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Multi-Sensor Fusion for Wheel-Inertial-Visual Systems Using a Fuzzification-Assisted Iterated Error State Kalman

Guohao Huang1, Haibin Huang1, Yaning Zhai1

  • 1School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
Summary

This study enhances mobile robot localization by fusing wheel, inertial, and visual odometry data using a Fuzzy Inference System (FIS) and Iterated Error State Kalman Filter (IESKF). The approach improves 6-DoF robot positioning accuracy in challenging indoor environments.

Keywords:
fuzzy inference system (FIS)iterative error state Kalman filter (IESKF)multi-sensor fusionsystem noise covariancewheel-inertial-visual odometry

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Area of Science:

  • Robotics
  • Sensor Fusion
  • Artificial Intelligence

Background:

  • Odometry drift is a significant challenge for indoor mobile robots, particularly in unstructured environments.
  • Traditional localization methods struggle with dynamic conditions, varying lighting, and complex robot kinematics.

Purpose of the Study:

  • To propose a multi-sensor fusion framework for accurate 6-DoF (six degrees-of-freedom) localization of differential-drive indoor mobile robots.
  • To enhance robot localization robustness in unstructured and dynamic indoor scenes.

Main Methods:

  • Developed a Wheel-Inertial-Visual Odometry (WIVO) framework incorporating a Fuzzy Inference System (FIS).
  • Integrated FIS with an Iterated Error State Kalman Filter (IESKF) to adaptively adjust noise covariance matrices.
  • Optimized fuzzy inference rule parameters for dynamic noise prediction.

Main Results:

  • The proposed FIS-IESKF fusion method significantly improves localization accuracy compared to traditional approaches.
  • Demonstrated enhanced system robustness for differential-drive robots in dynamic environments and movements.
  • Successfully addressed limitations of fixed covariance matrices in Kalman filtering for odometry.

Conclusions:

  • The multi-sensor fusion approach using FIS-IESKF is effective for precise and robust 6-DoF indoor mobile robot localization.
  • Adaptive noise handling via FIS improves performance in challenging, unstructured, and dynamic environments.
  • This framework offers a promising solution for reliable odometry in real-world robotic applications.