基于SHOT特征融合的散乱工件点云配准算法
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摘要
针对随机箱体抓取过程中目标识别和定位问题,提出一种基于SHOT特征融合的点云配准方法.对结构光三维测量获取的点云进行预处理和分割,得到去除噪声点后的多个工件点云数据集;提出基于方向包围盒裁剪的方法,得到去除稀疏边缘点后的工件点云,结合均匀采样算法获取关键点集;通过改进SHOT特征描述子对关键点进行唯一性描述;采用最小方差法查找工件点云的关键点在模板点云中的对应点,根据对应关系求解初始变换矩阵;最后,使用ICP算法进行精确配准,得到工件的精确位姿信息.实验结果表明,将本文算法与基于FPFH特征配准、SHOT特征配准算法进行对比,配准精度分别提高了30. 07%和37. 10%,配准速度分别提高了35. 64%和21. 21%.
Aiming at the problem of target recognition and location in the process of random bin picking,a point cloud registration method based on SHOT feature fusion is proposed. The point cloud obtained from the three-dimensional measurement of structured light is preprocessed and segmented to obtain the point cloud dataset of multiple workpieces. A newmethod based on the oriented bounding box is proposed to get the point cloud of the workpiece after removing the sparse edge points,and to obtain the key point set with the uniform sampling algorithm. The key points is described uniquely by improving the SHOT feature descriptor. The minimum variance method is used to find the corresponding points of the point cloud of the workpiece in the template point cloud,and the initial transformation matrix is solved according to the corresponding relation. Finally,the accurate alignment of the workpiece is obtained by using the ICP algorithm. The experimental results showthat the algorithm is compared with the algorithm based on SHOT feature registration and FPFH,the registration accuracy is increased by 30. 07% and 37. 1% respectively,and the registration speed is increased by 35. 64% and 21. 21% respectively.
Aiming at the problem of target recognition and location in the process of random bin picking,a point cloud registration method based on SHOT feature fusion is proposed. The point cloud obtained from the three-dimensional measurement of structured light is preprocessed and segmented to obtain the point cloud dataset of multiple workpieces. A newmethod based on the oriented bounding box is proposed to get the point cloud of the workpiece after removing the sparse edge points,and to obtain the key point set with the uniform sampling algorithm. The key points is described uniquely by improving the SHOT feature descriptor. The minimum variance method is used to find the corresponding points of the point cloud of the workpiece in the template point cloud,and the initial transformation matrix is solved according to the corresponding relation. Finally,the accurate alignment of the workpiece is obtained by using the ICP algorithm. The experimental results showthat the algorithm is compared with the algorithm based on SHOT feature registration and FPFH,the registration accuracy is increased by 30. 07% and 37. 1% respectively,and the registration speed is increased by 35. 64% and 21. 21% respectively.
引文
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[9]黄源,达飞鹏,陶海跻.一种基于特征提取的点云自动配准算法[J].中国激光,2015,42(3):242-248.
[10]柯科勇.基于双目视觉的散乱堆放工件拾取系统[D].广州:广东工业大学,2016.
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[15]石爱军,白瑞林,田青华.Gray码结合线移的结构光三维测量[J].光电工程,2016,43(11):26-32.
[2]Bielicki J,Sitnik R.A method of 3D object recognition and localization in a cloud of points[J].Eurasip Journal on Advances in Signal Processing,2013,29(1):1-13.
[3]Bogue Consultant R.Random bin picking:has its time finally come[J].Assembly Automation,2014,34(3):217-221.
[4]Cao J,Wushour S,Yao X,et al.Sharp feature extraction in point clouds[J].Iet Image Processing,2012,6(7):863-869.
[5]Zuo Li-ying.Research on automatic identification and location of scattered parts for robot picking[D].Harbin:Harbin Institute of Technology,2015.
[6]Tombari F,Salti S,Stefano L D.Unique signatures of histograms for local surface description[C].European Conference on Computer Vision Conference on Computer Vision,Springer-Verlag,2010:356-369.
[7]Salti S,Tombari F,Stefano L D.SHOT:unique signatures of histograms for surface and texture description[J].Computer Vision&Image Understanding,2014,125(8):251-264.
[8]Zhang Kai-lin,Zhang Liang.3D object recognition and 6DoF pose estimation in scenes w ith occlusions and clutter based on C-SHOT3D descriptor[J].Journal of Computer-Aided Design&Computer Graphics,2017,29(5):846-853.
[9]Huang Yuan,Da Fei-peng,Tao Hai-ji.An automatic registration algorithm for point cloud based on feature extraction[J].Chinese Journal of Lasers,2015,42(3):242-248.
[10]Ke Ke-yong.Scattered parts bin-picking system based on binocular vision[D].Guangzhou:Guangdong University of Technology,2016.
[11]Hoang D C,Chen L C,Nguyen T H.Sub-OBB based object recognition and localization algorithm using range images[J].M easurement Science&Technology,2017,28(2):1-11.
[12]Shah S A A,Bennamoun M,Boussaid F.Keypoints-based surface representation for 3D modeling and 3D object recognition[J].Pattern Recognition,2017,64:29-38.
[13]Zhu De-hai.Point cloud library PCL study tutorial[M].Beijing:Beijing University of Aeronautics and Astronautics Press,2012.
[14]Tian Qing-hua,Bai Rui-lin,Li Du.Point cloud segmentation of scattered workpiece based on improved euclidean clustering[J].Laser&Optoelectronics Progress,2017,54(12):121503-1-121503-9.
[15]Shi Ai-jun,Bai Rui-lin,Tian Qing-hau.3D measurement method based on structured light using a combination of gray code and lineshift fringe[J].Opto-Electronic Engineering,2016,43(11):26-32.
[5]佐立营.面向机器人抓取的散乱零件自动识别与定位技术研究[D].哈尔滨:哈尔滨工业大学,2015.
[8]张凯霖,张良.复杂场景下基于C-SHOT特征的3D物体识别与位姿估计[J].计算机辅助设计与图形学学报,2017,29(5):846-853.
[9]黄源,达飞鹏,陶海跻.一种基于特征提取的点云自动配准算法[J].中国激光,2015,42(3):242-248.
[10]柯科勇.基于双目视觉的散乱堆放工件拾取系统[D].广州:广东工业大学,2016.
[13]朱德海.点云库PCL学习教程[M].北京:北京航空航天大学出版社,2012.
[14]田青华,白瑞林,李杜.基于改进欧式聚类的散乱工件点云分割[J].激光与光电子学进展,2017,54(12):121503-1-121503-9.
[15]石爱军,白瑞林,田青华.Gray码结合线移的结构光三维测量[J].光电工程,2016,43(11):26-32.
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