Improving handheld Laser Scanning Point Cloud Quality in Forests via RTK-GNSS integrated SLAM

Abstract. Accurate forest inventories are essential for sustainable forest management. Handheld personal laser scanning (H-PLS) enables efficient and flexible forest data acquisition. However, ensuring reliable point cloud quality in complex environments remains challenging. While Simultaneous Localization and Mapping (SLAM)-based H-PLS allows rapid data collection, trajectory drift and accumulated registration errors can reduce the accuracy of derived tree parameters and structural metrics. In contrast, Global Navigation Satellite System (GNSS)-based Real-Time Kinematic (RTK) positioning provides centimetre-level absolute accuracy and drift-free trajectories, although its application in forested environments is still emerging. This study evaluates the impact of RTK-GNSS integration on point cloud geometry compared to SLAM-based point clouds without GNSS across two Central European forest plots with contrasting canopy structures. Analyses focused on tree parameter accuracy, structural metrics based on quantitative structural models, point density and noise characteristics. To isolate the effect of GNSS integration, data from the RTK-GNSS enabled H-PLS device were additionally processed without GNSS information, and an open-trajectory scan without loop closure was included for comparison. Results show that RTK-GNSS improves point cloud consistency and especially enhances the estimation of volume- and branch-related metrics. In the dense canopy plot, RTK-GNSS information reduced mean errors in branch number (−6100 to −5369) and crown volume (−492.75 to −357.21 m³). However, overall performance in tree parameter estimation depends on point density. These findings highlight RTK-GNSS H-PLS as a promising approach for flexible and efficient forest data acquisition in inventory applications.