Abstract
The accuracy of photogrammetric and Lidar dataset integration is dependent on the quality of a group of parameters that models accurately the conditions of the system at the moment of the survey. In this sense, this paper aims to study the effect of the sub-block position in the entire image block to estimate the interior orientation parameters (IOP) in flight conditions to be used in integrated sensor orientation (ISO). For this purpose, five sub-blocks were extracted in different regions of the entire block. Then, in situ camera calibrations were performed using sub-blocks and sets of Lidar control points (LCPs), computed by a three planes’ intersection extracted from the Lidar point cloud on building roofs. The ISO experiments were performed using IOPs from in situ calibrations, the entire image block, and the exterior orientation parameters (EOP) from the direct sensor orientation (DSO). Analysis of the results obtained from the ISO experiments performed show that the IOP from the sub-block positioned at the center of the entire image block can be recommended.
Highlights
The integration of Lidar and photogrammetric datasets has been an important research subject aiming to increase the automation of geoinformation extraction in photogrammetric mapping procedures
They were used as control or check points to perform the experiments of the in situ camera calibration and integrated sensor orientation (ISO)
The 37 Lidar control points (LCPs) were extracted from medium density-point cloud
Summary
The integration of Lidar and photogrammetric datasets has been an important research subject aiming to increase the automation of geoinformation extraction in photogrammetric mapping procedures. Using Lidar data as a source of positional information, methods to extract geometric primitives (points, lines and areas) are required, since the Lidar dataset does not show, directly, such primitives. Delara et al [2] performed research to extract Lidar control points (LCPs) from Lidar intensity images for application in aerial triangulation using a low-cost digital camera. Csanyi and Toth [5] conducted research to develop an optimal ground-control target to improve the accuracy of Lidar data in mapping projects. Gneeniss [9] conducted research to integrate photogrammetric dense tie points and Lidar cloud using a robust 3D least-squares surface-matching algorithm. Li et al [10] proposed an approach using sand ridges as primitives to solve the registration problem between Lidar and imagery in regions where ground-control points (GCPs) are not available, for instance, desert areas
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