Research on Underwater Positioning Method Based on Vision-Inertia-Pressure Fusion

ZHANG Jian; HU Qiao; XIA Yin; SHI Lin; LI Yangyang

doi:10.11993/j.issn.2096-3920.2024-0061

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Article Navigation > Journal of Unmanned Undersea Systems > 2024 > Accepted Manuscript

ZHANG Jian, HU Qiao, XIA Yin, SHI Lin, LI Yangyang. Research on Underwater Positioning Method Based on Vision-Inertia-Pressure Fusion[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0061

Citation:

ZHANG Jian, HU Qiao, XIA Yin, SHI Lin, LI Yangyang. Research on Underwater Positioning Method Based on Vision-Inertia-Pressure Fusion[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0061

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Research on Underwater Positioning Method Based on Vision-Inertia-Pressure Fusion

doi: 10.11993/j.issn.2096-3920.2024-0061

ZHANG Jian¹,
HU Qiao^{1, 2
,},
XIA Yin¹,
SHI Lin¹,
LI Yangyang¹

1.
School of Mechanical Engineering, Xi′an Jiaotong University, Xi′an 710049, China
2.
Shanxi Key Laboratory of Intelligent Robots, Xi′an Jiaotong University, Xi′an 710049, China

Received Date: 2024-04-03
Accepted Date: 2024-06-04
Rev Recd Date: 2024-05-27

Available Online: 2024-11-11

Abstract

Abstract

In underwater unstructured environments, robots face difficulties in relying on external base stations for localization. Therefore, autonomous localization using multi-sensor fusion has significant application value in such settings. This paper addresses issues such as poor stability in visual localization and substantial drift in inertial navigation within underwater multi-sensor fusion localization. We propose a tightly integrated multi-sensor fusion localization method that combines visual, inertial, and pressure sensors. By utilizing graph optimization techniques for multi-sensor fusion and identifying errors in visual-inertial data based on depth information, the quality of the fused data is enhanced. To address drift and localization loss during the fusion localization process, a depth sensor is employed for weight allocation to provide more detailed system initialization. Additionally, loop closure detection and relocalization methods are introduced to effectively mitigate drift and localization loss. Experimental validation demonstrates that the proposed fusion localization algorithm improves accuracy by 48.4% compared to visual-inertial fusion localization methods, achieving superior precision and robustness.
- underwater positioning,
- multi-sensor fusion positioning,
- graph optimization

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References(18)

References

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