Estimating Tree Diameters from an Autonomous Below-Canopy UAV with Mounted LiDAR

Below-canopy UAVs hold promise for automated forest surveys because their sensors can provide detailed information on below-canopy structures, especially in dense forests, which may be inaccessible to above-canopy UAVs, aircraft, and satellites. We present an end-to-end autonomous system estimating tree diameters using a UAV parklands. used simultaneous localization mapping (SLAM) LiDAR data produced at flight time as inputs diameter-estimation algorithms post-processing. The SLAM path was initial compilation of horizontal scans into 2D cross-sectional map, then optimization aligned the each within map achieve precision suitable diameter measurement. successfully identified 12 objects, 11 were trees one lamppost. For these, estimated from survey highly correlated with manual ground-truthed (R2=0.92, root mean squared error = 30.6%, bias 18.4%). Autonomous measurement most effective larger (>300 mm diameter) 10 m path, medium (200–300 5 m, regular cross sections. conclude that fully are promising, but still nascent, technology suggest directions future research.