Simulation and Analysis of Photogrammetric UAV Image Blocks—Influence of Camera Calibration Error

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 scene and corridor configuration, the focal length of camera is highly correlated with the height of the 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 order to better investigate the impact of camera calibration errors, a synthetic, error-free aerial image block is generated to simulate several issues of interest. Firstly, the erroneous focal length in the case of camera pre-calibration is studied. Nadir images are not able to prevent camera poses from drifting to compensate for the erroneous focal length, whereas the inclusion of oblique images brings significant improvement. Secondly, the case where the focal length varies gradually (e.g., when the camera subject to temperature changes) is investigated. The neglect of this phenomenon can substantially degrade the 3D measurement accuracy. Different flight configurations and flight orders are analyzed, the combination of oblique and nadir images shows better performance. At last, the rolling shutter effect is investigated. The influence of camera rotational motion on the final accuracy is negligible compared to that of the translational motion. The acquisition configurations investigated are not able to mitigate the degradation introduced by the rolling shutter effect. Other solutions such as correcting image measurements or including camera motion parameters in the bundle block adjustment should be exploited.

Uav Block Georeferencing and Control by on-Board GNSS Data

“…Strengths comes from a dense and homogenous block control, as all camera stations are measured. Weaknesses are the risks that inaccurate inner orientation parameters may affect tie points ground coordinates (Zhou et al, 2020). Unlike nadiral blocks with GCP, where with dense enough ground control these errors are to some extent adsorbed by exterior orientation parameters, if block control is limited to camera stations the ground coordinates are not effectively shielded.…”

Canopy Volume Extraction of Citrus reticulate Blanco cv. Shatangju Trees Using UAV Image-Based Point Cloud Deep Learning

“…Shatangju tree. With the development of UAV technology, UAV images have also been widely used [49][50][51][52][53][54][55][56]. Studies [10][11][12][13] have shown that UAV tilt photogrammetry can quickly and conveniently obtain images and texture information of forests, pine trees, eucalyptus, etc.…”

LiDAR-Aided Interior Orientation Parameters Refinement Strategy for Consumer-Grade Cameras Onboard UAV Remote Sensing Systems

“…Based on the impact analysis of different camera parameters, the capability of LiDAR to provide control information for decoupling the involved IOPs is evaluated. Although the impact of an erroneous/gradually varying principal distance and rolling shutter effect on the accuracy of 3D reconstruction has been experimentally investigated by Zhou et al [41], this section will focus on analytically and experimentally analyzing the impact of the principal distance and distortion parameters.…”