Fruit Tree Point Cloud Registration Based on Normal Vector Angles and Branch-Leaf Segmentation Method
In realizing full automation of orchard operations,it is urgent to construct a 3D model of fruit tree branches and trunks in the natural environment directly.Point clouds of fruit trees collected from different views in the natural environment were registered.Considering that sampling consistency(SAC-IA)+iterative nearest point(ICP)registration algorithm took a long time and had low accuracy in point cloud registration.Thus,the feature points of the source point cloud and target point cloud were extracted by combining the angle of the normal vector of the point cloud,and then matching point pairs were found in the feature points of the source and target point clouds based on the cosine value of the angle of the normal vector of the point cloud.Using the matching point pairs of fruit tree point clouds,an improved SAC-IA+ICP point cloud registration algorithm was proposed.Further,the registered fruit tree point cloud was partitioned by using the partitioning technology of minimum box partition,and then the branches and leaves of the partitioned sub-blocks were roughed by using the geometric features of the point cloud;finally,the branches and leaves were partitioned by using Euclidean clustering.Compared with the original SAC-IA+ICP algorithm,the average rotation error was reduced by 85.44％,and the registration root mean square error can be reduced by 71.74％,the registration time was reduced by 97.99％.Meantime,compared with the SAC-IA+NDT algorithm,the average rotation error was reduced by 90.38％,and the registration root mean square error can be reduced by 85.39％,the registration time was reduced by 98.04％.The segmentation algorithm can complete the segmentation of branches and leaves,and the accuracy can reach 94.77％compared with manual segmentation.
fruit treespoint cloudnormal vector anglepoint cloud registrationbranch-leaf segmentation
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