Precision landing systems for unmanned aerial vehicles (UAVs) present a significant challenge that must be addressed to prevent damage and accidents. Traditional object detection systems for precision landing often require complex computations and substantial processing power, which can be difficult to implement on the limited resources of a typical flight computer. This study proposes the use of computer vision technology, specifically ArUco markers, to detect and track a drone's landing pad. In this research, a precision landing system utilizing ArUco markers is implemented on a quadcopter drone. The system employs a webcam module to detect the ArUco marker on the landing pad and a computer vision algorithm to track it. A flight controller estimates the relative position and orientation of the quadcopter, enabling precise landing on the pad. The proposed methodology includes camera modeling and calibration, marker detection, marker pose estimation, and quadcopter control. Experimental results demonstrate a significant improvement in landing accuracy when compared to traditional GPS-based landing. The system achieved an average landing accuracy of 34.1 cm, compared to 121.1 cm with GPS alone, resulting in an 87% accuracy improvement. These findings highlight the effectiveness of the proposed ArUco marker-based precision landing system in enhancing the landing accuracy of UAVs.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Computer Vision-Driven Precision Landing in Quadcopter Drones Using ArUco Markers




    Publication date :

    2024-08-07


    Size :

    729240 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    ArUco markers pose estimation in UAV landing aid system

    Marut, Adam / Wojtowicz, Konrad / Falkowski, Krzysztof | IEEE | 2019


    Embedded ArUco: a novel approach for high precision UAV landing

    Khazetdinov, Artur / Zakiev, Aufar / Tsoy, Tatyana et al. | IEEE | 2021


    Vision-Based Autonomous Ship Deck Landing of an Unmanned Aerial Vehicle Using Fractal ArUco Markers

    Prachand, Chiranjeev / Rustagi, Rahul / Shankar, Ritwik et al. | AIAA | 2025


    Autonomous Docking for Underwater Drones Using ArUco Marker Based Localization

    Saju Joseph, Benaiah / Gunturu, Shasank / Shrote, Sumukh et al. | IEEE | 2024


    Monocular Visual Autonomous Landing System for Quadcopter Drones Using Software in the Loop

    Saavedra-Ruiz, Miguel / Pinto-Vargas, Ana Maria / Romero-Cano, Victor | IEEE | 2022