Vegetation versus man-made object detection from imagery for unmanned vehicles in off-road environments

There have been several major advances in autonomous navigation for unmanned ground vehicles in controlled urban environments in recent years. However, off-road environments still pose several perception and classification challenges. This paper addresses two of these challenges: detection and classification of vegetation vs. man-made objects. In order for a vehicle or remote operator to traverse cross-country terrain, automated decisions must be made about obstacles in the vehicle's path. The most common obstacle is vegetation, but some vegetation may be traversable, depending on the size of the vehicle and the type of vegetation. However, man­ made objects should generally be detected and avoided in navigation. We present recent research towards the goal of vegetation and man-made object detection in the visible spectrum. First, we look at a state-of-the-art approach to image segmentation and image saliency using natural scene statistics. Then we apply recent work in multi-class image labeling to several images taken from a small unmanned ground vehicle (UGV). This work will attempt to highlight the recent advances and challenges that lie ahead in the ultimate goal of vegetation and man-made object detection and classification in the visual spectrum from UGV.