Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories

S. Nikoohemat (Corresponding Author), M.S. Peter, S.J. Oude Elberink, G. Vosselman

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
19 Downloads (Pure)

Abstract

The data acquisition with Indoor Mobile Laser Scanners (IMLS) is quick, low-cost and accurate for indoor 3D modeling. Besides a point cloud, an IMLS also provides the trajectory of the mobile scanner. We analyze this trajectory jointly with the point cloud to support the labeling of noisy, highly reflected and cluttered points in indoor scenes. An adjacency-graph-based method is presented for detecting and labeling of permanent structures, such as walls, floors, ceilings, and stairs. Through occlusion reasoning and the use of the trajectory as a set of scanner positions, gaps are discriminated from real openings in the data. Furthermore, a voxel-based method is applied for labeling of navigable space and separating them from obstacles. The results show that 80% of the doors and 85% of the rooms are correctly detected, and most of the walls and openings are reconstructed. The experimental outcomes indicate that the trajectory of MLS systems plays an essential role in the understanding of indoor scenes
Original languageEnglish
Article number1754
Pages (from-to)1-23
Number of pages23
JournalRemote sensing
Volume10
Issue number11
DOIs
Publication statusPublished - 7 Nov 2018

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scanner
laser
trajectory
data acquisition
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modeling
labelling
method

Keywords

  • mobile laser scanner
  • Trajectory
  • occlusion reasoning
  • semantic labeling
  • indoor point
  • point clouds
  • lidar
  • space sub
  • indoor modeling
  • 3d modeling
  • ITC-GOLD
  • ITC-ISI-JOURNAL-ARTICLE

Cite this

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title = "Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories",
abstract = "The data acquisition with Indoor Mobile Laser Scanners (IMLS) is quick, low-cost and accurate for indoor 3D modeling. Besides a point cloud, an IMLS also provides the trajectory of the mobile scanner. We analyze this trajectory jointly with the point cloud to support the labeling of noisy, highly reflected and cluttered points in indoor scenes. An adjacency-graph-based method is presented for detecting and labeling of permanent structures, such as walls, floors, ceilings, and stairs. Through occlusion reasoning and the use of the trajectory as a set of scanner positions, gaps are discriminated from real openings in the data. Furthermore, a voxel-based method is applied for labeling of navigable space and separating them from obstacles. The results show that 80{\%} of the doors and 85{\%} of the rooms are correctly detected, and most of the walls and openings are reconstructed. The experimental outcomes indicate that the trajectory of MLS systems plays an essential role in the understanding of indoor scenes",
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year = "2018",
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Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories. / Nikoohemat, S. (Corresponding Author); Peter, M.S.; Oude Elberink, S.J.; Vosselman, G.

In: Remote sensing, Vol. 10, No. 11, 1754, 07.11.2018, p. 1-23.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Nikoohemat, S.

AU - Peter, M.S.

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