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WO2023002237A1 - Capteur lidar multifonctionnel polarimétrique pour reconnaissance de cibles - Google Patents

Capteur lidar multifonctionnel polarimétrique pour reconnaissance de cibles Download PDF

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Publication number
WO2023002237A1
WO2023002237A1 PCT/IB2021/056652 IB2021056652W WO2023002237A1 WO 2023002237 A1 WO2023002237 A1 WO 2023002237A1 IB 2021056652 W IB2021056652 W IB 2021056652W WO 2023002237 A1 WO2023002237 A1 WO 2023002237A1
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WIPO (PCT)
Prior art keywords
polarimetric
targets
multifunctional
data
lidar
Prior art date
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Ceased
Application number
PCT/IB2021/056652
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English (en)
Inventor
Irene ESTÉVEZ CARIDE
Filipe André PEIXOTO OLIVEIRA
Eduardo J. NUNES-PEREIRA
Nazar ROMANYSHYN
Nelssom FERNANDEZ CUNHA
Manuel José DE LIMA FERREIRA RODRIGUES
Nuno Miguel SILVA TELES OLIVEIRA
Pedro BRAGA FERNANDES
José Carlos VIANA GOMES
Rui Miguel SOARES PEREIRA
Manuel Filipe Pereira Da Cunha Martins Costa
Mário Rui DA CUNHA PEREIRA
Gueorgui VITALIEVITCH SMIRNOV
Luis Manuel FERNANDES REBOUTA
Moisés Alexandre SILVA DUARTE
Mikhail IGOREVICH VASILEVSKIY
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Universidade do Minho
Bosch Car Multimedia Portugal SA
Original Assignee
Universidade do Minho
Bosch Car Multimedia Portugal SA
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Universidade do Minho, Bosch Car Multimedia Portugal SA filed Critical Universidade do Minho
Priority to EP21748959.0A priority Critical patent/EP4352536A1/fr
Publication of WO2023002237A1 publication Critical patent/WO2023002237A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/499—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using polarisation effects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section

Definitions

  • the present application describes a polarimetric multifunctional LiDAR system and method for detecting and recognizing targets in the surroundings, for autonomous driving applications.
  • a LiDAR Light Detection and Ranging
  • ranges distances
  • targets By illuminating the scene and by detecting the light returned from objects in the environment where the scan is performed, a LiDAR sensor is able to determine obstacles, and how far away they are. This information allows for building real-time distance maps of surrounding objects to navigate in unknown environments.
  • LiDARs are essential for self-driving vehicles safety. In this sense, one of the main open challenges is not only to detect, but also to recognize different kinds of static or moving objects, such as walls, pedestrians, cyclists or cars, in an attempt to reduce the risk of vehicles' accidents.
  • One of the signals (the so-called reference signal) is used as zero reference, while the other (the measurement signal) is the one that travels towards the target, which is located at a distance d, and returns.
  • the reference signal is used as zero reference
  • the measurement signal is the one that travels towards the target, which is located at a distance d, and returns.
  • polarization When an optical beam interacts with matter, its polarization state can be changed as a function of the material's properties, the target surface, and the beam characteristics (wavelength, polarization, etc.). Under this scenario, different objects can reflect differently the same polarized incident beam, and consequently, polarization can enhance discriminative power between materials, when compared to unpolarized light. Thus, polarization can be used as an additional degree of freedom to discriminate between materials or to remotely obtain further information about targets.
  • Polarimetry is the measurement and interpretation of polarization.
  • One important method used to analyze polarization changes produced by different materials is based on the measurement of the Mueller matrix.
  • This matrix consists of 4x4 real elements that describe the polarimetric properties of materials, enabling us to analyze the interaction of a totally or partially polarized or depolarized wave reflected/transmitted/scattered by a sample.
  • the Stokes-Mueller formalism is the most appropriate representation of polarization for radiometric measurements when considering not fully polarized light.
  • the Stokes vector, S (composed by the Stokes parameters So, i, S 2 and S 3) describes the state of polarization of light and it can be written as where E s and E p are the parallel and perpendicular, with respect to the plane of incidence, components of the electric field vector. Partially polarized light or depolarized light can be conveniently described based on a Stokes vector.
  • a Mueller matrix codifies the polarimetric content of its respective sample, which can be synthetized by properly arranging the information in the Mueller matrix elements.
