Abstract
Abstract. Unmanned aerial vehicles (UAV) are increasingly used for topographic mapping. The camera calibration for UAV image blocks can be performed a priori or during the bundle block adjustment (self-calibration). For an area of interest with flat, corridor configuration, the focal length of camera is highly correlated with the height of camera. Furthermore, systematic errors of camera calibration accumulate on the longer dimension and cause deformation. Therefore, special precautions must be taken when estimating camera calibration parameters. In this paper, a simulated, error-free aerial image block is generated. error is then added on camera calibration and given as initial solution to bundle block adjustment. Depending on the nature of the error and the investigation purpose, camera calibration parameters are either fixed or re-estimated during the bundle block adjustment. The objective is to investigate how certain errors in the camera calibration impact the accuracy of 3D measurement without the influence of other errors. All experiments are carried out with Fraser camera calibration model being employed. When adopting a proper flight configuration, an error on focal length for the initial camera calibration can be corrected almost entirely during bundle block adjustment. For the case where an erroneous focal length is given for pre-calibration and not re-estimated, the presence of oblique images limits the drift on camera height hence gives better camera pose estimation. Other than that, the error on focal length when neglecting its variation during the acquisition (e.g., due to camera temperature increase) is also investigated; a bowl effect is observed when one focal length is given in camera pre-calibration to the whole image block. At last, a local error is added in image space to simulate camera flaws; this type of error is more difficult to be corrected with the Fraser camera model and the accuracy of 3D measurement degrades substantially.
Highlights
The derivation of geospatial information from unmanned aerial vehicles (UAV) is becoming increasingly ubiquitous (Nex, Remondino, 2014)
The procedure consists of identifying common feature points between overlapping images and recovering their poses at first in a relative coordinate system, followed by the georeferencing phase with the help of, e.g. ground control points (GCP), or the camera positions measured with global navigation satellite system (GNSS) (Heipke et al, 2002, Cramer et al, 2000)
Different types of errors are added on the camera calibration; the erroneous camera calibration is given as an initial solution to the bundle block adjustment
Summary
The derivation of geospatial information from unmanned aerial vehicles (UAV) is becoming increasingly ubiquitous (Nex , Remondino, 2014). A 3D scene can be reconstructed from aerial images with high accuracy. The bundle block adjustment is a basic tool for photogrammetric scene reconstruction. Camera calibration parameters can be considered pre-calibrated and constant, or their values are re-estimated in the self-calibrating bundle block adjustement. We generate a simulated, error-free aerial image block which is of flat, corridor configuration. Different types of errors are added on the camera calibration; the erroneous camera calibration is given as an initial solution to the bundle block adjustment. Depending on the nature of the error and the investigation purpose, camera calibration parameters are either fixed or re-estimated during the bundle block adjustment. The impacts of each type of error on camera calibration and photogrammetric accuracy are investigated
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