Vibration-based Terrain Classification Using Support Vector Machines

In outdoor environments, there is a variety of different types of ground surfaces. If some of them are slippery or bumpy, for example, the ground surface itself is a possible hazard for an autonomous mobile vehicle traversing the surface. Therefore, it is beneficial if the vehicle is able to estimate, which terrain it is currently traversing. Using this estimation, the vehicle can adapt its driving style to the terrain. In this paper, we present a method for terrain classification based on vibration induced in the vehicle's body. An accelerometer mounted on the vehicle measures the vibration perpendicular to the ground surface. We experimentally compare representations of the data based on the fast Fourier transform (FFT) and on the power spectral density (PSD). Additionally, we suggest a simpler and more compact representation based on features calculated from the raw data vectors and a combination of this representation with the PSD. We train and classify the data with a support vector machine (SVM). Experiments on a large real-world dataset containing seven different terrain types evaluate our approach