Vision-based estimation of bolt-hole location using circular hough transform

Yungeun Choe, Hoo Cheol Lee, Young Joong Kim, Dae Hie Hong, Sin Suk Park, Myo Taeg Lim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

In order to use a robot in the construction automation filed, we proposed the concept of Bolting Robot and a visual servo control scheme to track a bolting tool to a bolt hole in the structural steel frame. For estimating a location of a bolt hole, Circular Hough Transform (CHT) was used to extract circles. Generally, CHT is computationally complex due to a power of the dimensionality of a circle. A distance from a camera to a steel frame can be measured by using laser range-finder installed. The radius of a bolt hole can be calculated with the distance to a steel frame. Since the radius is known, the processing of CHT can be reduced to 2D. In addition, it contains image pre-processing to make an image of bolt holes to be clear. Pre-processing has 4 steps which consist of compensating lens distortion, noise filtering, histogram equalization, and edge detection.

Original languageEnglish
Title of host publicationICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
Pages4821-4826
Number of pages6
Publication statusPublished - 2009
EventICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka, Japan
Duration: 2009 Aug 182009 Aug 21

Publication series

NameICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings

Other

OtherICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009
Country/TerritoryJapan
CityFukuoka
Period09/8/1809/8/21

Keywords

  • Bolt hole detection
  • Circular hough transform
  • Construction automation
  • Image processing
  • Robot

ASJC Scopus subject areas

  • Information Systems
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Fingerprint

Dive into the research topics of 'Vision-based estimation of bolt-hole location using circular hough transform'. Together they form a unique fingerprint.

Cite this