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UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes.

Jun Dai1,2, Songlin Liu1, Xiangyang Hao1

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

Sensors (Basel, Switzerland)
|August 12, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel factor graph optimization (FGO) algorithm for Unmanned Aerial Vehicle (UAV) localization using Inertial Measurement Unit (IMU), Global Navigation Satellite System (GNSS), and Visual Odometry (VO) sensors. The FGO algorithm significantly enhances positioning accuracy and robustness in complex environments.

Keywords:
UAVfactor graph optimizationmulti-source fusionrobustness

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Area of Science:

  • Robotics
  • Navigation Systems
  • Artificial Intelligence

Background:

  • The increasing integration of Unmanned Aerial Vehicles (UAVs) in various applications necessitates reliable autonomous navigation and precise localization capabilities.
  • Existing localization methods often struggle in complex environments with sensor occlusions or signal loss, limiting UAV operational scope.
  • Accurate state estimation is crucial for safe and efficient UAV operations, especially in GPS-denied or challenging terrains.

Purpose of the Study:

  • To develop and evaluate a robust multi-source sensor fusion framework for UAV localization in complex scenarios.
  • To enhance the accuracy, real-time performance, and fault tolerance of UAV state estimation.
  • To provide a generalizable framework for multi-sensor fusion applicable to diverse UAV platforms and operational contexts.

Main Methods:

  • A novel multi-source fusion framework utilizing factor graph optimization (FGO) was developed for UAV localization.
  • The framework integrates data from Inertial Measurement Unit (IMU), Global Navigation Satellite System (GNSS), and Visual Odometry (VO) sensors.
  • The iSAM incremental inference algorithm was employed to build the factor graph, incorporating IMU pre-integration, IMU bias, GNSS, and VO factors.

Main Results:

  • Mathematical simulations and validation on the EuRoC dataset demonstrated the real-time capability and accuracy of the FGO algorithm with a sliding window size of 30.
  • The FGO algorithm exhibited a plug-and-play functionality, maintaining performance during local sensor failures.
  • Positioning accuracy was improved by 1.5-2 fold compared to traditional federated Kalman and adaptive federated Kalman algorithms.

Conclusions:

  • The proposed FGO algorithm offers a significant improvement in UAV localization accuracy and robustness for complex environments.
  • The multi-source fusion framework enhances the reliability and flexibility of autonomous navigation systems.
  • The general nature of the framework allows for adaptation to different sensor combinations and operational scenarios, paving the way for more versatile UAV applications.