薛珊, 吕南方, 沈雨鹰, 刘正彬, 郭建波. 基于激光三维点云的机械工件识别方法[J]. 红外与激光工程, 2019, 48(4): 442002-0442002(8). DOI: 10.3788/IRLA201948.0442002
引用本文: 薛珊, 吕南方, 沈雨鹰, 刘正彬, 郭建波. 基于激光三维点云的机械工件识别方法[J]. 红外与激光工程, 2019, 48(4): 442002-0442002(8). DOI: 10.3788/IRLA201948.0442002
Xue Shan, Lv Nanfang, Shen Yuying, Liu Zhengbin, Guo Jianbo. Identification method for machine workpiece based on laser 3D point cloud[J]. Infrared and Laser Engineering, 2019, 48(4): 442002-0442002(8). DOI: 10.3788/IRLA201948.0442002
Citation: Xue Shan, Lv Nanfang, Shen Yuying, Liu Zhengbin, Guo Jianbo. Identification method for machine workpiece based on laser 3D point cloud[J]. Infrared and Laser Engineering, 2019, 48(4): 442002-0442002(8). DOI: 10.3788/IRLA201948.0442002

基于激光三维点云的机械工件识别方法

Identification method for machine workpiece based on laser 3D point cloud

  • 摘要: 随着中国制造2025的到来,运用工业机器人在线加工机械工件是大势所趋。为了能够智能抓取工件,工业机器人需要识别工件的类型以及工件的位姿。针对流水线上识别工件类型难的问题,提出了一种基于激光扫描三维点云的工件类型识别方法,该方法主要能够识别工件是哪种工件。首先对流水线上杂乱无序的工件进行激光扫描,得到工件的三维激光点云数据,将三维激光点云数据初步去噪。运用MATLAB软件对得到的三维激光点云进行中心切片,得到点云的主视切片、俯视切片、左视切片;运用HALCON软件对点云切片去噪、增强、分割,提取中心切片的边界信息并得到提取区域的特征参数,进而识别工件的类型。最后运用自主研发设备进行实验,分别以步距为0.05 mrad、测距精度0.2 mm、测角精度为0.02 mrad进行扫描,实验结果表明,识别准确性达96.67%。该方法对同类问题有较大的借鉴意义。

     

    Abstract: With the coming of 2025 in China, it is an irresistible trend to use industrial robots to process agricultural machinery online. In order to grasp workpiece intelligently, industrial robots need to identify the type of workpiece and pose of workpiece. In view of the difficulty in identifying the types of work pieces on the pipeline, an online recognition method based on the 3D point cloud of laser scanning was proposed. This method can identify which workpiece is the moving piece. First, the disordered workpiece on the assembly line was scanned, the 3D laser point cloud data of the workpiece was obtained, and the 3D laser point cloud data was initially denoised. Using MATLAB software to slice the 3D laser point cloud, the main view slicing, top view slicing, left view slicing was obtained. By using the HALCON software, the boundary information of the center slice was extracted, enhanced, segmented, and the characteristic parameters of the extracted area were extracted. Then the type of the workpiece was identified. Experimental results show that the accuracy of recognition is 96.67%. This method can be used for reference to similar problems.

     

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