A Structured Light Scanner for Hyper Flexible Industrial Automation

Kent Hansen, Jeppe Pedersen, Thomas Sølund, Henrik Aanaes, Dirk Kraft

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

A current trend in industrial automation implies a need for doing automatic scene understanding, from optical 3D sensors, which in turn imposes a need for a lightweight and reliable 3D optical sensor to be mounted on a collaborative robot e.g., Universal Robot UR5 or Kuka LWR. Here, we empirically evaluate the feasibility of structured light scanners for this purpose, by presenting a system optimized for this task. The system incorporates several recent advances in structured light scanning, such as Large-Gap Gray encoding for dealing with defocusing, automatic creation of illumination masks for noise removal, as well as employing a multi exposure approach dealing with different surface reflectance properties. In addition to this, we investigate expanding the traditional structured light setup to using three cameras, instead of one or two. Also, a novel method for fusing multiple exposures and camera pairs is given. We present an in-depth evaluation, that lead us to conclude, that this setup performs well on tasks relevant for an industrial environment, where many metallic and other surfaces with difficult reflectance properties are in abundance. We demonstrate, that the added components contribute to the robustness of the system. Hereby, we demonstrate that structured light scanning is a technology well suited for hyper flexible industrial automation, by proposing an appropriate system.
Original languageEnglish
Title of host publication2nd International Conference on 3D Vision (3DV)
Volume1
PublisherIEEE
Publication dateDec 2014
Pages401-408
DOIs
Publication statusPublished - Dec 2014
Event2nd International Conference on 3D Vision - The University of Tokyo, Tokyo, Japan
Duration: 8. Dec 201411. Dec 2014

Conference

Conference2nd International Conference on 3D Vision
LocationThe University of Tokyo
Country/TerritoryJapan
CityTokyo
Period08/12/201411/12/2014

Keywords

  • 3D reconstruction
  • 3D robot vision
  • Data fusion
  • Large-gap gray code
  • Robotics
  • Structured light

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