Collaborative Research: NRI: INT: Cooperative Underwater Structure Inspection and Mapping

合作研究：NRI：INT：合作水下结构检查和测绘

• 批准号: 2024741
• 负责人: Ioannis Rekleitis
• 金额: $ 54.37万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024741&HistoricalAwards=false
• 关键词: Collaborative; Research; NRI; INT; Cooperative

This project develops a system of co-robots collaborating with a human operator to map underwater structures. Underwater structure mapping is an important capability applicable to multiple domains: marine archaeology, infrastructure maintenance, resource utilization, security, and environmental monitoring. The underwater environment is challenging and dangerous for humans in many aspects, while robotic operations face additional challenges compared to the above-water ones. In particular, both sensing and communications are restricted, and planning is required in three dimensions with limited information. The project will generate a 3D model of the underwater structure providing a high-resolution photo-realistic representation. Autonomous Underwater Vehicles (AUVs)will be operating in close cooperation, generating a dense vision-based reconstruction of the observed surface, and coordinated with remote human operators.. The project integrates research and education through training of undergraduate and graduate students, who will have the opportunity to work in an inclusive, interdisciplinary team across South Carolina, New Jersey, and New Hampshire. The system will be integrated and tested for archaeological mapping at field sites. Research will be conducted along three directions. (1) Robust underwater state estimation based on a deep learning approach and a hybrid representation for 3-D reconstruction that will encode probabilistic occupancy for both navigation and initial inspection from users. (2) Collaborative planning, for the proximal observers based on a local optimization framework that originally considers multiple criteria, including information gain, uncertainty reduction, and loop closure, active positioning of distal observers, and user preference to make joint measurements and inform proximal observers on where to go. (3) Information driven communications, with careful design of efficient data representation of the 3-D reconstruction and of a cross-layer optimization for deciding when and how to share. These three components will contribute towards the overarching goal of enabling a team of co-robots to operate autonomously and produce a realistic map of an underwater structure.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.

该项目开发了一个协作机器人系统，可与人类操作员协作绘制水下结构图。水下结构测绘是一项适用于多个领域的重要能力：海洋考古、基础设施维护、资源利用、安全和环境监测。水下环境对人类来说在很多方面都具有挑战性和危险性，而与水上环境相比，机器人操作面临着更多的挑战。特别是传感和通信都受到限制，需要在信息有限的情况下进行三维规划。该项目将生成水下结构的 3D 模型，提供高分辨率的逼真表示。自主水下航行器（AUV）将密切合作，对观察到的表面进行基于视觉的密集重建，并与远程人类操作员协调。该项目通过对本科生和研究生的培训将研究和教育结合起来，他们将拥有有机会在南卡罗来纳州、新泽西州和新罕布什尔州的包容性跨学科团队中工作。该系统将被集成并测试用于现场考古测绘。 研究将沿着三个方向进行。 (1) 基于深度学习方法和 3D 重建混合表示的鲁棒水下状态估计，它将对导航和用户初始检查的概率占用进行编码。 （2）协同规划，基于局部优化框架的近端观察者，该框架最初考虑了多种标准，包括信息增益、不确定性减少和闭环、远端观察者的主动定位以及用户偏好，以进行联合测量并通知近端观察者关于去哪里。 (3) 信息驱动的通信，仔细设计 3D 重建的有效数据表示以及决定何时以及如何共享的跨层优化。这三个组成部分将有助于实现使协作机器人团队能够自主操作并生成水下结构的真实地图的总体目标。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势进行评估，被认为值得支持以及更广泛的影响审查标准。

项目成果

Weakly Supervised Caveline Detection for AUV Navigation Inside Underwater Caves

水下洞穴内 AUV 导航的弱监督洞穴线检测

• DOI: 10.1109/iros55552.2023.10342435
• 发表时间: 2023-10
• 期刊: Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems
• 作者: Yu, Boxiao;Tibbetts, Reagan;Barua, Titon;Morales, Ailani;Rekleitis, Ioannis;Islam, Md Jahidul
• 通讯作者: Islam, Md Jahidul

SM/VIO: Robust Underwater State Estimation Switching Between Model-based and Visual Inertial Odometry

SM/VIO：基于模型和视觉惯性里程计之间的鲁棒水下状态估计切换

• DOI: 10.1109/icra48891.2023.10161407
• 发表时间: 2023-05
• 期刊: Proceedings IEEE International Conference on Robotics and Automation
• 作者: Joshi, Bharat;Damron, Hunter;Rahman, Sharmin;Rekleitis, Ioannis
• 通讯作者: Rekleitis, Ioannis

SVIn2: A multi-sensor fusion-based underwater SLAM system

SVIn2：基于多传感器融合的水下SLAM系统

• DOI: 10.1177/02783649221110259
• 发表时间: 2022-07
• 期刊: The International Journal of Robotics Research
• 作者: Rahman, Sharmin;Quattrini Li, Alberto;Rekleitis, Ioannis
• 通讯作者: Rekleitis, Ioannis

Hybrid Visual Inertial Odometry for Robust Underwater Estimation

用于稳健水下估计的混合视觉惯性里程计

• DOI: 10.23919/oceans52994.2023.10336994
• 发表时间: 2023-09
• 期刊: OCEANS 2023 - MTS/IEEE U.S. Gulf Coast
• 作者: Joshi, Bharat;Bandara, Chanaka;Poulakakis, Ioannis;Tanner, Herbert G.;Rekleitis, Ioannis
• 通讯作者: Rekleitis, Ioannis

Real-Time Dense 3D Mapping of Underwater Environments

水下环境的实时密集 3D 测绘

• DOI: 10.1109/icra48891.2023.10160266
• 发表时间: 2023-05
• 期刊: IEEE
• 作者: Wang, Weihan;Joshi, Bharat;Burgdorfer, Nathaniel;Batsos, Konstantinos;Quattrini Li, Alberto;Mordohai, Philippos;Rekleitis, Ioannis
• 通讯作者: Rekleitis, Ioannis