Paper:
Proposal of UAV-SLAM-Based 3D Point Cloud Map Generation Method for Orchards Measurements
Soki Nishiwaki, Haruki Kondo, Shuhei Yoshida, and Takanori Emaru
Faculty and Graduate School of Engineering, Hokkaido University
Kita 13 Nishi 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan
This paper proposes a method for generating highly accurate point cloud maps of orchards using an unmanned aerial vehicle (UAV) equipped with light detection and ranging (LiDAR). The point cloud captured by the UAV-LiDAR was converted to a geographic coordinate system using a global navigation satellite system / inertial measurement unit (GNSS/IMU). The converted point cloud is then aligned with the simultaneous localization and mapping (SLAM) technique. As a result, a 3D model of an orchard is generated in a low-cost and easy-to-use manner for pesticide application with precision. The method of direct point cloud alignment with real-time kinematic-global navigation satellite system (RTK-GNSS) had a root mean square error (RMSE) of 42 cm between the predicted and true crop height values, primarily due to the effects of GNSS multipath and vibration of automated vehicles. Contrastingly, our method demonstrated better results, with RMSE of 5.43 cm and 2.14 cm in the vertical and horizontal axes, respectively. The proposed method for predicting crop location successfully achieved the required accuracy of less than 1 m with errors not exceeding 30 cm in the geographic coordinate system.

Orchard point cloud map by UAV-SLAM
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