高级检索

面向大飞机外形测量的渐进式全局点云配准方法

A Progressive Global Point Cloud Registration Method for Large Aircraft Exterior Measurement

  • 摘要: 为满足大飞机整机外形数字化检测对高精度与高效率的双重要求,本文面向整机外形的点云获取与全局配准开局研究;首先设计柔性组合式测量装备并采用分区扫描获取整机高密度点云,然后提出三阶段渐进式全局点云配准方法,依次完成粗配准、Predator++细配准与ICP精配准,最后在大飞机实测数据及3DMatch、3DLoMatch数据集上验证有效性。实验结果表明,多视角配准MRE为1.71°,MTE为0.12 mm,RR为98.1%,平均耗时1.5 h;3DMatch上IR、FMR和RR分别为64.6%、98.4%和90.0%,较SMVR分别提高4.1、0.2和0.4个百分点;3DLoMatch上IR、FMR和RR分别为30.3%、84.1%和68.2%,RR较SMVR提高0.3个百分点。

     

    Abstract: Large aircraft impose stringent requirements on performance, structural stability, and reliability, and high-precision 3D measurement of the complete aircraft exterior is critical for manufacturing quality and assembly accuracy. To address insufficient flexibility, automation, and global registration accuracy, a flexi-ble modular high-precision automated 3D measurement method is presented. The method first acquires dense point-cloud data through partitioned scanning; then performs three-stage progressive registration, including coarse alignment, Predator++-based fine registration, and ICP refinement; finally, it is evaluated on real aircraft data and the 3DMatch and 3DLoMatch benchmarks. Multiview experiments achieve an MRE of 1.71°, an MTE of 0.12 mm, an RR of 98.1%, and an average runtime of 1.5 h; pairwise registration on 3DMatch reaches IR/FMR/RR values of 64.6%, 98.4%, and 90.0%, improving over SMVR by 4.1, 0.2, and 0.4 percentage points; on 3DLoMatch, IR/FMR/RR reach 30.3%, 84.1%, and 68.2%, with RR improving over SMVR by 0.3 percentage points.

     

/

返回文章
返回