铁路场景三维点云分割与分类识别算法

铁路限界侵入检测对保障高速铁路安全具有重要意义,基于激光三维点云分割与分类识别的异物侵入检测具有准确、直观的优点,在诸如隧道口和站台的铁路重点区域监测中具有广泛应用前景。设计了一种带动二维激光雷达进行俯仰运动的装置用于铁路三维点云的采集,基于法线方向一致性原则提出采用区域生长分割算法解决欧氏聚类分割和随机采样一致性(RANSAC)分割造成的过分割和欠分割问题;针对分割后的单物体点云,提出利用视点特征直方图(VFH)进行不同目标的三维点云特征提取,基于不同物体VFH建立KD树,并利用最近点搜索方法完成单物体点云分类识别。铁路场景典型物体的分类实验结果表明,本算法对铁路场景典型物体的分类识别准确率大于90%。

3D point cloud segmentation, classification and recognition algorithm of railway scene

Railway clearance intrusion detection is critical to the safety of high speed railway. The foreign object intrusion detection based on 3D laser point cloud segmentation, classification and recognition has the merits of accuracy and intuition, and has broad application prospects in the monitoring of railway key regions such as tunnel entrance and platform. In this paper, an equipment is designed, which drives the 2D laser radar to implement pitching movement and acquires the 3D point cloud of railway scene. Based on the normal consistency principle, the region growing segmentation algorithm is proposed to solve the over segmentation and under segmentation problems caused by Euclidean cluster segmentation and RANSAC segmentation methods. Aiming at the segmented single object point cloud, the Viewpoint Feature Histogram (VFH) is used to extract the 3D point cloud features of different objects; then, based on the VFHs of different objects k dimensional (KD) tree is built, and the closest point searching algorithm is adopted to achieve the classification recognition of single object point cloud. The result of the classification experiment on the typical objects in railway scene shows that the classification recognition accuracy of the proposed algorithm for the typical objects in railway scene is higher than 90%.