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Frame-to-Frame Visual Odometry: The Importance of Local Transformations

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Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017 (CORES 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 578))

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Abstract

Trajectory estimation is of pivotal importance for mobile robots. Visual Odometry (VO) allows localizing a robot from passive vision data in frame-to-frame fashion. The VO problem can be solved in different ways, hence an evaluation of these algorithms in the context of real benchmark data is interesting. We focus on feature-based n-point methods based on RGB images. These methods used in monocular vision allow for camera rotation estimation, but only a few of them provide translation estimates up to the unknown scale. In the context of the use of commodity RGB-D cameras, we also compare these methods with the Kabsch algorithm, which uses full depth information.

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Authors and Affiliations

  1. Institute of Control and Information Engineering, Poznań University of Technology, ul. Piotrowo 3A, 60-965, Poznań, Poland

    Aleksander Kostusiak

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  1. Aleksander Kostusiak
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Correspondence to Aleksander Kostusiak .

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Editors and Affiliations

  1. Department of Systems and Computer Networks, Wrocław University of Technology, Wrocław, Poland

    Marek Kurzynski

  2. Department of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michal Wozniak

  3. Department of Systems and Computer Networks, Wrocław University of Technology , Wroclaw, Poland

    Robert Burduk

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Kostusiak, A. (2018). Frame-to-Frame Visual Odometry: The Importance of Local Transformations. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_37

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  • DOI: https://doi.org/10.1007/978-3-319-59162-9_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59161-2

  • Online ISBN: 978-3-319-59162-9

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