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Star-Tracker Algorithm for Smartphones and Commercial Micro-Drones.

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Summary
This summary is machine-generated.

This study introduces an efficient star-tracking algorithm for autonomous platforms like drones and satellites. Utilizing smartphone cameras, it achieves accurate global orientation even with noisy data.

Keywords:
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Area of Science:

  • Aerospace Engineering
  • Computer Vision
  • Robotics

Background:

  • Accurate global orientation is crucial for autonomous platforms like nano-satellites, UAVs, and micro-drones.
  • Existing star-tracking methods often require specialized hardware or struggle with partial sky views and noisy data.
  • Commercial-off-the-shelf (COTS) mobile devices offer a cost-effective alternative for navigation systems.

Purpose of the Study:

  • To develop and validate a computationally efficient star-tracking algorithm for determining the accurate global orientation of autonomous platforms.
  • To leverage the cameras of affordable COTS mobile devices, such as smartphones, for autonomous navigation.
  • To ensure the algorithm's robustness against noisy measurements and outliers in real-world conditions.

Main Methods:

  • Implementation of a novel star-tracking algorithm designed for computational efficiency.
  • Utilizing cameras from COTS mobile devices (smartphones, drones, Raspberry Pi) for sky image acquisition.
  • Developing algorithms to handle partial sky views, continuous operation, and noisy sensor data.

Main Results:

  • The star-tracking algorithm demonstrated computational efficiency and robustness against noise and outliers.
  • Successful implementation and testing on various platforms including Android devices, micro-drones, and Raspberry Pi.
  • Achieved an expected orientation accuracy in the range of [0.1°, 0.5°].

Conclusions:

  • The proposed star-tracking algorithm provides an affordable and accurate solution for global orientation of autonomous platforms using COTS mobile devices.
  • The framework is suitable for applications requiring continuous and autonomous navigation, overcoming challenges of partial sky views and noisy data.
  • This approach significantly reduces the cost barrier for advanced navigation systems in small-scale autonomous platforms.