Markov random field-based clustering of vibration data

A safe traversal of a mobile robot in an unknown environment requires the determination of local ground surface properties. As a first step, a broad structure of the underlying environment can be established by clustering terrain sections which exhibit similar features. In this work, we focus on an unsupervised learning approach to segment different terrain types according to the clustering of acquired vibration signals. Therefore, we present a Markov random field-based clustering approach taking the inherent temporal dependencies between consecutive measurements into account. The applied generative model assumes that the class labels of neighboring vibration segments are generated by prior distributions with similar parameters. A temporally constrained expectation maximization algorithm enables the efficient estimation of its parameters considering a predefined set of neighboring vibration segments. Since the size of the neighbor set proves to be data-dependent, we derive a general means of estimating this set size from the observed data. We show that the Markov random field clustering approach generates valid models for a variety of driving speeds even in situations of frequent terrain changes.