Abstract
Aiming at the attitude estimation of the telescopic arm of the boarding bridge in the process of docking with the offshore wind turbine, an attitude estimation algorithm for the telescopic arm of the boarding bridge based on YOLOv5 and EPnP is proposed in this paper. YOLOv5 algorithm is used to detect four marks in the offshore wind turbine logo image, and then the EPnP algorithm is used to solve attitude angles of the telescopic arm relative to the landing port of the offshore wind turbine according to the 2D pixel coordinates of the center point of each mark. The experimental results show that the attitude estimation error of the proposed algorithm is less than 0.4° and the data update frequency of the algorithm implemented in NVIDIA Jetson AGX Xavier is 25Hz, which meets the real-time and accuracy requirements for the attitude detection of the boarding bridge telescopic arm.
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.
