Vision-guided autonomous landing technology for noncooperative target

Quanrui Chen; Zhiying Chen; Zhuo Zhang; Liangchao Guo; Xiaoliang Sun

doi:10.1117/12.3035597

16 August 2024 Vision-guided autonomous landing technology for noncooperative target

Quanrui Chen, Zhiying Chen, Zhuo Zhang, Liangchao Guo, Xiaoliang Sun

Author Affiliations +

Proceedings Volume 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024); 132302K (2024) https://doi.org/10.1117/12.3035597
Event: Third International Conference on Machine Vision, Automatic Identification and Detection, 2024, Kunming, China

Abstract

Traditional vision-guided aircraft autonomous landing technology relies on artificial design features such as cooperative markers. In complex environments, these systems are unreliable. For the above problems, this paper proposes that vision-guided autonomous landing technology for non-cooperative target, which utilizes the geometric features of the target without relying on the cooperative target. The system first extracts 2D key points under the object and key point detection based on deep learning, and then solves the Perspective-n-Point problem to obtain the pose. The aircraft can landing by taking images and calculate the pose steadily. In this paper, a visual guidance system composed of hardware such as UAV and camera is used for experiments. Experiments show that the system can successfully achieve visual guided landing. Pose update frequency is higher than 10 Hz, the average error of key point prediction is less than 2.5 pixels, and the reprojection error is less than 4 pixels.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Quanrui Chen, Zhiying Chen, Zhuo Zhang, Liangchao Guo, and Xiaoliang Sun "Vision-guided autonomous landing technology for noncooperative target", Proc. SPIE 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024), 132302K (16 August 2024); https://doi.org/10.1117/12.3035597

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE