Robust vehicle detection using 3D Lidar under complex urban environment

Robust vehicle detection is one of the key task for autonomous vehicle under the complex urban environment. Using 3D Lidar, the difficulty of the task lies in that the appearance of a vehicle in the sparse range data changes greatly with the distance, the angle of view, as well as occlusions. In this paper we present a new algorithm to detect vehicles using the finely segmented 3D object points. In segmentation, RGLOS (Ring Gradient based Local Optimal Segmentation) algorithm is proposed. Instead of processing in the grid map, the point-wise segmentation method keeps the connection between points and is able to extract object points in far distance. Using the local optimal ground height, it produces much more correct object points with less wrong ground points. In feature extraction stage, three novel features: position-related shape, object height along length and reflective intensity histogram are proposed. Finally the kernel based SVM is used to finish the classification task. Experiments are carried out using the real data acquired from urban environment. The results demonstrate the superior performance comparing with previous methods, thanks to the improved segmentation and new features.