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Updated: Feb 23, 2026

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Remote Marker-Based Tracking for UAV Landing Using Visible-Light Camera Sensor.

Phong Ha Nguyen1, Ki Wan Kim2, Young Won Lee3

  • 1Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea. stormwindvn@dongguk.edu.

Sensors (Basel, Switzerland)
|September 5, 2017
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Summary
This summary is machine-generated.

This study presents a novel computer vision method for safely landing drones without GPS signals. The developed marker-based tracking algorithm ensures accurate and efficient autonomous navigation in challenging environments.

Keywords:
UAV landingremote marker-based trackingunmanned aerial vehicle (UAV)visible light camera sensor

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Area of Science:

  • Robotics
  • Computer Vision
  • Aerospace Engineering

Background:

  • Unmanned Aerial Vehicles (UAVs), or drones, are increasingly used for diverse applications beyond military use, including surveillance, rescue, and cargo.
  • Global Positioning System (GPS) is crucial for drone navigation, but signal loss in GPS-denied environments poses significant challenges for autonomous operation.
  • Developing robust autonomous navigation and landing systems for UAVs is essential for expanding their operational capabilities.

Purpose of the Study:

  • To investigate and develop a method for safe drone landing in the absence of GPS signals.
  • To create an autonomous navigation and landing guidance system utilizing computer vision.
  • To enhance drone reliability and operational safety in GPS-denied scenarios.

Main Methods:

  • A novel remote marker-based tracking algorithm was developed using a visible light camera sensor.
  • A unique marker was designed as a tracking target for precise landing procedures.
  • The algorithm was tested on an embedded system in various environmental conditions.

Main Results:

  • The proposed marker-based tracking algorithm demonstrated superior performance compared to state-of-the-art object trackers.
  • The method achieved significant improvements in both accuracy and processing time for drone landing.
  • Experimental validation confirmed the algorithm's effectiveness in diverse environments.

Conclusions:

  • The developed computer vision-based method enables safe and reliable drone landing without GPS.
  • The marker-based tracking algorithm offers a robust solution for autonomous navigation in GPS-denied areas.
  • This research contributes to advancing UAV capabilities for operations in challenging and unpredictable environments.