基于卷积神经网络的激光雷达点云目标分割

Convolutional Neural Nets for Real-time Road-object Segmentation from 3D LiDAR Point Cloud

对激光雷达点云进行道路对象的语义分割,尤其是对感兴趣实例(如汽车、行人和自行车)的检测与归类。将此问题明确表达为逐点分类问题,并以卷积神经网络(Convolutional Neural Nets,CNN)为基准网络设计网络结构,对预处理过的点云数据进行语义分割。具体地,CNN将转化后的雷达点云数据作为输入,直接输出逐点标号的预测图,再通过条件随机场(Conditional Random Field,CRF)对预测图进行完善。CNN模型在KITTI数据集产生的LiDAR点云上训练,逐点分割标签源自KITTI产生的3D边界框。实验表明,设计的网络结构基本满足自动驾驶所需要的高精确度和快处理速度(每帧13.8 ms左右)。

This paper performs semantic segmentation of road objects from 3 D LiDAR point cloud,especially the detection and categorization of interesting instances(such as cars,pedestrians,and bicycles).This problem is clearly expressed as a point-wise classification problem,and an end-to-end pipeline based on convolutional neural networks(CNN)is proposed:the CNN takes a transformed LiDAR point cloud as input and directly outputs a point-wise label map,which is then refined by a conditional random field(CRF),implemented as a recurrent layer.The CNN model is trained on LiDAR point clouds from the KITTI dataset,and point-wise segmentation labels are derived from 3 D bounding boxes from KITTI.The experiments results indicate that the pipeline achieves high accuracy with astonishingly fast and stable runtime(about 13.5 ms per frame),highly desirable for autonomous driving applications.