Vision-based system identification of a quadrotor

İz, Selim Ahmet and Ünel, Mustafa (2023) Vision-based system identification of a quadrotor. In: 8th International Conference on Image, Vision and Computing (ICIVC 2023), Dalian, China

[thumbnail of ICIVC2023-D14191.pdf] PDF

Download (1MB)


This paper explores the application of vision-based system identification techniques in quadrotor modeling and control. Through experiments and analysis, we address the complexities and limitations of quadrotor modeling, particularly in relation to thrust and drag coefficients. Grey-box modeling is employed to mitigate uncertainties, and the effectiveness of an onboard vision system is evaluated. An LQR controller is designed based on a system identification model using data from the onboard vision system. The results demonstrate consistent performance between the models, validating the efficacy of vision-based system identification. This study highlights the potential of vision-based techniques in enhancing quadrotor modeling and control, contributing to improved performance and operational capabilities. Our findings provide insights into the usability and consistency of these techniques, paving the way for future research in quadrotor performance enhancement, fault detection, and decision-making processes.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: onboard sensing system; quadrotor modeling; system identification; vision-based localization
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ163.12 Mechatronics
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Mechatronics
Faculty of Engineering and Natural Sciences
Depositing User: Mustafa Ünel
Date Deposited: 03 Oct 2023 21:52
Last Modified: 07 Feb 2024 15:25

Actions (login required)

View Item
View Item