LiDAR-Based Tree Detection and Parameterization for SLAM in Autonomous Forestry Machinery

The demand for autonomous functions in forestry operations is growing due to labor shortages, safety risks, and the need for precision in unstructured forest environments. However, dense vegetation, occlusions, and unreliable GNSS signals make autonomous navigation challenging for forest machinery. To address these issues, this paper proposes a LiDAR-based tree detection and parameterization method to support Simultaneous Localization and Mapping (SLAM), which is essential for reliable long-term autonomy. The proposed approach consists of three components. First, density-based spatial clustering segments tree-like structures from raw LiDAR point clouds. Then, parametric modeling fits cylindrical representations to trunks using RANSAC and numerically stable circle fitting. Finally, validation constraints, including a radius consistency check for maintaining stable trunk geometry and a probabilistic visibility filter for excluding occluded or back-facing points, enhance robustness against noise and occlusion-induced outliers. The method is evaluated in controlled 2D LiDAR simulations with realistic noise and branch interference. It achieves an RMSE of 0.077 m for trunk radius and 0.062 m for center localization compared to the simulated tree radius of 0.27 m. ... mehrThese results demonstrate the method’s ability to extract typical tree landmarks from single scans, even under challenging conditions. By enabling accurate tree modeling in cluttered forest environments, the proposed method lays a foundation for incorporating structured environmental features into SLAM pipelines.