Optimization of radial distortion self-calibration for structure from motion from uncalibrated UAV images
Structure from motion (SfM) and self-calibration from images of unknown radial distortions could fail under some critical configurations and produce distorted reconstruction results. In this paper, we propose an effective approach to optimize the estimation of radial distortion coefficient by taking full advantage of GPS information, which allows for more accurate SfM results. A feedback function is designed as the metric to indicate the magnitude of the distortion error. Heuristic search strategies are applied to search for the optimal distortion coefficient. Extensive experimental results show that our approach can effectively reduce the distorted deformation error and improve the estimation accuracy of the distortion coefficient.
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Optimization of radial distortion self-calibration for structure from motion from uncalibrated UAV images
Semantic Scholar · Engineering · 2016
Abstract
Structure from motion (SfM) and self-calibration from images of unknown radial distortions could fail under some critical configurations and produce distorted reconstruction results. In this paper, we propose an effective approach to optimize the estimation of radial distortion coefficient by taking full advantage of GPS information, which allows for more accurate SfM results. A feedback function is designed as the metric to indicate the magnitude of the distortion error. Heuristic search strategies are applied to search for the optimal distortion coefficient. Extensive experimental results show that our approach can effectively reduce the distorted deformation error and improve the estimation accuracy of the distortion coefficient.