Efficiency analysis of photogrammetric system by simulation modelling
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ, Matematičeskoe modelirovanie i programmirovanie, Tome 11 (2018) no. 1, pp. 109-123 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

We developed a universal simulation model of photogrammetric systems that utilizes artificial target points and fiducial marks (coded targets). The model allows to analyze the efficiency of the system in particular, measurement errors as well as system performance (scalability). Simulation modeling allows to estimate how different factors influence the measurement error and the performance of the system. It also allows to vary the values of factors easily and in a broad range. It reduces organizational, time, and financial cost of the testing system as well. We have implement the proposed simulation model in several configurations with GNU Octave. We have also run a series of simulation experiments, thus estimating how the measurement error of the system depends on errors in various input factors. It was determined that the measurement error of pixel coordinates of circle targets is the key factor that influences the resulting measurement error of targets' 3D coordinates. Other factors, such as deviations of the parameters of the camera model from its initial calibrations, or uncertainties in estimation of cameras' initial pose, do not influence the resulting measurement error significantly due to effect of system automatic calibration. We have also estimated the impact of the linear size and instrument error of scale bar on system accuracy. The proposed simulation model may be also used for the verification of various algorithms in the field of computer vision in conditions that are hard to implement during the process of natural experiments.
Keywords: photogrammetry; simulation modelling; accuracy estimation; computer vision.
@article{VYURU_2018_11_1_a9,
     author = {S. A. Tushev and B. M. Sukhovilov},
     title = {Efficiency analysis of photogrammetric system by simulation modelling},
     journal = {Vestnik \^U\v{z}no-Uralʹskogo gosudarstvennogo universiteta. Seri\^a, Matemati\v{c}eskoe modelirovanie i programmirovanie},
     pages = {109--123},
     year = {2018},
     volume = {11},
     number = {1},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VYURU_2018_11_1_a9/}
}
TY  - JOUR
AU  - S. A. Tushev
AU  - B. M. Sukhovilov
TI  - Efficiency analysis of photogrammetric system by simulation modelling
JO  - Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ, Matematičeskoe modelirovanie i programmirovanie
PY  - 2018
SP  - 109
EP  - 123
VL  - 11
IS  - 1
UR  - http://geodesic.mathdoc.fr/item/VYURU_2018_11_1_a9/
LA  - ru
ID  - VYURU_2018_11_1_a9
ER  - 
%0 Journal Article
%A S. A. Tushev
%A B. M. Sukhovilov
%T Efficiency analysis of photogrammetric system by simulation modelling
%J Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ, Matematičeskoe modelirovanie i programmirovanie
%D 2018
%P 109-123
%V 11
%N 1
%U http://geodesic.mathdoc.fr/item/VYURU_2018_11_1_a9/
%G ru
%F VYURU_2018_11_1_a9
S. A. Tushev; B. M. Sukhovilov. Efficiency analysis of photogrammetric system by simulation modelling. Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ, Matematičeskoe modelirovanie i programmirovanie, Tome 11 (2018) no. 1, pp. 109-123. http://geodesic.mathdoc.fr/item/VYURU_2018_11_1_a9/

[1] S.A. Tushev, B.M. Sukhovilov, “Effective Graph-Based Point Matching Algorithms”, Proceedings of the 2nd International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) (Chelyabinsk, Russia, May 19–20, 2016), Institute of Electrical and Electronics Engineers, N.Y., 2017, 1462–1466 | DOI

[2] B.M. Sukhovilov, E.M. Sartasov, E.A. Grigorova, “Improving the Accuracy of Determining the Position of the Code Marks in the Problems of Constructing Three-Dimensional Models of Objects”, Proceedings of the 2nd International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) (Chelyabinsk, Russia, May 19–20, 2016), Institute of Electrical and Electronics Engineers, N.Y., 2017, 1690–1696 | DOI

[3] Tushev S. A., Sukhovilov B. M., “Some Ways to Improve the Performance of Automatic Calibration of Digital Cameras”, Young Researcher, Materials of the 2nd Scientific Exhibition-Conference of Scientific, Technical and Creative Works of Students, Publishing center of South Ural State University, Chelyabinsk, 2015, 434–439 (in Russian)

[4] Sukhovilov B. M., Grigorova E. A., “Development of a Photogrammetric System for Measuring the Spatial Coordinates of Structural Elements of the Frame of a Low-Floor Tram”, Science of SUSU. Materials of the 67th Scientific Conference. Section of Economics, Management and Law, Publishing center of South Ural State University, Chelyabinsk, 2015, 458–463 (in Russian)

[5] Sukhovilov B. M., Grigorova E. A., Sartasov E. M., Gornyh E. N., “Experimental Analysis of Photogrammetry System Errors in Measuring Spatial Coordinates”, Science of SUSU. Materials of the 68th Scientific Conference. Section of Economics, Management and Law, Publishing center of South Ural State University, Chelyabinsk, 2016, 221–228 (in Russian)

[6] Brown J., V-STARS/S Acceptance Test Result, (accessed January 02, 2017) https://www.geodetic.com/v-stars/papers.aspx

[7] R. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, Cambridge, 2004 | DOI | MR

[8] Z. Zhang, “A Flexible New Technique for Camera Calibration”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22:11 (2000), 1330–1334 | DOI

[9] Tushev S. A., Sukhovilov B. M., “Parallel Algorithms for Effective Correspondence Problem Solution in Computer Vision”, Bulletin of the South Ural State University. Series: Computational Mathematics and Software Engineering, 6:2 (2017), 49–68 | DOI

[10] B. Triggs, P. McLauchlan, R. Hartley, A. Fitzgibbon, “Bundle Adjustment – A Modern Synthesis”, Vision Algorithms: Theory and Practice, 1883 (2000), 298–372 | DOI

[11] B. Jähne, Practical Handbook on Image Processing for Scientific Applications, CRC Press, Boca Raton, 1997

[12] D. Forsyth, J. Ponce, Computer Vision: A Modern Approach, Pearson, New Jersey, 2011

[13] R. Godding, “Geometric Calibration of Digital Imaging Systems”, Computer Vision and Applications, Academic Press, San Diego, 2000, 153–175

[14] GNU Octave, (accessed May 23, 2017) https://www.gnu.org/software/octave

[15] N. Metropolis, S. Ulam, “The Monte Carlo Method”, Journal of the American Statistical Association, 44:247 (1949), 335–341 | DOI | MR

[16] G.S. Fishman, Monte Carlo: Concepts, Algorithms, and Applications, Springer, N.Y., 1996 | DOI | MR

[17] S.A. Tushev, B.M. Sukhovilov, E.M. Sartasov, “Architecture of Industrial Close-Range Photogrammetric System with Multi-Functional Coded Targets”, Proceedings of the 2017 2nd International Ural Conference on Measurements (UralCon) (Chelyabinsk, Russia, October 16–19, 2017), Institute of Electrical and Electronics Engineers, N.Y., 2017, 435–442 | DOI