Metasurface-enhanced light detection and ranging technology

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.

Full text

Article https://doi.org/10.1038/s41467-022-33450-2
Metasurface-enhanced light detection and
ranging technology
Renato Juliano Martins
1
, Emil Marinov
1
,M.AzizBenYoussef
1
, Christina Kyrou
1
,
Mathilde Joubert
1
, Constance Colmagro
1,2
,ValentinGâté
2
, Colette Turbil
2
,
Pierre-Marie Coulon
1
,DanielTurover
2
,SamiraKhadir
1
, Massimo Giudici
3
,
Charalambos Klitis
4
,MarcSorel
4,5
&PatriceGenevet
1
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.
Autonomous mobile systems such asautonomous carsand warehouse
robots include multiple sensors to acquire information of their sur-
rounding environments, defining their position, velocity, and accel-
eration in real time. Among them, range sensors, and in particular
optical ranging sensors, provide vision to robotic systems1–3and are
thus at the core of the automation of industrial processes, theso-called
4.0 industrial revolution. Several optical imaging techniques are cur-
rently integrated into industrial robots for 3D image acquisition,
including stereoscopic camera, RADAR, structured light illumination,
and laser range finders or LiDARs. LiDAR is a technological concept
introduced in the early 60s, when Massachusetts Institute of Tech-
nology (MIT) scientists reported on the detection of echo signals upon
sending optical radiation to the moon surface4. Since the pioneering
MIT work,LiDARs have been using laser sources to illuminate targeted
objects and to collect the returning echo signals offering the
possibility of reconstructing highly resolved three-dimensional (3D)
images. Conventional LiDARs rely on time-of-flight (ToF) measure-
ment, which employs a pulsed laser directed toward a distant reflective
object tomeasure the round-trip time of light pulses propagating from
the laser to the scanned scene and back to a detection module. All
LiDAR components must act synchronously to tag single returning
pulses for ranging imaging reconstruction. The formula, 2d=cToF ,
holds for the recovered distance, where cis the speed of light and ToF
is the ToF. To sense the space, the LiDAR source must be able to sweep
a large Field of View (FoV). The objects in the scene are then detected,
point-by-point by measuring the ToF from every single direction to
build an optical echo map. The other measurement processes known
as Amplitude Modulation Continuous Wave (AMCW)5,6,Frequency
Modulation Continuous Wave (FMCW)7,8or Stepped Frequency Con-
tinuous Wave (SFCW)9employ continuous waves with constant or
Received: 7 April 2022
Accepted: 20 September 2022
Check for updates
1
Université Cote d’Azur, CNRS, CRHEA, Rue Bernard Gregory, Sophia Antipolis, 06560 Valbonne, France.
2
NAPA-Technologies, 74160 Archamps, France.
3
Université Côte d’Azur, Centre National de La Recherche Scientifique, Institut de Physique de Nice, F-06560 Valbonne, France.
4
School of Engineering,
University of Glasgow, Glasgow G12 8LT, UK.
5
Institute of Technologies for Communication, Information and Perception (TeCIP), Sant’Anna School of
Advanced Studies, Via Moruzzi 1, 56127 Pisa, Italy. e-mail: Patrice.Genevet@crhea.cnrs.fr
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time-modulated frequency to measure the round-trip time of the
modulated light information. LiDAR systems enable the real-time 3D
mapping of objects located at long, medium or short-range distances
from the source, finding a vast variety of applications beyond robotic
vision, spanning from landscape mapping Chase10–12,atmospheric
particle detection13–16, wind speed measurements17,18, static and/or
moving object tracking19–22,AR/VR
23, among others. Generally, LiDARs
are classified into scanning or non-scanning (Flash LiDAR) systems
depending on whether the laser sources simply illuminate24 or scan the
targeted scene. A scanning LiDAR system can be essentially described
in terms ofthree key components, (i) the light source for illumination,
(ii) the scanning module for fast beam direction at different points in
the scene, and (iii) the detection system for high-speed recovering of
the optical information received from the scene. Over the past dec-
ades, nanophotonics-based LiDAR systems have blossomed, and more
advanced scanning and detection techniques have been proposed25,26.
