KLTVO: Algoritmo de Odometria Visual estéreo baseada em seleção de pontos chaves pela imposição das restrições da geometria epipolar
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2020-07-23
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Universidade Federal de Goiás
Resumo
Self-localization is one of the key tasks for applications such as robotics, self-driving cars, and augmented
reality. The cameras have been broadly used because of their affordable cost, lower energy consumption,
rich information, and the ability to provide results comparable to more expensive sensors. Among the visual
localization methods, the feature-based Visual Odometry (VO) has attracted substantial attention, due to
their low computation demand which makes them suitable for embedded systems. This is due to the nature
of the information used since the pose of the camera is estimated based on a geometric consistency of
feature matching. On the other hand, these methods tend to be more sensitive to errors resulting from bad
correspondence. In this present work is proposed a correspondence methodology based on a circular
matching procedure, which fuses well-known strategies in Computer Vision in order to enhance the quality
of feature matching. The process combines the INSAD (Illumination Normalized Sum of Absolute
Differences) metric, for stereo feature matching, and the KLT algorithm (Kanade-Lucas-Tomasi feature
tracker) for feature tracking between consecutive frames. In both approaches is imposed the constraints of
the epipolar geometry, in order to obtain a fast and accurate feature matching. The proposed methodology
was evaluated in the KITTI dataset and against other methods. Experimental results demonstrate that the
proposed method contributes to faster convergence and achieves high local accuracy. Furthermore, even
without global optimizations, the proposed method demonstrated to be accurate for long term tracking,
compared to other methods.
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DIAS, N. J. B. KLTVO: Algoritmo de Odometria Visual estéreo baseada em seleção de pontos chaves pela imposição das restrições da geometria epipolar. 2020. 103 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2020.