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AUV SLAM method based on SO-CFAR and ADT feature extraction.

Xiaokai Mu1,2, Haiyang Chen2, Jiahao Wang2

  • 1Science and Technology on Underwater Vehicle Technology Laboratory, Harbin Engineering University, Harbin, China.

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|October 3, 2024
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Summary
This summary is machine-generated.

This study applies factor graph optimization SLAM for autonomous underwater vehicles using forward-looking sonar. The method improves localization accuracy by 8.52% compared to dead reckoning.

Keywords:
AUVICPSLAMfactor graphforward-looking sonar

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Area of Science:

  • Robotics
  • Marine Engineering
  • Computer Vision

Background:

  • Autonomous Underwater Vehicles (AUVs) require robust Simultaneous Localization and Mapping (SLAM) for navigation.
  • Forward-looking sonar offers advanced detection capabilities suitable for SLAM applications.

Purpose of the Study:

  • To investigate the application of factor graph optimization SLAM using feature maps for AUVs.
  • To enhance the accuracy and reliability of AUV localization in underwater environments.

Main Methods:

  • Feature extraction from forward-looking sonar using combined SO-CFAR and ADT noise filtering.
  • Feature point registration via the Weighted Iterative Closest Point (WICP) algorithm.
  • Implementation of factor graph optimization for SLAM.

Main Results:

  • Successful extraction of feature point clouds from sonar data.
  • Effective registration of features using WICP.
  • Demonstrated an 8.52% improvement in Root Mean Square Error (RMSE) over dead reckoning (DR) using field data.

Conclusions:

  • The proposed factor graph optimization SLAM method is effective for AUVs utilizing forward-looking sonar.
  • The integration of SO-CFAR, ADT, and WICP significantly improves localization accuracy.
  • This approach offers a viable solution for precise AUV navigation in complex underwater environments.