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Task and Spatial Planning by the Cognitive Agent with Human-Like Knowledge Representation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

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

Authors : Andrey Bokovoy, Konstantin Yakovlev

Published in: Interactive Collaborative Robotics

Publisher: Springer International Publishing

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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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Footnotes
1
For the sake of simplicity we assume that the image is grayscale and pixels are real numbers.
 
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Metadata
Title
Sparse 3D Point-Cloud Map Upsampling and Noise Removal as a vSLAM Post-Processing Step: Experimental Evaluation
Authors
Andrey Bokovoy
Konstantin Yakovlev
Copyright Year
2018
Book
Interactive Collaborative Robotics

Print ISBN: 978-3-319-99581-6

Electronic ISBN: 978-3-319-99582-3

Copyright Year: 2018

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

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