Abstract: Addressing the automation challenges in boulder point cloud measurement using UAV-borne LiDAR,particularly in boulder region extraction and volume estimation,this paper proposes an automated processing method based on cloth simulation filtering and distance clustering.Furthermore,a volume calculation strategy is presented based on the difference between loaded and unloaded deck point cloud models.First,ship hull points are extracted to identify the carrier structure,and then the cloth simulation algorithm is employed to accurately extract the bottom points (i.e.,the deck points within the cargo area),leveraging the spatial distribution characteristics of the loaded stone point clouds.Subsequently,a local coordinate system is constructed to analyze the geometric features of the bottom points,determining the horizontal boundaries of the boulder distribution and obtaining an initial boulder point cloud set.A distance clustering algorithm is then introduced to process these initial points,enabling precise separation of boulder points from residual hull points.Finally,by constructing 3D models of both the boulder point clouds and the corresponding empty-ship deck point clouds,volume estimation is achieved through model differencing.Accuracy assessments indicate that the proposed method achieves high precision in boulder point cloud extraction,with volume results consistent with manual estimations,effectively meeting the practical requirements of navigation channel improvement projects.

Key words: waterway improvement, volume calculation, unmanned aircraft carrier, cloth simulation, regional extraction