A Fruit-Tree Mapping System for Semi-Structured Orchards Based on Multi-Sensor-Fusion SLAM

Robots have been increasingly applied in orchard management, with fruit tree localization and mapping being crucial for precision and automation. Existing methods require real-time kinematic (RTK) global navigation satellite system (GNSS) receivers. However, signal strength is diminished in orchards due to being occluded by tree canopies. Therefore, this study proposes a novel system for global fruit tree positioning and mapping in semi-structured orchards based on multi-sensor fusion simultaneous localization and mapping (SLAM), without dependence of positioning signals from satellites. As the specific contributions, firstly, a SLAM system which fuse a camera, an inertial measurement unit (IMU), and a light detection and ranging (LiDAR) sensor was constructed to improve the accuracy of robot odometry estimation in complex orchard environments. Secondly, a fruit tree localization algorithm was developed to localize the fruit trees around the robot using both images and LiDAR point clouds, after which the global positions of the detected fruit trees were optimized using the SLAM-derived robot pose real-time. Our method has been extensively evaluated in real pear and persimmon orchards. The results showed that the average root mean square error (RMSE) for odometry was 6.04 cm and 14.59 cm in pear and persimmon orchards, respectively, and the positioning errors of the detected fruit trees were 0.57 cm and 3.23 cm, respectively. The results indicate that our method can achieve accurate positioning and mapping of fruit trees in complex environments and build fruit-tree maps.