Metasurface-enhanced light detection and ranging technology

• Published in: Nature communications Vol. 13; no. 1; p. 5724
• Main Authors: Juliano Martins, Renato, Marinov, Emil, Youssef, M. Aziz Ben, Kyrou, Christina, Joubert, Mathilde, Colmagro, Constance, Gâté, Valentin, Turbil, Colette, Coulon, Pierre-Marie, Turover, Daniel, Khadir, Samira, Giudici, Massimo, Klitis, Charalambos, Sorel, Marc, Genevet, Patrice
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group 29.09.2022; Nature Publishing Group UK; Nature Portfolio
• Subjects: Algorithms; Computer vision; Decision making; Deflectors; Eye; Field of view; Fovea; Imaging; Lidar; Machine learning; Machine vision; Metasurfaces; Mimicry; Optical components; Optimization; Retina; Robotics; Technology
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-33450-2

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.