To increase autonomous ground vehicle (AGV) safety and efficiency on outdoor terrains
the control system should have settings for individual terrains. A first step in such a terrain-dependent control system is classification of the terrain upon which the AGV is traversing. This paper considers vision-based terrain classification for the path directly in front of the vehicle (< 1 m). Previous vision-based approaches to classifying traversable terrain have relied on stand-alone cameras, which due to their passive nature will not work in the dark. In contrast, this research uses a laser stripe-based structured light sensor, which uses a
laser in conjunction with a camera, and hence can work at night. Also, unlike previous results, the classification here does not rely on color since color changes with illumination and weather and certain terrains have multiple colors (e.g., sand may be red or white). Instead, it relies only on spatial relationships, specifically spatial frequency response and texture, which captures spatial relationships between different gray levels. Terrain classification using each of these features separately is conducted by using a probabilistic neural network. Experimental results based on classifying four outdoor terrains demonstrate the effectiveness of the proposed methods.
