Road detection based on the fusion of Lidar and image data

In this article, we propose a road detection method based on the fusion of Lidar and image data under the framework of conditional random field. Firstly, Lidar point clouds are projected into the monocular images by cross calibration to get the sparse height images, and then we get high-resolution height images via a joint bilateral filter. Then, for all the training image pixels which have corresponding Lidar points, we extract their features from color image and Lidar point clouds, respectively, and use these features together with the location features to train an Adaboost classifier. After that, all the testing pixels are classified into road or non-road under a conditional random field framework. In this conditional random field framework, we use the scores computed from the Adaboost classifier as the unary potential and take the height value of each pixel and its color information into consideration together for the pairwise potential. Finally, experimental tests have been carried out on the KITTI Road data set, and the results show that our method performs well on this data set.