For fruit picking robot, it is an essential prerequisite for achieving fruit picking using machine vision technology to accurately identify the fruits growing in the natural environment. This article presents a vision system of fruit picking robot to perform fruit location and three-dimensional model reconstruction. Firstly, combining the features of color and shape of fruit to reconstruct the actual contour of overlapped and sheltered fruits. Secondly, the least square method was used to reconstruct the three-dimensional model of each fruit according to the spatial coordinates corresponding to image location. Finally, fruit picking experiments in the laboratory environment are used to verify the feasibility of the proposed vision system. Three parameters including Segmentation Error, Intersection Over Union, and False Negative Rate are used to evaluate the performance of the algorithm. The average Segmentation Error, Intersection Over Union, and False Negative Rate of the fruit location algorithm based on geometry were 6.36%, 87.9%, and 6.72%, respectively. The experimental results showed that the average computation time of the algorithm is 3.2 s and the reconstructed three-dimensional model matched the size and position of fruits in the actual scene. The research results can be applied to the vision system of fruit picking robot.

对于水果采摘机器人来说，利用机器视觉技术准确识别生长在自然环境中的水果是实现水果采摘的必要前提。本文介绍了一种水果采摘机器人的视觉系统，用于进行水果定位和三维模型重建。首先，结合果实的颜色和形状特征，重建重叠遮蔽果实的实际轮廓。其次，根据图像位置对应的空间坐标，采用最小二乘法重建每个水果的三维模型。最后通过实验室环境下的水果采摘实验验证了所提视觉系统的可行性。三个参数包括分割误差，交集超过联合，和 False Negative Rate 用于评估算法的性能。基于几何的水果定位算法的平均分割误差、联合交叉和假阴性率分别为6.36%、87.9%和6.72%。实验结果表明，该算法的平均计算时间为3.2 s，重构的三维模型与实际场景中水果的大小和位置相匹配。研究成果可应用于水果采摘机器人的视觉系统。2 s 和重建的三维模型与实际场景中水果的大小和位置相匹配。研究成果可应用于水果采摘机器人的视觉系统。2 s 和重建的三维模型与实际场景中水果的大小和位置相匹配。研究成果可应用于水果采摘机器人的视觉系统。