Research on Orchard Navigation Path Planning Based on 3D LiDAR SLAM Considering Terrain Roughness

ABSTRACT Autonomous navigation in orchards is essential for enhancing operational efficiency and ensuring safe agricultural operations. However, autonomous navigation in orchard environments presents significant challenges due to uneven surfaces and limited visual information in natural environments. To address these issues, this study proposed a shortest‐path planning method for autonomous orchard navigation based on 3D LiDAR SLAM. First, a global 3D map was constructed using the LIO‐SAM algorithm. Ground points were then separated using the Cloth Simulation Filter (CSF), and terrain roughness information was extracted from the ground point cloud to identify rugged areas that might compromise robot stability. In parallel, an improved Random Forest model was used to segment fruit‐tree points, after which DBSCAN was applied to extract individual tree centers and the Kernel Density Estimation (KDE) method was used to estimate tree‐row direction. Finally, a cost map integrating fruit‐tree distribution and terrain roughness information was constructed, and an improved A* algorithm was employed to generate efficient and terrain‐adaptive paths. The proposed method was evaluated in both a simulation and a real pear orchard. The results showed that the proposed approach reduced traversal over rugged terrain by more than 50% and lowered estimated energy consumption by nearly 48%, while maintaining comparable path lengths and high computational efficiency. Field experiments further demonstrated reliable path‐following performance, with average lateral and longitudinal deviations within 0.18 meters and heading deviation below 3.1°. These findings highlight the practical value of incorporating terrain roughness into path planning for robust and efficient orchard navigation.