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Sparse 3D Point-Cloud Map Upsampling and Noise Removal as a vSLAM Post-Processing Step: Experimental Evaluation

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Interactive Collaborative Robotics (ICR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11097))

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Abstract

The monocular vision-based simultaneous localization and mapping (vSLAM) is one of the most challenging problem in mobile robotics and computer vision. In this work we study the post-processing techniques applied to sparse 3D point-cloud maps, obtained by feature-based vSLAM algorithms. Map post-processing is split into 2 major steps: (1) noise and outlier removal and (2) upsampling. We evaluate different combinations of known algorithms for outlier removing and upsampling on datasets of real indoor and outdoor environments and identify the most promising combination. We further use it to convert a point-cloud map, obtained by the real UAV performing indoor flight to 3D voxel grid (octo-map) potentially suitable for path planning.

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Notes

  1. 1.

    For the sake of simplicity we assume that the image is grayscale and pixels are real numbers.

  2. 2.

    https://github.com/raulmur/ORB_SLAM2

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Acknowledgments

This work was partially supported by the “RUDN University Program 5-100” and by the RFBR project No. 17-29-07053.

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Authors and Affiliations

  1. Peoples Friendship University of Russia (RUDN University), Moscow, Russia

    Andrey Bokovoy

  2. Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, Moscow, Russia

    Andrey Bokovoy & Konstantin Yakovlev

  3. National Research University Higher School of Economics, Moscow, Russia

    Konstantin Yakovlev

Authors
  1. Andrey Bokovoy
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  2. Konstantin Yakovlev
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Correspondence to Andrey Bokovoy .

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Editors and Affiliations

  1. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia

    Andrey Ronzhin

  2. TU Munich, Munich, Germany

    Gerhard Rigoll

  3. Institute for Control Problem of the Russian Academy of Sciences, Moscow, Russia

    Roman Meshcheryakov

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Bokovoy, A., Yakovlev, K. (2018). Sparse 3D Point-Cloud Map Upsampling and Noise Removal as a vSLAM Post-Processing Step: Experimental Evaluation. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2018. Lecture Notes in Computer Science(), vol 11097. Springer, Cham. https://doi.org/10.1007/978-3-319-99582-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-99582-3_3

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