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Degeneration-Aware Localization with Arbitrary Global-Local Sensor Fusion.

Xiaqing Ding1,2, Fuzhang Han1,2, Tong Yang1,2

  • 1College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.

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This study introduces a novel sensor fusion method for mobile robots, enhancing global localization accuracy. The approach effectively combines different sensor data, even during challenging movements, improving robot navigation.

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degeneration-aware state estimationglobal localizationmulti-sensor fusion

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Area of Science:

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

Background:

  • Mobile robot navigation relies heavily on accurate global localization.
  • Limitations of single-sensor systems necessitate sensor fusion for robust performance.
  • Integrating diverse sensor data with complementary properties is crucial for real-time localization.

Purpose of the Study:

  • To develop a decoupled, optimization-based framework for fusing global and local sensor measurements.
  • To achieve real-time 6D global localization by combining intermittent 3D global positions and high-frequency 6D odometry poses.
  • To address system observability issues and improve robustness under degenerated movements.

Main Methods:

  • A decoupled optimization framework for global-local sensor fusion.
  • Formulation of the fusion process involving relative transformation estimation, translational extrinsic calibration, and local pose scale estimation.
  • Development of a degeneration-aware method to detect and constrain unobservable directions during optimization.

Main Results:

  • Full system observability validated under general movements.
  • Demonstrated effectiveness of the degeneration-aware method in mitigating noise effects.
  • Achieved superior accuracy and robustness compared to other decoupled sensor fusion methods in simulations and real-world tests.

Conclusions:

  • The proposed degeneration-aware global-local sensor fusion method significantly enhances mobile robot global localization.
  • The framework provides accurate and robust real-time localization by effectively fusing intermittent global and continuous local pose data.
  • The approach offers a valuable solution for improving robot navigation in complex environments.