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Error Reduction in Vision-Based Multirotor Landing System.

Juan Pedro Llerena Caña1, Jesús García Herrero1, José Manuel Molina López1

  • 1Applied Artificial Intelligence Group (GIAA), Carlos III University of Madrid, 28270 Madrid, Spain.

Sensors (Basel, Switzerland)
|May 28, 2022
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Summary
This summary is machine-generated.

This study introduces a novel drone landing system using monocular vision and navigation data to precisely estimate helipad position. The system incorporates error correction and filtering, significantly improving landing accuracy and stability.

Keywords:
SITLUAVautonomous landingcomputer visionfilteringglobal positionhelipad contextnavigation system

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Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Aerospace Engineering

Background:

  • Drones require precise landing maneuvers for new applications.
  • Existing control systems can cause instability during landing.
  • Vision systems are crucial for advanced drone navigation.

Purpose of the Study:

  • To develop a robust drone landing system using monocular vision and navigation data.
  • To enhance helipad position estimation accuracy.
  • To mitigate undesired behaviors from embedded control systems.

Main Methods:

  • Utilizing monocular vision and onboard navigation information for global helipad position estimation.
  • Implementing a position error correction module via cylinder space transformation.
  • Applying a sliding window filtering system for enhanced stability.

Main Results:

  • The proposed system accurately estimates the helipad's global position.
  • Cylinder space transformation effectively corrects position errors.
  • Stationary filtering significantly improves landing system performance.

Conclusions:

  • The integrated landing system enhances drone landing precision and stability.
  • The novel approach addresses limitations of traditional embedded control systems.
  • This research contributes to safer and more reliable autonomous drone operations.