  • Stokes vectors and Mueller matrices are defined and extensively discussed.
  • LiDARs that allow to perform polarization measurements.
  • cloud research i.e. to observe cloud characteristics, remotely identifying characteristics of atmospheric aerosol particles or urban aerosols.
  • LiDAR sensor for detecting an object
  • US20180106901A1 discloses a LiDAR sensor able to perform polarization measurements with a beam splitter for splitting the light to two detectors for detecting an object.
  • This device is able to locate the object, nevertheless, it cannot be recognized. In this sense, this LiDAR sensor does not perform measurements of the Mueller matrix or Stokes vector, and it does not propose the use of Artificial Intelligence to achieve this recognition.
  • authors in reference Erandi Wijerathna, Charles D. Creusere, David Voelz, and Juan Castorena, "Polarimetric LIDAR with FRI sampling for target characterization", Proc.
  • SPIE 10407, Polarization Science and Remote Sensing VIII, 104070R, (2017) propose a polarimetric LIDAR for target characterization, using linearly polarized light with finite rate of innovations.
  • One limitation of this method is that they do not use elliptical or circular polarizations.
  • Inventors in reference (Annemarie I. Holleczek, Andre Albuquerque, Alexandre Correia, Pedro M. Caldelas, Angela R. Rodrigues, and Eduardo Pereira, "Method for material discrimination and respective implementation system", W02020021306, (2020)) propose to combine information on the backscattered light parameters and image processing techniques for identification of obstacles.
  • This method results in a 6D analysis where a 2D location in an image is combined with information regarding range, reflectivity, velocity and polarization of light in order to provide material discrimination and, consequently, target classification.
  • This method requires a system working with different types of sensors at the same time.
  • the present invention aims to overcome state of the art limitations with regard to the development of a remote sensor system capable of acquiring information of target distances and/or velocities and recognizing these targets.
  • the present invention describes a polarimetric multifunctional LiDAR system for target recognition in autonomous driving applications, comprising: an emission system, configured to emit polarized signals; a detection system, configured to detect return signals from targets; a control and data acquisition system, configured to control both the emission system and detection system and to acquire and digitize the return signals; and a data processing system, connected to the control and data acquisition system; wherein the data processing system is configured to determine Stokes parameters of the return signals and/or Mueller matrix elements of the targets and combine the determined Stokes parameters and/or Mueller matrix elements with Artificial Intelligence to determine information about surrounding environment, providing targets recognition, along with targets positions and/or velocity.
  • the emission system comprises at least one light source and at least one optical emission unit.
  • the detection system comprises at least one detector and at least one optical detection unit.
  • the at least one optical emission unit and the at least one optical detection unit are comprised of at least one of an active and/or passive non-polarizing optics and active and/or passive polarizing optics, configured to manipulate, polarize and steer light signals.
  • control and data acquisition system comprises a collection of software and hardware, configured to control the emission system and the detection systems; acquire return signals from targets; and digitize the return signals.
  • the data processing system is configured to receive data from the control and data acquisition system; calculate Stokes parameters and/or Mueller matrix elements; calculate the distance to targets and/or targets' velocities; and recognize targets supported by Artificial Intelligence.
  • the at least one light source of the emission system comprises at least one emitted polarized light signal and/or at least one transmitted wavelength.
  • the at least one optical emission unit of the emission system comprises at least one polarization generator configured to modify the polarization of the emitted polarized light signals.
  • the at least one optical detection unit of the detection system comprises at least one polarization analyzer configured to analyze the polarization of the return signals from targets.
  • the data processing system can be integrated in the described polarimetric multifunctional LiDAR system, or the central computer of the vehicle where the LiDAR sensor is installed, or even in a remote system.
  • the Polarimetric multifunctional LiDAR system comprises an optical reference unit configured to steer and/or manipulate a reference signal defined as a portion of the emitted polarized light signals.
  • control and data acquisition system is configured to control the optical reference unit and acquire the reference signal.
  • the polarimetric multifunctional LiDAR system comprises a non-coaxial configuration or a coaxial configuration.
  • the polarimetric multifunctional LiDAR system comprises an optical coaxial unit configured to steer and/or manipulate the transmitted polarized light signal and the return signal in the same optical axis.
  • control and data acquisition system is configured to control the optical coaxial unit.
