INTEGRATION OF TERRESTRIAL AND AIRBORNE LIDAR DATA FOR SYSTEM CALIBRATION

The ever improving capabilities of the direct geo-referencing technology is having a positive impact on the widespread adoption of LiDAR systems for the acquisition of dense and accurate surface models over extended areas. LiDAR systems can quickly provide accurate surface models with a dense set of irregular points, surpassing the quality of those derived from other techniques, such as manual photogrammetric DSM generation, radar interferometry, and contour interpolation. A typical LiDAR system consists of three main components: a GNSS to provide position information, an INS for attitude determination, and a laser scanner to provide the range/distance from the laser-beam firing point to its footprint. The accuracy of the LiDAR point cloud is ensured by the quality of the measurements from the individual system components and their spatial relationship as defined by the bore-sighting parameters. Even though the measurements of the individual system components (GNSS, INS and laser scanner) are quite precise, serious errors can result from inaccurate estimation of the bore-sighting parameters. For this reason, bore-sighting parameters should be well defined at the beginning of the work process and will be the focus of this paper. This paper presents a new methodology for simultaneous estimation of the LiDAR bore-sighting parameters using control features that are automatically extracted from a reference control surface. In this approach, the reference control surface is derived from a terrestrial LiDAR system. The shorter ranges and the high point density associated with terrestrial LiDAR systems would ensure the generation of a reference surface, which is accurate enough for reliable estimation of the calibration parameters associated with airborne LiDAR systems. After introducing the mathematical models for the proposed methodologies, this paper outlines the optimal configuration of the control data for a reliable estimation of the calibration parameters, while avoiding possible correlations among these parameters. Finally, the feasibility test presents experimental results from real datasets while highlighting the advantages and the limitations of the proposed methodologies. * Corresponding author