Continuous and Autonomous Digital Twinning of Large-Scale Dynamic Indoor Environments

Michael G. Adam, Martin Piccolrovazzi, Ahmed Dalloul, Christian Werner, Eckehard Steinbach

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In the context of Industry 4.0, the interest in digitalizing manufacturing environments is steadily increasing. Hence, the need of a frequently updated digital twin of the facilities is also growing. In order to create a digital twin, specialized hardware is used to capture data with lidars and cameras. The data is then processed by a SLAM algorithm. However, the data acquisition process is typically done manually by multiple employees. This requires dedicated training and many working hours and hence is not feasible to do on a daily basis. In this paper, we present a solution to automate the data acquisition process, by combining an autonomous mobile robot and a scanning device. We show that the quality of the resulting point clouds matches the one of the manual scanning process and is hence ready to be deployed in real environments.

Original languageEnglish
Title of host publication2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023
PublisherIEEE Computer Society
ISBN (Electronic)9798350320695
DOIs
StatePublished - 2023
Event19th IEEE International Conference on Automation Science and Engineering, CASE 2023 - Auckland, New Zealand
Duration: 26 Aug 202330 Aug 2023

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2023-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference19th IEEE International Conference on Automation Science and Engineering, CASE 2023
Country/TerritoryNew Zealand
CityAuckland
Period26/08/2330/08/23

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