Classification of Binary Obstacle Terrain Based on 3D World Models for Unmanned Robots

Recently, the applications of unmanned robots are increasing in various fields including surveillance and reconnaissance, planet exploration and disaster relief. To perform their missions with success, the robots should be able to evaluate terrain's characteristics quantitatively and identify traversable regions to progress toward a goal using mounted sensors. Recently, the authors have proposed techniques that extract terrain information and analyze traversability for off-road navigation of an unmanned robot. In this paper, we examine the use of 3D world models(terrain maps) to classify obstacle and safe terrain for increasing the reliability of the proposed techniques. A world model is divided into several patches and each patch is classified as belonging either to an obstacle or a non-obstacle using three types of metrics. The effectiveness of the proposed method is verified on real terrain maps.