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

  • Andrey BokovoyEmail author
  • Konstantin Yakovlev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11097)

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.

Keywords

3D Point-cloud Outlier removal Upsampling vSLAM 3D path planning Sparse map Feature-based vSLAM 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Peoples Friendship University of Russia (RUDN University)MoscowRussia
  2. 2.Federal Research Center “Computer Science and Control” of Russian Academy of SciencesMoscowRussia
  3. 3.National Research University Higher School of EconomicsMoscowRussia

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