The expected massive use of LiDARs in the automotive industry for
advanced driver-assistance systems (ADAS) or even full-autonomous
driving brought out new challenges for the scanning systems, includ-
ing low fabrication complexity, potential for scalable manufacturing,
cost, lightweight, tolerance to vibrations and so on. Today, industrially
relevant LiDARs mainly use macro-mechanical systems to scan the
entire 360° FoV. Besides their large FoV, these bulk systems present
limited imaging rates of the order of few tens of Hz. A promising
evolution in mechanical scanners are the micro-electromechanical
systems27 (MEMS) which shift the scanning frequency to the kHz range.
However, a major drawback of MEMS is the low FoV, typically not
exceeding 25° for horizontal and 15° for vertical scanning. At the
research level, beam steering with optical phased arrays (OPA)28,29
provides remarkable speeds while reaching FoV around 60°. However,
OPA technology is less likely to be massively deployed in industrial
systems due to its manufacturing challenges. The industrially mature
liquid crystal modulators are also not adequate as LiDAR scanners due
to their poor FoVs usually remaining below 20° depending on the
wavelength, as well as their kHz modulation frequency30,31.Moreover,
acousto-optic deflectors (AODs) enabling ultrafast MHz scanning32,33,
have never been considered in LiDARs because of their narrow FoV
reaching at maximum 2°, imposing a compromise between high-speed
imaging and large FoV.
During the last decade, metasurfaces (MS)34 have spurred the
interest of the entire international photonic community by unveiling
the possibility of engineering the properties (i.e., the amplitude, the
phase, the frequency and/or the polarization) oflight at will35.Theyare
flat optical components made of arrangements of scattering objects
(meta-atoms) of subwavelength size and periodicity. Currently, four
light modulation mechanisms are used to create metasurfaces: light
scattering from resonant nanoparticles36,37,geometric phase occurring
during polarization conversion (Pancharatnam–Berry phase)38,accu-
mulated propagation phase in pillars with controllable effective
Refractive Index (ERI)39 and the topological phase in vicinity of
singularities40. Usually, MSs comprise inherently passive components,
designed to perform a fixed optical functionality after fabrication.
For instance, by properly selecting the size and the spacing of the
meta-atoms, MSs allow to redirect a laser beam at any arbitrary but
fixed angle dictated by the generalized Snell’s law. Clearly, passive
MS alone cannot be used in LiDARs requiring real-time beam scan-
ning. On the contrary, dynamic MSs designed by—or combined with—
materials possessing tunable optical properties caused by external
stimuli41–45 stand as promising alternatives for real-time deflection.
Recently, the US startup company LUMOTIVE introduced electrically
addressable reflective resonant MSs infiltrated with liquid crystals
and demonstrated scanning frequency that exceeds the switching
speed of common liquid crystal displays, as well as a FoV of around
120°46. The latter approach has been proven auspicious for minia-
turized, scalable LiDARs but it involves complex electronic
architectures, and likely significant optical losses in case of metallic
MS building blocks.
Here, we propose an alternative high-frequency beam scanning
approach that exploits the light deflecting capabilities of passive MSs
to expand the LiDAR FoV to 150× 150°, and to achieve simultaneous
low- and high-resolution multizone imaging. We make use of an ERI
multibeam deflecting MS cascaded with a commercial AOD. The sys-
tem offers large flexibilities in terms of beam scanning performance,
operation wavelength and materials. The angular resolution, referring
to the ability of the system to distinguish adjacent targets and retrieve
shapes, becomes very important in applications requiring simulta-
neous long and short-range detections. Our multizone LiDAR imaging
demonstration can mimic human vision by achieving simultaneous
high frame rate acquisition of high- and low-field zones with different
spatial resolution. The large design flexibility of MSs provides imaging
capabilities of interest to LiDAR systems, meanwhile offering new
industrial applications.
