基于结构光3D视觉的槟榔姿态识别与定位系统

Betel Nut Pose Recognition and Localization System Based on Structured Light 3D Vision

枸杞槟榔的放料工序目前仍需工人手动完成,生产效率低且存在食品卫生问题。为了解决这个问题,设计了一种基于结构光3D视觉的槟榔姿态识别与定位系统。首先,由数字投影仪向槟榔投影蓝光正弦条纹,相机采集变形条纹图像后,通过计算机进行三维重建,获得高精度槟榔点云;然后,融合2D图像和3D点云信息,采用所提特征线法估计槟榔的姿态参数;最后,定位槟榔空腔中心并将其作为放料点,根据手眼标定将放料点坐标转换到机械臂基坐标系下,使机械臂自动放料。利用500个槟榔进行姿态识别与定位实验,并统计处理时间和分类准确率。实验结果表明,1个槟榔的处理时间为0.39~0.59 s。整体的识别准确率为95.6%。对放料点的定位误差在0.25 mm之内,低于放料要求的0.3 mm。所提方法能有效解决形状复杂且自由放置目标的姿态识别与定位问题,具有较高的定位精度以及良好的稳定性,满足实际生产要求。

The process of feeding wolfberry into betel nut still needs to be accomplished manually by workers,which has low production efficiency and food safety issues.To address this problem,betel nut pose recognition and positioning system based on structured light three-dimensional(3D)vision is designed.First,a digital projector projects blue sinusoidal fringe patterns onto betel nuts,and once the deformed fringe images are acquired,the computer performs 3D reconstruction to obtain a high-precision betel nut point cloud.Subsequently,two-dimensional(2D)image and 3D point cloud information are fused,and the proposed feature line method is used to estimate the betel nut pose parameters.Finally,the center of the betel nut cavity is positioned as the feeding point,which is subsequently converted to the base coordinate system of a robot arm according to hand-eye calibration to complete automatic feeding.Experiments on pose recognition and localization were conducted using 500 betel nuts,and both processing time and classification accuracy were evaluated.Results show that the processing time of one betel nut is 0.39‒0.59 s.The overall recognition accuracy is 95.6%.The localization error for the feeding point is within 0.25 mm,which is lower than the 0.3 mm required for feeding.This demonstrates that the proposed method can effectively solve the problem of attitude recognition and positioning of freely placed targets within complex shapes and has high positioning accuracy and good stability to meet the actual production requirements.