Metasurface-enhanced light detection and ranging technology

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 Khadir, Massimo Giudici, Charalambos Klitis, Marc Sorel and Patrice Genevet ()
Additional contact information
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
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
Patrice Genevet: Université Cote d’Azur, CNRS, CRHEA, Rue Bernard Gregory

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-33450-2

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