Robust lane detection and tracking with ransac and Kalman filter

In a previous paper, a simple approach to lane detection using the Hough transform and iterated matched filters was described. This paper extends this work by incorporating an inverse perspective mapping to create a bird's-eye view of the road, applying random sample consensus to help eliminate outliers due to noise and artifacts in the road, and a Kalman filter to help smooth the output of the lane tracker.