Abstract
Digital twin technology is a new tool for automatic network planning and optimization. However, for indoor private networks, such as Industry 4.0 supported manufacturing plants, an exact “twin” of the real environment further leads to overly complex radio propagation modeling. We propose a feature and object-based monocular simultaneous localization and mapping (SLAM) algorithm called Simultaneous Localization and Mapping with Object REcognition (SLAMORE), that can reconstruct an “extracted” version of the radio propagation environment by detecting, tracking, localizing, and reconstructing the major obstacles for electromagnetic waves. The embedded convolutional object detector helps recognize and reconstruct major obstacles, and provides reference to estimate the room scale and real world coordinate. We conduct a proof of concept in an office environment and show that the mean absolute percentage errors for room size estimation and object position estimation achieve approximately 2.6-5.7% and 1.8-2.7%, respectively.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
