This work proposes a novel deep network architecture to solve the camera ego-motion estimation problem. A motion estimation network generally learns features similar to optical flow (OF) fields starting from sequences of images. This OF can be described by a lower dimensional latent space. Previous research has shown how to find linear approximations of this space. We propose to use an autoencoder network to find a nonlinear representation of the OF manifold. In addition, we propose to learn the latent space jointly with the estimation task, so that the learned OF features become a more robust description of the OF input. We call this novel architecture latent space visual odometry (LS-VO). The experiments show that LS-VO achieves a considerable increase in performances with respect to baselines, while the number of parameters of the estimation network only slightly increases.

LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation / Costante, G.; Ciarfuglia, T. A.. - In: IEEE ROBOTICS AND AUTOMATION LETTERS. - ISSN 2377-3766. - 3:3(2018), pp. 1735-1742. [10.1109/LRA.2018.2803211]

LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation

Ciarfuglia T. A.
2018

Abstract

This work proposes a novel deep network architecture to solve the camera ego-motion estimation problem. A motion estimation network generally learns features similar to optical flow (OF) fields starting from sequences of images. This OF can be described by a lower dimensional latent space. Previous research has shown how to find linear approximations of this space. We propose to use an autoencoder network to find a nonlinear representation of the OF manifold. In addition, we propose to learn the latent space jointly with the estimation task, so that the learned OF features become a more robust description of the OF input. We call this novel architecture latent space visual odometry (LS-VO). The experiments show that LS-VO achieves a considerable increase in performances with respect to baselines, while the number of parameters of the estimation network only slightly increases.
2018
Computer vision for transportation; deep learning in robotics and automation; visual learning; visual-based navigation
01 Pubblicazione su rivista::01a Articolo in rivista
LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation / Costante, G.; Ciarfuglia, T. A.. - In: IEEE ROBOTICS AND AUTOMATION LETTERS. - ISSN 2377-3766. - 3:3(2018), pp. 1735-1742. [10.1109/LRA.2018.2803211]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1494365
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