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A Sensor Fusion Approach to Observe Quadrotor Velocity.

Sensors (Basel, Switzerland)·2024
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Scale Factor Estimation for Quadrotor Monocular-Vision Positioning Algorithms.

Alejandro Gómez-Casasola1, Hugo Rodríguez-Cortés1

  • 1Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Av. Instituto Politécnico Nacional 2508, Col. San Pedro Zacatenco, Ciudad de Mexico 07360, Mexico.

Sensors (Basel, Switzerland)
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Summary

This study presents a new method for unmanned aerial vehicle (UAV) navigation using sensor fusion. It accurately estimates velocity and corrects position scale factors for safer autonomous flight.

Keywords:
observer designquadrotorsensor fusion

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Area of Science:

  • Robotics
  • Control Systems
  • Computer Vision

Background:

  • Autonomous navigation for unmanned aerial vehicles (UAVs) requires accurate state estimation.
  • Existing methods often rely on multiple sensors due to limitations of single-sensor systems.
  • Monocular vision-based simultaneous localization and mapping (SLAM) presents challenges in determining accurate position scale factors.

Purpose of the Study:

  • To develop a deterministic estimator for reconstructing the position scale factor in monocular SLAM for quadrotor UAVs.
  • To design a deterministic observer for estimating quadrotor translational velocity using only onboard sensor data.
  • To validate the proposed methods using inertial and visual measurements.

Main Methods:

  • Implementation of a deterministic estimator and observer based on the immersion and invariance (I&I) method.
  • Fusion of inertial measurement unit (IMU) and visual odometry data.
  • Utilization of Lyapunov stability theory to prove convergence of estimation errors.

Main Results:

  • The proposed estimator successfully reconstructs the unknown position scale factor for monocular SLAM.
  • The observer accurately estimates the quadrotor's translational velocity.
  • Numerical simulations confirm the asymptotic convergence of estimation errors to zero.

Conclusions:

  • The developed deterministic approach enhances the accuracy of UAV state estimation.
  • This method enables more reliable autonomous navigation for quadrotors using monocular vision and IMU data.
  • The validated techniques contribute to the advancement of robust UAV navigation systems.