Terrain surface classification with a control mode update rule using a 2D laser stripe-based structured light sensor

It is necessary for autonomous ground vehicles operating on outdoor terrains to identify and adapt to different terrains in order to improve their mobility and safety. This work presents a classification scheme to identify outdoor terrains and an update rule to reduce the possibility of implementing control modes based on classification inaccuracies. A laser stripe-based structured light sensor, which has a laser and infrared camera component, is used to sense terrains directly in front of the vehicle (<1m). Use of this infrared vision system allows sensing at night unlike many previous vision-based approaches that rely on stand-alone cameras. Also, unlike many previous results the classification algorithm presented here does not rely on measures of color which are subject to illumination and weather conditions. Instead, the method presented here relies on spatial relationships which are captured in two quantities: spatial frequency and texture. The presented terrain classification scheme uses a probabilistic neural network classifier to exploit the spatial differences in four terrains: asphalt, grass, gravel and sand. This approach yields empirical results that report a greater than 97% classification accuracy when both spatial frequency and texture features are used. Color robustness and lighting robustness is then shown through additional experiments. Furthermore, instead of implementing control modes directly from the identified terrains, it is shown that the use of current and past terrain detections allows for the rejection of misclassifications with minimal effect on the rate at which a new control mode can be implemented.