  • the optical emission unit and/or the optical detection unit comprises at least one of a lens, mirror, prism, filter, attenuator, diffraction grating, beam splitter, optical modulator, etc., positioned in a path of the emitted signal and the return signal configured to manipulate and steer light signals.
  • the optical emission unit and optical detection unit comprises at least one of a linear polarizer, circular polarizer, retarder, variable retarder, Pockels cell, photo-elastic modulator, electro-optic modulator, etc., positioned in a path of the emitted signal and/or the return signal.
  • the present invention further describes a method for target recognition in autonomous driving applications based on the described Polarimetric multifunctional LiDAR system, comprising an emission system, emitting polarized light signals toward surrounding environment; a detection system, detecting the return signals that exhibit polarization changes which results in radiometric flux variations; a control and data acquisition system, controlling the polarimetric multifunctional LiDAR system, acquiring return signals from targets and digitizing the collected data; and a data processing system, processing the data to: calculate Stokes parameters and/or Mueller matrix elements, calculate the distance to targets and/or targets' velocities, and process the data to recognize targets.
  • the data computed by the data processing system is structured as a data point cloud, including the plurality of points indicating the position and/or velocity of each of the one or more objects in the environment with respect to the LiDAR system (100) and the Stokes parameters and/or elements of the Mueller matrix associated to each point.
  • the polarimetric data comprises Stokes parameters of the return light and/or elements of the Mueller matrix of targets in the data processing system, where the polarimetric data is processed by Artificial Intelligence to recognize targets.
  • the data processed by Artificial Intelligence further comprises locations and/ or velocities from the point cloud data to determine targets' shapes that are also used for target recognition.
  • the data processed by Artificial Intelligence to recognize targets is combined with other data from the polarimetric multifunctional LiDAR system, comprising other point cloud data, illumination angle, used wavelength, generated polarizations, etc.
  • the data processing system is further configured to process data associated with the reference signal and data associated with the return signal, to calculate distances to targets and/or targets' velocities.
  • the data processed by Artificial Intelligence for target recognition is combined with data from other sensors from the vehicle where the system is installed or data remotely received from other devices.
  • the proposed system discloses a polarimetric multifunctional LiDAR system that joins in a single device the capability of light ranging (LiDAR) and active polarimetry (emission and detection of polarized light, even if the return light is partially polarized or depolarized) with integrated system for control, data acquisition and data processing.
  • the polarimetric multifunctional LiDAR sensor is able to measure distances and/or velocities to objects and perform polarimetric measurements that, assisted by Artificial Intelligence, allow the proposed system to recognize targets.
  • the disclosed device is particularly developed for advanced driver assistance systems and autonomous driving.
  • LiDAR sensors are combined with other types of sensors, such as video cameras, SONARs and RADARs, to complement object information (the so-called sensor fusion).
  • sensor fusion object information
  • they could still be insufficient to distinguish similar shape objects of different materials, and the distance evaluation of said objects can result in some errors due to misinterpretation of highly reflective surfaces and foreign light signals that can induce ghost targets. It is an object of the present invention to provide an alternative to the prior state of the art, which covers the gaps found therein.
  • the herein disclosed LiDAR system aims to solve the above-mentioned flaws, particularly ensuring all the capacities and specifications of conventional LiDARs, with regard to wide range detection, distance measurement, resolution and eye-safety.
  • the introduction of polarimetry in the herein disclosed LiDAR system by polarizing the emitted light, will allow obtaining additional information, which was not possible to get by measuring just intensity.
  • the polarimetric information increases the capacity of the sensor to correctly distinguish targets, as well as to enhance the contrast of objects illuminated by the sensor.
  • the developed polarimetric multifunctional LiDAR system joins in a single device, the ability to light ranging (LiDAR) and active polarimetry (emission and detection of polarized light, even if the light scattered by the target is partially polarized or depolarized).
  • the proposed system simultaneously measures the distance to the target and performs polarimetric measurements, by measuring partially or totally the Mueller matrix of the sample or partially or totally the Stokes vector of the return light.
  • the herein disclosed device could measure the targets' velocity.
  • the objects located around this polarimetric multifunctional LiDAR are recognized by processing the measured data and by using at least one Artificial Intelligence, introducing a faster and precise discrimination of targets, allowing to minimize decisioning times.
