Learning Visual Odometry with a Convolutional Network

Kishore Konda; Roland Memisevic

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Paper

Learning Visual Odometry with a Convolutional Network

Topics: Early and Biologically-Inspired Vision; Features Extraction; Machine Learning Technologies for Vision; Vision for Robotics

In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, 486-490, 2015 , Berlin, Germany

Authors: Kishore Konda ¹ and Roland Memisevic ²

Affiliations: ¹ Goethe University Frankfurt, Germany ; ² University of Montreal, Canada

Keyword(s): Visual Odometry, Convolutional Networks, Motion, Stereo.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Early and Biologically-Inspired Vision ; Features Extraction ; Image and Video Analysis ; Pattern Recognition ; Robotics ; Software Engineering

Abstract: We present an approach to predicting velocity and direction changes from visual information (”visual odometry”) using an end-to-end, deep learning-based architecture. The architecture uses a single type of computational module and learning rule to extract visual motion, depth, and finally odometry information from the raw data. Representations of depth and motion are extracted by detecting synchrony across time and stereo channels using network layers with multiplicative interactions. The extracted representations are turned into information about changes in velocity and direction using a convolutional neural network. Preliminary results show that the architecture is capable of learning the resulting mapping from video to egomotion.

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Paper citation in several formats:

Konda, K. and Memisevic, R. (2015). Learning Visual Odometry with a Convolutional Network. In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 2: VISAPP; ISBN 978-989-758-089-5; ISSN 2184-4321, SciTePress, pages 486-490. DOI: 10.5220/0005299304860490

@conference{visapp15,
author={Kishore Konda. and Roland Memisevic.},
title={Learning Visual Odometry with a Convolutional Network},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 2: VISAPP},
year={2015},
pages={486-490},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005299304860490},
isbn={978-989-758-089-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 2: VISAPP
TI - Learning Visual Odometry with a Convolutional Network
SN - 978-989-758-089-5
IS - 2184-4321
AU - Konda, K.
AU - Memisevic, R.
PY - 2015
SP - 486
EP - 490
DO - 10.5220/0005299304860490
PB - SciTePress