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Computer Engineering ›› 2019, Vol. 45 ›› Issue (11): 315-320. doi: 10.19678/j.issn.1000-3428.0052375

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Kinect Point Cloud Registration for Vehicle Outline Based on Robotic Arm

WU Mengnan, LI Lihong   

  1. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030000, China
  • Received:2018-08-13 Revised:2018-11-20 Published:2018-11-30

基于机械臂的Kinect车辆轮廓点云配准

武梦楠, 李丽宏   

  1. 太原理工大学 电气与动力工程学院, 太原 030000
  • 作者简介:武梦楠(1993-),男,硕士研究生,主研方向为智能机器人、计算机视觉;李丽宏(通信作者),副教授。

Abstract: The 3D point cloud model of vehicle contour plays a key role in the intelligent manufacturing and maintenance of automobiles.In order to improve the accuracy and efficiency of point cloud registration,this paper taking the vehicle maintenance robot as the research object,proposes a vehicle outline scanning and positioning method based on point cloud data processing technology,as well as a date registration method for point cloud.Install a Kinect depth sensor at the end of the arm for precise movement.The point cloud data is collected and pre-processed around the car.The sensor sample pose is calculated according to the kinematic equation of the manipulator to complete the preliminary registration.On this basis,the iterative nearest point algorithm is used to implement precise point cloud registration for vehicle outline.Experimental results show that the method can complete the accurate and fast registration of cloud data from various viewpoints and obtain a complete 3D point cloud digital model.

Key words: point cloud of vehicle outline, depth sensor, iterative nearest point algorithm, point cloud processing, robotic arm

摘要: 车辆轮廓的三维点云模型在汽车智能化制造及维保过程中具有重要作用。为提高点云配准的精度和效率,以汽车维保机器人为研究对象,提出一种基于点云数据处理技术的车辆轮廓扫描定位及点云数据配准方法。在机械臂末端安装Kinect深度传感器实现精准移动,在汽车四周采集点云数据并进行预处理,根据机械臂运动学方程计算传感器采样位姿,完成初步配准。在此基础上,使用迭代最近点算法完成车辆轮廓点云的精确配准。实验结果表明,该方法可完成各视角点云数据的准确、快速配准,得到完整的三维点云数字模型。

关键词: 车辆轮廓点云, 深度传感器, 迭代最近点算法, 点云处理, 机械臂

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