测绘通报 ›› 2024, Vol. 0 ›› Issue (7): 1-5.doi: 10.13474/j.cnki.11-2246.2024.0701

• 导航定位研究 •    下一篇

一种毫米波雷达里程计自主定位技术

吕轩凡, 柳景斌, 毛井锋   

  1. 武汉大学测绘遥感信息工程国家重点实验室, 湖北 武汉 430079
  • 收稿日期:2023-11-10 发布日期:2024-08-02
  • 作者简介:吕轩凡(1999—),男,硕士生,研究方向为点云数据处理和SLAM。E-mail:lxf0626@whu.edu.cn
  • 基金资助:
    南方电网公司科技项目(GDKJXM20220188);武汉大学测绘遥感信息工程国家重点实验室自主科研课题;湖北省自然科学基金(2024AFD403);武汉市知识创新专项基础研究项目(2022010801010109);2022年度人工智能创新专项(2023010402040029);深圳市科技计划(JCYJ20210324123611032)

A millimeter-wave radar odometry autonomous localization technique

LÜ Xuanfan, LIU Jingbin, MAO Jingfeng   

  1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • Received:2023-11-10 Published:2024-08-02

摘要: 本文通过研究毫米波雷达的数据基本特性,提出了一种毫米波雷达里程计自主定位技术。该方法通过特征匹配的方式,对毫米波雷达图像提取AKAZE特征点,并基于雷达图像自身所具备的几何先验信息,提出了3种匹配约束条件,从而大大降低了特征点误匹配率,保证了传感器位姿和里程计解算的可靠性。在牛津雷达机器人数据集上的试验结果表明,本文方法在不引入局部建图和回环检测等优化策略的情况下,水平位移相对定位误差和航向角误差可分别降低至6.8%和0.89(°)/m,单帧的定位时间仅为0.081 s,为恶劣环境下的室内外导航定位提供了新的思路。

关键词: 雷达里程计, 自动驾驶, 毫米波雷达数据处理, 特征匹配, 几何先验约束

Abstract: By studying the fundamental characteristics of millimeter-wave radar data,we propose a millimeter-wave radar odometry autonomous localization technique.Our approach extracts AKAZE feature points from millimeter-wave radar images through feature matching.And we introduce three matching constraint conditions based on the geometric priors inherent in radar images,which significantly reduces feature point mismatches,ensuring the reliability of sensor pose and odometry calculation.Experimental results on the Oxford radar robot dataset show that our method achieves a relative positioning error of 6.8% for horizontal displacement and 0.89 degrees per meter for heading angle without the local mapping and loop closure detection optimization strategies,and the single-frame positioning time is only 0.081 s,which provides a new idea for indoor and outdoor navigation and positioning in harsh environments.

Key words: radar odometry, automatic driving, millimeter-wave radar data processing, feature matching, geometric prior constraints

中图分类号: