基于Kinect v2传感器的果树枝干三维重建方法

针对果树三维重构中存在建模精度低、成本高、拓扑结构差等问题,提出一种基于Kinect v2传感器的果树表型三维重建与骨架提取方法。首先,使用Kinect v2传感器采集不同视角下的果树点云数据;其次,对植株点云进行尺度不变特征变换的特征点检测,对关键点使用快速点特征直方图算法进行特征向量计算,通过随机抽样一致性方法提纯点云的初始位置,经初始变换后使用改进的迭代最近点算法进行精配准、拼接形成完整点云;最后,使用Delaunay三角剖分解构点云数据对缺失点云进行填充,使用Dijkstra最短路径算法对最小生成树进行求取,通过迭代去除冗余分量对骨架进行简化,使用圆柱拟合算法估算枝干骨架,将枝干骨架变为封闭凸包多面体,实现果树的枝干三维重建。实验结果表明：采用本文所提建模方法点云平均配准误差为0.52cm,枝干平均重构误差不超过3.52%,重建效果良好。研究成果可为果园评估作物状态、智能化修剪等研究提供数据支持。

3D Reconstruction Method for Fruit Tree Branches Based on Kinect v2 Sensor

Aiming at the problems of low modeling accuracy, high cost and poor topology structure in the three-dimensional (3D) reconstruction of fruit trees, a 3D reconstruction method of fruit tree phenotype and skeleton extraction based on Kinect v2 sensor was proposed. Firstly, the Kinect v2 sensor was used to collect fruit tree point cloud data from different perspectives. Secondly, the characteristic point detection of scale invariant feature transformation was carried out on the plant point cloud, the eigenvector vector calculation was carried out by using the fast point feature histogram algorithm, the initial position of the point cloud was purified by the random sampling consistency method, and the improved iterative nearest point algorithm was used to finely register and stitch to form a complete point cloud after the initial transformation. Finally, the Delaunay triangulation of the point cloud data was used to fill the missing point cloud, the Dijkstra shortest path algorithm was used to obtain the minimum spanning tree, the skeleton was simplified by iteratively removing redundant components, the tree skeleton was estimated by the cylindrical fitting algorithm, and the tree skeleton was transformed into a closed convex polyhedron, and the 3D reconstruction of the branches of the fruit tree was realized. The experimental results showed that the average error of point cloud registration was 0.52cm, and the average error of branch reconstruction was not more than 3.52%, and the reconstruction effect was good. The research results can provide data support for orchard assessment of crop status, intelligent pruning and other research.