Research on 3D Point Cloud Pose Estimation Technology for Automotive Sheet Metal Parts
In response to the problem of traditional 3D point cloud pose estimation for automotive sheet metal parts,this study proposes the use of the Random Sampling Consistency Registration Algorithm(SAC-IA algo-rithm)for point cloud coarse registration.After obtaining a more accurate initial pose,a Multidimensional Bina-ry Search Tree(K-D tree)is used to improve the Iterative Nearest Point Algorithm(IPC algorithm),achieving precise point cloud registration and obtaining a more accurate point cloud registration scheme,which is used for 3D point cloud pose estimation of automotive sheet metal parts.The experimental results show that this research method has better comprehensive registration performance compared to other registration methods,with a practi-cal application position error of less than 4mm,an angle error of less than 4.5°,and a calculation time of less than 6 seconds,which can meet engineering requirements.
Automotive sheet metal parts3D point cloudPose estimation technology
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