Abstract: Road detection is always the key problem of re-searches on areas of unmanned ground vehicle and computer vision. A road detection method is proposed based on online learning and multi-sensor fusion. First of all, the Lidar point clouds are projected onto the images via the joint calibration of these two kinds of sensors. Then Simple Linear Iterative Clustering is used to segment images into many superpixles. Based on that, a multilayer online learning method is proposed, in which 2 Support Vector Machines are trained to detect the road. To be specific, the superpixel layer Support Vector Machine is used to detect road roughly, and the pixel layer Support Vector Machine is then trained to classify the edge pixels of the road areas, which is classified by the upper-layer Support Vector Machine. These 2 Support Vector Machines are updated online at each frame to be adapted to the changing environment. At last, some experiments are carried out on KITTI RAW dataset and an autonomous land vehicle, and the results show the effectiveness of proposed method. The main contributions of this work lie on as follows: 1) a multilayer learning model is proposed to detect road more robustly and accurately; 2) an online learning method is proposed which can be adapted to the changing environment.
Keywords: Road detection; data fusion; unmanned ground vehicle; online learning; image segmentation