TY - GEN
T1 - Continuous and Autonomous Digital Twinning of Large-Scale Dynamic Indoor Environments
AU - Adam, Michael G.
AU - Piccolrovazzi, Martin
AU - Dalloul, Ahmed
AU - Werner, Christian
AU - Steinbach, Eckehard
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85174396270&partnerID=8YFLogxK
U2 - 10.1109/CASE56687.2023.10260595
DO - 10.1109/CASE56687.2023.10260595
M3 - Conference contribution
AN - SCOPUS:85174396270
T3 - IEEE International Conference on Automation Science and Engineering
BT - 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023
PB - IEEE Computer Society
T2 - 19th IEEE International Conference on Automation Science and Engineering, CASE 2023
Y2 - 26 August 2023 through 30 August 2023
ER -