Target-less registration of UAV-LiDAR point clouds based on graph matching of tree locations in forest environments

Abstract

Accurate forest mapping is essential for understanding and managing forest ecosystems. In recent years, Light Detection and Ranging (LiDAR) technology has become a powerful tool for forest monitoring. However, registering UAV-LiDAR strips remains challenging in GNSS-degraded forest environments due to sparse ground features, canopy occlusion, and limited availability of reliable control points. Existing registration methods often rely on surface-based or feature-plane matching, which can be suboptimal in structurally heterogeneous forests. To address this gap, we present a robust and target-less registration framework for UAVLS data that leverages tree localization as structural anchors. Each detected tree is encoded into a graph using three spatial descriptors: (a) planimetric distance, (b) vertical distance, and (c) height difference relative to neighboring trees. Initial correspondences between trees across overlapping strips are established using the Hungarian algorithm, which maximizes a similarity function derived from these descriptors. Subsequently, a three-dimensional rigid transformation (rotation and translation) is estimated via Particle Swarm Optimization (PSO) to refine the spatial alignment. The method was validated on eight circular forest plots (30 m in diameter) derived from two overlapping UAVLS strips. The root mean square error (RMSE) of residual distances between registered tree pairs ranged from ±17.6 cm to ±27.4 cm for poplar plots and from ±17.5 cm to ±24.3 cm for dawn blackwood plots. Furthermore, the proposed method improved the tree matching ratio by 17%–27% compared to a baseline approach, demonstrating higher alignment accuracy. By integrating graph-based correspondence with swarm-based optimization, this study contributes a scalable, accurate, and GNSS-independent registration solution tailored to the complexities of forest environments. The framework has strong potential to support UAV-LiDAR-based forest mapping, monitoring, and inventory tasks in both research and operational settings.