High resolution visual terrain classification for outdoor robots

In this paper we investigate SURF features for visual terrain classification for outdoor mobile robots. The image is divided into a grid and SURF features are calculated on the intersections of this grid. These features are then used to train a classifier that can differentiate between different terrain classes. Images of five different terrain types are taken using a single camera mounted on a mobile outdoor robot. We further introduce another descriptor, which is a modified form of the dense Daisy descriptor. Random forests are used for classification on each descriptor. Classification results of SURF and Daisy descriptors are compared with the results from traditional texture descriptors like LBP, LTP and LATP. It is shown that SURF features perform better than other descriptors at higher resolutions. Daisy features, although not better than SURF features, also perform better than the three texture descriptors at high resolution.