Ultrafast and high-FoV metasurface scanning module
MHz beam scanning can be achieved over a large FoV, by coupling
AODs with ERI MSs exhibiting spatially varying deflection angles.
Figure 1a illustrates the experimental concept where a modulated
laser source at λ= 633 nm (TOPTICA i-beam smart) generates single
pulses at any arbitrary rate up to 250 MHz. For single-pulse LIDAR,
the repetition rate frep is related to the maximum ranging distance
dmax by the expression:
dmax =1
2
c
frep
:ð1Þ
The focused beam with a small deflection is angularly increased
to scan in both azimuthal θand elevation φangles. A detailed scheme
of the FoV amplifying system is shown in Fig. 1b. A photograph of the
built proof-of-concept system is shown in Fig. 1c where we high-
lighted (shaded red region) the expansion of the small two degrees
(2°) AOD FoV into an enhanced 150° FoV. The deflected angle by the
MS is controlled by the impact position of the impinging focused
beam on the MS plane, associated with the radial and angular coor-
dinates rand θMS, respectively (see Fig. 1e). By applying voltage into
the AOD, one can actively re-point the beam at any arbitrary angle
within the 2° × 2° FoV, thus sweeping the focused beam across the
metasurface to vary θMS and r, in the range of [0 2π]and[0rmax ],
respectively, where rmax is the radius of the metasurface. Note that
θMS and rdenote, in polar coordinate, the position of the impact
beam on the metasurface according to Fig. 1e. For simplicity in
connecting incident and deflected angles, we designed a circular
metasurface with radially symmetric phase-delaying response, but
given the versatility in controlling the optical wavefront, various MS
with any other beam defecting properties can be adjusted according
to specific application. We must also highlight that, in principle,
there is no limitation on the observed FoV as it is fully dependent on
the metasurface phase function, within the limit ½0, πfor transmis-
sion scheme. In this initial demonstration, we implemented (Fig. 1f, g)
the simple concept of ERI MS designed to spatially impart linearly
increasing momentum with respect to the radial dimension rgiven
by the expression:
∂Φ
∂r=k0
r
rmax
ð2Þ
where, k0is the free space momentum, and Φthe local-phase
retardation. Such design results in parabolic-phase retardation as
represented in Fig. 1d. In this design, the deflected beam will be
delayed by a maximum phase retardation of Φ=∓πrmax
λand Φ=0 for
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the peripherical points ± rmax and central points, respectively. More-
over, Eq. (2) transformed in Cartesian coordinates determines the
value of the deflected angles in both axes, denoted as ðθ,φÞ,according
to the generalized Snell laws41:
kx,t=kx,i+∂Φ
∂x=k0sinθisinφi+∂Φ
∂r
∂r
∂x
ky,t=ky,i+∂Φ
∂y=k0sinθicosφi+∂Φ
∂r
∂r
∂y
,
(ð3Þ
where the phase gradient is defined at the metasurface plane at z=0.
Considering small incident angles originating from the AOD, the
expressions simplify as:
k0sinθtsinφt=k0r
rmax cosθMS
k0sinθtcosφt=k0r
rmax sinθMS
(,ð4Þ
Such expression validates the linearity observed for small angles
[−40°, 40°] according to the experimental measurements of the vol-
tage dependence of the deflection angles (Supplemental Fig. S2c).
Results
2D and 3D LiDAR image acquisition
To show the angular and depth 2D imaging capabilities of our LIDAR
system, we start performing 1D scanning of three distinct objects
placed on a table, (1) a square reflector mounted on a post, (2) a round
deflector and (3) a box reflector, angularly distributed at different
locations as shown in Fig. 2a. The associated 2D LIDAR ranging image is
displayed in Fig. 2b, indicating that high reflectivity objects are
observed at LIDAR positions matching to those observed with a con-
ventional camera (Fig. 2a). Particularly, we found that the three objects
shown in Fig. 2c were located at the following width [x], and depth [z]
positions: [−0.4m,1.5m],[−0.1m, 2.4 m] and [0.6 m, 3.5 m] for the
square, the round, and the box reflector, respectively. In the graph, we
also observe the difference in reflectivity of the three objects at various
distances leading to distinct intensities: the objects on the left and
right (square and box deflectors) correspond to lower signals due to
their angular locations, size and distance, while the round deflector in
the middle has higher reflectivity and appears with higher reflectance.
