Abstract

Abstract. High-end consumer quadcopter UAVs or so-called “prosumer devices”, have made inroads into the mapping industry over the past few years, arguably displacing more expensive purpose-built systems. In particular, the DJI Phantom series quadcopters, marketed primarily for videography, have shown considerable promise due to their relatively high-quality cameras. Camera pre-calibration has long been a part of the aerial photogrammetric workflow with calibration certificates being provided by operators for every project flown. Most UAV data, however, is processed today in Structure-from-Motion software where the calibration is generated “on-the-fly” from the same image-set being used for mapping. Often the scenes being mapped and their flight-plans are inappropriate for calibration as they do not have enough variation in altitude to produce a good focal-length solution, and do not have cross-strips to improve the estimation of the principal point. What we propose is a new type of flight-plan that can be run on highly textured scenes of varying height prior to mapping missions that will significantly improve the estimation of the interior orientation parameters and, as a consequence, improve the overall accuracy of projects undertaken with these sorts of UAV systems. We also note that embedded manufacturer camera profiles, which correct for distortion automatically, should be removed prior to all photogrammetric processing, something that is often overlooked as these profiles are not made visible to the end user in most image conversion software, particularly Adobe’s CameraRAW.

Highlights

  • As the UAV industry continues to mature, sophisticated quadcopter UAVs with features hitherto reserved for purposebuilt systems are becoming available at low price-points and, as a consequence, to a wider range of potential users

  • In conjunction with Structure-from-Motion photogrammetry software, the data from these UAVs can produce what appears to be a high-quality mapping product with next to no user intervention. This new generation of enthusiastic users often do not have a background in photogrammetry or aerial photography and are largely ignorant of practices long-established in the aerial mapping industry (Fraser, 2013)

  • The accuracy of the flight plans described above was established in CalibCam by generation matching points by Normalized Cross-Correlation Least Squares Matching, followed by a bundle adjustment to solve for the interior orientation parameters: Focal Length (C), Radial Distortion (K1, K2, K3), Principal Point Offset (Xp, Yp), Decentering Distortion (P1, P2) and the Pixel Scaling Factors (B1, B2)

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Summary

INTRODUCTION

As the UAV industry continues to mature, sophisticated quadcopter UAVs with features hitherto reserved for purposebuilt systems are becoming available at low price-points and, as a consequence, to a wider range of potential users. A significant source of systematic error in camera calibration of “prosumer” UAVs occurs when geometric distortion models are imposed on in-camera JPEGs or are embedded in RAW files and imposed during the conversion of these files to a usable format for photogrammetry; like JPEG or TIFF These geometric distortion corrections are generalized for the lens and camera combination and cannot take into account the manufacturing variances that are obtained between different instances of the camera model. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W13, 2019 ISPRS Geospatial Week 2019, 10–14 June 2019, Enschede, The Netherlands underestimate, the real interior parameters These geometric corrections are frequently not apparent to many end users who even insist on the use of RAW imagery as the basis of photogrammetric processing. The foundation of our calibration procedure will be a workflow to recover the original images with no geometric corrections applied

METHODS AND MATERIALS
Site Locations and Scene Selection
Flight Plan Creation
Flight Planning Software
Flight Plans
Post-Processing and Calibration
RESULTS AND ANALYSIS
Camera Calibration Assessment
C Cσ Xpσ Ypσ K1σ K2σ K3σ
In-Situ use of Calibration Model and Control Network Check
CONCLUSION
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