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Multimodal 2D Image to 3D Model Registration via a Mutual Alignment of Sparse and Dense Visual Features

Nathan Crombez 1, 2 Ralph Seulin 1, 2, * Olivier Morel 1, 2 David Fofi 1, 2 Cédric Demonceaux 1, 2
* Corresponding author
2 VIBOT - VIsion pour la roBOTique [VIBOT CNRS ERL 6000]
Le2i - Laboratoire d'Electronique, d'Informatique et d'Image [EA 7508], CNRS - Centre National de la Recherche Scientifique : ERL6000
Abstract : Many fields of application could benefit from an accurate registration of measurements of different modalities over a known 3D model. However, aligning a 2D image to a 3D model is a challenging task and is even more complex when the two have a different modality. Most of the 2D/3D registration methods are based on either geometric or dense visual features. Both have their own advantages and their own drawbacks. We propose, in this paper, to mutually exploit the advantages of one feature type to reduce the drawbacks of the other one. For this, an hybrid registration framework has been designed to mutually align geometrical and dense visual features in order to obtain an accurate final 2D/3D alignment. We evaluate and compare the proposed registration method on real data acquired by a robot equipped with several visual sensors. The results highlights the robustness of the method and its ability to produce wide convergence domain and a high registration accuracy.
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Submitted on : Thursday, April 26, 2018 - 2:35:46 PM
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Nathan Crombez, Ralph Seulin, Olivier Morel, David Fofi, Cédric Demonceaux. Multimodal 2D Image to 3D Model Registration via a Mutual Alignment of Sparse and Dense Visual Features. IEEE International Conference on Robotics and Automation - ICRA,2018, May 2018, Brisbane, Australia. pp.6316-6322. ⟨hal-01779176⟩

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