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Prediction-Based Perspective Warping of Feature Template for Improved Visual SLAM Accuracy

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Man–Machine Interactions 4

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 391))

Abstract

The paper presents an improved method for feature matching in the visual simultaneous localization and mapping (SLAM) system. The appearance of the point feature’s neighborhood observed from a different camera pose is estimated according to the predicted displacement of the camera. As a result the precision of feature matching increases and so does the accuracy of the trajectory’s reconstruction. The proposed method was compared with the state-of-the-art feature detectors and descriptors in the context of visual SLAM. The obtained results place it on par with the best feature descriptors in terms of the system’s accuracy while having significantly smaller computational requirements.

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Acknowledgments

This research was supported by the Polish National Science Centre grant funded according to the decision DEC-2011/01/N/ST7/05940.

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Authors and Affiliations

  1. Poznan University of Technology, Poznan, Poland

    Adam Schmidt

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  1. Adam Schmidt
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Correspondence to Adam Schmidt .

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Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Aleksandra Gruca

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Agnieszka Brachman

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Tadeusz Czachórski

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Schmidt, A. (2016). Prediction-Based Perspective Warping of Feature Template for Improved Visual SLAM Accuracy. In: Gruca, A., Brachman, A., Kozielski, S., Czachórski, T. (eds) Man–Machine Interactions 4. Advances in Intelligent Systems and Computing, vol 391. Springer, Cham. https://doi.org/10.1007/978-3-319-23437-3_14

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  • DOI: https://doi.org/10.1007/978-3-319-23437-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23436-6

  • Online ISBN: 978-3-319-23437-3

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