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2D Forward Looking Sonar Simulation with Ground Echo Modeling
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- Yusheng Wang
- Graduate School of Engineering, The University of Tokyo,Department of Precision Engineering,Japan
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- Chujie Wu
- Graduate School of Engineering, The University of Tokyo,Department of Precision Engineering,Japan
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- Hajime Asama
- Graduate School of Engineering, The University of Tokyo,Department of Precision Engineering,Japan
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- Yonghoon Ji
- Graduate School for Advanced Science and Technology, JAIST,Japan
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- Hiroshi Tsuchiya
- Research Institute, Wakachiku Construction Co., Ltd.,Japan
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- Atsushi Yamashita
- Graduate School of Frontier Sciences, The University of Tokyo,Department of Human and Engineered Environmental Studies,Japan
Bibliographic Information
- Published
- 2023-06-25
- Rights Information
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- https://doi.org/10.15223/policy-029
- https://doi.org/10.15223/policy-037
- DOI
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- 10.1109/ur57808.2023.10202279
- 10.48550/arxiv.2304.08146
- Publisher
- IEEE
Description
Imaging sonar produces clear images in underwater environments, independent of water turbidity and lighting conditions. The next generation 2D forward looking sonars are compact in size and able to generate high-resolution images which facilitate underwater robotics research. Considering the difficulties and expenses of implementing experiments in underwater environments, tremendous work has been focused on sonar image simulation. However, sonar artifacts like multi-path reflection were not sufficiently discussed, which cannot be ignored in water tank environments. In this paper, we focus on the influence of echoes from the flat ground. We propose a method to simulate the ground echo effect physically in acoustic images. We model the multi-bounce situations using the single-bounce framework for computation efficiency. We compare the real image captured in the water tank with the synthetic images to validate the proposed methods.
Final version of UR2023
Journal
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- 2023 20th International Conference on Ubiquitous Robots (UR)
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2023 20th International Conference on Ubiquitous Robots (UR) 724-729, 2023-06-25
IEEE
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Details 詳細情報について
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- CRID
- 1360584346466980224
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- Data Source
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- Crossref
- OpenAIRE
