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

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
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-02-01 至 2020-01-31

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中文摘要
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英文摘要
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.
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会议论文
NRI: INT: COLLAB: Shared Autonomy for Unstructured Underwater Environments through Vision and Language
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