Unstructured road recognition and following for mobile robots via image processing using Anns

Dilan, Rasim Aşkın
For an autonomous outdoor mobile robot ability to detect roads existing around is a vital capability. Unstructured roads are among the toughest challenges for a mobile robot both in terms of detection and navigation. Even though mobile robots use various sensors to interact with their environment, being a comparatively low-cost and rich source of information, potential of cameras should be fully utilized. This research aims to systematically investigate the potential use of streaming camera images in detecting unstructured roads. The investigation focused on the use of methods employing Artificial Neural Networks (ANNs). An exhaustive test process is followed where different kernel sizes and feature vectors are varied systematically where trainings are carried out via backpropagation in a feed-forward ANN. The thesis also claims a contribution in the creation of test data where truth images are created almost in realtime by making use of the dexterity of human hands. Various road profiles v ranging from human-made unstructured roads to trails are investigated. Output of ANNs indicating road regions is justified against the vanishing point computed in the scene and a heading vector is computed that is to keep the robot on the road. As a result, it is shown that, even though a robot cannot fully rely on camera images for heading computation as proposed, use of image based heading computation can provide a useful assistance to other sensors present on a mobile robot.