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The calibration of multi-sensor systems to facilitate direct georeferencing is essential for ensuring high accuracy photogrammetry products. Integrated solutions require estimations and compensations of misalignments - positional and/or angular - between individual sensors. This paper discusses the calibration of angular misalignments, or boresight...
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... order to perform the field calibration, 30 GCPs were distributed across an area of almost 20,000 m 2 (Fig. 3). The position of every target was measured using survey- grade equipment (Fig. 4) to obtain accurate results of exterior orientation estimation. UAS carrying the multi-sensor system collected 137 aerial images of the area from an altitude of 50 meters; flight parameters were set to attain 80% forward and 80% side overlap. Thanks to the sensor synchronization, GNSS/INS derived exterior orientation of every image was ...
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Citations
Unmanned Aerial Systems (UAS) Photogrammetry has become widely used for spatial data acquisition. Nowadays, RTK (Real Time Kinematic) and PPK (Post Processed Kinematic) are the main correction methods for accurate positioning used for direct measurements of camera station coordinates in UAS imagery. Thus, 3D camera coordinates are commonly used as additional observations in Bundle Block Adjustment to perform Global Navigation Satellite System-Assisted Aerial Triangulation (GNSS-AAT). This process requires accurate Interior Orientation Parameters to ensure the quality of photogrammetric intersection. Therefore, this study investigates the influence of on-site camera calibration with a sub-block of images on the accuracy of spatial data obtained by PPK-based UAS Photogrammetry. For this purpose, experiments of on-the-job camera self-calibration in the Metashape software with the SfM approach were performed. Afterward, experiments of GNSS-Assisted Aerial Triangulation with on-site calibration in the Erdas Imagine software were performed. The outcomes show that only the experiment of GNSS-AAT with three Ground Control Points yielded horizontal and vertical accuracies close to nominal precisions of the camera station positions by GNSS-PPK measurements adopted in this study, showing horizontal RMSE (Root-Mean Square Error) of 0.222 m and vertical RMSE of 0.154 m. Furthermore, the on-site camera calibration with a sub-block of images significantly improved the vertical accuracy of the spatial information extraction.
To determine the 3D coordinates of the object in images taken using a drone, not only camera calibration but alsosystem calibration must precede. Factors to consider when calibrating the system include the accuracy and layout of ground control points(GCP), the specification of the camera accuracy of GNSS/IMU mounted on the drone, the measurement accuracy of image coordinates, and flight path. It is necessary to analyze the impact of each factor on the determination of system parameters. Therefore, in this study, simulation data was generated based on the specification and design of the equipment to perform actual system calibration. Then, the accuracy of system parameters according to the GNSS/IMU performance and GCP layout was compared.
