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Using Dense 3D Reconstruction for Visual Odometry Based on Structure from Motion Techniques

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10073))

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Abstract

Aim of intense research in the field computational vision, dense 3D reconstruction achieves an important landmark with first methods running in real time with millimetric precision, using RGBD cameras and GPUs. However, these methods are not suitable for low computational resources. The goal of this work is to show a method of visual odometry using regular cameras, without using a GPU. The proposed method is based on techniques of sparse Structure from Motion (SFM), using data provided by dense 3D reconstruction. Visual odometry is the process of estimating the position and orientation of an agent (a robot, for instance), based on images. This paper compares the proposed method with the odometry calculated by Kinect Fusion. Odometry provided by this work can be used to model a camera position and orientation from dense 3D reconstruction.

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Author information

Authors and Affiliations

  1. Department of Informatics, Pontifical Catholic University of Rio de Janeiro, PUC-Rio, Rio de Janeiro, Rio de Janeiro, Brazil

    Marcelo de Mattos Nascimento, Manuel Eduardo Loaiza Fernandez & Alberto Barbosa Raposo

Authors
  1. Marcelo de Mattos Nascimento
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  2. Manuel Eduardo Loaiza Fernandez
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  3. Alberto Barbosa Raposo
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Corresponding author

Correspondence to Manuel Eduardo Loaiza Fernandez .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University, O’Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs, Redwood City, California, USA

    Sandra Skaff

  7. University of Florida, Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc., Mountain View, California, USA

    Jianyuan Min

  9. Osaka University, Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute, Monterey, California, USA

    Amela Sadagic

  11. University of Arizona, Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud, Orsay, France

    Tobias Isenberg

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de Mattos Nascimento, M., Fernandez, M.E.L., Raposo, A.B. (2016). Using Dense 3D Reconstruction for Visual Odometry Based on Structure from Motion Techniques. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_47

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  • DOI: https://doi.org/10.1007/978-3-319-50832-0_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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