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Application of multi-sensor fusion localization algorithm based on recurrent neural networks.

Zexia Huang1, Guoyang Ye2, Pu Yang3

  • 1School of Electrical Engineering and Automation, Anhui University, Hefei, 230601, Anhui, China. 18756902365@163.com.

Scientific Reports
|March 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an EKF-RNN hybrid framework for precise mobile robot localization in complex environments. The novel approach significantly improves accuracy and efficiency, outperforming existing methods.

Keywords:
Dynamic environment navigationExtended Kalman filterLocalization accuracyMulti-sensor fusionRecurrent neural network

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Sensor Fusion

Background:

  • Mobile robots require high-precision localization in dynamic environments.
  • Existing multi-sensor fusion methods face challenges like sensor asynchrony and noise.

Purpose of the Study:

  • To propose a novel hybrid fusion framework combining Extended Kalman Filter (EKF) and Recurrent Neural Network (RNN) for enhanced mobile robot localization.
  • To address sensor frequency asynchrony, drift accumulation, and measurement noise.

Main Methods:

  • A hybrid EKF-RNN framework was developed for real-time statistical estimation and temporal dependency modeling.
  • Light Detection and Ranging (LiDAR) data was integrated for enhanced robustness.
  • Simulations were conducted on the Gazebo platform for validation.

Main Results:

  • The EKF-RNN framework achieved a localization error within 8 cm under various noise levels and dynamic disturbances.
  • The proposed method demonstrated superior accuracy and computational efficiency compared to Particle Filter (PF) and Graph SLAM.
  • An average runtime of 30.1 ms per frame was recorded, suitable for real-time applications.

Conclusions:

  • The EKF-RNN framework offers a robust, accurate, and computationally efficient solution for autonomous robotic navigation.
  • This approach significantly advances multi-sensor fusion technologies for mobile robots in unstructured environments.