Abstract
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 R2 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.
Highlights
The fruit tree canopy is the main focus of photosynthesis and fruit production, and canopy information can reflect the yield and growth status of fruit trees and can provide a certain level of guidance for fertilization, irrigation, pruning, and other operations
With the development of sensors and Simultaneous Localization and Mapping (SLAM) technologies [3], mobile LiDAR scanning systems have been increasingly used in areas such as robot environment perception and autonomous driving, which have laid a solid foundation for the use of mobile LiDAR scanning systems in fruit tree canopy measurements
The system was divided into three modules: a high-precision orchard point cloud map building module, a canopy parameter module based on the point cloud model, and a test module based on manual measurement values and Global Navigation Satellite System (GNSS)
Summary
The fruit tree canopy is the main focus of photosynthesis and fruit production, and canopy information can reflect the yield and growth status of fruit trees and can provide a certain level of guidance for fertilization, irrigation, pruning, and other operations. André Freitas Colaço et al [9] developed a mobile ground-based laser scanner suitable for large commercial orange groves to estimate canopy volume and height, relying on GNSS with LiDAR and using the alpha-shape method to perform canopy measurements It can be seen from the above that GNSS is widely used in orchard mapping, but due to excessive canopy height and density in orchards, GNSS can suffer from signal loss and canopy measurement failure [10]. The recently developed LiDAR-IMU tightly coupled odometry algorithm has greatly improved the accuracy and robustness of point cloud map construction The application of this technology enables better results for tree crown measurements.The Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping) algorithm [14] uses the Visual-Inertial State Estimator (VINS-mono) monocular odometry’s dynamic initialization scheme [15], which relies on variable motion with roll angles, but large agricultural tracked robots cannot perform movement in rolls and pitches in a garden or orchard. This method can provide theoretical support and technical guidance for canopy measurements and can provide accurate decision information for orchard managers
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