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 journalConference articlepeer-review


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 languageEnglish
Pages (from-to)211-218
Number of pages8
JournalCEUR Workshop Proceedings
Publication statusPublished - 2019
Externally publishedYes
EventShort Paper of the 10th International Conference on Indoor Positioning and Indoor Navigation - Work-in-Progress Papers, IPIN-WiP 2019 - Pisa, Italy
Duration: 2019 Sep 302019 Oct 3


  • Indoor Mapping
  • OGC IndoorGML
  • Point Cloud

ASJC Scopus subject areas

  • Computer Science(all)


Dive into the research topics of 'From point cloud to indoorGML'. Together they form a unique fingerprint.

Cite this