Real-time road surface mapping using stereo matching, v-disparity and machine learning

We present and evaluate a computer vision approach for real-time mapping of traversable road surfaces ahead of an autonomous vehicle that relies only on a stereo camera. Our system first determines the camera position with respect to the ground plane using stereo vision algorithms and probabilistic methods, and then reprojects the camera raw image to a bidimensional grid map that represents the ground plane in world coordinates. After that, it generates a road surface grid map from the bidimensional grid map using an online trained pixel classifier based on mixture of Gaussians. Finally, to build a high quality map, each road surface grid map is integrated to a probabilistic bidimensional grid map using a binary Bayes filter for estimating the occupancy probability of each grid cell. We evaluated the performance of our approach for road surface mapping in comparison to manually classified images. Our experimental results show that our approach is able to correctly map regions at 50 m ahead of an autonomous vehicle, with True Positive Rate (TPR) of 90.32% for regions between 20 and 35 m ahead and False Positive Rate (FPR) not superior to 4.23% for any range.