  • the disclosed system allows the targets classification even when their shapes are difficult to detect (i.e. when they are partially obstructed from view, or the resolution of the LiDAR is low). Nevertheless, the shape of the objects can be used as additional information combined with polarimetric measurements and Artificial Intelligence to enhance the performance of the disclosed sensor.
  • the proposed polarimetric multifunctional LiDAR system combined with Artificial Intelligences can promote a great impact on autonomous vehicles enhancing the recognition of targets, improving the classification accuracy of targets, being possible to apply this technology to different kinds of autonomous vehicles (cars, trucks, buses and agricultural vehicles, among others).
  • FIG. 1 - illustrates one of the proposed embodiments for the Polarimetric multifunctional LiDAR system, in a non coaxial configuration, wherein the reference numbers refer to:
  • both emission (11) and detection (12) systems are controlled by a control and data acquisition system (21), and a data processing system (22) is adapted for ranging and target recognition.
  • Fig. 2 - illustrates the basic operating principle of the proposed Polarimetric multifunctional LiDAR system, wherein the reference numbers refer to:
  • Fig. 3 - illustrates one of the proposed embodiments for the Polarimetric multifunctional LiDAR system, in a coaxial configuration, wherein the reference numbers refer to:
  • both emission (11) and detection (12) systems are controlled by a control and data acquisition system (21), and a data processing system (22) is adapted for ranging and target recognition.
  • Fig. 4 - illustrates one of the proposed embodiments for the Polarimetric multifunctional LiDAR system, wherein both emission (11) and detection (12) systems comprise at least one light source (13) and at least one detector (14), respectively controlled by a control and data acquisition system (21), and a data processing system (22) adapted for ranging and target recognition.
  • emission (11) and detection (12) systems comprise at least one light source (13) and at least one detector (14), respectively controlled by a control and data acquisition system (21), and a data processing system (22) adapted for ranging and target recognition.
  • the reference numbers refer to:
  • Fig. 5 - illustrates a polarimetric multifunctional LiDAR system adapted to a vehicle for driver-assistance system, wherein the reference numbers refer to:
  • the disclosed polarimetric multifunctional LiDAR system measures the target's (51) distance and/or velocity.
  • the disclosed embodiment requires flux measurements (or intensity measurements) to perform polarization analysis.
  • two or more polarization generators that could be generated by the optical emission unit (15)
  • analyzers by the optical detection unit (16)
  • the data processing system (22) receives the measured information from the control and data acquisition system (21) and processes data to calculate target's (51) distance and/or velocity, and one or more Stokes parameters, or one or more elements of the Mueller matrix.
  • the main advantage of Stokes-Mueller polarimetry is that, although the return light (32) is not fully polarized, it can be studied.
  • the disclosed polarimetric multifunctional LiDAR system is configured to collect points to produce a point cloud of distances and/or velocities and polarimetric information. The polarimetric information is then used in the recognition procedure.
  • Artificial Intelligence uses the previously processed information to discriminate relevant targets for autonomous driving applications (for example, but not to be taken in a limiting sense, cars, pedestrians, traffic signs, etc.).
  • additional information from the point cloud can also be used by Artificial Intelligence, particularly shape, orientation or velocity determination of the targets. Additional information can be used complementary.
  • the information obtained from the disclosed polarimetric multifunctional LiDAR system (100) can be used by vehicles for dynamic obstacle detection and tracking to improve road safety.
  • Figures 1 through 4 illustrate several possible embodiments of the proposed polarimetric multifunctional LiDAR system (100), which is comprised of an emission system (11) and a detection system (12), both controlled by a control and data acquisition system (21), and a data processing system (22) configured to perform ranging and target recognition.
  • the polarimetric multifunctional LiDAR system (100) can also include additional units for ranging and for optical coaxial or non-coaxial configurations.
  • FIG. 1 it is illustrated a non-coaxial polarimetric multifunctional LiDAR system (100), where the emission system (11) is composed of a light source (13) and an optical emission unit (15).
  • the light source (13) can include at least one of a LASER, diode LASER, single frequency LASER diode, tunable LASER, LED, etc.
  • the detection system (12) is composed of a detector (14) and optical detection unit (16).
  • the detector (14) can include at least one of a photodiode, phototransistor, avalanche photodiode, photocounter, array of detectors, focal plane sensor, etc.
