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¹Aeronautical Sciences Laboratory, Aeronautical and Spatial Studies Institute, Blida 1 University, Blida 0900, Algeria.

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Summary

Indoor drone testing faces GPS signal limitations. This study introduces a computer vision-based Indoor Positioning System (IPS) for accurate UAV localization, enhancing navigation and testing reliability.

Keywords:

computer vision depth estimation indoor positioning system stereo vision triangulation unmanned aerial vehicles visual odometry

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Area of Science:

Robotics
Computer Vision
Aerospace Engineering

Background:

Drone integration across sectors necessitates robust testing protocols.
Indoor drone testing is crucial for safety and privacy but hindered by GPS signal unreliability.
Accurate positioning is vital for unmanned aerial vehicle (UAV) autopilot systems.

Purpose of the Study:

To implement an Indoor Positioning System (IPS) using computer vision for UAVs.
To address the challenge of inaccurate indoor drone positioning caused by GPS limitations.
To enhance the reliability and accuracy of indoor drone navigation and performance assessment.

Main Methods:

Development of a computer vision-based Indoor Positioning System (IPS).
Utilizing an enhanced vision-based triangulation approach for UAV detection and localization.
Comparative analysis against alternative indoor positioning methodologies.

Main Results:

The proposed system demonstrates efficiency and precision in detecting and localizing various UAV types indoors.
The computer vision approach effectively overcomes GPS signal limitations in indoor environments.
The system's accuracy supports reliable indoor drone navigation and testing.

Conclusions:

The developed Indoor Positioning System (IPS) provides a viable solution for accurate indoor UAV localization.
This technology is crucial for advancing the capabilities of indoor drone navigation and testing.
The system enhances the reliability and resilience of unmanned aerial vehicles (UAVs) in controlled environments.