This first example validates the short-range (~5 m) imaging capabilities
of our LiDAR system.
To further investigate the capabilities of the system, we exten-
ded the performance to achieve 3D imaging. To this end, an addi-
tional FoV dimension is added by cascading a second AOD,
orthogonally oriented, in the elevation axis. The extended FoV is now
improved over both dimensions considering a MS with radial sym-
metry, as schematized in Fig. 1b. To demonstrate the two-axis scan-
ning capability, we present in Fig. 3a the elevation (top) and the
azimuthal (bottom) line scanning, respectively, to highlight that 150°
FoV (Supplementary Materials S1) is accessible for both scanning axis
(see video V1 in supplement materials). These examples of line
scanning are realized by fixing the voltage value on the one deflector
and scanning the voltage of the second deflector over the entire
range at a scanning rate that exceeds the acquisition speed of either
our eye or the CCD refreshing frame rate, resulting in an apparent
continuous line scan. We prepared a scene (Fig. 3b)—bottom) with
three different actors located at different angular and depth posi-
tions of 1.2, 2.7, and 4.9 m to demonstrate 3D imaging. Due to low
laser pulse peak power (about 10 mW), we performed our demon-
strations in an indoor environment using high reflective suits, con-
siderations of power and losses are addressed in Section S2 of
Supplemental Materials. For the demonstration, we choose a visible
laser operating at λ= 633 nm, which is very convenient to observe
and monitor the deflected beam. After calibrating the system (see
Fig. 1 | Concept of a metasurface-augmented FoV LidAR. a Schematic repre-
sentation of the LIDAR system. A triggered laser source, emitting single pulses for
ToF detection, is directed to a synchronized acousto-opticdeflector (AOD)offering
ultrafast light scanning with low FoV (~2°). The deflectedbeamisdirectedtoa
scanning lens to scan the laser spot on the metasurface at different radial and
azimuthal positions. The transmitted light across the metasurface is deviated
according to the position of the impinging beam on the component to cover a
scanning range between 75and 75. The scattered light from the scene is col-
lected using a fast detector. Data are processed to extract the single echo ToF for
2D and 3D imaging of the scene. bDetail of the cascaded AOD-metasurface
assembled deflection system. cTop view photography of the optical setup.
dBottom: Graphical representation of the metasurface phase distribution along
the radial axis. Top: Representations of beam deflection according to the incident
beam positioningon the metasurface. Inset equation represents thephase function
designed. eIllustration of axial symmetry for the laser impact point. fPhotography
of the 1 cm MS fabricated using nanoimprinting lithography. gSEM image of the
sample showing the nanopillar building blocks of varying sizes employed to
achievebeam deflection by considering lateral effective refractiveindex variations.
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Supplementary Material), arbitrary—or random access—beam scan-
ning along high FoV can be realized and arbitrary intensity patterns
can be projected by rapidly steering the beam at different locations
at very short time intervals (see Supplementary Video V2). Figure 3c
shows examples of several scanning profiles implemented to the
metasurface beam scanner to project Lissajous curves demonstrat-
ing random-point access mode.
Mimicking human peripheral and fovea vision with multizone
LiDAR imaging
Previous experiments were performed by focusing the light deflected
by the AOD onrelatively small metasurfaces (1, 2, and 3 mm diameters)
using a scanning lens. This configuration favors a small spot (of the
order of 50 μm) to contain the MS angular divergence to a small
parametric region, i.e., scanning the MS with smallspot prevents large
Fig. 2 | 1D time-of-flight imaging. a Photography of the scene. bRanging image of
three objects displaced on a table using high reflective tapes to improve the
intensity of the returned signal. In (1) a post with a small reflector was used in (2) a
round object with a reflector and in (3)there is a box reflector witha tape around it.
