Integrating visual and range data for road detection

This paper presents a new method for detecting drivable road surfaces in a single image. The method takes advantage of range and visual information so that reliable results are achieved. Specifically, given LIDAR data and an aligned image, it first makes use of 3D points to estimate the ground plane and determine the horizon. Then, subsets of road and obstacle points are extracted from the 3D points based on the plane and LIDAR properties. The pixels registered to the extracted points are used to build apriori road and non-road appearance models. The road detection problem is further formulated using Markov random field whose energy function is defined based on the learned models. Constraints are also added on the energy function to place high confidence on the pixels that are registered to extracted 3D points. Extensive experiments on urban roads and highways show that our method is robust even in complicated environments.