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Алгоритм навигации беспилотного летательного аппарата на основе улучшенного алгоритма…

ISSN 0236-3933. Вестник МГТУ им. Н.Э. Баумана. Сер. Приборостроение. 2017. № 3

91

8.

Chatterjee A., Ray O., Chatterjee A., Rakshit A.

Development of a real-life EKF based SLAM sys-

tem for mobile robots employing vision sensing // Expert Systems with Applications. 2011. Vol. 38.

No. 7. P. 8266–8274. DOI: 10.1016/j.eswa.2011.01.007

URL:

http://www.sciencedirect.com/science/article/pii/S0957417411000273

9.

Sola J., Vidal-Calleja T., Civera J., Montiel J.M.M.

Impact of landmark parametrization on mo-

nocular EKF–SLAM with points and lines // Int. Journal of Computer Vision. 2012. Vol. 97. No. 3.

P. 339–368. DOI: 10.1007/s11263-011-0492-5

URL:

https://link.springer.com/article/10.1007/s11263-011-0492-5

10.

Consistency

of the EKF–SLAM algorithm / T. Bailey, J. Nieto, J. Guivant, M. Stevens, E. Nebot

//

IEEE/RSJ Int. Conf. on Intelligent Robots and Systems. 2006. P. 3562–3568.

DOI: 10.1109/IROS.2006.281644 URL:

http://ieeexplore.ieee.org/document/4058955

11.

Geng Ke Ke

. An improved EKF–SLAM algorithm for mobile robot // Интернаука. 2016. № 2.

С. 74–78.

12.

Krig S

. Computer vision metrics: Survey, taxonomy, and analysis. Apress, 2014.

13.

Smith S.M

.,

Brady J.M

. SUSAN — a new approach to lowlevel image processing // Int. Journal

of Computer Vision. 1997. Vol. 23. No. 1. P. 45–78. DOI: 10.1023/A:1007963824710

URL:

https://link.springer.com/article/10.1023%2FA%3A1007963824710

14.

Гэн Кэ Кэ, Чулин Н.А.

Метод реконструкции облачно-точечной карты окружающей сре-

ды на основе монокулярного компьютерного зрения в режиме реального времени // Меж-

дународный журнал экспериментального образования. 2015. № 12-3. С. 437–442.

URL:

https://expeducation.ru/ru/article/view?id=9163

15.

Di Stefano L., Mattoccia S., Mola M.

An efficient algorithm for exhaustive template matching

based on normalized cross correlation // Proc. 12th Int. Conf. on Image Analysis and Processing.

2003. P. 322–327. DOI: 10.1109/ICIAP.2003.1234070

URL:

http://ieeexplore.ieee.org/document/1234070

16.

Armangué X., Salvi J.

Overall view regarding fundamental matrix estimation // Image and

Vision Computing. 2003. Vol. 21. No. 2. P. 205–220. DOI: 10.1016/S0262-8856(02)00154-3

URL:

http://www.sciencedirect.com/science/article/pii/S0262885602001543

17.

Camera

calibration toolbox for Matlab // Vision.caltech: веб-сайт

URL:

http://www.vision.caltech.edu/bouguetj/calib_doc

(дата обращения: 19.10.2016).

18.

Hartley R.I

. Estimation of relative camera positions for uncalibrated cameras // European Conf.

on Computer Vision. 1992. P. 579–587. DOI: 10.1007/3-540-55426-2_62

URL:

https://link.springer.com/chapter/10.1007/3-540-55426-2_62?no-access=true

19.

Hartley R., Zisserman A.

Multiple view geometry in computer vision. Cambridge University

Press, 2003.

20.

Bunschoten R., Krose B.

Visual odometry from an omnidirectional vision system // IEEE Int.

Conf. on Robotics and Automation. 2003. Vol. 1. P. 577–583. DOI: 10.1109/ROBOT.2003.1241656

URL:

http://ieeexplore.ieee.org/document/1241656

21.

Nistér D., Naroditsky O., Bergen J.

Visual odometry // Proc. 2004 IEEE Computer Society Conf.

on Computer Vision and Pattern Recognition. 2004. No. 1. P. 652–659.

DOI: 10.1109/CVPR.2004.1315094 URL:

http://ieeexplore.ieee.org/document/1315094

22.

A solution

to the simultaneous localization and map building (SLAM) problem / M.G. Dis-

sanayake, P. Newman, S. Clark, H.F. Durrant-Whyte, M. Csorba // IEEE Transactions on Robotics

and Automation. 2001. Vol. 17. No. 3. P. 229–241. DOI: 10.1109/70.938381

URL:

http://ieeexplore.ieee.org/document/938381