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Vision-based safe autonomous UAV docking with panoramic sensors.

Phuoc Thuan Nguyen1,2, Tomi Westerlund1, Jorge Peña Queralta1

  • 1Turku Intelligent Embedded and Robotic Systems (TIERS) Lab, University of Turku, Turku, Finland.

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Summary
This summary is machine-generated.

This study introduces a vision-based system for safe unmanned aerial vehicle (UAV) landings. An upward-facing camera detects people, enabling autonomous UAVs to land safely with minimal infrastructure.

Keywords:
deep learningobject detectionpanoramic camerasafe landingunmanned aerial vehicle (UAV)vision-based localization

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Area of Science:

  • Robotics
  • Computer Vision
  • Aerospace Engineering

Background:

  • The proliferation of unmanned aerial vehicles (UAVs) necessitates enhanced safety protocols, particularly during autonomous landing operations.
  • UAVs pose potential risks to individuals in their vicinity during docking maneuvers.
  • Existing safety measures often require significant infrastructure, limiting widespread adoption.

Purpose of the Study:

  • To develop a vision-based system for safe autonomous UAV landings using minimal infrastructure.
  • To detect and localize people in the landing area using an upward-facing omnidirectional camera.
  • To enable real-time communication with UAVs for dynamic landing state adjustments.

Main Methods:

  • Utilized a single omnidirectional panoramic camera positioned on the landing pad, pointing upwards.
  • Employed a YOLOv7 object detection model for identifying people and an XGBoost model for localization.
  • Integrated the open-source Robot Operating System (ROS) and PX4 autopilot framework for UAV communication and control.
  • Implemented real-time image processing on an embedded computer.

Main Results:

  • Demonstrated the system's capability to detect and estimate the position of people in the landing zone.
  • Successfully enabled real-time communication for transitioning UAVs between landing, hovering, and emergency landing states.
  • Showcased the system's effectiveness in both simulated and real-world indoor experimental settings.
  • Validated the system's ability to assist in finding optimal landing positions during critical situations like low battery.

Conclusions:

  • The proposed vision-based system significantly enhances the safety of autonomous UAV landings.
  • Minimal infrastructure requirements make the solution practical for diverse deployment scenarios.
  • Real-time detection and communication are crucial for safe human-robot interaction in aerial robotics.