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IBM PAIRS: Scalable Big Geospatial-Temporal Data and Analytics As-a-Service Read chapter Read first chapter

2021 | OriginalPaper | Chapter

3. Indoor 3D: Overview on Scanning and Reconstruction Methods

Authors : Ville V. Lehtola, Shayan Nikoohemat, Andreas Nüchter

Published in: Handbook of Big Geospatial Data

Publisher: Springer International Publishing

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Abstract

This chapter covers the essentials regarding indoor 3D data, from scanning to reconstruction. It is aimed for education and professionals. The order of presentation is background, history in measurement method development, sensors, sensor systems, positioning algorithms, reconstruction, and applications. The authors’ backgrounds are in indoor 3D, mobile laser scanning, indoor reconstruction, and robotics. In order to maintain a coherence in the text and provide some useful tools for the reader, we have selected to focus solely on the ICP version of simultaneous localization and mapping (SLAM). Regardless, this should give a solid base for the reader to understand other (e.g. probabilistic) indoor SLAM methods as well. Reconstruction algorithms (starting from room segmentation and opening detection) are discussed with the help of abundant figures. At the very end, we discuss future trends with a connection to the current applications and propose some exercise questions for students.

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Footnotes
1
The terms dead reckoning and pedestrian dead reckoning are used in the field of positioning and navigation.
 
2
Note that ready open source SLAM codes are also available, e.g. https://​github.​com/​googlecartograph​er/​ (Hess et al. 2016).
 
3
Typically, the origin is chosen to be at the start point of scanning, i.e. (x, y, z) = (0,  0,  0).
 
4
Navipedia of European Space Agency: https://​gssc.​esa.​int/​navipedia/​
 
5
In reconstruction literature, voxel maps are also referred to as Manhattan world approximation.
 
6
Today the commercial RGB-D cameras have a range of only up to 10 m.
 
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Metadata
Title
Indoor 3D: Overview on Scanning and Reconstruction Methods
Authors
Ville V. Lehtola
Shayan Nikoohemat
Andreas Nüchter
Copyright Year
2021
Book
Handbook of Big Geospatial Data

Print ISBN: 978-3-030-55461-3

Electronic ISBN: 978-3-030-55462-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-55462-0_3

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