  • the optical emission unit (15) and the optical detection unit (16) can be composed of active and/or passive non-polarizing optics and active and/or passive polarizing optics.
  • the non-polarizing optics can include at least one of a lens, diffractive lens, variable focus liquid lens, mirror, micromirror device, prism, filter, attenuator, diffraction grating, beam splitter, optical modulator, etc.
  • the polarizing optics can include at least one of a linear polarizer, circular polarizer, retarder, variable retarder, Pockels cell, photo-elastic modulator, electro-optic modulator, etc.
  • the control and data acquisition system (21), comprising a collection of software and hardware, is configured to control the polarimetric multifunctional LiDAR system (100) and also to digitize the acquired data.
  • the control and data acquisition system (21) can be responsible for steering, polarizing and manipulating the signal, by controlling the active elements, acquiring and digitizing the detected signals, the emission electronics, the timing electronics, communication electronics, among others.
  • the data processing system (22) is responsible for processing the measured data from the polarimetric multifunctional LiDAR (100).
  • the data processing system (22) computes the Stokes parameters and/or the Mueller matrix elements as well as the position and/or velocity of targets.
  • the plurality of points indicating the position and/or velocity of each of the one or more objects in the environment with respect to the LiDAR system (100) and the Stokes parameters and/or elements of the Mueller matrix associated to each point are structured as a data point cloud. Then, by using Artificial Intelligence, the data processing system (22) processes the data from the point cloud to recognize targets. Nonetheless, the final recognition process and other computation processes could be performed in the data processing system (22), as well as in the central computer of the vehicle where the LiDAR sensor (100) is installed, or even remotely.
  • the light source (13) is configured to emit a light signal (31) that passes through an optical emission unit (15) adapted to generate an emitted polarization state and steering and/or manipulating the emitted signal (31).
  • the outgoing polarized transmitted signal (31) interacts with surrounding targets (51), being scattered and/or reflected back to the LiDAR system (100).
  • this interaction of the transmitted light (31) with the target (51) can modify its state of polarization, mainly reducing the degree of polarization.
  • the return signal (32) resulting from the target (51) reflection passes through the optical detection unit (16), where it can be steered and filtered; for example, to reduce background light; and its polarization state is analyzed.
  • the control and data acquisition system (21) receives the light flux measurements from the detector (14) and digitizes it.
  • the embodiment described in Figure 1 can work in temporal mode, being the optical emission unit (15) able to generate multiple states of polarization sequentially in time or modulate the polarization of the transmitted signal (31).
  • the states of polarization generated by the optical emission unit (15) resort to the use of, for example, polarizers, photoelastic modulators, electro-optical modulators, variable retarders, magneto-optical modulators, etc.
  • the optical detection unit (16) could therefore analyze the return signal (32) resorting to the use of several polarization analyzers, taking sequential measurements in time or modulating the return signal (32).
  • the time of flight principle can be used to calculate the target (51) distance to the LiDAR system (100).
  • an additional signal the so-called reference signal (30)
  • the disclosed polarimetric multifunctional LiDAR (100) is configured to send the reference signal (30) from the light source (13) to the detector (14), passing through the optical reference unit (17).
  • the optical reference unit (17) is responsible for steering and/or manipulating the reference signal (30) in order to reach the detector (14).
  • the optical reference unit (17) can be composed of active and/or passive non polarizing optics and active and/or passive polarizing optics.
  • Other design variations for reference zero value can comprise the use of a light source (13) with an integrated detector or an independent detector dedicated for time reference.
  • the reference signal (30) could also pass through some elements of the optical emission unit (15) and optical detection unit (16).
  • Other methods to measure the target (51) distance and/or velocity to the LiDAR system (100) are not excluded, such as frequency-modulated continuous-wave (FMCW).
  • FMCW frequency-modulated continuous-wave
  • the digitized data from the control and data acquisition system (21) is computed by the data processing system (22), where the Stokes parameters and/or Mueller matrix elements are calculated.
  • the distances to targets (51) and/or their velocities can be also computed in the data processing system (22).
  • the point cloud with the polarimetric information from the scene is used by an Artificial Intelligence, duly trained for recognizing the desired target (51) categories.
  • Other information such as, for example, the shape of the surrounding objects, illumination angle, used wavelength, etc.; can also be used by the Artificial Intelligence.
