计算机工程

• 开发研究与工程应用 • 上一篇    下一篇

基于激光点云的智能挖掘机目标识别

朱建新 1,沈东羽 1,吴钪 2   

  1. (1.中南大学 机电工程学院,长沙 410083; 2.山河智能装备股份有限公司,长沙 410100)
  • 收稿日期:2016-01-13 出版日期:2017-01-15 发布日期:2017-01-13
  • 作者简介:朱建新(1965—),男,教授、博士、博士生导师,主研方向为目标识别、机电一体化;沈东羽,硕士;吴钪,工程师、博士。
  • 基金项目:
    国家“十二五”科技支撑计划项目(2013BAF07B02)。

Target Recognition for Intelligent Excavator Based on Laser Point Cloud

ZHU Jianxin  1,SHEN Dongyu  1,WU Kang  2   

  1. (1.College of Mechanical and Electrical Engineering,Central South University,Changsha 410083,China; 2.Sunward Intelligent Equipment Co.,Ltd.,Changsha 410100,China)
  • Received:2016-01-13 Online:2017-01-15 Published:2017-01-13

摘要: 传统智能工程机械的环境目标识别方法为单目或双目视觉识别,识别速度慢、效率低且工况适应能力差。为进一步提升挖掘机的环境目标识别能力,提出一种基于点云聚类特征直方图的目标识别方法。对原始点云进行滤波预处理,通过聚类分离取得单个识别聚类,建立待识别聚类的点云特征直方图,在模型库中采用近邻搜索算法获得k个近邻,并根据其匹配度得到最终识别结果。实验结果表明,该方法针对挖掘机作业工况目标识别有较强的稳健性,能在复杂工况下识别出多个目标且识别率高。

关键词: 智能挖掘机, 激光扫描, 点云数据, 特征直方图, 目标识别

Abstract: Traditional intelligent engineering machinery uses monocular or binocular recognition for environmental target recognition,which is inefficient and has low recognition rate and poor adaptation to the environment.A target recognition method based on point cloud clustering feature histogram is proposed in order to improve the ability of environmental target recognition for the excavator.After processing the raw point cloud data by filtering algorithm,the data is split into several single clusters using clustering algorithm.Point cloud characteristics histogram of be recognited cluster is built.In the model library,k neighbor is got by using neighbor search algorithm,and the final recognition result is got according to matching degree.The experimental results show that the method works with strong robustness for target recognition of intelligent excavator.It can identify multiple targets in complex conditions and has high recognition rate.

Key words: intelligent excavator, laser scanning, point cloud data, feature histogram, target recognition

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