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Research on UAV Robust Adaptive Positioning Algorithm Based on IMU/GNSS/VO in Complex Scenes.

Jun Dai1,2, Xiangyang Hao1, Songlin Liu1

  • 1Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China.

Sensors (Basel, Switzerland)
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a robust adaptive positioning algorithm for Unmanned Aerial Vehicles (UAVs). The algorithm enhances navigation accuracy and reliability in complex environments, even with interrupted Global Navigation Satellite System (GNSS) signals.

Keywords:
UAVerror state Kalman filter (ESKF)information sharing coefficientmulti-source fusionrobust adaptation filter

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Area of Science:

  • Robotics
  • Navigation Systems
  • Artificial Intelligence

Background:

  • Unmanned Aerial Vehicles (UAVs) require robust autonomous positioning for intelligent systems.
  • Challenges arise from Global Navigation Satellite System (GNSS) signal interruption and insufficient visual features in complex environments.
  • Existing methods struggle with accuracy and reliability in degraded sensor conditions.

Purpose of the Study:

  • To develop a novel robust adaptive positioning algorithm for UAVs.
  • To enhance the accuracy and robustness of autonomous navigation and positioning systems.
  • To address limitations in current positioning methods under challenging environmental conditions.

Main Methods:

  • A multi-source fusion model based on a federated Kalman filter (FKF) was established, combining vision/inertial navigation and satellite/inertial navigation.
  • An Extended State Kalman Filter (ESKF) was utilized within the fusion framework.
  • A robust adaptive localization algorithm was proposed, employing robust equivalent weights and adaptive information sharing coefficients.

Main Results:

  • The proposed algorithm effectively mitigates the impact of gross errors and model deviations.
  • It adaptively updates information sharing coefficients based on sub-filter covariance.
  • Position, velocity, and attitude measurement accuracy improved by 2-3 times compared to classical and adaptive FKF algorithms.
  • Real-time navigation requirements are met.

Conclusions:

  • The robust adaptive localization algorithm significantly improves the reliability and accuracy of autonomous UAV navigation in complex scenes.
  • The algorithm demonstrates generality, applicable to various sensor combinations and environments.
  • It offers a reliable solution for autonomous positioning challenges faced by UAVs.