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Adaptive Multi-Sensor Fusion Localization with Eigenvalue-Based Degradation Detection for Mobile Robots.

Weizu Huang1, Long Xiang2,3, Ruohao Chen2

  • 1School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces an adaptive sensor fusion framework for autonomous mobile robots, enhancing localization accuracy and robustness. The method dynamically integrates LiDAR, IMU, and RTK-GNSS data for reliable centimeter-level positioning in challenging environments.

Area of Science:

  • Robotics
  • Sensor Fusion
  • Simultaneous Localization and Mapping (SLAM)

Background:

  • Autonomous mobile robots require precise localization in complex, dynamic environments.
  • Single-sensor solutions (LiDAR-inertial odometry, RTK-GNSS) face limitations like drift and signal unreliability.
  • Robust localization is critical for safe and efficient robot operation.

Purpose of the Study:

  • To develop an adaptive multi-sensor fusion framework for robust robot localization.
  • To dynamically integrate LiDAR, Inertial Measurement Unit (IMU), and Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) data.
  • To improve localization accuracy and robustness in challenging environments.

Main Methods:

  • A tightly coupled LiDAR-IMU iterative extension Kalman filter (IEKF) as the core estimator.
Keywords:
LiDAR-inertial odometryRTK-GNSSadaptive localizationdegradation detectionmobile robotsmulti-sensor fusion

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  • Loop detection and incremental factor graph optimization to mitigate long-term drift.
  • A novel degradation detection method using Jacobian matrix minimum eigenvalue for real-time constraint quality assessment.
  • A smooth fusion strategy based on covariance weighting to handle sensor data quality fluctuations.

    • Main Results:

    • Significantly improved localization accuracy and robustness compared to LiDAR-only methods.
    • Achieved stable centimeter-level positioning performance.
    • Demonstrated real-time capability on embedded platforms.
    • Validated through experiments on the KITTI benchmark and self-collected datasets.

    Conclusions:

    • The proposed adaptive multi-sensor fusion framework enhances robot localization accuracy and robustness.
    • The dynamic fusion strategy effectively addresses sensor degradation and data quality fluctuations.
    • The method offers a reliable solution for centimeter-level autonomous robot navigation in complex environments.