Research on the Efficient Suppression of Multiple Reflections in Laser Galvanometer Scanning Systems

The structured light 3D measurement technology based on laser galvanometer has been widely applied 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 during the measurement process and ensure the accuracy of the measurement information provided by vision, this paper proposes a binocular point cloud sensor with a laser galvanometer as the key projection module. Without adding hardware, it realizes two different modes of image projection to deal with complex measurement situations within the scene. Among them, the anti-multiple reflection projection mode proposed in this paper, 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 were 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 paper reaches 98.03%, an increase of 18.98% compared to the conventional measurement mode. It effectively solves the problem of point cloud loss in measurement scenarios with multiple reflection interference. The accuracy and completeness of the information obtained by visual measurement are guaranteed, and the six-axis compensation values obtained by the robot for each part pose state after teaching will be more accurate, ensuring that the previously taught robot trajectory can be accurately reused for the next pose, reducing the time for manual robot debugging and improving production efficiency.