Vision-based obstacle detection and avoidance for autonomous land vehicle navigation in outdoor roads

An effective approach to obstacle detection and avoidance for autonomous land vehicle (ALV) navigation in outdoor road environments using computer vision and image sequence techniques is proposed. To judge whether an object newly appearing in the image of the current cycle taken by the ALV is an obstacle, the object shape boundary is first extracted from the image. After the translation from the ALV location in the current cycle to that in the next cycle is estimated, the position of the object shape in the image of the next cycle is predicted, using coordinate transformation techniques based on the assumption that the height of the object is zero. The predicted object shape is then matched with the extracted shape of the object in the image of the next cycle to decide whether the object is an obstacle. We use a reasonable distance measure to compute the correlation measure between two shapes for shape matching. Finally, a safe navigation point is determined, and a turn angle is computed to guide the ALV toward the navigation point for obstacle avoidance. Successful navigation tests show that the proposed approach is effective for obstacle detection and avoidance in outdoor road environments.