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Single and Multi-channel Direct Visual Odometry with Binary Descriptors

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Intelligent Computing Methodologies (ICIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11645))

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Abstract

Visual odometry is a popular area of computer vision that has seen a paradigm shift towards direct methods, where whole image alignment is used to determine camera poses. Current methods not robust to lighting changes in the scene and rely on standard feature-based methods for multi-camera systems. Binary descriptors are an option for alleviating both problems, but current methods do not scale well to larger and more robust descriptors. We present a method for performing direct tracking with binary descriptors of any size by approximating the gradient and descent direction with Hamming weights. We also present alternative methods that approximate the entire descriptor by its Hamming weights. Our results show improved accuracy compared to tracking on intensity alone, and our primary method improves significantly upon similar methods.

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Acknowledgements

This research has been conducted with the support of the Australian Government Research Training Program Scholarship.

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Authors and Affiliations

  1. University of Wollongong, Northfields Avenue, Wollongong, NSW, 2522, Australia

    Brendan Halloran, Prashan Premaratne, Peter James Vial & Inas Kadhim

Authors
  1. Brendan Halloran
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  2. Prashan Premaratne
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  3. Peter James Vial
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  4. Inas Kadhim
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Corresponding author

Correspondence to Brendan Halloran .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Nanchang Institute of Technology, Nanchang, China

    Zhi-Kai Huang

  3. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Halloran, B., Premaratne, P., Vial, P.J., Kadhim, I. (2019). Single and Multi-channel Direct Visual Odometry with Binary Descriptors. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_9

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  • DOI: https://doi.org/10.1007/978-3-030-26766-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26765-0

  • Online ISBN: 978-3-030-26766-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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