Material Sensing Light Imaging, Detection, and Distancing (LIDAR) System

Abstract

The Company provides material-sensing optical imaging, detection, and ranging (LIDAR) systems. [Solution] The system includes a laser configured to generate light pulses, a beam steering unit configured to generate polarization-adjusted light pulses that are emitted toward the object, at least one polarizer configured to polarize reflected, scattered, or emitted light returned from the object, and a processor configured to detect at least one material of the object based on the intensity and polarization of the polarized reflected, scattered, or emitted light from the object.

Description

Technical Field

[0001] Government Support This invention was made with government support under Grant No. 1240264 awarded by the National Science Foundation. The government has certain rights in this disclosure.

[0002] Cross - Reference to Related Applications This application claims the benefit of U.S. Provisional Application No. 62 / 571,986, filed on October 13, 2017. The entire disclosure of the above application is incorporated herein by reference.

[0003] This disclosure relates to optical imaging, detection, and ranging (LIDAR) systems, and more particularly, to material - sensing LIDAR systems, and methods for fabricating and using the same.

Background Art

[0004] This section provides background information related to this disclosure, which is not necessarily prior art.

[0005] LIDAR is a surveying method for measuring the distance to an object by illuminating the object with pulsed laser light and measuring the reflected pulse with a sensor. And differences in the return time and wavelength of the laser can be used to create a digital 3D representation of the detected object. LIDAR is used in geodesy, geoinformatics, archaeology, geography, geology, topography, seismology, forestry, atmospheric physics, laser guidance, airborne laser swath mapping (ALSM), and laser altimetry, and may be used to generate high - resolution maps. LIDAR technology may also be used for the control and navigation of autonomous vehicles.

[0006] Conventional Lidar devices may operate as follows: A laser light source generates polarized or unpolarized pulses at a specific wavelength. Once the light is first emitted, a time-of-flight sensor records the initial time. The time of flight is used to determine the total distance the light travels from the light source to the detector, using the speed at which light travels.

[0007] The emitted light is then "directed" at a given angle. This "direction" may also include splitting the light pulse into multiple pulse components directed at various angles. The direction angle will change over time to obtain a specific field of view for comprehensive mapping of the environment. After it has been directed, the light passes through a linearly polarized optical system before and after emission. These types of LiDARs are known as polarized LiDARs and may use a polarized optical system in the registration step.

[0008] Conventional LiDAR devices typically utilize bulky and expensive optical lenses. Furthermore, the optical lenses used in conventional LiDAR devices require extensive protective packaging due to their sensitivity to moisture, increasing the weight, size, and complexity of the LiDAR devices in which they are used. One well-known challenge in implementing LiDAR systems with rotating optics (e.g., the Velodyne-HDL64™ model) in autonomous vehicles and robots is their large size and high cost. The rotation of the entire device for maneuvering the laser beam reduces reliability, limits miniaturization, and increases energy consumption. While solid-beam-driven LiDAR systems address this challenge, their implementation is hindered by insufficient precision and range. Another issue is the performance of LiDAR and all other sensors in adverse weather conditions. Laser beams with wavelengths of approximately 900-940 nm, currently in use, can be strongly scattered by rain, fog, and snow, making their readings highly unreliable under such conditions.

[0009] Furthermore, conventional LiDAR devices and their accompanying analysis systems have been found to have limitations in their ability to accurately perform object recognition. For example, LiDAR point clouds are known to rely solely on reading the distance from the laser source to the object. In this representation of the human world, a person resting on a bench and their image are the same. This challenge also applies to a sleeping baby and a plastic doll of the same size next to it, or trying to distinguish a black car in the distance from the pavement. The burden of distinguishing these objects and decoding their surroundings is borne by the computational processing of these 3D maps.

[0010] The proper classification of objects based on their shapes is not a trivial matter, requiring complex algorithms and significant computing power, especially considering the highly dynamic nature of various environments. Furthermore, current beam manipulation methods cause point clustering and banding in LiDAR arrays, resulting in unclear interpretation of 3D images and their individual points, making proper object recognition and classification even more difficult with typical LiDAR hardware. As a result, perception based on the surrounding shape requires high computational costs, high energy consumption, and long processing times. [Prior art documents] [Patent Documents]

[0011] [Patent Document 1] U.S. Patent Application Publication No. 2016 / 0299270 [Overview of the project] [Problems that the invention aims to solve]

[0012] Therefore, improved LIDAR systems and methods are desired, in particular, LIDAR systems and methods that provide the ability to identify the material on which an object is formed.

[0013] This section provides a general overview of this disclosure and is not a comprehensive disclosure of its entire scope or all of its features. [Means for solving the problem]

[0014] In certain embodiments, the Disclosure provides a laser configured to generate light pulses, a beam maneuver configured to generate polarization-adjusted light pulses emitted toward an object, at least one polarizer configured to polarize reflected, scattered, or emitted light returned from the object, and a processor configured to detect at least one material of the object based on the intensity and polarization of the polarized reflected, scattered, or emitted light from the object. We provide a system that includes the following features.

