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An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph.

Qinghua Zeng1,2, Weina Chen3,4, Jianye Liu5,6

  • 1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China. zengqh@nuaa.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a novel factor graph-based multi-sensor fusion algorithm for Micro Unmanned Aerial Vehicle (MUAV) navigation. The method efficiently integrates asynchronous and non-linear sensor data, enhancing navigation accuracy.

Keywords:
factor graphmicro unmanned aerial vehiclemulti-sensor information fusionprobability density

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Area of Science:

  • Robotics
  • Navigation Systems
  • Sensor Fusion

Background:

  • Micro Unmanned Aerial Vehicles (MUAVs) benefit from integrated navigation systems using multiple sensors due to data redundancy and complementarity, improving accuracy.
  • Efficiently processing asynchronous and potentially non-linear measurements from diverse sensors presents a significant challenge in MUAV navigation.

Purpose of the Study:

  • To propose a real-time multi-sensor fusion algorithm for MUAV navigation that effectively handles asynchronous and non-linear sensor data.
  • To leverage factor graph methodology for optimal integration of multi-sensor information.

Main Methods:

  • A factor graph-based multi-sensor fusion algorithm is proposed, factorizing the global optimum solution via the graph's chain structure.
  • The algorithm accommodates a general form of conditional probability density, enabling the fusion of measurements without strict adherence to sensor update frequencies or fusion periods.
  • The approach converts sensor measurements into connecting factors within the graph.

Main Results:

  • An experimental MUAV system was developed to validate the proposed algorithm.
  • Experimental results demonstrated the effectiveness of the factor graph-based fusion method in improving navigation accuracy.

Conclusions:

  • The proposed factor graph-based multi-sensor fusion algorithm provides an effective solution for real-time navigation in MUAVs.
  • This approach successfully addresses the challenges of asynchronous and non-linear sensor data integration, enhancing overall system performance.