果树重建与果实识别方法在采摘场景中的应用

Application of fruit tree reconstruction and fruit recognition methods in picking scenes

为了实现柑橘采摘机器人的精准采摘,以及在采摘过程中为避障路径规划提供有效的指导,提出了一种自然场景下柑橘树三维重建与成熟柑橘识别的方法。利用Kinect v2获取柑橘树的彩色图像和深度图像并通过深度阈值分割的方法去除背景;通过彩色图像和深度图像配准得到点云,并通过统计滤波器中的Kd-Tree算法进行离群噪声点去除,利用ICP算法对其进行迭代融合,对融合后的点云,采用泊松曲面重建算法对柑橘树进行三维重建;通过结合成熟柑橘的颜色特征信息在不同的颜色空间下对其进行分割识别。实验结果表明:所研究的方法对柑橘树重建精度达到94. 83%,对成熟柑橘的识别率达到94. 72%。

In order to achieve accurate picking of citrus picking robots and provide effective guidance for obstacle avoidance path planning during picking,a method three-dimensional reconstruction of citrus trees and mature citrus recognition in natural scenes is proposed.Color image and depth image of the citrus tree are obtained by Kinect v2 and the background is removed by depth threshold segmentation.Point cloud is obtained by color image and depth image registration,and the outlier noise point is removed by the Kd-Tree algorithm in the statistical filter.It is iteratively fused by the ICP algorithm,for the fused point cloud,Poisson’s surface reconstruction algorithm is used for three-dimensional reconstruction of the citrus tree.Combined with the color feature information of mature citrus to segment and identify it under different color spaces.The experimental results show that the precision of citrus tree reconstruction is 94.83%,and the recognition rate of mature citrus is 94.72%.