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A LiDAR SLAM-Assisted Fusion Positioning Method for USVs.

Wei Shen1,2, Zhisong Yang1,2, Chaoyu Yang1,2

  • 1School of Marine Science, Shanghai Ocean University, Shanghai 201306, China.

Sensors (Basel, Switzerland)
|February 11, 2023
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Summary

This study introduces a LiDAR SLAM-assisted fusion positioning method for unmanned surface vessels (USVs) to ensure reliable navigation when Global Navigation Satellite System (GNSS) signals are lost. The new approach significantly improves positioning accuracy and stability in challenging operational environments.

Keywords:
GNSS/INSLiDAR-SLAMfusion positioningunmanned surface vessel (USV)

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Area of Science:

  • Robotics
  • Marine Engineering
  • Navigation Systems

Background:

  • Unmanned Surface Vessels (USVs) face navigation challenges due to Global Navigation Satellite System (GNSS) signal unavailability.
  • Accurate and stable positioning is critical for USV operations in diverse marine environments.
  • Existing fusion methods may not adequately address GNSS signal loss scenarios.

Purpose of the Study:

  • To develop and validate a novel LiDAR SLAM-assisted fusion positioning method for USVs.
  • To enhance USV positioning accuracy and stability during GNSS signal outages.
  • To improve the reliability of USV navigation systems in obstructed environments.

Main Methods:

  • Integration of GNSS/INS (Inertial Navigation System) with LiDAR SLAM (Simultaneous Localization and Mapping).
  • Application of a carrier phase differential GNSS/INS loosely coupled integration strategy in open water.
  • Implementation of a dynamic switching strategy to activate LiDAR SLAM when GNSS signals are unavailable.
  • Compensation of LiDAR data with precise angle information for robust localization.

Main Results:

  • Significant reduction in positioning error by 55.4% compared to traditional Kalman filter and 43.5% vs. adaptive Kalman filter.
  • Substantial decrease in velocity error by 78.2% and 57.9% respectively.
  • Maintained positioning accuracy with standard deviation and root mean square error within 0.1 m.
  • Effective control of positioning anomalies, ensuring data stability.

Conclusions:

  • The proposed LiDAR SLAM-assisted fusion positioning method offers superior accuracy and stability for USVs, especially during GNSS signal loss.
  • This advanced navigation system enhances USV operational reliability in complex marine scenarios.
  • The method outperforms conventional fusion algorithms, demonstrating its practical applicability.