Research On 3D Reconstruction Method And Feature Extraction Of Agricultural And Forestry Crops Based On 3d Point Cloud

With the application and development of computer technology,3D reconstruction technology has been applied in the field of agriculture and forestry.This paper mainly researched the 3D reconstruction technology of agricultural and forestry crops,forest areas and orchards.The research contents and innovations of this article are as follows:(1)For the 3D reconstruction of a single agricultural and forestry crop,a registration method based on calibration balls was proposed in this paper.The trunk,branch,and crown were selected as experimental objects,and three calibration balls were placed around the experimental objects to ensure different distances between two ball centers.The Kinect V2 depth camera was exploitd to collect the point cloud of the experimental scene from four different viewpoints,the Passthrough Filter algorithm was used for point cloud filtering in each view of the experimental scenes.The Euclidean cluster extraction algorithm was employed for point cloud clustering and segmentation to extract the experimental object and the calibration ball.The RANSAC algorithm was applied to fit the point cloud of a ball and calculate the coordinates of the ball center so that the distance between two ball centers under different viewpoints can be obtained by using the coordinates of the ball center.Comparing the distance between the ball centers from different viewpoints to determine the corresponding relationship between the ball centers from different viewpoints,and then using the SVD method,the initial registration matrix was obtained.Finally,ICP and its improved algorithm were used for accurate registration and the least square method was used to smooth the 3D model.(2)As to the 3D reconstruction of forest areas,a handheld lidar scanning equipment was utilized to obtain point cloud based on SLAM technology,3D reconstruction of large-scale forest areas was performed and DBH was extracted.The area of 71 trees was selected as the experimental forest plot,and the 3D map of forest area was generated by handheld lidar scanning equipment.The ground point cloud of the 3D point cloud map was removed by the RANSAC algorithm.The trees in the experimental area were segmented by the European clustering algorithm,and the DBH component of the tree point cloud was extracted and projected onto a 2D plane,fitting the diameter of the circle using the RANSAC algorithm in the plane.Comparing the fitting result and the true value,the mean absolute and relative errors of 71 trees were 0.43 cm and 2.27%,the corresponding variances were 0.50 and 15.09,and the RMSE value was 0.70 cm.(3)For the 3D reconstruction of the orchard area,the handheld lidar scanning equipment and SLAM-based technology were also exploited for 3D reconstruction,and the Alpha-shape algorithm was employed to calculate the canopy volume which was optimized by the slice method.Two orchards containing 36 grapefruit trees and 74 citrus trees were selected as test sites,and 3D maps of orchards were generated using handheld lidar scanning equipment.CSF was applied to filter out the orchard ground point cloud in the 3D map,a single fruit tree was extracted by the European clustering algorithm,and the Alpha-shape algorithm to construct a 3D model of the fruit tree canopy and calculate the canopy volume.In the case that the branch structure of the 3D canopy model was clear,the corresponding optimal parameter ? was selected,and the slice method was proposed to optimize the canopy volume results.