From point cloud to indoorGML

Ki Joune Li; Dean Hintz; Nathan Doh; Mohsen Kalantari; Sung Hwan Kim; Jeb Benson; Bart De Lathouwer; Scott Seriell

From point cloud to indoorGML

Ki Joune Li, Dean Hintz, Nathan Doh, Mohsen Kalantari, Sung Hwan Kim, Jeb Benson, Bart De Lathouwer, Scott Seriell

Research output: Contribution to journal › Conference article › peer-review

Abstract

While a number of methods have been proposed to build indoor map data, LiDAR is also a promising approach among them.A pilot project was launched in 2018 with an aim to investigate the feasibility of generating indoor maps in a standard format, OGC IndoorGML [4] with public safety features from point cloud data. It may be the eventual goal of the pilot to generate IndoorGML data in fully automated ways but many technical challenges prevent us from achieving this goal in reality. In this paper, we discuss important technical issues that we encountered during the pilot project and present a semi-automatic approaches to generate IndoorGML data from point cloud data collected by LiDAR sensors.

Original language	English
Pages (from-to)	211-218
Number of pages	8
Journal	CEUR Workshop Proceedings
Volume	2498
Publication status	Published - 2019
Externally published	Yes
Event	Short Paper of the 10th International Conference on Indoor Positioning and Indoor Navigation - Work-in-Progress Papers, IPIN-WiP 2019 - Pisa, Italy Duration: 2019 Sept 30 → 2019 Oct 3

Bibliographical note

Publisher Copyright:
© 2019 CEUR Workshop Proceedings. All rights reserved.

Keywords

Indoor Mapping
OGC IndoorGML
Point Cloud

ASJC Scopus subject areas

General Computer Science

Cite this

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title = "From point cloud to indoorGML",

abstract = "While a number of methods have been proposed to build indoor map data, LiDAR is also a promising approach among them.A pilot project was launched in 2018 with an aim to investigate the feasibility of generating indoor maps in a standard format, OGC IndoorGML [4] with public safety features from point cloud data. It may be the eventual goal of the pilot to generate IndoorGML data in fully automated ways but many technical challenges prevent us from achieving this goal in reality. In this paper, we discuss important technical issues that we encountered during the pilot project and present a semi-automatic approaches to generate IndoorGML data from point cloud data collected by LiDAR sensors.",

keywords = "Indoor Mapping, OGC IndoorGML, Point Cloud",

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year = "2019",

language = "English",

volume = "2498",

pages = "211--218",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

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TY - JOUR

T1 - From point cloud to indoorGML

AU - Li, Ki Joune

AU - Hintz, Dean

AU - Doh, Nathan

AU - Kalantari, Mohsen

AU - Kim, Sung Hwan

AU - Benson, Jeb

AU - De Lathouwer, Bart

AU - Seriell, Scott

PY - 2019

Y1 - 2019

N2 - While a number of methods have been proposed to build indoor map data, LiDAR is also a promising approach among them.A pilot project was launched in 2018 with an aim to investigate the feasibility of generating indoor maps in a standard format, OGC IndoorGML [4] with public safety features from point cloud data. It may be the eventual goal of the pilot to generate IndoorGML data in fully automated ways but many technical challenges prevent us from achieving this goal in reality. In this paper, we discuss important technical issues that we encountered during the pilot project and present a semi-automatic approaches to generate IndoorGML data from point cloud data collected by LiDAR sensors.

AB - While a number of methods have been proposed to build indoor map data, LiDAR is also a promising approach among them.A pilot project was launched in 2018 with an aim to investigate the feasibility of generating indoor maps in a standard format, OGC IndoorGML [4] with public safety features from point cloud data. It may be the eventual goal of the pilot to generate IndoorGML data in fully automated ways but many technical challenges prevent us from achieving this goal in reality. In this paper, we discuss important technical issues that we encountered during the pilot project and present a semi-automatic approaches to generate IndoorGML data from point cloud data collected by LiDAR sensors.

KW - Indoor Mapping

KW - OGC IndoorGML

KW - Point Cloud

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M3 - Conference article

AN - SCOPUS:85075630901

SN - 1613-0073

VL - 2498

SP - 211

EP - 218

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - Short Paper of the 10th International Conference on Indoor Positioning and Indoor Navigation - Work-in-Progress Papers, IPIN-WiP 2019

Y2 - 30 September 2019 through 3 October 2019

ER -

From point cloud to indoorGML

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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