基于点云错误纠正和全球校准优化的多视图3D激光扫描测量的精度提高
Optics express
|January 5, 2024
概括
这项研究引入了一种新的方法,以提高多摄像头3D激光扫描测量的精度. 通过优化全局校准和纠正点云错误,它显著提高了3D光学测量精度.
科学领域:
- 计量学和测量科学 计量学和测量科学
- 光学工程是指光学工程.
- 计算机视觉 计算机视觉
背景情况:
- 多摄像头3D激光扫描提供了改进的数据采集,但由于复杂的系统算法,在测量准确性方面面临挑战.
- 减少阻塞和增加3D数据收集是多视图系统的关键好处.
- 解决全局校准和错误校正对于高精度的多视图3D测量至关重要.
研究的目的:
- 提出和验证一种用于提高多视图3D激光扫描测量精度的新方法.
- 开发一个系统,同时校准摄像头,激光飞机和初始的全球转换.
- 通过点云校正和全局校准来建立点准确度的错误模型并优化测量.
主要方法:
- 设计了一个平面不对称的圆形网格目标,用于同时校准.
- 分析了激光平面位置的影响,并建模了新的数学错误因子.
- 为回归分析开发了一个反向传播 (BP) 神经网络错误模型.
- 通过点云校正和使用标准球体板的全球校准优化优化测量.
主要成果:
- 提出的方法有效地提高了多视图3D激光扫描探头的测量精度.
- 实验证明了使用标准球形板和平面目标的可靠性和易于实施.
- 整合点云错误校正和校准优化可以显著提高准确性.
结论:
- 开发的方法为提高多视图3D激光扫描精度提供了可靠和实用的解决方案.
- 同时校准和基于BP神经网络的错误建模是实现高精度的关键.
- 这种方法为3D测量提供了光学计量学的重大进步.
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