A simple and efficient road detection algorithm for real time autonomous navigation based on monocular vision

Navigation of a Mobile Robot is based on its interaction with the environment, through information acquired by sensors. By incorporating several kinds of sensors in autonomous vehicles, it is possible to improve their autonomy and "intelligence", specially regarding Mobile Robot Navigation in unknown environment. The type and number of sensors determines the data volume necessary for the processing and composition of the image from the environment. Nevertheless, the excess of information imposes a great computational cost in data processing. Currently many applications for control of autonomous vehicles are being developed, for example, the Grand Challenge, world-wide championship organized by DARPA (Defense Advanced Research Projects Agency). Machine Vision is, together with a set of sensors, one of the tools used in the championship. In our work, we propose a Mobile Robot Navigation Software based in monocular vision. Our system is organized in hierarchic and independent layers, with distributed processing. After reading an image, the system processes, segments and identifies the navigation area (road detection), that will be used as input by a motion generator system. The considered system is based on images acquired by a single camera and uses simple filtering and thresholding techniques, which make it fast and efficient for real time navigation.