基于机器视觉的散乱柱类零件抓取系统

Scattered column parts grabbing system based on machine vision

  • 摘要: 针对散乱摆放的柱类零件抓取问题,提出一种基于圆柱拟合的散乱柱类零件识别抓取系统。首先,采用直通滤波,离群点去除和体素滤波降低密度采样对点云数据进行预处理,然后使用平面拟合算法去除载物台点云,接着使用区域增长算法将不同圆柱的点云分离,由于分割后的圆柱点云存在大量噪点,采用随机抽样一致法和最小二乘法相结合的方式去除噪点,拟合圆柱面,最后将点云重心向圆柱轴线投影,得到准确的柱体中心。实验证明该算法鲁棒性好,精度较高,适用于柱类零件抓取。

     

    Abstract: Aiming at the problem of scaffolding of column parts placed in disorder, a classification and grabbing system for scattered column parts based on cylindrical fitting is proposed. First, through-pass filtering, outlier removal and voxel filtering downsampling are used to preprocess the point cloud data, then the plane fitting algorithm is used to remove the point cloud, and then the region growth algorithm is used to separate the point clouds of different cylinders. Because there is a lot of noise in the segmented cylindrical point cloud, the random sampling consistency method and the least square method are combined to remove the noise, the cylindrical surface is fitted, and finally the center of gravity of the point cloud is projected onto the cylinder axis to obtain an accurate center of the cylinder. The experiment proves that the algorithm has good robustness and high precision, and is suitable for the grabbing of column parts.

     

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