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.

The topography of a rough surface determines many of its physical properties, for instance, tribology, optical properties etc. Nowadays, a deep understanding of such physical phenomena requires the knowledge of the topography at appropriate length scales. Apart from performing multi-scale measurements of the surface topography, it also requires the use of proper statistical estimators for the analysis of such topography maps. Moreover, when dealing with light scattering in the visible spectral range, the scale at which the estimators of local topography properties are defined is extremely important. Here we present a multi-scale and statistical study of the surface topography of blasted aluminum samples which all have rather different visual appearance. Various statistical estimators of surface topography are examined, including estimators related to the height distribution, the lateral correlation and local topology. The combination of these various estimators unveils a scale separation between a micro-scale roughness inherited from the initial cold-rolled aluminum surface and a large scale roughness fully controlled by the blasting process. A special emphasis is given to the crucial importance of length scales in the estimation of local slopes. The present analysis establishes a quantitative link between the statistical properties of the surface topography and the blasting process used to fabricate the samples.

The transmission of light through low coverage regular and random arrays of glass supported silica micropillars of diameters 10 to 40 µm and height 10 µm is studied experimentally. Angle-resolved measurements of the transmitted intensity are performed at visible wavelengths by either a goniospectrophotometer or a multimodal imaging (Mueller) polarimetric microscope. It is demonstrated that for the regular arrays, the angle-resolved measurements are capable of resolving many of the densely packed diffraction orders that are expected for periodic structures of lattice constants 20 to 80 µm, but they also display features that are due to the scattering and guiding of light in individual micropillars or in the supporting glass slides. These latter features are also found in angle-resolved measurements on random arrays of micropillars of the same surface coverage. Finally we perform a comparison of direct measurements of haze in transmission for our patterned glass samples with what can be calculated from the angle-resolved transmitted intensity measurements. Good agreement between the two types of results are found which testifies to the accuracy of the angle-resolved measurements that we report.

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