  Forests, as critical components of our ecosystem, demand effective monitoring
and management. However, conducting real-time forest inventory in large-scale
and GNSS-interrupted forest environments has long been a formidable challenge.
In this paper, we present a novel solution that leverages robotics and
sensor-fusion technologies to overcome these challenges and enable real-time
forest inventory with higher accuracy and efficiency. The proposed solution
consists of a new SLAM algorithm to create an accurate 3D map of large-scale
forest stands with detailed estimation about the number of trees and the
corresponding DBH, solely with the consecutive scans of a 3D lidar and an imu.
This method utilized a hierarchical unsupervised clustering algorithm to detect
the trees and measure the DBH from the lidar point cloud. The algorithm can run
simultaneously as the data is being recorded or afterwards on the recorded
dataset. Furthermore, due to the proposed fast feature extraction and transform
estimation modules, the recorded data can be fed to the SLAM with higher
frequency than common SLAM algorithms. The performance of the proposed solution
was tested through filed data collection with hand-held sensor platform as well
as a mobile forestry robot. The accuracy of the results was also compared to
the state-of-the-art SLAM solutions.
