PhD Defense: ‘From Efficient Airborne LiDAR Data Processing and Classification to 3D Point Cloud Visualization'
Over the last two decades, LiDAR (Light Detection And Ranging), an active remote sensing technique, has gained significant adoption. LiDAR allows acquiring a 3D record of the target scene in the form of point cloud with high accuracy. The goal of this thesis is to develop efficient methods for the classification of LiDAR data. For this, both general-purpose methods (segmentation and classification) and application-specific methods (building and road points extraction) are proposed, which have been efficiently implemented in a middle-to-low level language with an optimal spatial indexing and multi-core parallelisation.
Also, the feasibility of real-time processing is explored by presenting a method for ground filtering that exploits the scan-line acquisition pattern of the LiDAR data and providing an implementation in a SoC using FPGA acceleration. Finally, an OpenGL-based point cloud visualisation tool, namely OLIVIA, is developed, which offers an easy way to create customised visualisations with support for 3D stereoscopic view.