[0015] In one embodiment, the beam control section includes a paper-cut nanocomposite material.

[0016] In one embodiment, at least one polarizer comprises a cut paper nanocomposite.

[0017] In one embodiment, the processor is further configured to classify objects based on at least one detected material of the object.

[0018] In a further embodiment, the processor is configured to classify objects based on at least one detected material of the object by applying a machine learning algorithm.

[0019] Further improvements include machine learning algorithms, such as artificial neural network algorithms.

[0020] In one embodiment, the beam manipulator is configured to adjust the polarization of the light pulse in order to generate a polarization-adjusted light pulse.

[0021] In one embodiment, the beam manipulator is configured to adjust the polarization of an optical pulse by at least one of the following: giving polarization to an unpolarized optical pulse and changing the polarization of a polarized optical pulse.

[0022] In one aspect, the beam steering unit is configured to adjust the polarization of the optical pulse by applying at least one of linear polarization, circular polarization, and elliptical polarization.

[0023] In a further aspect, applying linear polarization includes applying at least one of s - type linear polarization and p - type linear polarization.

[0024] In one aspect, at least one polarizer is configured to polarize the reflected, scattered, or emitted light returned from the object by applying at least one of linear polarization, circular polarization, and elliptical polarization.

[0025] In a further aspect, applying is applying linear polarization that includes applying at least one of s - type linear polarization and p - type linear polarization.

[0026] In one aspect, at least one polarizer includes a plurality of polarizers.

[0027] In one aspect, the system further includes at least one polarizer and at least one polarization detector connected to a processor, and the at least one polarization detector is configured to detect the intensity of the polarized reflected, scattered, or emitted light from the object.

[0028] In a further aspect, at least one polarization detector includes a plurality of polarization detectors.

[0029] In a further aspect, at least one polarization detector is configured to detect the angle of incidence related to the polarized reflected, scattered, or emitted light from the object.

[0030] In a further aspect, the processor is further configured to detect at least one material of the object based on the angle of incidence related to the polarized reflected, scattered, or emitted light from the object.

[0031] In further variations, the Disclosure provides a method comprising the steps of generating a light pulse, adjusting the polarization of the light pulse to generate a polarization-adjusted light pulse to be emitted toward an object, polarizing the reflected, scattered, or emitted light that is returned from the object, and detecting at least one material of the object based on the intensity and polarization of the polarized reflected, scattered, or emitted light from the object.

[0032] In one embodiment, the step of adjusting the polarization of the light pulse is performed by a beam maneuvering unit that includes a paper-cut nanocomposite.

[0033] In one embodiment, the paper-cut nanocomposite is manufactured via a vacuum-assisted filtration (VAF) process.

[0034] In one embodiment, the paper-cut nanocomposite is manufactured via a multilayer (LBL) deposition process.

[0035] In one embodiment, the method further includes the step of classifying an object based on at least one material detected of the object.

[0036] In one embodiment, the step of classifying objects includes the step of classifying objects by applying a machine learning algorithm.

[0037] In one embodiment, the machine learning algorithm includes an artificial neural network algorithm.

[0038] Further areas of application will become apparent from the descriptions provided herein. The descriptions and specific examples in this summary are intended for illustrative purposes only and are not intended to limit the scope of this disclosure.

[0039] The drawings described herein are for illustrative purposes only of selected embodiments and do not represent all possible implementations, nor are they intended to limit the scope of this disclosure. [Brief explanation of the drawing]

[0040] [Figure 1] This is a functional diagram illustrating an M-LIDAR system according to a particular aspect of this disclosure. [Figure 2a] Scanning electron microscope (SEM) images of nano-cut paper nanocomposite sheets configured for use in an M-LIDAR system, according to a particular aspect of this disclosure. [Figure 2b] Scanning electron microscope (SEM) images of nano-cut paper nanocomposite sheets configured for use in an M-LIDAR system, according to a particular aspect of this disclosure. [Figure 3a] This image depicts the laser diffraction pattern from a nano-cut paper-based graphene composite at a 0% strain level, according to a particular aspect of the present disclosure. [Figure 3b] This image depicts the laser diffraction pattern from a nano-cut paper-based graphene composite material at a 50% strain level, according to a particular aspect of the present disclosure. [Figure 3c] This image depicts the laser diffraction pattern from a nano-cut paper-based graphene composite material to a 100% strain level, according to a particular aspect of the present disclosure. [Figure 4] A typical simplified process for manufacturing nano-cut-based optical elements according to a particular aspect of this disclosure is illustrated. [Figure 5a] The present disclosure illustrates a nano-cut paper nanocomposite optical element manufactured on a wafer according to a particular aspect of this disclosure. A photograph of the nano-cut paper nanocomposite optical element according to a particular aspect of this disclosure is shown. [Figure 5b] Figure 5a shows an SEM image of a nano-cut paper nanocomposite optical element under 0% strain, according to a specific aspect of this disclosure. [Figure 5c] An example of a particular aspect of this disclosure is shown: an SEM image of the nano-cut paper nanocomposite optical element in Figure 5a under 100% strain. [Figure 6]This disclosure illustrates a confusion matrix for MST using artificial intelligence algorithms and polarization information, according to a specific aspect of this disclosure. [Figure 7] A confusion matrix for simulated thin ice detection compared to other materials, according to a particular aspect of this disclosure, is illustrated. [Figure 8] An example of a thin ice detection unit incorporating an M-LIDAR system, according to a particular aspect of this disclosure, is provided. [Figure 9] This flowchart shows a method for performing object classification using an M-LIDAR system according to a particular aspect of the present disclosure. [Figure 10] This is a schematic diagram of a planar composite material having several representative paper-cut patterns formed within it. [Figure 11] This figure illustrates an M-LIDAR system for vehicle mounting according to a particular aspect of the present disclosure. [Modes for carrying out the invention]

