College of Engineering, Nanjing Agricultural University, Nanjing 210031, China.
Jiangsu Province Key Laboratory of Intelligent Agricultural Equipment, Nanjing 210031, China.
Sensors (Basel). 2021 Jan 6;21(2):339. doi: 10.3390/s21020339.
To meet the demand for canopy morphological parameter measurements in orchards, a mobile scanning system is designed based on the 3D Simultaneous Localization and Mapping (SLAM) algorithm. The system uses a lightweight LiDAR-Inertial Measurement Unit (LiDAR-IMU) state estimator and a rotation-constrained optimization algorithm to reconstruct a point cloud map of the orchard. Then, Statistical Outlier Removal (SOR) filtering and European clustering algorithms are used to segment the orchard point cloud from which the ground information has been separated, and the k-nearest neighbour (KNN) search algorithm is used to restore the filtered point cloud. Finally, the height of the fruit trees and the volume of the canopy are obtained by the point cloud statistical method and the 3D alpha-shape algorithm. To verify the algorithm, tracked robots equipped with LIDAR and an IMU are used in a standardized orchard. Experiments show that the system in this paper can reconstruct the orchard point cloud environment with high accuracy and can obtain the point cloud information of all fruit trees in the orchard environment. The accuracy of point cloud-based segmentation of fruit trees in the orchard is 95.4%. The R and Root Mean Square Error (RMSE) values of crown height are 0.93682 and 0.04337, respectively, and the corresponding values of canopy volume are 0.8406 and 1.5738, respectively. In summary, this system achieves a good evaluation result of orchard crown information and has important application value in the intelligent measurement of fruit trees.
为满足果园树冠形态参数测量需求，设计了一种基于 3D 同时定位与地图构建（SLAM）算法的移动扫描系统。该系统采用轻量级的 LiDAR-惯性测量单元（LiDAR-IMU）状态估计器和旋转约束优化算法，重建果园点云地图。然后，采用统计离群点剔除（SOR）滤波和欧式聚类算法，从果园点云中分割出地面信息，并采用 K 最近邻（KNN）搜索算法，对过滤后的点云进行恢复。最后，采用点云统计法和 3D alpha 形状算法，获取果树高度和树冠体积。为验证算法，在标准化果园中使用配备 LiDAR 和 IMU 的跟踪机器人进行实验。实验结果表明，本文所提系统能够高精度地重建果园点云环境，并能获取果园环境中所有果树的点云信息。基于点云的果园果树分割精度达到 95.4%。冠层高度的 R 和均方根误差（RMSE）值分别为 0.93682 和 0.04337，树冠体积的 R 和 RMSE 值分别为 0.8406 和 1.5738。总之，该系统实现了果园树冠信息的良好评价结果，在果树智能测量中具有重要的应用价值。
