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UAPF: A UWB Aided Particle Filter Localization For Scenarios with Few Features.

Yang Wang1, Weimin Zhang1,2,3, Fangxing Li1,2,3

  • 1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)
|December 2, 2020
PubMed
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This summary is machine-generated.

This study introduces an improved Particle Filter localization (UAPF) for robust robot navigation. UAPF enhances robot kidnap recovery and pose accuracy, outperforming traditional lidar-based methods.

Area of Science:

  • Robotics
  • Simultaneous Localization and Mapping (SLAM)

Background:

  • Lidar-based localization struggles with accuracy in feature-poor environments and robot kidnap situations.
  • Existing methods lack robust mechanisms for detecting robot kidnapping and compensating for pose errors.

Purpose of the Study:

  • To develop an improved Particle Filter localization (UAPF) for enhanced robot kidnap detection and pose error compensation.
  • To improve the accuracy and robustness of robot localization in challenging environments.

Main Methods:

  • Adaptive Particle Filter (UAPF) utilizing Ultra-wideband (UWB) Jacobian for covariance updates.
  • Novel Kidnap Probability (KNP) criterion for robust robot kidnap detection.
  • Pose fusion of ranging-based and Particle Filter-based localization for reduced uncertainty.
Keywords:
particle filtersrobot kidnap recoveryrobot localizationsensor fusionultra wideband technology

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  • Linear regression of ranging data using maximum probability values for improved accuracy.

    • Main Results:

    • UAPF achieved robot kidnap recovery in under 2 seconds.
    • Positioning error was less than 0.1 meters in a 40x15m hall.
    • Outperformed prevalent lidar-based localization, which took over 90 seconds and converged to incorrect positions.

    Conclusions:

    • The proposed UAPF significantly enhances robot localization robustness and accuracy, particularly in scenarios with few features and during robot kidnaps.
    • UAPF offers a superior alternative to current lidar-based localization methods, providing faster recovery and higher precision.