Lidar-based Object Classification with Explicit Occlusion Modeling
This addresses occlusion issues in object classification for unmanned ground vehicles, representing an incremental improvement.
The paper tackled the problem of object classification in lidar point clouds by explicitly modeling occlusion as an intrinsic property, resulting in significantly improved performance on the KITTI dataset.
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.