The graph shows the image in the correct ranging distance X(scanning dimension)
and Z(ranging dimension) showing the capabilitiesto sense all of the three objects.
cPositionof single objects according to ranging image in (b). dRawsignal collected
for the respective image, showing that objects oriented in the normal direction
have bigger scattering intensity, the inset display single pulses used to determine
the ToF ranging distance.
Fig. 3 | 3D imaging and wide-angle scanning capabilities. a LIDAR line scanning
of our laboratory room that show the large FoV on both Elevation (top) and Azi-
muth (bottom) angles. Note the top picture showing a scanning line profile cov-
ering thewhole range from theground to the ceiling of the testing room over 150°.
b3D ranging demonstration (top): the scene (bottom) was set up with actors
wearing reflective suits positioned in the scene at distance Zvarying from 1.2 to
4.9 m. Colors encodes distance. cLissajous scanning using deflecting functions as
θ=Asin αt+ΨðÞand = Bsin βtðÞfor different parameters αand βto illustrate the
laser projectioncapabilitieson a fast beam scanning, in a large FoV configuration. Ψ
was set to be 0° and A=B= 30, although any configuration can be activelychanged.
Article https://doi.org/10.1038/s41467-022-33450-2
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overlap with thespatially varying deflecting area. The beam divergence
as a function of the metasurface size is provided in Supplementary
Material S5, indicating that a 3 mm device results in a divergence lower
than 1.5°. Robotic systems interested in reproducing human vision
requires peripheral and central vision as illustrated in Fig. 4a, where
several zones featuring different spatial resolutions are acquired
simultaneously. A low-resolution peripheral field provides coarse
scene exploration, usually needed for human to direct the eye to focus
to a highly resolved fovea region for sharp imaging. The scene thus
needs to be scanned differently according to the zones of interest. To
reduce further beam divergence and improve as needed the resolu-
tion, it is necessary to increase the diameter and complexity of the
metasurface and work with fully collimated beams. For this purpose,
we realized a cm-size metasurface deflector using nanoimprint litho-
graphy(NIL), as shown in Fig. 1f, g (further details on the fabrication are
provided in S9). In the latter configuration, the deflector is directly
placed after the AOD without utilizing a scanning lens. We specifically
designed a large area deflector that achieve moderate 1st order
deflection efficiency of ~40% and took advantage of the non-deflected
zero-order narrow scanning FoV to simultaneously scan two zones
with different FoVs and resolutions. This demonstration specifically
exploits the multibeam addressing capability of metasurfaces, result-
ing in a dual mode imaging: (i) a high-resolution scanning provided by
the near collimated zero-order beam deflected by the AOD only, and
(ii) a large FoV, lower resolution image provided by the 1st order beam
deflected by the metasurface. As illustrated in Fig. 4b, inset, we spa-
tially selected the returned/scattered signal from the different parts of
the scene. For this purpose, we used a double-detector monitoring
scheme. The first detector collects light from the full numerical aper-
ture (~2πsolid angle) but it blocks the central small numerical aperture
(a beam blocker is placed in front of the detector). The second
detector covers only a small NA for the narrow FoV resulting from
zero-order light scanning (a spatial filter is used to select the obser-
vation area). A dual-beam metasurface scanning scheme is used to
imageascene(Fig.4b, top) with two fields of interest: (i) three actors
placed at different regions of the space periphery, as measured in
Fig. 4c (top) and a highly resolved chessboard-like object placed in the
forward direction at a small FoV, measured in Fig. 4c (bottom). The
images presented in Fig. 4c correspond to low- and highly resolved
imaging, acquired by both detectors simultaneously. Multizones
scanning with a high resolution forward, and low lateral resolution
over a high-FoV peripherical vision could be a disruptive solution for
addressing the needs of advanced driver-assistance systems (ADAS).
