关键词: 无人驾驶, 深度学习, 激光雷达, 目标检测, 三维点云

Abstract: Abstract: With rapid development of autonomous driving technology in automobile industry, Lidar based approaches for on-road obstacle detection play a more critical role than ever. Based on various point cloud data of on-road vehicles acquired by mobile Lidar, this paper proposes an on-road obstacle detection algorithm using deep learning. Firstly, the clustering algorithm is applied to filter and eliminate the outliers from the raw data, improving the roughness of point clouds. Then, an end-to-end trainable deep network is proposed to construct the object detection network, by optimizing RPN (region proposal network) and adding a max pooling layer. Experiments are conducted to show that the proposed approach significantly improves the accuracy of 3D-objects detection. For easy, moderate and hard detection tasks on orientation in the KITTI benchmark, the average precisions are improved by11.30%, 6.02% and 3.89%, respectively, compared with the VoxelNet.

Key words: autonomous vehicles, deep learning, mobile Lidar, 3D-object detection, point cloud