Study On 3D Point Cloud Data Detection System For Workpieces In Wood Coating Industry

The processing of wood has always been of great concern to the people.From the previous manual processing methods to the numerical control processing methods,this greatly improved people’s processing level has promoted the economic development.The process of wood spraying is important in the entire production process of panel furniture.However,in many domestic furniture industries,artificial hand-held spray guns are still used to achieve this,resulting in poor production environment throughout the entire production process,low productivity,lack of an effective production industry standard,and the country’s high pollution levels are now high.The extreme concern with the dangerous types of work has caused the entire industry to urgently upgrade.Researchers at home and abroad are now fully implementing smart spraying equipment to automate the entire wood spraying process.In today’s era,machine vision technology has been well developed.Therefore,the use of its advantages of real-time,non-contact,visualization,and automation in the wood spraying industry is a key technology for obtaining three-dimensional point clouds on the surface of wood.Through the acquired3 D point cloud information of the wood,the key position information of the paper is provided for the entire intelligent device behind.This makes the wood-spraying industrial production environment environmentally friendly,safe,efficient and convenient.With the rise of optoelectronic technology,image detection technology,and computer vision,the non-contact optical measurement technology has developed rapidly.This method is also considered to be the most popular because of its advantages such as high resolution,no damage,and fast data acquisition.Future measurement methods.In this paper,this method is used when obtaining the 3D point cloud information of the wood surface.The Lord’s main work is as follows:(1)Based on the analysis of the status quo of the wood spraying industry,to reduce the harm of paint to human body and improve the production efficiency,a proposal was made to achieve intelligent spraying equipment by acquiring information on the surface of the wood through a non-contact visual 3D point cloud,and the 3D pointof the wood was designed.The cloud hardware platform system includes a visual sensor section and a mechanical movement section.(2)In order to realize the analysis and calculation of three-dimensional point cloud,a mathematical model of the entire wood three-dimensional point cloud measurement system was established based on a camera built by a laboratory,a shooting system composed of a line laser,and a point cloud acquisition system of a one-dimensional linear translation stage mobile system.(3)In order to improve the detection accuracy and accuracy,after establishing the mathematical model of the entire wood 3D point cloud system,two of them need parameter calibration.The first part is the parameter calibration of the entire camera sensor,and the second part is the calibration of the line laser plane.This chapter through the design of experiments to complete the parameter calibration and analysis of these two parts,compared and analyzed the advantages and disadvantages of the use of MATLAB-based and Opencv calibration,found that combining the advantages of the two calibration results better calibration results.(4)In order to improve the accuracy of the point cloud data acquisition system,based on the analysis and comparison of existing algorithms,the Steger algorithm was used to extract the center of the sub-pixel of the light bar,and the change in the width of the laser line due to the Steger algorithm was introduced.The accuracy of the extraction problem,the use of real-time calculation of the line width method to improve the Steger algorithm,and experimental testing and analysis verify the effectiveness of the algorithm.(5)Based on the experimental system design,the 3D point cloud data collection is realized,and combined with the algorithm of(4),the 3D point cloud data is extracted for outline,which provides basic data for the later visual-based 3D workpiece retrieval.