RT Dissertation/Thesis
T1 HPC Solutions for ALS Point Cloud Processing in Pathfinding and Powerline Detection and Characterization
A1 Yermo, Miguel
K1 HPC
K1 lidar
K1 pointcloud
K1 powerlines
K1 pathfinding
AB This thesis addresses the processing of LiDAR point clouds using high-performance computing techniques. By employing efficient data structures and the shared-memory parallelization paradigm, two methods have been implemented for point cloud analysis. First, a path planning algorithm is used to find the route between any two points within an airborne LiDAR point cloud, considering terrain features such as trafficability, slope, roughness, presence of vegetation, and roads. It is guaranteed that the found route is optimal in terms of cost. Second, the problem of detecting and characterizing powerlines in general-purpose airborne LiDAR point clouds has been tackled. The method can detect multiple powerlines in a given scene with a precision of 97.2%, and it can model the conductors with a mean error of 0.14 meters.
YR 2024
FD 2024
LK http://hdl.handle.net/10347/34947
UL http://hdl.handle.net/10347/34947
LA eng
DS Minerva
RD 28 abr 2026