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Dettaglio pubblicazione

2018, IEEE ROBOTICS AND AUTOMATION LETTERS, Pages 1735-1742 (volume: 3)

LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation (01a Articolo in rivista)

Costante G., Ciarfuglia T. A.

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.
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