Accuracy comparison of interior orientation parameters from different photogrammetric software and direct linear transformation method

Abstract

The integration of computer vision algorithms and photogrammetric methods leads to procedures that increasingly automate the image-based 3D modeling process. The main objective of photogrammetry is to obtain a three-dimensional model using terrestrial or aerial images. Calibration of the camera and detection of the orientation parameters are important for obtaining accurate and reliable 3D models. For this purpose, many methods have been developed in the literature. However, since each method has different mathematical background, calibration results may be different. In this study, the effect of camera interior orientation parameters obtained from different methods on the accuracy of three-dimensional model will be examined. In this context, a test area consisting of 21 points was used. The test network was coordinated in a local coordinate system using geodetic methods. Some points of the test area were selected as the check point and accuracy analysis was performed. Direct Linear Transformation (DLT) method, MATLAB, Agisoft Lens, Photomodeler, 3D Flow Zephyr software were analysed. The lowest error value of 7.7 cm was achieved by modelling with Agisoft Lens.

Keywords

• Camera Calibration
• Accuracy Assessment
• Three Dimensional Model
• Photogrammetry
• Interior Orientation

References

1. Abdel-Aziz Y I & Karara H M (2015). Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. Photogrammetric Engineering & Remote Sensing. 81(1), 103–107. DOI: 10.14358/PERS.81.2.103
2. Akçay O, Erenoğlu R C & Avsar E O (2017). The effect of JPEG compression in close range photogrammetry. International Journal of Engineering and Geosciences, 2(1), 35-40. DOI: 10.26833/ijeg.287308
3. Drap P & Lefèvre J (2016). An exact formula for calculating inverse radial lens distortions. Sensors, 16(6), 807. DOI: 10.3390/s16060807
4. Duran Z & Aydar U (2012). Digital modeling of world's first known length reference unit: The Nippur cubit rod. Journal of Cultural Heritage 13(3), 352-356. DOI: 10.1016/j.culher.2011.12.006
5. Hemayed E E (2003). A survey of camera self-calibration. Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 351-357. DOI: 10.1109/AVSS.2003.1217942
6. Kraus K (1993). Photogrammetry, I. Fundamentals and standard processes. Dümmlers, 1. ISBN 978-3427786849
7. Lichti D D, Kim C & Jamtsho S (2010). An integrated bundle adjustment approach to range camera geometric self-calibration. ISPRS Journal of Photogrammetry and Remote Sensing, 65(4), 360-368. DOI: 10.1016/j.isprsjprs.2010.04.002
8. Linder W (2009). Digital Photogrammetry – A Practical Course, 3 ed. Springer-Verlag Berlin, Heidelberg. ISBN 978-3-662-50463-5

9. Nex F & Remondino F (2014). UAV for 3D mapping applications: a review. Applied Geomatics, 6, 1-15.
10. Ozdemir E & Duran Z (2017). Comparison of commonly used camera calibration software. Afyon Kocatepe University Journal of Science and Engineering, 17(4), 1-11. (in Turkish)
11. Reis H Ç (2018). Bone anomaly of the foot detection using medical photogrammetry. International Journal of Engineering and Geosciences, 3(1), 1-5. DOI: 10.26833/ijeg.333686
12. Song L, Wu W, Guo J & Li X (2013). Survey on camera calibration technique. 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, 2, 389-392. DOI: 10.1109/IHMSC.2013.240
13. Tasdemir S, Urkmez A, Yakar M & Inal S (2009). Determination of camera calibration parameters at digital image analysis. 5th International Advanced Technologies Symposium (IATS’09). (in Turkish)
14. Ulvi A & Toprak A S (2016). Investigation of three-dimensional modelling availability taken photograph of the unmanned aerial vehicle; sample of Kanlidivane Church. International Journal of Engineering and Geosciences, 1(1), 1-7. DOI: 10.26833/ijeg.285216
15. Yemenicioglu C, Kaya S & Seker D Z (2016). Accuracy of 3D (Three-dimensional) terrain models in simulations. International Journal of Engineering and Geosciences, 1(1), 30-33. DOI: 10.26833/ijeg.285223
16. Zhang Z (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330–1334. DOI: 10.1109/34.888718
17. Zhao H, Wang Z, Jiang H, Xu Y & Dong C (2015). Calibration for stereo vision system based on phase matching and bundle adjustment algorithm. Optics and Lasers in Engineering, 68, 203-213. DOI:10.1016/j.optlaseng.2014.12.001