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A Multi-Sensor Fusion Underwater Localization Method Based on Unscented Kalman Filter on Manifolds.

Yang Wang¹, Chenxi Xie¹, Yinfeng Liu²

¹Department of Automation, Beijing Information Science and Technology University, Beijing 102206, China.

Sensors (Basel, Switzerland)

|October 16, 2024

Summary

This summary is machine-generated.

This study introduces an Unscented Kalman Filter on Manifolds (UKF-M) for Autonomous Underwater Vehicle (AUV) localization. The UKF-M method enhances trajectory estimation accuracy by reducing cumulative errors in complex underwater environments.

Keywords:

Lie groups manifolds underwater localization unscented Kalman filter

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Area of Science:

Robotics
Nonlinear Systems Analysis
State Estimation

Background:

Lie groups and algebra simplify nonlinear system computations for robot localization.
Unscented Kalman Filter (UKF) handles nonlinearities but faces high computational complexity on Lie groups.
Applying Lie groups to manifolds simplifies computation while maintaining geometric constraints.

Purpose of the Study:

To investigate a multi-sensor fusion localization method using an Unscented Kalman Filter on Manifolds (UKF-M).
To enhance the accuracy and robustness of Autonomous Underwater Vehicle (AUV) localization in challenging underwater environments.

Main Methods:

Developed a system and multi-sensor model for an AUV.
Designed a UKF-M for the AUV system.
Implemented a multi-sensor fusion strategy integrated with the UKF-M.

Main Results:

The proposed UKF-M method demonstrated suitability for underwater environments.
The method significantly corrected cumulative trajectory estimation errors.
Accurate underwater localization was achieved using multi-sensor fusion.

Conclusions:

The UKF-M provides an efficient and accurate approach for AUV localization.
Multi-sensor fusion on manifolds effectively addresses challenges in underwater navigation.
The method shows promise for improving the reliability of AUV operations.