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Pose-Aware Visual Place Recognition (PAVPR)

Project description

Advancing camera motion estimation and VPR technology

Camera motion can be analysed with millimetre-level accuracy using local image features. However, this efficiency declines in real-world situations due to changes in viewpoint, dynamic scene elements and variable lighting or weather conditions. Visual place recognition (VPR) is robust against appearance changes but is limited to coarse geometric models. Supported by the Marie Skłodowska-Curie Actions programme, the PAVPR project will overcome the current limitations of VPR and advance VPR and camera motion estimation to the next level. The project will use 3D modelling within VPR embeddings and develop novel mapping and localisation methods, enabling both accurate motion estimation and robustness to appearance changes.

Objective

Camera motion can be estimated from images with millimeter-level accuracy through local features, but only in carefully controlled data. In real-world conditions, performance often deteriorates under moderate illumination or viewpoint changes, dynamic scene elements or weather conditions. By contrast, Visual Place Recognition (VPR) exhibits strong robustness to visual appearance changes, yet remains limited to coarse geometric models, as it encodes image similarities without explicitly exploiting 3D information.

The goal of our Pose-Aware Visual Place Recognition (PAVPR) is to advance VPR and camera motion estimation by integrating explicit 3D modeling in VPR embeddings, thereby combining robustness to appearance changes with accurate motion estimation. Building on this foundation, PAVPR will also develop localization and mapping methods that rely on holistic image descriptors rather than traditional local feature-based pipelines. This approach offers several advantages, including improved resilience to visual variations, stronger privacy guarantees, and reduced computational costs. Finally, PAVPR will design novel benchmarks that account for the geometric proportionality of VPR embeddings, addressing a key limitation of current datasets and recall-based evaluations. The project also includes a secondment at VANDAL (Politecnico di Torino), enabling further collaboration with leading VPR experts and fostering deeper insights into the proposed research tasks.

Overall, this work aligns with the EU Work Programme on trustworthy, safe, and resilient AI and robotics systems. By enhancing both the efficiency and robustness of localization technologies, PAVPR will strengthen Europe’s leadership in trustworthy AI and deliver significant impact to the scientific community and industrial stakeholders alike.

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2025-PF

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Coordinator

UNIVERSIDAD DE ZARAGOZA
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.

€ 194 074,56
Address
CALLE PEDRO CERBUNA 12
50009 Zaragoza
Spain

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Region
Noreste Aragón Zaragoza
Activity type
Higher or Secondary Education Establishments
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Total cost

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Partners (1)