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Metasurface-enhanced light detection and ranging technology

Author

Listed:
  • Renato Juliano Martins

    (Université Cote d’Azur, CNRS, CRHEA, Rue Bernard Gregory)

  • Emil Marinov

    (Université Cote d’Azur, CNRS, CRHEA, Rue Bernard Gregory)

  • M. Aziz Ben Youssef

    (Université Cote d’Azur, CNRS, CRHEA, Rue Bernard Gregory)

  • Christina Kyrou

    (Université Cote d’Azur, CNRS, CRHEA, Rue Bernard Gregory)

  • Mathilde Joubert

    (Université Cote d’Azur, CNRS, CRHEA, Rue Bernard Gregory)

  • Constance Colmagro

    (Université Cote d’Azur, CNRS, CRHEA, Rue Bernard Gregory
    NAPA-Technologies)

  • Valentin Gâté

    (NAPA-Technologies)

  • Colette Turbil

    (NAPA-Technologies)

  • Pierre-Marie Coulon

    (Université Cote d’Azur, CNRS, CRHEA, Rue Bernard Gregory)

  • Daniel Turover

    (NAPA-Technologies)

  • Samira Khadir

    (Université Cote d’Azur, CNRS, CRHEA, Rue Bernard Gregory)

  • Massimo Giudici

    (Université Côte d’Azur, Centre National de La Recherche Scientifique, Institut de Physique de Nice)

  • Charalambos Klitis

    (University of Glasgow)

  • Marc Sorel

    (University of Glasgow
    Sant’Anna School of Advanced Studies)

  • Patrice Genevet

    (Université Cote d’Azur, CNRS, CRHEA, Rue Bernard Gregory)

Abstract

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.

Suggested Citation

  • Renato Juliano Martins & Emil Marinov & M. Aziz Ben Youssef & Christina Kyrou & Mathilde Joubert & Constance Colmagro & Valentin Gâté & Colette Turbil & Pierre-Marie Coulon & Daniel Turover & Samira K, 2022. "Metasurface-enhanced light detection and ranging technology," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33450-2
    DOI: 10.1038/s41467-022-33450-2
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    References listed on IDEAS

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    1. Christopher Rogers & Alexander Y. Piggott & David J. Thomson & Robert F. Wiser & Ion E. Opris & Steven A. Fortune & Andrew J. Compston & Alexander Gondarenko & Fanfan Meng & Xia Chen & Graham T. Reed , 2021. "A universal 3D imaging sensor on a silicon photonics platform," Nature, Nature, vol. 590(7845), pages 256-261, February.
    2. Pin Chieh Wu & Ragip A. Pala & Ghazaleh Kafaie Shirmanesh & Wen-Hui Cheng & Ruzan Sokhoyan & Meir Grajower & Muhammad Z. Alam & Duhyun Lee & Harry A. Atwater, 2019. "Dynamic beam steering with all-dielectric electro-optic III–V multiple-quantum-well metasurfaces," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
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    Cited by:

    1. Dawoon Jeong & Hansol Jang & Min Uk Jung & Taeho Jeong & Hyunsoo Kim & Sanghyeok Yang & Janghyeon Lee & Chang-Seok Kim, 2024. "Spatio-spectral 4D coherent ranging using a flutter-wavelength-swept laser," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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