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arXiv · 1709.06019

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

Gabriele Costante·Thomas A. Ciarfuglia
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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 Auto-Encoder network to find a non-linear 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 LS-VO. The experiments show that LS-VO achieves a considerable increase in performances in respect to baselines, while the number of parameters of the estimation network only slightly increases.

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Gabriele Costante, Thomas A. Ciarfuglia. 2017-09-18. LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation. https://doi.org/10.1109/lra.2018.2803211

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