基于激光雷达回波信号的自适应阈值车道线检测  

Lane Marking Detection of Adaptive Threshold Based on Echo Signal of Lidar

作　　者：吴毅华[1,2] 梁华为[2] 王智灵[2] 梅涛[2] 辛煜[1,2] 黄如林[1,2] 

机构地区：[1]中国科学技术大学自动化系,安徽合肥230061 [2]中国科学院合肥物质科学研究院应用技术研究所,安徽合肥230088

出　　处：《机器人》2015年第4期451-458,共8页Robot

基　　金：国家自然科学基金资助项目(61005091,91120307,91320301)

摘　　要：基于激光雷达的车道线检测目前使用最多的是基于雷达扫描点密度的检测方法,但它的抗干扰能力差.为此,本文利用激光雷达的回波脉冲宽度对于车道线与路面的区分度进行特征提取,提出一种特征提取方法,分两步进行——基于脉冲宽度动态阈值的种子点提取和基于高斯核加权搜索的区域生长.然后引入FCL(fuzzy C-means of line)算法识别车道线(以线为中心进行聚类),最后通过最小二乘法拟合车道线.通过实车在6个不同的道路场景下进行实验,都能够准确检测出车道线,同时具有较高的检测精度.The method of lidar scanning point density is widely applied to lane detection based on lidar, but it has poor anti-interference performance. As an improvement, the echo pulse width of lidar is used to extract features because of its good differentiation between road surface and lane. What＇s more, a feature extraction method is proposed which contains 2 steps： the seed points extraction based on dynamic threshold of echo pulse width, and the region growth based on Gaussian kernel weighting searching. Then, fuzzy C-means of line （FCL） algorithm clustering based on line character （based on the center of the line） is introduced to identify the lane markings. Finally, least square method is used to fit lane markings. The method is tested on an unmanned vehicle application platform under six different road conditions, and lanes are all detected with high detection precision.

关 键 词：回波脉冲宽度 车道线检测 动态阈值 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]