计算机工程

• 开发研究与工程应用 • 上一篇    下一篇

基于激光雷达的舞蹈机器人室内行人跟踪方法

刘召1,宋立滨1,2,耿美晓1,于涛1,王增喜1,郭凯1   

  1. (1.清研华宇智能机器人(天津)有限责任公司,天津 300304; 2.清华大学 机械工程系,北京 100084)
  • 收稿日期:2016-05-27 出版日期:2017-06-15 发布日期:2017-06-15
  • 作者简介:刘召(1979—),男,博士,主研方向为机器人仿生学;宋立滨,博士;耿美晓、于涛、王增喜、郭凯,硕士。
  • 基金项目:
    国家自然科学基金(51005126)。

Indoor Pedestrian Tracking Method of Dancing Robot Based on Laser Radar

LIU Zhao  1,SONG Libin  1,2,GENG Meixiao  1,YU Tao  1,WANG Zengxi  1,GUO Kai  1   

  1. (1.Tsingyan Huayu Robot and Automation (Tianjin) Co.,Ltd.,Tianjin 300304,China;2.Department of Mechanical Engineering,Tsinghua University,Beijing 100084,China)
  • Received:2016-05-27 Online:2017-06-15 Published:2017-06-15

摘要: 为使舞蹈机器人根据行人的运动轨迹进行路径的动态规划,增强与人共处的能力,提出一种基于激光雷达的室内行人跟踪方法。获取激光原始数据并进行预处理,根据激光数据的分布特点对DBSCAN算法进行优化,实现激光数据的快速聚类,完成环境分割,给出基于类簇到激光雷达的距离及行人身体宽度的行人识别方法,并将行人簇的位置作为初始跟踪位置,将激光数据图形化显示,激光数据转换成视频数据,利用粒子滤波算法实现行人跟踪并实时绘制轨迹。实验结果表明,该方法能获得较好的行人识别以及跟踪效果,且实时性较强。

关键词: 激光雷达, 舞蹈机器人, DBSCAN算法, 行人识别, 粒子滤波, 行人跟踪

Abstract: To make the dancing robot plan a dynamic path based on the motion of pedestrians and enhance its ability of getting along with people,an indoor pedestrian tracking method based on laser radar is proposed.The source data collected by the laser is gotten and preprocessed.According to the distribution characteristics of laser data,the DBSCAN algorithm is optimized to cluster the data rapidly to realize the segmentation of the environmental information.A pedestrian recognition method is proposed based on the distance from cluster to laser radar and the pedestrian width.The position of the pedestrian cluster is used as the initial tracking position.The laser data is displayed graphically and converted into video data.The particle filtering algorithm is applied to track the pedestrian,and the trajectory is drawn at the same time.Experimental results show that the proposed method has good effect on pedestrian recognition and tracking.It also has a good real-time performance.

Key words: laser radar, dancing robot, DBSCAN algorithm, pedestrian recognition, particle filtering, pedestrian tracking

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