关键词: 移动机器人, 点云环境地图, 深度学习, 点云识别, 树冠参数

Abstract: Aiming at the problem that manual measurement of nursery canopy parameters was timeconsuming and laborintensive, and fruit tree canopy parameters  could not be quickly extracted, a tree canopy information extraction method based on environmental point cloud recognition algorithm was proposed in this paper. Firstly, the LiDARIMU tightly coupled odometer was used for point cloud correction and feature point extraction, and the rotation constraint was used to solve the Zaxis migration problem in the construction map to complete the environment reconstruction of the measurement area. After the point cloud map was transferred to the background workstation, European clustering and 3DFVDNNs algorithm were used to segment and identify the tree point cloud. Finally, after finding the first main branch, the canopy volume was modeled by cubic voxel method, and the canopy area parameters were extracted by twodimensional raster method. The test showed that the mapping algorithm adopted in this paper could reconstruct the complete orchard environment with high accuracy. The Bet value of PR curve obtained by the nursery recognition method based on DNN deep learning classifier was 0.0641 and 0.0999 higher than that obtained by SVM and RF classifier. In addition, R2 and RMSE of crown volume and area were 0.74677 and 0.6978, 0.09754 and 0.07677, respectively. The results showed that the canopy parameters measured by the proposed algorithm were strongly correlated with the manual measurements, which provided important support for the fine management of orchards.

Key words:  , mobile robot, point cloud environment map, deep learning, point cloud recognition, canopy parameters