Joint calibration of Machine Vision subsystems for robuster surrounding 3D perception

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

  • Ruben Alaniz-Plata
  • Fernando Lopez-Medina
  • Oleg Sergiyenko
  • José A. Nuñez-López
  • Cesar Sepulveda-Valdez
  • David Meza-García
  • J. Fabián Villa-Manríquez
  • Humberto Andrade-Collazo
  • Wendy Flores-Fuentes
  • Julio C. Rodríguez-Quiñonez
  • Vera Tyrsa
  • Moisés Jesús Castro-Toscano
  • Paolo Mercorelli

To achieve successful autonomous navigation, a vision system that provides continuous and precise three-dimensional data of the system's surroundings is always needed. An indispensable step in the acquisition of reliable data is the calibration of the system, preferably with a time-efficient and low-complexity approach. In this paper, a robust and efficient calibration method is proposed for the information fusion of a stereo vision system and a Technical Vision System. The proposed methodology achieves an error of xe = 6.9401 mm, ye = 8.0997 mm and ze = 15.1822 mm in 3.0202 seconds of processing time, as proven through experimental results.

Original languageEnglish
Title of host publicationIECON 2024- 50th Annual Conference of the IEEE Industrial Electronics Society : Proceedings, Sheraton Grand Chicago Riverwalk Chicago, Illinois, USA 3 - 6 November 2024
Number of pages6
Place of PublicationPiscataway
PublisherIEEE Industrial Electronics Society
Publication date2024
ISBN (print)978-1-6654-6455-0
ISBN (electronic)978-1-6654-6454-3
DOIs
Publication statusPublished - 2024
Event50th Annual Conference of the IEEE Industrial Electronics Society - IECON 2024 - Chicago, United States
Duration: 03.11.202406.11.2024
Conference number: 50
https://www.iecon-2024.org/

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

    Research areas

  • Artificial Vision, Calibration, Sensor Fusion
  • Engineering