High-speed velocimetry and time-series imaging
To characterize the MHz deflection speed and the possibility of
achieving real-time frame rate imaging, we measured the beam
deflection speed, i.e., the minimum frequency at which the beam can
be re-pointed to a new direction. To do so, we placed highly reflecting
tapes on the wall, and measured the amplitude of the backscattered
signal for distinct scanning frequencies. We define as “system cutoff
frequency”the condition when the amplitude of the reflected signal
decays to −3dB point (see Supplementary Information S4). The mea-
surements were made by considering: (i) a single scanner in the
Fig. 4 | Multizone imaging. a Schematic representation of a human multizone
viewing with the concept to be adapted in ADAS systems. Such mimicking char-
acteristics enables double vision for dual-purpose imaging features for high-reso-
lution, long range, in the center and lower resolution, bigger FoV, for the
peripherical view. bExperimental realization to test the dual-zone imaging func-
tionality of the LIDAR system, including dual detection scheme (inset) for simul-
taneousimage multiplexed collection. The central0th diffractionorder beam scans
a small area with high resolution directed at the center of the image while the 1st
diffracted order scans the whole field. cTop: We show the result of the scanned
scenes described in (b). Top represents the LIDAR large FoV ranging image. The
image is obtained by blocking the central part of the numerical aperture using an
obstacle as sketched in (b). The bottom LIDAR ranging high-resolution image
presentsthe central part scene captured using the0th diffractionbeam, covering a
FoV of about 2°.
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azimuth angle (see Supplementary Fig. S5b red curve) and (ii) a cas-
caded system comprised by two orthogonally oriented deflectors, for
scanning at both azimuthal and elevation angles, (see Supplementary
Fig. S5b) blue curve). The results indicate less than −3dB loss up to
around 6 MHz and 10 MHz for single and double-axis scanning,
respectively. We also demonstrate the modulation of a laser beam over
an large FoV (>140°) at MHz speed and correct imaging with scanning
frequency up to 6.25 MHz (see Supplemental Fig. S5c)). This corre-
sponds to about two orders of magnitude faster than any other beam-
pointing technology reported so far. Operating beyond the −3dBloss
at higher frequency was also realized, leading to reduced resolution
but increased imaging frame rate, up to 1 MHz for 1D scanning at
40 MHz (see discussion in Supplementary Materials in Section S7).
Measurements of time events were performed to investigate
dynamic imaging. The most convenient dynamic system observable in
our laboratory was a spinning chopper composed of a rotating wheel
at nominally 100 Hz rotation speed. We prepared the scene composed
of a chopper, located at 70 cm away from the source, decorated with a
high reflective tape in one of the mechanical shutters, as illustrated in
Fig. 5a (top). As described in Supplemental Table 1, we performed
three time-series experiments using acquisition frame rates of 741,
1020, and 3401 fps (see Supplementary Information S8 and Supple-
mentary Videos SGIF 1–3).Wetrackedthecenterpositionofthe
reflective tape in both the space and time domains by integrating the
radial axis of the ranging image from the center of the chopper and
fitting a Gaussian curve plotted over the entire ½0,2πangular axis (see
Fig. 5a, bottom). The curves are manually offset by 6πto differentiate
the experiments. All experiments revealed an averaged rotation speed
valueof92.71Hz.Weattributethe~7.3Hzdifferencebetweenthe
measured and nominal speed of 100 Hz to the phase-jitter control
mechanism on the chopper. In principle, rotating mechanical shutters
are designed with a closed loop circuitry providing an electronic signal
that maintains linear rotation speed. Interestingly, displaying time
events on the angular dimension reveals small wobbling wheel
imperfection caused by the presence of the reflective tape, resulting in
a slowdown at the angles around 3π=2asevidencedinFig.5b
(Experiment 2—1020 fps). One can indeed observe a rotation slope-
change during periodic times corresponding to the position of the
reflective tape at the bottom (for instance at t= 1.0 ms/10.8 ms in
Fig. 5b) (bottom panel)). Using the recovered ranging information, we
estimate the size of the tape to 4 cm, as illustrated in Fig. 5c (bottom).
The 1 cm difference to the real object (Fig. 5c, top) is due to the high
reflectivity of the screws located close to the center and causing
additional scattering at the same ranging distance.
