From efficient airbone LIDAR data processing and classification to 3D point cloud visualisation

Abstract

The acquisition of knowledge about the world is an essential endeavour of science. However, performing on-site observations at a global scale is unfeasible, therefore remote sensing is an appealing alternative. 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 ground filtering is explored exploiting the scan-line acquisition pattern of the LiDAR data. This implementation was ported into a development board using FPGA acceleration where experiments demonstrated that it can cope with the data acquisition rates of the current lightweight scanners used in UAVs. Furthermore, a point cloud visualisation tool, namely OLIVIA, is presented. OLIVIA is an OpenGL-based open-source project implemented in Java, that offers an easy way to create customised visualisation and the capability of 3D stereoscopic view.