All-terrain estimation for mobile robots in precision agriculture

This paper presents a novel multi-sensor terrain classification approach using visual and proprioceptive data, to support autonomous operations by an agricultural vehicle. The novelty of the proposed method lies in the possibility to identify the terrain type relying not only on classical appearance-based features, such as color and geometric properties, but also on contact-based features, which measure the dynamic effects related to the vehicle-terrain interaction and directly affect vehicle's mobility. Using methods from the machine learning community, it is shown that it is not only possible to classify various kinds of terrain using either sensor modality, but that these modalities are complementary to each other, and can be therefore combined to improve classification results.