Discussion
We realize an ultrafast beam scanning system composed of a fast
deflector and a passive metasurface to achieve beam steering at MHz
speed over 150 × 150°FoV, improving the wide-angle scanning rate of
mechanical devices by five orders of magnitude. We performed fast
steering in one and two angular dimensions and retrieved the asso-
ciated time of flight for ranging measurements leading to high-speed
LiDAR imaging of very fast-moving objects on a large FoV. Employing
parameters described on the second row of Supplementary Table 1, we
achieved a time step of 980 µs, see Fig. 5b. An object traveling at the
speed of the sound (1234 Km/h) at 15m away from the source will take
~74 ms to cover a 120°FoV. Such supersonic object can be detected
within 76 time-series events. Considering the Nyquist limit i.e., four
time series to recover the speed, the maximum event detection can
increase up to a speed of 47 mega-meter/h.
High-speed scanning modules for LiDAR applications have to
trade-off between the maximum distance and spatial resolution (see
Supplementary Information S5, S10). The frame rate of a single ToF
system can be expressed as:
fRate =c
2ndmax
ð5Þ
where cis the speed of the light. Equation (5) thus indicates that both
the number of pixels in the image (n) and the maximum ambiguity
distance, ðdmaxÞ,defines theimaging frame rate. Suchechoing time can
be reduced by encoding the signal sent in each scanning direction with
aspecificidentification code namely Code-division multiple access
(CDMA)47. Multiplexed observation is realized by decorrelating the
ToF signal using matched filter technique. LiDAR companies often
multiplex the source with an array of diode lasers to increase frame
rate, increasing the lidar complexity, and multiplying the system cost
by the number of sources. Such CDMA technique realistically could be
exploited in combination with our fast beam deflection system to
reach imaging frame rate of 125 frames/s with high spatial resolution of
200 × 200 pixels. Beyond application for ADAS industry, beam
steering systems with similar performances have potential in real-
time imaging for applications requiring short ambiguity distance, for
example in microscopy and wide-angle optical coherence
tomography48. Our main limitation to achieve high frame-rate is
related to the extremely large volume of real-time data treatment to be
realized synchronously during the acquisition. Here we only per-
formed calculation using conventional CPU—LabView based—as such,
we cannot output and save data as the same speed as their acquisition.
Fig. 5 | Measurement of fast in real-time-series events. a Top: Illustration of the
scene: a mechanical chopper of was set up with a nominal speed of 100 Hz and
some slabs were covered using a reflective tape. Bottom: Measurement of the
rotation speed for three different frame rates. bTop: Normalized intensity map for
the radial axis, illustrating the dynamics of the wheel. Note the different slope for
the rotation angles around 3π=2 representing a lessening of the speed. Bottom:
Single-frame intensity data illustrating various angular positions. cTop: photo-
graphy of the chopper and the size of the reflective tape. Bottom: Ranging image
for t= 1.0ms and the measurement of the tape from the recovered data.
Article https://doi.org/10.1038/s41467-022-33450-2
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The Supplementary Video V3 showing a moving person in 3D space is
taken by achieving the best compromise, that is by acquiring single
frames raw data (with 200 ×200 pixels for instance) and outputting
data directly to SSD driverframe-by-frame. Our data treatment process
creates latencies related to asynchronous data storage, which result in
stuttered or choppy movements with occasional video speeding-up
movements. This problem is generally mitigated in LiDAR by
implementing FPGA/ASICS processing.
Our approach also offers random-access beam steering cap-
abilities. Multizone ranging images mimicking human vision at high
frame rate have been realized. The versatility of MS for wavefront
engineering could improve the capabilities of simultaneous localiza-
tion and mapping algorithms. Furthermore, incorporating this system
in ADAS could provide a disruptive solution for medium/long-range
perception, in which the central view scans the front scene, while the
peripheral view provides additional sensing for pedestrian safety for
example. We finally demonstrated time-event series for imaging at a
real-time regime (>1k fps and up to MHz frame rate for 1D scanning).
