RI: Small: Computational Imaging for Underwater Exploration

RI：小型：水下勘探的计算成像

• 批准号: 2122068
• 负责人: Jinwei Ye
• 金额: $ 49.95万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122068&HistoricalAwards=false
• 关键词: RI; Small; Computational; Imaging; Underwater; RI; Small; Computational; Imaging; Underwater

The underwater environment poses great challenges for vision sensing due to the refraction, absorption, and scattering that occurs in the water column. Although a variety of manned vehicles, remotely operated vehicles (ROVs), and autonomous underwater vehicles (AUVs) have been developed for various underwater missions, the vision sensing capacity of these underwater vehicles still stays at a limited level. This project aims at developing novel computational imaging solutions to facilitate underwater robotic tasks, such as autonomous navigation and in-detail sea floor mapping. The project is expected to produce new imaging systems, scene reconstruction algorithms, and control theories that are tailored for underwater vehicles. The project benefits marine research, oil & gas industry, and the military by developing underwater vision systems that decrease the costs, challenges, and risks associated with ocean exploration. The project tightly integrates research with education by introducing new curricula on computer vision and marine robotics. The project will provide training and research experience to both undergraduate and graduate students, in particular women and underrepresented minorities. This project focuses on three specific research objectives: 1) angular ray sampling for nonlinear light transport analysis; 2) underwater reflectance modeling and geometry estimation; and 3) visual perceptive tracking for underwater vehicles. A critical component of this project is to develop a novel angularly sampled imaging system that strategically emits and collects light rays, which allows the analysis of nonlinear light paths through water. Specifically, the team will first investigate the underwater light transport model in presence of refraction, scattering, and absorption. Based on the light transport model, the team will develop new surface reflectance models and three-dimensional shape reconstruction algorithms that follow the physics of underwater ray optics. Finally, the team will integrate the imaging system with underwater vehicles and develop a novel visual perceptive tracking navigation system that leverages the angular ray sampling scheme.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

水下环境对水柱中发生的折射，吸收和散射引起的视力感应构成了巨大挑战。 尽管已经为各种水下任务开发了各种各样的载人车，远程操作的车辆（ROV）和自动驾驶水下车辆（AUV），但这些水下车辆的视觉感应能力仍保持有限水平。该项目旨在开发新颖的计算成像解决方案，以促进水下机器人任务，例如自动导航和详细信息映射。预计该项目将生成新的成像系统，场景重建算法以及为水下车辆量身定制的理论。该项目通过开发降低与海洋勘探相关的成本，挑战和风险来使海洋研究，石油和天然气行业以及军事力量受益。该项目通过引入有关计算机视觉和海洋机器人技术的新课程将研究与教育紧密整合。该项目将为本科生和研究生，尤其是妇女和代表性不足的少数民族提供培训和研究经验。该项目侧重于三个特定的研究目标：1）非线性光传输分析的角度射线采样； 2）水下反射率建模和几何估计； 3）水下车辆的视觉感知跟踪。该项目的一个关键组成部分是开发一种新型的角度采样成像系统，该系统从策略上排放和收集光线，从而可以通过水对非线性光路进行分析。具体而言，团队将在折射，散射和吸收的情况下首先研究水下光传输模型。基于轻型传输模型，该团队将开发新的表面反射率模型和遵循水下射线光学物理学的三维重建算法。最后，该团队将将成像系统与水下车辆整合在一起，并开发出一种新型的视觉感知跟踪导航系统，该导航系统利用Angular Ray采样方案。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来评估通过评估来获得支持的。