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Exploration of Unknown Environments for Digital Twins

Project description

Putting Digital Twins to work

Digital Twins are virtual 3D replicas of complex environments such as cities, factories, and construction sites, capturing both objects and their movements. While they hold great potential for monitoring and planning, current technology limits their widespread adoption. The ERC-funded Explorer project aims to develop methods for automatically capturing and labelling video data in ‘open worlds’ to support the creation and maintenance of Digital Twins. The project will guide autonomous systems, including robotic platforms and UAVs, through complex environments to capture visual data. Inspired by AI techniques, this approach will create a dataset of annotated video sequences from work sites to evaluate advancements.

Objective

"In the 'explorer' project, we will develop methods for automatically capturing and labelling video data in ""open worlds"". The ultimate goal is the great facilitation of the creation and maintenance of Digital Twins: Digital Twins are virtual 3D copies of complex scenes such as cities, factories, or construction sites. Not just a 3D reconstruction, they should capture the scene's semantics, i.e. the identity of each object and the scene's dynamics, i.e. how objects move. Because Digital Twins have the potential to be extremely useful for monitoring large complex sites and planning the development of these sites, their forecast market is huge, they remain mostly a concept because of important limitations of the current technology. Our methods will guide autonomous systems such as robotic platforms and UAVs through complex and unknown environments to capture visual data for creating and maintaining Digital Twins. This is extremely challenging as these systems will encounter objects without any prior knowledge about them and will have to collect sufficient data about them. To the best of our knowledge, this active and automatic capture in complex real environments is a new problem. It is however very important to solve it as this will relax the need for human expertise and time: Currently, capturing such data is done manually only by researchers and requires strong understanding of what the learning algorithms require. To tackle the complexity of this problem, our approach is inspired by techniques from Artificial Intelligence applied to the exploration of extremely large trees. This approach will allow us to bring the perception part and the planning part of the problem together under the same optimization framework, to formalize it and solve it efficiently. To evaluate our developments, we will create a dataset of annotated video sequences from working sites, which we will share with the community."

Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2022-ADG

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Host institution

ECOLE NATIONALE DES PONTS ET CHAUSSEES
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 2 476 718,00
Address
AVENUE BLAISE PASCAL-CITE DESCARTES-CHAMPS-SUR-MARNE 6-8
77455 Marne La Vallee Cedex 2
France

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Region
Ile-de-France Ile-de-France Seine-et-Marne
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 2 476 718,00

Beneficiaries (1)

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