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An Iterative Closest Point Method for Measuring the Level of Similarity of 3D Log Scans in Wood Industry

  • Cyrine Selma
  • Hind Bril El Haouzi
  • Philippe Thomas
  • Jonathan Gaudreault
  • Michael Morin
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 762)

Abstract

In the Canadian’s lumber industry, simulators are used to predict the lumbers resulting from the sawing of a log at a given sawmill. Giving a log or several logs’ 3D scans as input, simulators perform a real-time job to predict the lumbers. These simulators, however, tend to be slow at processing large volumes of wood. We thus explore an alternative approximation techniques based on the Iterative Closest Point (ICP) algorithm to identify the already processed log to which an unseen log resembles the most. The main benefit of the ICP approach is that it can easily handle 3D scans with a variable number of points. We compare this ICP-based nearest neighbour predictor, to predictors built using machine learning algorithms such as the K-nearest-neighbour (kNN) and Random Forest (RF). The implemented ICP-based predictor enabled us to identify key points in using the 3D scans directly for distance calculation. The long-term goal of this on-going research is to integrated ICP distance calculations and machine learning.

Keywords

Sawing simulation Iterative closest point Machine learning application 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Cyrine Selma
    • 1
    • 2
  • Hind Bril El Haouzi
    • 1
    • 2
  • Philippe Thomas
    • 1
    • 2
  • Jonathan Gaudreault
    • 3
    • 4
  • Michael Morin
    • 4
    • 5
  1. 1.Université de LorraineVandœuvre-lès-Nancy CedexFrance
  2. 2.CNRS, CRAN, UMR 7039Vandœuvre-lès-Nancy CedexFrance
  3. 3.Department of Computer Science & Software EngineeringUniversité LavalQuébecCanada
  4. 4.FORAC Research Consortium, Université LavalQuébecCanada
  5. 5.Department of Mechanical & Industrial EngineeringUniversity of TorontoTorontoCanada

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