Abstract
Abstract. Due to the increasing range of work carried out with UAV in recent years, the importance of final product accuracy appreciates. However, obtaining survey-grade accuracy requires to perform bundle adjustment processes that could be affected by multiple factors like unstable camera calibration, a correlation between interior and exterior orientation, insufficient georeferenced information, and software settings. During the project, multi-variant flight over the test field was conducted. The flights were performed with a fixed-wing airframe equipped with PPK receiver on-board. Based on the conducted flights, the database for multifactorial data sets has been prepared. The database containing hundreds of independent adjustment variants which differ as follows: georeferencing method, flight configuration, additional camera calibration corrections, tie points filtering, and a priori accuracy settings. The database allowed to investigate the separate influence of each factor on the final results using ANOVA statistical models.
Highlights
The use of drones in surveying is developing rapidly and areas of UAVs application are getting the more and more various [Nex, 2014]
The estimation of those parameters is affected due to their high correlation. On this account the classical photogrammetry capture images using metric cameras which internal is orientation is performed autonomously in laboratory or in field test. This way does not work in UAV survey application due to using light consumer-grade cameras with unstable internal orientation
For non-metric cameras used in UAV a method of self-calibration is considered as the most reliable due to the issue of poor stability of the internal orientation
Summary
The use of drones in surveying is developing rapidly and areas of UAVs application are getting the more and more various [Nex, 2014]. The estimation of those parameters is affected due to their high correlation On this account the classical photogrammetry capture images using metric cameras which internal is orientation is performed autonomously in laboratory or in field test. For non-metric cameras used in UAV a method of self-calibration is considered as the most reliable due to the issue of poor stability of the internal orientation In this process additional unknowns like principal distance, principal point and distortion are determined simultaneously with the exterior orientation that makes it very sensitive to the structure of image block and input observations and raises the question about actual accuracy and reliability [Luhman, 2015]. There are some experiences that confirm the expectation [Gerke, Stöcker, 2019]
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