2020-07-31T15:56:26Z (GMT) by Lisa Marie Laforest

Unmanned aerial vehicles (UAVs) equipped with imaging systems and integrated global navigation satellite system/inertial navigation system (GNSS/INS) are used for a variety of applications. Disaster relief, infrastructure monitoring, precision agriculture, and ecological forestry growth monitoring are among some of the applications that utilize UAV imaging systems. For most applications, accurate 3D spatial information from the UAV imaging system is required. Deriving reliable 3D coordinates is conditioned on accurate geometric calibration. Geometric calibration entails both spatial and temporal calibration. Spatial calibration consists of obtaining accurate internal characteristics of the imaging sensor as well as estimating the mounting parameters between the imaging and the GNSS/INS units. Temporal calibration ensures that there is little to no time delay between the image timestamps and corresponding GNSS/INS position and orientation timestamps. Manual and automated spatial calibration have been successfully accomplished on a variety of platforms and sensors including UAVs equipped with frame and push-broom line cameras. However, manual and automated temporal calibration has not been demonstrated on both frame and line camera systems without the use of ground control points (GCPs). This research focuses on manual and automated spatial and temporal system calibration for UAVs equipped with GNSS/INS frame and line camera systems. For frame cameras, the research introduces two approaches (direct and indirect) to correct for time delay between GNSS/INS recorded event markers and actual time of image exposures. To ensure the best estimates of system parameters without the use of ground control points, an optimal flight configuration for system calibration while estimating time delay is rigorously derived. For line camera systems, this research presents the direct approach to estimate system calibration parameters including time delay during the bundle block adjustment. The optimal flight configuration is also rigorously derived for line camera systems and the bias impact analysis is concluded. This shows that the indirect approach is not a feasible solution for push-broom line cameras onboard UAVs due to the limited ability of line cameras to decouple system parameters and is confirmed with experimental results. Lastly, this research demonstrates that for frame and line camera systems, the direct approach can be fully-automated by incorporating structure from motion (SfM) based tie point features. Methods for feature detection and matching for frame and line camera systems are presented. This research also presents the necessary changes in the bundle adjustment with self-calibration to successfully incorporate a large amount of automatically-derived tie points. For frame cameras, the results show that the direct and indirect approach is capable of estimating and correcting this time delay. When a time delay exists and the direct or indirect approach is applied, horizontal accuracy of 1–3 times the ground sampling distance (GSD) can be achieved without the use of any ground control points (GCPs). For line camera systems, the direct results show that when a time delay exists and spatial and temporal calibration is performed, vertical and horizontal accuracy are approximately that of the ground sample distance (GSD) of the sensor. Furthermore, when a large artificial time delay is introduced for line camera systems, the direct approach still achieves accuracy less than the GSD of the system and performs 2.5-8 times better in the horizontal components and up to 18 times better in the vertical component than when temporal calibration is not performed. Lastly, the results show that automated tie points can be successfully extracted for frame and line camera systems and that those tie point features can be incorporated into a fully-automated bundle adjustment with self-calibration including time delay estimation. The results show that this fully-automated calibration accurately estimates system parameters and demonstrates absolute accuracy similar to that of manually-measured tie/checkpoints without the use of GCPs.