CAREER: Real-Time 3D Reconstruction and Manipulation for Underwater Intervention - A Career Development Plan

职业：水下干预的实时 3D 重建和操纵 - 职业发展计划

• 批准号: 1452793
• 负责人: Matthew Johnson-Roberson
• 金额: $ 53.19万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1452793&HistoricalAwards=false
• 关键词: CAREER; Real; Time; 3D; Reconstruction

Underwater robotic vehicles like AUVs (Autonomous Underwater Vehicles) and ROVs (Remotely Operated Vehicles) have occasion to interact with the environment in many tasks including deep-water science, resource extraction, and sub-sea construction, and maintenance. Crucial to these tasks is the ability to interpret the operating environment to ensure safety and effectiveness. This project proposes the development of algorithms for quickly constructing 3D models of the environment from pictures and video obtained with an underwater camera. In the short term, this means augmenting how humans control AUVs and ROVs. In the long term, the PI's career plan aims to produce systems that can safely interact with the environment without direct human control. Beyond the technical goals, this proposal lays out a plan for education and outreach to encourage, foster, and promote both engineering science and marine exploration to a broad audience.Real-time underwater 3D reconstruction is an enabling technology for many other research areas, including manipulation, navigation, obstacle avoidance, intelligent sampling, and adaptive surveying. This career development plan approaches the general vision problem by analyzing the constraints imposed by the underwater domain. The PI proposes a principled approach for online 3D reconstruction that handles both the propagation of light in water and a formulation that includes prior shape knowledge. Additionally, scene understanding is framed to allow for the segmentation and classification of underwater optical data based upon a joint model of the 3D structure and the photometric properties of the scene using a state-of-the-art dimensionality reduction approach.

水下机器人如AUV(自主水下机器人)和ROV(遥控潜水器)在深水科学、资源开采、海底建设和维护等许多任务中都有机会与环境互动。对这些任务至关重要的是能够解释操作环境，以确保安全性和有效性。该项目提出了开发算法，从水下相机获得的图片和视频中快速构建环境的3D模型。在短期内，这意味着加强人类控制AUV和ROV的方式。从长远来看，PI的职业计划旨在生产能够在没有直接人类控制的情况下与环境安全互动的系统。除了技术目标之外，这项建议还列出了一项教育和推广计划，以鼓励、培养和促进工程科学和海洋勘探向广大受众。实时水下3D重建是许多其他研究领域的一项使能技术，包括操纵、导航、避障、智能采样和自适应测量。这份职业发展计划通过分析水下领域施加的限制来解决一般的视力问题。PI为在线三维重建提出了一种原则性的方法，既处理了光在水中的传播，又提出了包括先验形状知识的公式。此外，场景理解的框架允许基于3D结构的联合模型和使用最先进的降维方法的场景的光度特性来对水下光学数据进行分割和分类。