CUI Shuyang; WU Yuxiang; CAI Yudong; LI Xuan; SHAO Xiaopeng

doi:10.7498/aps.74.20250716

Abstract

The structured light 3D measurement technology based on laser galvanometer has been widely used in industrial scenarios such as robot grasping, handling, and loading/unloading in welding and assembly workshops. However, in actual measurement scenarios, there are complex measurement structures such as depressions, overlaps, and occlusions. Light is prone to multiple reflections between micro-faces, causing intensity information to be mixed within the micro-face area and ultimately resulting in point cloud loss in this measurement area. To address the issue of point cloud loss in complex structure areas in the measurement process and ensure the accuracy of the measurement information provided by vision, a binocular point cloud sensor with a laser galvanometer as the key projection module is proposed in this work. Without adding hardware, it realizes two different image projection modes to deal with complex measurement situations within the scene. Among them, the anti-multiple reflection projection mode proposed in this work, by regulating the timing coordination relationship between key components, completes the measurement of complex structure positions and solves the problem of point cloud loss caused by multiple reflection interference. Finally, multiple experiments are conducted in actual scenarios to verify the feasibility of the proposed strategy. The experimental results show that in measurement scenarios with multiple reflection interference, the integrity of the black part point cloud measured by the anti-multiple reflection projection mode proposed in this work reaches 98.03%, which is 18.98% higher than the traditional measurement mode. It effectively solves the problem of point cloud loss in measurement scenarios with multiple reflection interference. Visual measurements ensure the accuracy and completeness of the information obtained. The six-axis compensation values determined by the robot for each part’s pose state during teaching become more precise. This ensures that the previously taught robot trajectory can be accurately reused for subsequent poses, thereby reducing the time needed for manual robot debugging and enhancing production efficiency.

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Corresponding author: LI Xuan, lixuan@xidian.edu.cn

Funds: Project supported by the Hangzhou Regional Innovation and Development Joint Fund of Natural Science Foundation of Zhejiang Province, China (Grant No. LHZSZ25F010002).

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DownLoad: Full-Size Img PowerPoint

	左相机	右相机
相机内参	$ \left(\begin{array}{ccc}1685.17&0&1022.08\\0&1684.51&764.886\\ 0&0&1\end{array}\right) $	$ \left(\begin{array}{ccc}1689.73&0&1036.15\\ 0&1689.53&763.744\\ 0&0&1\end{array}\right) $
平均重投影误差	0.03 pixel	0.02 pixel
立体标定结果	$ \left(\begin{array}{cccc}0.93517&0.00656701&0.354139&-832.458\\ -0.00290955&0.999937&-0.0108592&2.80458\\ -0.354188&0.0912484&0.93513&155.423\\ 0&0&0&1\end{array}\right) $

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传感器	测量方式	球心距/mm	左球直径/mm	右球直径/mm
市面成熟传感器	—	909.53	101.71	101.66
本文搭建传感器	常规策略	909.70	101.44	101.70
本文搭建传感器	抗多重反射策略	909.46	101.84	100.96

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实验组数	常规策略重建点云数量	完整性/%	抗多重反射策略重建点云数量	完整性/%
1	2815	79.05	3491	98.03
2	2833	79.56	3516	98.74
3	2798	78.57	3482	97.78

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Vol. 74, Issue 19 - 2025-10-05

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