This paper presents an exploration of methods for estimating terrain trafficability from visual appearance. Two different
sets of data are used. The first set is extracted from video sequences and has a small number of different terrains. A fuzzy
c-means clustering algorithm is used to predict terrain type. The second set is derived from high-resolution still images
and has a large variety of terrains. A decision tree algorithm is used to provide a subjective assessment of trafficability.
A variety of local features are explored, based on color and texture, as input to the learning algorithms.