Metasurface-enhanced light detection and ranging technology.


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
29 09 2022
Historique:
received: 07 04 2022
accepted: 20 09 2022
entrez: 29 9 2022
pubmed: 30 9 2022
medline: 4 10 2022
Statut: epublish

Résumé

Deploying advanced imaging solutions to robotic and autonomous systems by mimicking human vision requires simultaneous acquisition of multiple fields of views, named the peripheral and fovea regions. Among 3D computer vision techniques, LiDAR is currently considered at the industrial level for robotic vision. Notwithstanding the efforts on LiDAR integration and optimization, commercially available devices have slow frame rate and low resolution, notably limited by the performance of mechanical or solid-state deflection systems. Metasurfaces are versatile optical components that can distribute the optical power in desired regions of space. Here, we report on an advanced LiDAR technology that leverages from ultrafast low FoV deflectors cascaded with large area metasurfaces to achieve large FoV (150°) and high framerate (kHz) which can provide simultaneous peripheral and central imaging zones. The use of our disruptive LiDAR technology with advanced learning algorithms offers perspectives to improve perception and decision-making process of ADAS and robotic systems.

Identifiants

pubmed: 36175421
doi: 10.1038/s41467-022-33450-2
pii: 10.1038/s41467-022-33450-2
pmc: PMC9523074
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

5724

Informations de copyright

© 2022. The Author(s).

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Auteurs

Renato Juliano Martins (R)

Université Cote d'Azur, CNRS, CRHEA, Rue Bernard Gregory, Sophia Antipolis, 06560, Valbonne, France.

Emil Marinov (E)

Université Cote d'Azur, CNRS, CRHEA, Rue Bernard Gregory, Sophia Antipolis, 06560, Valbonne, France.

M Aziz Ben Youssef (MAB)

Université Cote d'Azur, CNRS, CRHEA, Rue Bernard Gregory, Sophia Antipolis, 06560, Valbonne, France.

Christina Kyrou (C)

Université Cote d'Azur, CNRS, CRHEA, Rue Bernard Gregory, Sophia Antipolis, 06560, Valbonne, France.

Mathilde Joubert (M)

Université Cote d'Azur, CNRS, CRHEA, Rue Bernard Gregory, Sophia Antipolis, 06560, Valbonne, France.

Constance Colmagro (C)

Université Cote d'Azur, CNRS, CRHEA, Rue Bernard Gregory, Sophia Antipolis, 06560, Valbonne, France.
NAPA-Technologies, 74160, Archamps, France.

Valentin Gâté (V)

NAPA-Technologies, 74160, Archamps, France.

Colette Turbil (C)

NAPA-Technologies, 74160, Archamps, France.

Pierre-Marie Coulon (PM)

Université Cote d'Azur, CNRS, CRHEA, Rue Bernard Gregory, Sophia Antipolis, 06560, Valbonne, France.

Daniel Turover (D)

NAPA-Technologies, 74160, Archamps, France.

Samira Khadir (S)

Université Cote d'Azur, CNRS, CRHEA, Rue Bernard Gregory, Sophia Antipolis, 06560, Valbonne, France.

Massimo Giudici (M)

Université Côte d'Azur, Centre National de La Recherche Scientifique, Institut de Physique de Nice, F-06560, Valbonne, France.

Charalambos Klitis (C)

School of Engineering, University of Glasgow, Glasgow, G12 8LT, UK.

Marc Sorel (M)

School of Engineering, University of Glasgow, Glasgow, G12 8LT, UK.
Institute of Technologies for Communication, Information and Perception (TeCIP), Sant'Anna School of Advanced Studies, Via Moruzzi 1, 56127, Pisa, Italy.

Patrice Genevet (P)

Université Cote d'Azur, CNRS, CRHEA, Rue Bernard Gregory, Sophia Antipolis, 06560, Valbonne, France. Patrice.Genevet@crhea.cnrs.fr.

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Classifications MeSH