KLTVO: Algoritmo de Odometria Visual estéreo baseada em seleção de pontos chaves pela imposição das restrições da geometria epipolar

• Autor(a) principal: Dias, Nigel Joseph Bandeira
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFG
• Texto Completo: http://repositorio.bc.ufg.br/tede/handle/tede/10746
• topic: Odometria Visual; VO; Seleção de pontos chaves; Robótica móvel; Visual Odometry; VO; Keypoint selection; Robotics; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

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.