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Probabilistic Combination of Noisy Points and Planes for RGB-D Odometry

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Towards Autonomous Robotic Systems (TAROS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10454))

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Abstract

This work proposes a visual odometry method that combines points and plane primitives, extracted from a noisy depth camera. Depth measurement uncertainty is modelled and propagated through the extraction of geometric primitives to the frame-to-frame motion estimation, where pose is optimized by weighting the residuals of 3D point and planes matches, according to their uncertainties. Results on an RGB-D dataset show that the combination of points and planes, through the proposed method, is able to perform well in poorly textured environments, where point-based odometry is bound to fail.

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Acknowledgments

This work was supported by Sellafield Ltd.

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Authors and Affiliations

  1. Faculty of Engineering and Physical Sciences, Surrey Space Centre, University of Surrey, Guildford, UK

    Pedro F. Proença & Yang Gao

Authors
  1. Pedro F. Proença
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  2. Yang Gao
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Corresponding author

Correspondence to Pedro F. Proença .

Editor information

Editors and Affiliations

  1. University of Surrey, Guildford, United Kingdom

    Yang Gao

  2. University of Surrey, Guildford, United Kingdom

    Saber Fallah

  3. University of Surrey, Guildford, United Kingdom

    Yaochu Jin

  4. University of Surrey, Guildford, United Kingdom

    Constantina Lekakou

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Proença, P.F., Gao, Y. (2017). Probabilistic Combination of Noisy Points and Planes for RGB-D Odometry. In: Gao, Y., Fallah, S., Jin, Y., Lekakou, C. (eds) Towards Autonomous Robotic Systems. TAROS 2017. Lecture Notes in Computer Science(), vol 10454. Springer, Cham. https://doi.org/10.1007/978-3-319-64107-2_27

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  • DOI: https://doi.org/10.1007/978-3-319-64107-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64106-5

  • Online ISBN: 978-3-319-64107-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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