Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds

Soon-Wook Kwon, Frederic Nicolas Bosche, Changwan Kim, Carl Haas, Katherine Liapi

Research output: Contribution to journalArticlepeer-review

Abstract

Techniques to rapidly model local spaces, using 3D range data, can enable implementation of. (1) real-time obstacle avoidance for improved safety, (2) advanced automated equipment control modes, and (3) as-built data acquisition for improved quantity tracking, engineering, and project control systems. The objective of the research reported here was to develop rapid local spatial modeling tools. Algorithms for fitting sparse range point clouds to geometric primitives such as spheres, cylinders, and cuboids have been developed as well as methods for merging primitives into assemblies. Results of experiments are presented and practical usage and limitations are discussed. (C) 2003 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)67-81
Number of pages15
JournalAutomation in Construction
Volume13
Issue number1
Early online date21 Nov 2003
DOIs
Publication statusPublished - Jan 2004

Keywords

  • sparse range point clouds
  • 3D workspace modeling
  • fitting and matching objects
  • merging objects
  • AUTOMATION

Fingerprint Dive into the research topics of 'Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds'. Together they form a unique fingerprint.

Cite this