for vibration-based terrain classification

Abstract

Vibration signals acquired during robot traversal provide enough information to yield a reliable prediction of the current terrain type. In a recent approach, we combined a history of terrain class estimates into a final prediction. We therefore adopted a Bayes filter taking the posterior probability of each prediction into account. Posterior probability estimates, however, were derived from support vector machines only, disregarding the capability of other classification techniques to provide these estimates. This paper considers other classifiers to be embedded into our Bayes filter terrain prediction scheme, each featuring different characteristics. We show that the best classification results are obtained using a combined k-nearestneighbor and support vector machine approach which has not been considered for terrain classification so far. Furthermore, we demonstrate that other classification techniques also benefit from the temporal filtering of terrain class predictions.