A combination of vision- and vibration-based terrain classification

For safe navigation in outdoor environments, a mobile robot should be able to estimate the type of the current and forthcoming terrain. Based on this estimation, the robot can decide if the terrain is safe and may be traversed securely, or if the terrain is potentially dangerous and must be avoided or traversed carefully. This paper presents a terrain classification approach which fuses terrain predictions based on image data with predictions made by a vibration-based method. Using color images, the robot classifies terrain in front of it. When the robot later traverses the classified area, it uses vibration data to verify its former prediction. Our experiments on 14 different terrain types show that by fusing both predictions, the classification rates are significantly larger than predictions based on data from a single sensor alone.