研究基于RGB-D数据融合的曲面特征点的高精度定位方法
Enguo Wang1, Rui Zou1, Chengzhi Su2
1School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究引入了一种新的RGB-D融合方法,用于精确的工件定位. 它实现了分像素精度,显著改善了工业制造和检查流程.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 工业自动化 工业自动化
背景情况:
- RGB图像缺乏3D深度,而深度数据是杂和稀疏的.
- 传统的视觉定位方法的准确性低,计算成本高.
研究的目的:
- 使用RGB-D数据融合开发一种高精度的,分像素级定位方法,用于工件特征点.
- 为解决工业应用现有的视觉定位技术的局限性.
主要方法:
- 使用YOLOv10与背景错误检测过模块 (BMFM) 在RGB图像中进行特征点识别.
- 使用改进的Prewitt操作符和子像素精细化,提高了2D定位准确性.
- 将2D特征映射到3D点云,然后进行错误检测和四边形表面拟合以实现3D定位.
主要成果:
- 实现了0.17mm的平均绝对误差 (MAE),用于定位各种组件类型.
- 显示的最大定位误差小于0.22毫米.
- 验证了该方法适用于高精度工业应用的适用性.
结论:
- 拟议的RGB-D融合方法为工件定位提供了卓越的精度和效率.
- 这种技术符合精密制造和质量检查的严格要求.
- 副像素级本地化提高了自动化工业流程的可靠性.
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