Outperforming existing LiDAR technologies, our tool offers a per-
spective for future applications, in particular by participating to
reducing the low decision-making latency of robotic and advanced
driver-assistance systems.
Methods
Experimental methodology
A collimated beam is sent to an AOD device (AA Opto-electronic
DTSXY-400-633) to deflect light at small arbitrary angles, within 49
mrad. The AOD is driven by a voltage-controlled RF generator (AA
Opto-electronics DRFA10Y2X-D-34-90.210). The deflected signal is
directed to a scanning lens (THORLABS LSM03-VIS) that focuses the
light at different transverse positions on the MS. The MS acts as a
designer-defined passive device to convert the small 2° × 2° FoV into an
enhanced 150° × 150° FoV. ToF is obtained by monitoring the scattered
light at each scanned angle using a detector (Hamamatsu C14193-
1325SA); and the reconstructed ranging image is built by associating
each period ð1
frepÞto individual pixels and extracting the ToF. In our
detection scheme, the detection path is separated to the excitation
path, which may result in not overlapped illumination/observation
regions. We believe that a mono-static approach could as well be
implemented in our configuration by utilizing a beam splitter before
sending the laser beam into the acousto-optics deflector. A PXI
(National Instruments) system is used for data generation, recovery,
and treatment (more details can be found in Section S6 of Supple-
mental Materials). The angular scanning of the whole 1D was per-
formed in a single shot, during which we orchestrated pulse
repetition, scanning position angles and collection for precise mea-
surement of ToF in the system. With an acquisition scope card of
3Gsamples/s sample rate and considering a rise time on the detector
smaller than ~330 ps, the maximum z (depth) resolution of single
echo per laser shot measurement is about Δz=5cm.InFig.2d, we
show the collected raw signal corresponding to the three objects. For
ToF recovery, we used the derivative of the signal and collected the
peak of the differentiated signal. Single pulses were collected (inset
Fig. 2d) and separated to evaluate the ToF for each scanned direction
and then folded at the scanning frequency to form an image. The
fabrication of the different MS has been realized using GaN on sap-
phire nanofabrication processes. Details are available in the supple-
mentary materials.
Data availability
The Source data are available from the corresponding author upon
request. All data needed to evaluate the conclusion are present in the
manuscript and/or the Supplementary Information. Videos are avail-
able as Supplementary Materials, and the associated raw data would be
available upon request.
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Acknowledgements
This work was financially supported by the European Research Council
proof of concept (ERC POC) under the European Union’s Horizon 2020
research and innovation program (Project i-LiDAR, grant number
874986), the CNRS prématuration, and the UCA Innovation Program
(2020 startup deepTech) and the French defense procurement agency
under the ANR ASTRID Maturation program, grant agreement number
ANR-18-ASMA-0006. CK and MS acknowledge inputs of the technical
staff at the James Watt Nanofabrication Centre at Glasgow University. C.
Kyrou has been supported with a postdoctoral fellowship grant by the
Bodossaki Foundation (Athens, Greece).
Author contributions
Sample fabrication: C.C., V.G., C.T., P.M.C., D.T., C. Klitis, and M.S.;
conceptualization and supervision: P.G.; Instrumentation support: M.G.;
experimental realization: R.J.M. and P.G.; data collection: R.J.M., E.M.,
A.B.Y., and M.J.; data analysis: R.J.M., E.M., A.B.Y. and P.G.; manuscript
writing: R.J.M., P.G., C. Kyrou, E.M., A.B.Y., and S.K.
Competing interests
A patent has been filed on this technology/Renato J Martins, Samira
Khadir, Massimo Giudici, and Patrice Genevet, SYSTEM AND METHOD
FOR IMAGING IN THE OPTICAL DOMAIN, EP21305472 (2021).
Additional information
Supplementary information The online version contains
supplementary material available at
https://doi.org/10.1038/s41467-022-33450-2.
Correspondence and requests for materials should be addressed to
Patrice Genevet.
Peer review information Nature Communications thanks the other
anonymous reviewer(s) for their contribution to the peer review of this
work. Peer review reports are available.
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