Optimization of Underwater Mapping Using EKF-FastSLAM and GPR

CUI Peng; LI Xinda; ZHANG Feihu; DU Peng

doi:10.11993/j.issn.2096-3920.2025-0122

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CUI Peng, LI Xinda, ZHANG Feihu, DU Peng. Optimization of Underwater Mapping Using EKF-FastSLAM and GPR[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0122

Citation:

CUI Peng, LI Xinda, ZHANG Feihu, DU Peng. Optimization of Underwater Mapping Using EKF-FastSLAM and GPR[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0122

CUI Peng, LI Xinda, ZHANG Feihu, DU Peng. Optimization of Underwater Mapping Using EKF-FastSLAM and GPR[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0122

Citation:

CUI Peng, LI Xinda, ZHANG Feihu, DU Peng. Optimization of Underwater Mapping Using EKF-FastSLAM and GPR[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0122

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Optimization of Underwater Mapping Using EKF-FastSLAM and GPR

doi: 10.11993/j.issn.2096-3920.2025-0122

CUI Peng^1
,,
LI Xinda^1
,,
ZHANG Feihu^{2
,
,},
DU Peng^2
,

1.
Dalian Naval Academy, Dalian 116018, China
2.
Northwesten Polytechnical University, Xi′an 710077, China

Received Date: 2025-09-09
Accepted Date: 2025-11-28
Rev Recd Date: 2025-11-27

Available Online: 2026-03-16

Abstract

Abstract

With the advancement of underwater exploration technologies, multibeam echo sounders(MBES) have become a key tool for underwater terrain scanning due to their efficient measurement capabilities and high spatial resolution. However, constructing high-precision maps from sonar data in complex and dynamic aquatic environments remains a significant challenge. To address the issue of particle degradation commonly encountered in traditional FastSLAM algorithms under such conditions, this paper proposes an optimized FastSLAM method based on the Extended Kalman Filter(EKF). By incorporating EKF as a proposal distribution within the particle filtering process, the method effectively integrates the latest observation data, mitigates particle degeneration, and enhances the stability and accuracy of the filter. Furthermore, considering the sparsity and lack of overlap in underwater sonar measurements, Gaussian Process Regression(GPR) is introduced to perform nonlinear modeling and map extrapolation, thereby compensating for the discontinuities in MBES-based mapping. Experimental results demonstrate that the proposed EKF-FastSLAM significantly reduces trajectory errors and improves mapping accuracy compared to standard FastSLAM. The integration of GPR further enhances the overall mapping performance. Field experiments conducted in a lake environment confirm that the proposed method achieves meter-level mapping precision.
- multibeam echo sounder,
- underwater mapping,
- ,
- FastSLAM,
- Gaussian process regression

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References(31)

References

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