Canopy Volume Measurement of Fruit Trees Using Robotic Platform Loaded LiDAR Data

Abstract

Accurate fruit tree models are essential for canopy volume measurement work, we build an orchard mobile robot platform and develop a fruit tree model reconstruction algorithm based on it, optimize the LIDAR Odometry specifically for the orchard environment, fuse the LIDAR Odometry, Inertial Measurement Unit (IMU), Global Navigation Satellite System (GNSS) sensor information and loop closure detection in the form of factors to add factor maps for back-end optimization to reconstruct the orchard map model, use the sliding window method to process in real time The fused information and narrowed the range where the tree trunks are located for two times of line surface feature matching, and the point cloud data are processed to get the fruit tree model. In order to make the point cloud distribution of the reconstructed model uniform, the robot also needs to match a specific walking route, and use the Hough transform and K-Means clustering algorithm to extract the linear-circular-linear walking route autonomously according to the tree row arrangement. The experimental results show that the error of the map model is less than 0.160 m, and the correlation coefficient R2 and root-mean-square error (RMSE) of the canopy model for volume measurement are 0.984 and 0.102 m3, respectively. The collected LiDAR data based on the robotic platform meets the requirement of fruit canopy volume calculation.

Keywords

• Orchard robot
• multi-sensor fusion
• model reconstruction
• point cloud processing