Uav Cameras: Overview and Geometric Calibration Benchmark

Abstract

ABSTRACT: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.

Benchmarking the Optical Resolving Power of Uav Based Camera Systems

“…Hereafter the results given in this paper are connected to the work of Cramer et al (2017a). As part of their investigation different UAV camera systems have been calibrated with airborne image data in a calibration field.…”

“…Another topic is a) to combine the experiment results under static / laboratory conditions with radiometrical correction parameters (PRNU / DSNU) to provide more fundamental information and b) to verify radiometrical and geometrical quality characteristics presented by Cramer et al (2017a) under dynamic / operating conditions.…”

“…The results are given in this paper, which can be seen as benchmark for current UAV based cameras. It is recommended to read this publication in conjunction with the work of Cramer et al (2017a) -to be submitted to this UAV-g conference -where geometrical calibration and stability of these cameras is discussed and empirically investigated. Some of the cameras investigated from radiometric point of view here also were geometrically analysed by Cramer et al (2017a).…”

Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning

“…Camera calibration in GCP-controlled UAV blocks is discussed by several papers [10,37,45]. While pre-calibration is recommended in principle, the stability of camera parameters is questionable: Cramer et al [45] found less accurate results using laboratory calibration parameters, while Harwin et al [10] found on-the-job self-calibration just as accurate.…”

“…While pre-calibration is recommended in principle, the stability of camera parameters is questionable: Cramer et al [45] found less accurate results using laboratory calibration parameters, while Harwin et al [10] found on-the-job self-calibration just as accurate. Self-calibration is likely to be the Remote Sens.…”