Feature Selection for Depth Estimation in 2D Images on an Autonomous Ground Vehicle

In this paper we investigate the problem of driving a small sized autonomous ground vehicle in unstructured outdoor environments using only a single camera input. The used method is based on depth perception that estimates the distances of the obstacles in front of the vehicle. We apply an offline dimensionality reduction to minimize the extracted data from the images to accomplish the navigation task in real time. The autonomous vehicle is able to traverse an outdoor path approximately 110 m succesfully.