  • data collected by other vehicle sensors for example, RADARs, cameras, etc., can also be included and used by the Artificial Intelligence. Note that part of (or all) the data processing can instead be performed in the central data computer system of the vehicle where the LiDAR sensor (100) is installed or remotely.
  • Figure 3 discloses an alternative embodiment of the one depicted system in Figure 1, mainly differing on the use of an optical coaxial configuration.
  • an optical coaxial unit (18) is used to steer and/or manipulate the transmitted signal (31) and the return signal (32) in order to have the same optical axis at the output of the polarimetric multifunctional LiDAR system (100).
  • This optical coaxial unit (18) can be composed of at least one of an active or passive optical element including lenses, diffractive lenses, variable focus liquid lenses, mirrors, micro-mirrors, prisms, filters, attenuators, diffraction gratings, beam splitters, optical modulators, etc.
  • the data processing system (22) computes and interprets the measurements of the collected return signal (32).
  • the control and data acquisition system (21) is responsible for controlling the polarimetric multifunctional LiDAR system (100) and acquiring the light flux information.
  • the emission system (11) can comprise multiple light sources (13) and/or optical emission units (15) and/or the detection system (12) can have multiple detectors (14) and/or optical detection units (16).
  • This configuration can be used to obtain multiple emissions with the same or different generated states of polarization and with the same or different wavelengths.
  • the polarimetric multifunctional LiDAR system (100) can use multiple detectors (14) and/or multiple optical detection units (16). In this sense, several optical detection units (16), each one working as a different polarization analyzer, with its own associated detector (14), allow the disclosed system (100) to be capable of measuring the Stokes vector of the return signal (32) instantaneously.
  • control and data acquisition system (21) is responsible for controlling the polarimetric multifunctional LiDAR system (100), acquiring data and digitizing the measured information.
  • Figure 5 provides an illustration of a vehicle (40) comprising the disclosed polarimetric multifunctional LiDAR system (100).
  • the point cloud produced by conventional LiDARs can be used to determine location and/or velocity of targets (51), but also identify said targets through their shapes.
  • sensor fusion i.e., combining data from multiple sensors installed in the vehicle (40), such as video cameras, RADARs, conventional LiDARs, etc.
  • the accuracy in the target (51) recognition process can be improved.
  • an object partially obstructed / hidden can still be difficult to classify.
  • Polarimetry together with a trained Artificial Intelligence can be used to recognize targets at each data point of the point cloud.
  • each measurement of the point cloud of the disclosed polarimetric multifunctional LiDAR system (100) could be recognized as a particular target.
  • the method associated with the disclosed polarimetric multifunctional LiDAR system (100) improves the target recognition performance in cases where the object (51) is partially blocked by obstacles (52) from the point of view of the vehicle (40) where the proposed LiDAR system (100) is installed. Therefore, the disclosed polarimetric multifunctional LiDAR system (100) combines conventional methods for object recognition and point clouds with polarimetry, offering a new approach for improving object recognition.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Electromagnetism (AREA)
  • Optical Radar Systems And Details Thereof (AREA)

Abstract

La présente invention concerne un système lidar multifonctionnel polarimétrique (100) adapté pour effectuer des mesures de télémétrie et polarimétriques pour la reconnaissance de cibles dans des applications de conduite autonome. Le système décrit (100) comprend, dans un seul dispositif, des capacités de télédétection par laser (lidar) et de polarimétrie active, en mesurant partiellement ou totalement la matrice de Mueller des cibles et/ou les paramètres de Stokes du retour de la lumière des cibles. En combinant la polarimétrie avec l'intelligence artificielle, le système lidar polarimétrique multifonctionnel (100) est en mesure d'ajouter une nouvelle fonctionnalité qui permet de distinguer les cibles. En outre, le système décrit ici est en mesure de déterminer l'emplacement et/ou la vitesse des cibles. Le système selon l'invention comprend un système d'émission (11) et un système de détection (12), tous deux commandés par un système de commande et d'acquisition de données (21), et avec un système de traitement de données (22) pour calculer les informations de portée et la reconnaissance de la cible.
PCT/IB2021/056652 2021-07-21 2021-07-22 Capteur lidar multifonctionnel polarimétrique pour reconnaissance de cibles Ceased WO2023002237A1 (fr)

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