Vision-based waypoint following using templates and artificial neural networks

This paper presents a learning-based vehicle control system capable of navigating autonomously. Our approach is based on image processing, road and navigable area recognition, template matching classification for navigation control, and trajectory selection based on GPS way-points. The vehicle follows a trajectory defined by GPS points avoiding obstacles using a single monocular camera and maintaining the vehicle in the road lane. Different parts of the image, obtained from the camera, are classified into navigable and non-navigable regions of the environment using neural networks. They provide steering and velocity control to the vehicle. Several experimental tests have been carried out under different environmental conditions to evaluate the proposed techniques.