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The article examines methods of images analysis based on computer vision. We made a comparison between the detectors of feature points determined by Harris and Kanade–Lucas–Tomasi (KLT) methods. Found points are represented by Speed-Up Robust Feature (SURF) descriptor and then used to determine homography matrix. Analyses of accuracy of visual navigation is done by estimation of a camera rotation angle via factorization of homography matrix obtained from two detector methods. Errors of visual navigation follow the normal distribution for the given sample.

Аналіз ефективності використання детекторів Харріса та Канаде-Лукас-Томасі для задач візуальної навігації