PhD Defense: 'HPC Solutions for ALS Point Cloud Processing in Pathfinding and Powerline Detection and Characterization'

This thesis deals with the processing of point clouds, obtained with airborne LiDAR sensors, using high-performance computing techniques. One of the most common operations when processing point clouds is the determination of the neighbourhood of each point. For this purpose, an extensive study has been carried out to determine the most efficient data structure to perform this operation. Once the best data structure has been chosen, its scalability when parallelised under the shared memory paradigm has been studied.

On this basis, two point cloud processing algorithms have been developed. The first one consists of calculating the optimal path between any two points given a point cloud. The cost of the route is calculated in terms of slope, roughness, presence of roads and presence of vegetation that may hinder movement. The second algorithm consists of the detection and characterisation of power lines in general purpose point clouds. The algorithm is able to detect multiple power lines without prior information about them. In addition, each conductor is individually segmented for mathematical modelling using the catenary equation, which allows the calculation of very precise distances between each conductor and the obstacles that may exist around it.

Supervisors: Francisco Fernández Rivera and Tomás Fernández Pena