UAV CAMERAS: OVERVIEW AND GEOMETRIC CALIBRATION BENCHMARK

Different UAV platforms and sensors are used in mapping already, many of them equipped with (sometimes) modified cameras as known from the consumer market. Even though these systems normally fulfil their requested mapping accuracy, the question arises, which system performs best? This asks for a ben...

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Main Authors: M. Cramer, H.-J. Przybilla, A. Zurhorst
Format: Article
Language:English
Published: Copernicus Publications 2017-08-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W6/85/2017/isprs-archives-XLII-2-W6-85-2017.pdf
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spelling doaj-58b3c17f764f433ab13830ab4b6f06d72020-11-24T20:47:13ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342017-08-01XLII-2-W6859210.5194/isprs-archives-XLII-2-W6-85-2017UAV CAMERAS: OVERVIEW AND GEOMETRIC CALIBRATION BENCHMARKM. Cramer0H.-J. Przybilla1A. Zurhorst2Institute for Photogrammetry (ifp), Stuttgart University, Geschwister-Scholl-Str. 24D, 70174 Stuttgart, GermanyDepartment of Geodesy, Bochum University of Applied Sciences, Lennershofstr. 140, 44801 Bochum, Germanyaerometrics, Landwehrstraße 143, 59368 Werne, GermanyDifferent UAV platforms and sensors are used in mapping already, many of them equipped with (sometimes) modified cameras as known from the consumer market. Even though these systems normally fulfil their requested mapping accuracy, the question arises, which system performs best? This asks for a benchmark, to check selected UAV based camera systems in well-defined, reproducible environments. Such benchmark is tried within this work here. Nine different cameras used on UAV platforms, representing typical camera classes, are considered. The focus is laid on the geometry here, which is tightly linked to the process of geometrical calibration of the system. In most applications the calibration is performed in-situ, i.e. calibration parameters are obtained as part of the project data itself. This is often motivated because consumer cameras do not keep constant geometry, thus, cannot be seen as metric cameras. Still, some of the commercial systems are quite stable over time, as it was proven from repeated (terrestrial) calibrations runs. Already (pre-)calibrated systems may offer advantages, especially when the block geometry of the project does not allow for a stable and sufficient in-situ calibration. Especially for such scenario close to metric UAV cameras may have advantages. Empirical airborne test flights in a calibration field have shown how block geometry influences the estimated calibration parameters and how consistent the parameters from lab calibration can be reproduced.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W6/85/2017/isprs-archives-XLII-2-W6-85-2017.pdf
collection DOAJ
language English
format Article
sources DOAJ
author M. Cramer
H.-J. Przybilla
A. Zurhorst
spellingShingle M. Cramer
H.-J. Przybilla
A. Zurhorst
UAV CAMERAS: OVERVIEW AND GEOMETRIC CALIBRATION BENCHMARK
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
author_facet M. Cramer
H.-J. Przybilla
A. Zurhorst
author_sort M. Cramer
title UAV CAMERAS: OVERVIEW AND GEOMETRIC CALIBRATION BENCHMARK
title_short UAV CAMERAS: OVERVIEW AND GEOMETRIC CALIBRATION BENCHMARK
title_full UAV CAMERAS: OVERVIEW AND GEOMETRIC CALIBRATION BENCHMARK
title_fullStr UAV CAMERAS: OVERVIEW AND GEOMETRIC CALIBRATION BENCHMARK
title_full_unstemmed UAV CAMERAS: OVERVIEW AND GEOMETRIC CALIBRATION BENCHMARK
title_sort uav cameras: overview and geometric calibration benchmark
publisher Copernicus Publications
series The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
issn 1682-1750
2194-9034
publishDate 2017-08-01
description Different UAV platforms and sensors are used in mapping already, many of them equipped with (sometimes) modified cameras as known from the consumer market. Even though these systems normally fulfil their requested mapping accuracy, the question arises, which system performs best? This asks for a benchmark, to check selected UAV based camera systems in well-defined, reproducible environments. Such benchmark is tried within this work here. Nine different cameras used on UAV platforms, representing typical camera classes, are considered. The focus is laid on the geometry here, which is tightly linked to the process of geometrical calibration of the system. In most applications the calibration is performed in-situ, i.e. calibration parameters are obtained as part of the project data itself. This is often motivated because consumer cameras do not keep constant geometry, thus, cannot be seen as metric cameras. Still, some of the commercial systems are quite stable over time, as it was proven from repeated (terrestrial) calibrations runs. Already (pre-)calibrated systems may offer advantages, especially when the block geometry of the project does not allow for a stable and sufficient in-situ calibration. Especially for such scenario close to metric UAV cameras may have advantages. Empirical airborne test flights in a calibration field have shown how block geometry influences the estimated calibration parameters and how consistent the parameters from lab calibration can be reproduced.
url https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W6/85/2017/isprs-archives-XLII-2-W6-85-2017.pdf
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