A hybrid point cloud alignment method combining particle swarm optimization and iterative closest point method

• 作者: Quan Yu Kesheng Wang
• 来源期刊: 先进制造进展(英文版)
• 学科: 工学
• 关键词: 粒子群算法 迭代最近点 对准方法 点云 混合 质量检验 CAD模型 检验自动化
• 年，卷（期）: 2014,（1）
• 页码范围: 32-38
• 页数: 7页
• 分类号: TP391.41
• 语种: 中文

摘要

3D quality inspection is widely applied in many industrial fields including mould design, automotive and blade manufacturing, etc. A commonly used method is to obtain the point cloud of the inspected object and make a comparison between the point cloud and the corresponding CAD model or template. Thus, it is important to align the point cloud with the template first and foremost. Moreover, for the purpose of automatization of quality inspection, this alignment process is expected to be completed without manual interference. In this paper, we propose to combine the particle swarm optimization (PSO) with iterative closest point (ICP) algorithm to achieve the automated point cloud alignment. The combination of the two algorithms can achieve a balance between the alignment speed and accuracy, and avoid the local optimal caused by bad initial position of the point cloud.