上海交通大学学报

上海交通大学学报（自然版） ›› 2019, Vol. 53 ›› Issue (2): 203-208.doi: 10.16183/j.cnki.jsjtu.2019.02.011

• 学报（中文） • 上一篇    下一篇

基于地面快速鲁棒特征的智能车全局定位方法

胡兵,杨明,郭林栋,王春香,王冰   

  1. 上海交通大学 自动化系； 上海市北斗导航与位置服务重点实验室， 上海 200240
  • 出版日期:2019-02-28 发布日期:2019-02-28
  • 通讯作者: 杨明，教授，博士生导师，电话(Tel.):021-34204553；E-mail:mingyang@sjtu.edu.cn.
  • 作者简介:胡兵(1994-)，男，河南省南阳市人，硕士生，研究方向为视觉定位.
  • 基金资助:
    国家自然科学基金重大研究计划培育项目（91420101）

Global Localization for Intelligent Vehicles Using Ground SURF

HU Bing,YANG Ming,GUO Lindong,WANG Chunxiang,WANG Bing   

  1. Department of Automation; Shanghai Key Lab of Navigation and Location Services, Shanghai Jiao Tong University, Shanghai 200240, China
  • Online:2019-02-28 Published:2019-02-28

PDF (PC)

867

被引次数

6

摘要： 针对目前视觉定位方法大多基于地面语义特征(如车道线、停车线等)容易受到其他地面语义特征(如箭头、斑马线等)的影响,提出了一种基于地面快速鲁棒特征(SURF)点的全局定位方法.该方法首先在鸟瞰图中检测SURF点，结合高精度GPS构建地面SURF地图.然后在此基础上，使用迭代最近点算法，将在线检测结果与地图匹配获得车辆全局定位，并通过扩展卡尔曼滤波将定位结果与惯导和里程计数据进行融合，提高全局定位精度.实验结果表明，所提出的方法可获得分米级定位结果，能满足智能车的定位需求.

关键词: 快速鲁棒特征, 迭代最近点, 扩展卡尔曼滤波, 智能车定位

Abstract: Global localization is essential for intelligent vehicles as navigating on the urban road. Generally, visual localization methods are based on semantic landmarks such as lanes and stop-lines which are easily interfered by other semantic landmarks, such as arrows and zebra crossing. To solve the problem, a new global localization method using ground speeded up robust features (SURF) is proposed in this paper. Firstly, SURF extracted from bird-eye view images are fused with high-precision GPS data to create a priori map. Then, SURF extracted online are matched with the map to estimate the global localization using the iterative closest point (ICP) algorithm. Finally, the global localization is fused with other sensors data by the extented Kalman filter (EKF) for better accuracy. Experiment results show that localization can reach decimeter-level accuracy, which can meet the demand for intelligent vehicles.

Key words: speeded up robust features (SURF), iterative closest point (ICP), extended Kalman filter (EKF), localization for intelligent vehicle

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