计算机科学 ›› 2018, Vol. 45 ›› Issue (9): 294-298.doi: 10.11896/j.issn.1002-137X.2018.09.049

• 图形图像与模式识别 • 上一篇    下一篇

一种基于3D激光雷达的实时道路边缘提取算法

李广敬1, 鲍泓1, 徐成1,2   

  1. 北京联合大学北京市信息服务工程重点实验室 北京1001011
    北京邮电大学信息网络中心 北京1008762
  • 收稿日期:2017-08-14 出版日期:2018-09-20 发布日期:2018-10-10
  • 通讯作者: 鲍 泓(1958-),男,博士,教授,主要研究方向为数字图像处理、无人车环境感知与决策、智能驾驶技术,E-mail:xxtbaohong@buu.edu.cn
  • 作者简介:李广敬(1991-),男,硕士生,主要研究方向为无人车环境感知;徐 成(1988-),博士生,主要研究方向为信息安全。
  • 基金资助:
    本文受国家自然科学基金重大研究计划项目:智能车驾驶脑认知技术、平台与转化研究(91420202),Newton Fund Project:Talents Cultivation and Cooperation Oriented to Intelligent Vehicle Industrialization(UK-CIAPP\324)资助。

Real-time Road Edge Extraction Algorithm Based on 3D-Lidar

LI Guang-jing1, BAO Hong1, XU Cheng1,2   

  1. Beijing Key Laboratory of Information Service Engineering,Beijing Union University,Beijing 100101,China1
    Information Network Center,Beijing University of Posts and Telecommunications,Beijing 100876,China2
  • Received:2017-08-14 Online:2018-09-20 Published:2018-10-10

摘要: 无人驾驶车辆在道路中行驶时需要判定当前环境中的可行驶区域,针对这一问题,提出一种基于3D激光雷达的道路边缘实时提取算法。该算法首先在栅格化和分层处理后的激光雷达点云图中分别提取高度特征和平滑特征,以进一步通过道路宽度约束筛选得到候选边缘点,然后利用随机抽样一致性算法(RANSAC)对两侧路沿点进行多项式拟合,最后通过卡尔曼滤波对边缘点进行预测、跟踪。实验结果表明,该算法在园区场景和城市开放道路上都能实时、稳定地提取道路边缘,且此算法在“2017年世界智能驾驶挑战赛”中得到了成功应用。

关键词: 3D激光雷达, 道路边缘提取, 卡尔曼滤波, 随机抽样一致性算法, 无人驾驶车

Abstract: A real-time road edge extraction algorithm based on 3D-lidar was put forward for the environmental perception of driverless cars.In this algorithm,the height feature points and the smooth feature points are extracted separately in the maps rasterized and layered from the lidar points cloud followed by the constraint of the road width to obtain the candidate edge points.Then the candidate points are polynomial fitted by the algorithm of random sample consensus(RANSAC).Finally,Kalman filter is used to predict and track the road edge.The experimental results show that the proposed algorithm can extract the edge of road in real time and robustly in both park and urban roads.What’s more,this algorithm has been applied successfully in 2017 World Intelligent Driving Challenge.

Key words: 3D-Lidar, Driverless car, Kalman filter, Random sample consensus, Road edge extraction

中图分类号: 

  • TP242
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