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一种广泛适用的激光雷达数据特征提取方法
  • 摘要

    提出了一种新颖的、无需先验知识的、广泛适用于各种环境的激光雷达数据特征提取方法来解决同步定位与地图创建(SLAM)中的特征提取问题.这种方法采用经典的图像特征提取方法--Harris 角点探测器,具体来说,是多尺度Kanade-Tomasi角点探测器,来提取特征.这种方法可以从各种尺度的测量数据中提取稳定、精确的特征点,并同时可以得到特征点描述器和不确定性信息.文章将这种方法应用在了软件仿真环境及经典数据集上,包括:2维的维多利亚公园数据集、英特尔研究中心数据集(Intel Research Center dataset)以及3维的麻省理工学院美国国防部高级研究计划局城市竞赛数据集(MITDARPA Urban Challenge dataset).实验结果表明这种方法可以从各种环境中提取高精度、高重复性的稳定特征.

  • 作者

    李阳铭  孟庆虎  LI Yangming  MENG Max Q.-H. 

  • 作者单位

    中国科学院合肥智能机械研究所,安徽,合肥230031;中国科学技术大学信息科学与技术学院自动化系,安徽,合肥,230026;安徽省仿生感知与先进机器人技术重点实验室,安徽,合肥230031/中国科学院合肥智能机械研究所,安徽,合肥230031

  • 刊期

    2010年6期 ISTIC EI PKU

  • 关键词

    同步定位与地图创建  激光雷达  特征提取  角点探测器 

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