Techniques of feature points matching in the problem of UAV's visual navigation
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 4 (2015) no. 4, pp. 32-47 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

The paper is devoted to development and experimental comparison of techniques feature points matching on the images - images of the earth's surface with cameras mounted on unmanned aerial vehicles (UAVs) and artificial earth satellite. The main feature of the problem is that one of the images (satellite image) is divided into fragments. The developed techniques are part of a complex of algorithms for determining the position and orientation of the UAV using the methods and algorithms of machine vision. A description of the flight simulation technology and solving positioning tasks are described. Feature points on the image are extracted using an SURF algorithm. Also the approach to matching based on the partition of feature points' set into two subsets depending on the sign of the Laplacian are stidied. Methods of increasing the performance of matching points are offered.
Mots-clés : UAV, satellite images, brute-force
Keywords: feature points, SURF, computer vision, search index, image matching.
@article{VYURV_2015_4_4_a1,
     author = {D. N. Stepanov},
     title = {Techniques of feature points matching in the problem of {UAV's} visual navigation},
     journal = {Vestnik \^U\v{z}no-Uralʹskogo gosudarstvennogo universiteta. Seri\^a Vy\v{c}islitelʹna\^a matematika i informatika},
     pages = {32--47},
     year = {2015},
     volume = {4},
     number = {4},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VYURV_2015_4_4_a1/}
}
TY  - JOUR
AU  - D. N. Stepanov
TI  - Techniques of feature points matching in the problem of UAV's visual navigation
JO  - Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika
PY  - 2015
SP  - 32
EP  - 47
VL  - 4
IS  - 4
UR  - http://geodesic.mathdoc.fr/item/VYURV_2015_4_4_a1/
LA  - ru
ID  - VYURV_2015_4_4_a1
ER  - 
%0 Journal Article
%A D. N. Stepanov
%T Techniques of feature points matching in the problem of UAV's visual navigation
%J Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika
%D 2015
%P 32-47
%V 4
%N 4
%U http://geodesic.mathdoc.fr/item/VYURV_2015_4_4_a1/
%G ru
%F VYURV_2015_4_4_a1
D. N. Stepanov. Techniques of feature points matching in the problem of UAV's visual navigation. Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 4 (2015) no. 4, pp. 32-47. http://geodesic.mathdoc.fr/item/VYURV_2015_4_4_a1/

[1] The Cabinet of Ministers of the Russian Federation proposed to change the law for the use of UAVs, (accessed: 14.07.2015) http://ria.ru/politics/20150217/1048089808.html

[2] Correlation based similarity measures - Summary, (data obrascheniya: 14.07.2015) https://siddhantahuja.wordpress.com/tag/sum-of-absolute-differences-sad

[3] C. Harris, M. Stephens, “A combined corner and edge detector”, Proceedings of the 4th Alvey Vision Conference, 1988, 147–151 | DOI

[4] V. Gaganov, “Invariant algorithms of feature points' matching on images”, Computer graphics and multimedia, 7:1 (2009) (accessed: 14.07.2015) http://cgm.computergraphics.ru/issues/issue17/invariant_features

[5] D.G. Lowe, “Distinctive image features from scale-invariant keypoints”, International Journal of Computer Vision, 60:2, 91–110 | DOI

[6] H. Bay, A. Ess, T. Tuytelaars, L Van Gool, “SURF: Speeded Up Robust Features”, Computer Vision and Image Understanding (CVIU), 110:3 (2008), 14 | DOI

[7] K. Mikolajczyk, C. Schmid, “A performance evaluation of local descriptors”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 10:27, 1615–1630 | DOI

[8] J. Y. Bouguet, Pyramidal implementation of the Lucas Kanade feature tracker, Intel Corporation, Microprocessor Research Labs, 2000, 9 pp.

[9] M. Muja, D.G. Lowe, “Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration”, International Conference on Computer Vision Theory and Application VISSAPP'09, 331–340

[10] Stepanov D.N., Tishchenko I.P., Poljakov A.V., Vatutin V.M., Sobolev D.B., “The subsystem determine the position and orientation of the UAV”, Joint Stock Company «Russian Space Systems», Proc. of 5th All-Russian Scientific Conference «Actual problems of missile and space instrumentation and information technology» (Moscow, 5–7 June, 2012), Radiotehnika, M., 2013, 9–27

[11] J.-K. Kamarainen, V. Kyrki, H. Kälviäinen, “Invariance Properties of Gabor Filter Based Features - Overview and Applications”, IEEE Transactions on Image Processing, 15:5 (2006), 12 | DOI

[12] Stepanov D.N., “Methods and algorithms for determining the position and orientation UAV usage of on-board videocameras”, Programmnye produkty i sistemy (International Journal), 1:1 (2014) (accessed: 06.07.2015) http://www.swsys.ru/index.php?page=article&id=3776

[13] H. Stewenius, C. Engels, D. Nister, “Recent developments on direct relative orientation”, ISPRS Journal of Photogrammetry and Remote Sensing, 60:4 (2006), 284–294 | DOI

[14] M.A. Fischler, R.C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography”, Comm. Of the ACM, 24:6 (1981), 381–395 | DOI | MR

[15] A. Kravcov, V. Vezhnevec, “General formulation of the problem of extrinsic camera calibration”, Computer graphics and multimedia, 1:2 (2003) (accessed: 06.07.2015) http://cgm.computergraphics.ru/content/view/34

[16] FLANN - Fast Library for Approximate Nearest Neighbors, (data obrascheniya: 06.07.2015) http://www.cs.ubc.ca/ mariusm/index.php/FLANN/FLANN

[17] G. Bradski., A. Kaehler, Learning OpenCV, O'Reilly Media, 2008, 576 pp.