Fusion of depth, color, and thermal images towards digital twins and safe human interaction with a robot in an industrial environment

Publikation: Beitrag in FachzeitschriftKonferenzartikelBeigetragenBegutachtung

Beitragende

Abstract

Accurate detection of the human body and its limbs in real-time is one of the challenges toward human-robot collaboration (HRC) technology in the factory of the future. In this work, a new algorithm has been developed to fuse thermal, depth, and color information. Starting with the calibration of the sensors to each other, proceeding with data fusion and detection of human body parts, and ending with pose estimation. The proposed approach has been tested in various HRC scenarios. It has shown a significant decrease in errors and noise in industrial environments. Furthermore, it produces not only a higher positioning accuracy of the head and hands compared to the state of art algorithms (e.g., OpenPose), but it is also faster. Hence, such an algorithm could be joined with digital twins for safe and efficient human-robot collaboration.

Details

OriginalspracheEnglisch
Seiten (von - bis)532-537
Seitenumfang6
FachzeitschriftIEEE International Symposium on Robot and Human Interactive Communication proceedings
Jahrgang2022
PublikationsstatusVeröffentlicht - 2022
Peer-Review-StatusJa

Konferenz

Titel31st IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2022
Dauer29 August - 2 September 2022
StadtNapoli
LandItalien

Schlagworte

Schlagwörter

  • Digital Twins, human detection, Human robot collaboration, sensor fusion

Bibliotheksschlagworte