  LIDAR is one of the most important sensors for Unmanned Ground Vehicles
(UGV). Object detection and classification based on lidar point cloud is a key
technology for UGV. In object detection and classification, the mutual
occlusion between neighboring objects is an important factor affecting the
accuracy. In this paper, we consider occlusion as an intrinsic property of the
point cloud data. We propose a novel approach that explicitly model the
occlusion. The occlusion property is then taken into account in the subsequent
classification step. We perform experiments on the KITTI dataset. Experimental
results indicate that by utilizing the occlusion property that we modeled, the
classifier obtains much better performance.
