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Comparison of the Selected State-Of-The-Art 3D Indoor Scanning and Point Cloud Generation Methods

Authors :
Ville V. Lehtola
Harri Kaartinen
Andreas Nüchter
Risto Kaijaluoto
Antero Kukko
Paula Litkey
Eija Honkavaara
Tomi Rosnell
Matti T. Vaaja
Juho-Pekka Virtanen
Matti Kurkela
Aimad El Issaoui
Lingli Zhu
Anttoni Jaakkola
Juha Hyyppä
Source :
Remote Sensing, Vol 9, Iss 8, p 796 (2017)
Publication Year :
2017
Publisher :
MDPI AG, 2017.

Abstract

Accurate three-dimensional (3D) data from indoor spaces are of high importance for various applications in construction, indoor navigation and real estate management. Mobile scanning techniques are offering an efficient way to produce point clouds, but with a lower accuracy than the traditional terrestrial laser scanning (TLS). In this paper, we first tackle the problem of how the quality of a point cloud should be rigorously evaluated. Previous evaluations typically operate on some point cloud subset, using a manually-given length scale, which would perhaps describe the ranging precision or the properties of the environment. Instead, the metrics that we propose perform the quality evaluation to the full point cloud and over all of the length scales, revealing the method precision along with some possible problems related to the point clouds, such as outliers, over-completeness and misregistration. The proposed methods are used to evaluate the end product point clouds of some of the latest methods. In detail, point clouds are obtained from five commercial indoor mapping systems, Matterport, NavVis, Zebedee, Stencil and Leica Pegasus: Backpack, and three research prototypes, Aalto VILMA , FGI Slammer and the Würzburg backpack. These are compared against survey-grade TLS point clouds captured from three distinct test sites that each have different properties. Based on the presented experimental findings, we discuss the properties of the proposed metrics and the strengths and weaknesses of the above mapping systems and then suggest directions for future research.

Details

Language :
English
ISSN :
20724292
Volume :
9
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.5d19088c9ccd4a499a4e09c55b4fcdb6
Document Type :
article
Full Text :
https://doi.org/10.3390/rs9080796