Visual segmentation of lawn grass for a mobile robotic lawnmower

Abstract

We investigate the existence of computationally inexpensive first and second order statistics that uniquely describe grass for application in an autonomous lawnmower. We then segment images based on these statistics to determine locations of driveable terrain in an image. Tight statistical clustering of illuminated grass versus artificial texture suggests that this method is sufficient for identifying driveable terrain for an autonomous lawnmower.