A robust road profile estimation method for low texture stereo images

The estimation of road profiles from low texture stereo images is a problematic task because the disparity images computed from such class of images have a large number of noisy disparities. This paper presents a new method that is based on edge maps to guide the cost aggregation process in the stereo matching problem. Using the proposed aggregation method, the disparity images are smooth at low texture regions, but the boundaries of on-road objects are still preserved. The V-disparity images computed from such reliable disparity images can clearly show the road profiles. Thereby, the road profiles can be straightforwardly extracted by the dynamic programming technique. Experiments on a long and real stereo image sequence demonstrate that the proposed method can robustly estimate the road profiles. Furthermore, on-road objects can be detected by combining v- and u-disparity images as well because their boundaries are preserved in the disparity images.