Exteme Underwater Environment Sensing Using Acoustic Camera

使用声学相机进行极端水下环境传感

• 批准号: 21J12362
• 负责人: WANG YUSHENG
• 金额: $ 0.96万
• 依托单位: The University of Tokyo
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for JSPS Fellows
• 财政年份: 2021
• 资助国家: 日本
• 起止时间: 2021-04-28 至 2023-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21J12362/
• 关键词: 2d forward looking sonar; 3d reconstuction; deep learning; multi-path reflection; rendering; fiduicial marker; pose estimation

This year, several aspects of acoustic camera sensing in extreme underwater environments were achieved. The first is 3D reconstruction using two to three acoustic images. 3D reconstruction with a small number of acoustic images is a challenging task. An elevation plane sweeping stereo network was proposed. After training the network with a well-built dataset, it is possible to generate high-quality 3D model. This can be applied to underwater monitoring. The second is ground echo modeling, multi-path reflection may influence recognition results using acoustic images. By assuming the ground is flat, it is possible to explicitly model the ground echo. This is an important fundamental research in this field. The third is testing differentiable rendering in acoustic camera. This is the key to building the relationship between image simulation using computer graphics techniques and computer vision tasks like pose estimation and 3D reconstruction. It can be used to solve inverse rendering problems and be integrated into deep learning frameworks. Early results proved the feasibility of the method. A field experiment in water tank was also carried out for dataset collection. Three international conference papers were accepted on the three aspects.

今年，实现了极端水下环境中声学摄像头感测的几个方面。第一个是使用两到三个声学图像的3D重建。具有少量声学图像的3D重建是一项具有挑战性的任务。提出了一个高程平面的立体声网络。在使用完善的数据集训练网络后，可以生成高质量的3D模型。这可以应用于水下监测。第二个是地面回声建模，多路径反射可能会使用声学图像影响识别结果。通过假设地面是平坦的，可以明确对地面回波建模。这是该领域的重要基础研究。第三个是在声学相机中测试可区分渲染。这是使用计算机图形技术和计算机视觉任务（如姿势估计和3D重建）之间建立图像模拟之间关系的关键。它可用于解决反向渲染问题并将其集成到深度学习框架中。早期结果证明了该方法的可行性。还进行了水箱中的现场实验以进行数据集收集。这三个方面接受了三篇国际会议论文。

项目成果

Elevation Angle Estimation in 2D Acoustic Images Using Pseudo Front View

使用伪前视图进行 2D 声学图像中的仰角估计

• DOI: 10.1109/lra.2021.3058911
• 发表时间: 2021
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Wang Yusheng;Ji Yonghoon;Liu Dingyu;Tsuchiya Hiroshi;Yamashita Atsushi;Asama Hajime
• 通讯作者: Asama Hajime

Acoustic Camera-Based Pose Graph SLAM for Dense 3-D Mapping in Underwater Environments

基于声学相机的姿态图 SLAM，用于水下环境中的密集 3D 建图

• DOI: 10.1109/joe.2020.3033036
• 发表时间: 2021
• 期刊: IEEE Journal of Oceanic Engineering
• 影响因子: 4.1
• 作者: Wang Yusheng;Ji Yonghoon;Woo Hanwool;Tamura Yusuke;Tsuchiya Hiroshi;Yamashita Atsushi;Asama Hajime
• 通讯作者: Asama Hajime

研究室URL

实验室网址

Learning Pseudo Front Depth for 2D Forward-Looking Sonar-based Multi-view Stereo

基于 2D 前视声纳的多视图立体学习伪前方深度

• 发表时间: 2023
• 作者: Yusheng Wang;Yonghoon Ji;Hiroshi Tsuchiya;Hajime Asama and Atsushi Yamashita
• 通讯作者: Hajime Asama and Atsushi Yamashita

Simulator-aided Edge-based Acoustic Camera Pose Estimation

模拟器辅助的基于边缘的声学相机姿态估计

• 发表时间: 2022
• 作者: Yusheng Wang;Yonghoon Ji;Hiroshi Tsuchiya;Hajime Asama and Atsushi Yamashita;竹崎奏詠，辻勲平，ハザリカ・へマンタ，浅井光輝;Yusheng Wang
• 通讯作者: Yusheng Wang