A Lightweight LiDAR SLAM System Integrated with Semantic Segmentation for Forest Biomass Parameter Acquisition

Context : Efficient and automated acquisition of individual tree parameters is essential for intelligent forest resource inventory. Traditional methods, which rely heavily on manual measurements, are limited in their scalability and cannot meet the demands of large-scale, high-frequency surveys. Aims : This study aims to develop a lightweight LiDAR SLAM system integrated with semantic segmentation to improve the accuracy and real-time performance of individual tree extraction and DBH measurement in forest environments. Methods: The system is based on an improved LIO-SAM framework, incorporating the IKD-Tree to enhance efficiency. In the semantic segmentation module, the SqueezeSegV3 network with the added ELA attention mechanism is employed to improve semantic category recognition. The segmented point clouds are processed through clustering and cylindrical fitting to extract individual trees and estimate their DBH. Results : On both a public dataset and field-collected forest data, the semantic segmentation achieved accuracies of 0.85 and 0.89, with mean Intersection over Union values of 0.55 and 0.67, respectively. The average prediction accuracy for DBH reached 98.6%.. Conclusion The system integrates a lightweight semantic network with an efficient point cloud structure, offering both high accuracy and real-time performance. It meets the requirements of large-scale and high-efficiency measurements in forest resource inventory tasks.