SiameseVO-Depth: odometria visual através de redes neurais convolucionais siamesas
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2018-10-11
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Universidade Federal de Goiás
Resumo
Visual Odometry is an important process in image based navigation of robots. The
standard methods of this field rely on the good feature matching between frames where
feature detection on images stands as a well adressed problem within Computer Vision.
Such techniques are subject to illumination problems, noise and poor feature localization
accuracy. Thus, 3D information on a scene may mitigate the uncertainty of the features
on images. Deep Learning techniques show great results when dealing with common
difficulties of VO such as low illumination conditions and bad feature selection. While
Visual Odometry and Deep Learning have been connected previously, no techniques
applying Siamese Convolutional Networks on depth infomation given by disparity maps
have been acknowledged as far as this work’s researches went. This work aims to fill
this gap by applying Deep Learning to estimate egomotion through disparity maps on
an Siamese architeture. The SiameseVO-Depth architeture is compared to state of the art
techniques on OV by using the KITTI Vision Benchmark Suite. The results reveal that the
chosen methodology succeeded on the estimation of Visual Odometry although it doesn’t
outperform the state-of-the-art techniques. This work presents fewer steps in relation to
standard VO techniques for it consists of an end-to-end solution and demonstrates a new
approach of Deep Learning applied to Visual Odometry.
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Citação
SANTOS, V. A. SiameseVO-Depth: odometria visual através de redes neurais convolucionais siamesas. 2018. 73 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2018.