[0041] The corresponding reference numbers indicate the corresponding parts through several figures in the drawing.

[0042] Exemplary embodiments are provided to ensure that this disclosure is complete and fully conveys its scope to those skilled in the art. Numerous specific details, such as examples of particular compositions, components, devices, and methods, are described in order to provide a complete understanding of the embodiments of this disclosure. It will be apparent to those skilled in the art that specific details are not necessarily required, that the exemplary embodiments may be embodied in many different forms, and that none should be construed as limiting the scope of this disclosure. In some exemplary embodiments, well-known processes, well-known device structures, and well-known techniques are not described in detail.

[0043] The terms used herein are for the purpose of describing specific exemplary embodiments only and are not intended to be limiting. As used herein, the singular forms “a,” “an,” and “the” may also be intended to include the plural form unless the context otherwise explicitly indicates. The terms “comprise,” “comprising,” “including,” and “having” are inclusive and thus identify the presence of the described features, elements, compositions, steps, integers, actions, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, actions, elements, components, and / or groups thereof. The unrestrictive term “comprising” should be understood as an unrestrictive term used to describe and assert the various embodiments described herein, although in certain embodiments the term may instead be understood as a more restrictive and limiting term such as “consisting of” or “consisting essentially of.” As a result, for any given embodiment that enumerates compositions, materials, components, elements, features, integers, operations, and / or process steps, the Disclosure also specifically includes embodiments that consist of or are essentially composed of such enumerated compositions, materials, components, elements, features, integers, operations, and / or process steps. Where “consists of,” alternative embodiments exclude any additional compositions, materials, components, elements, features, integers, operations, and / or process steps; where “substantially from,” any additional compositions, materials, components, elements, features, integers, operations, and / or process steps that materially affect the basic and novel properties are excluded from such embodiments, but any compositions, materials, components, elements, features, integers, operations, and / or process steps that do not materially affect the basic and novel properties may be included in the embodiments.

[0044] The steps, processes, and actions of any method described herein should not be construed as necessarily requiring their execution in a specific order discussed or illustrated, unless specifically identified as such. It should also be understood that additional or alternative steps may be used unless otherwise indicated.

[0045] Where a component, element, or layer is referred to as "on top of," "engaged to," "connected to," or "joined to" another element or layer, it may directly be on top of, engaged to, connected to, or joined to, or there may be an intervening element or layer. In contrast, where an element is referred to as "directly on top of," "directly engaged to," "directly connected to," or "directly joined to" another element or layer, there may be no intervening element or layer. Other words used to describe relationships between elements should be interpreted similarly (e.g., "between" vs. "directly between," "adjacent" vs. "directly adjacent," etc.). As used herein, the term "and / or" includes any combination of one or more of the related enumerated items.

[0046] In this specification, terms such as First, Second, Third, etc., may be used to describe various steps, elements, components, regions, layers, and / or parts, but these steps, elements, components, regions, layers, and / or parts should not be limited by these terms unless otherwise indicated. These terms may be used only to distinguish one step, element, component, region, layer, or part from another step, element, component, region, layer, or part. Terms such as “First,” “Second,” and other numerical terms as used herein do not mean a sequence or order unless explicitly indicated by the context. As a result, the first step, element, component, region, layer, or part discussed below may be referred to as the second step, element, component, region, layer, or part without departing from the teaching of the exemplary embodiments.

[0047] Spatial or temporally relative terms such as "front," "back," "inside," "outside," "down," "below," "underside," "up," and "upper side" may be used herein to describe the relationship between one element or feature and another, as illustrated in the figures, for the sake of ease of explanation. Spatial or temporally relative terms may also be intended to encompass various orientations of a device or system in use or operation, in addition to the orientations depicted in the figures.

[0048] Throughout this disclosure, numerical values ​​represent an approximation or limit to a range, encompassing not only those that have the exact value mentioned, but also slight deviations from given values ​​and embodiments having respect to the value mentioned. Except for the examples provided at the end of the detailed description of the invention, all numerical values ​​of parameters in this specification (e.g., quantities or conditions), including the appended claims, should be understood to be modified in all cases by the term “about,” whether or not “about” actually appears before the numerical value. “About” indicates that the stated numerical value is tolerant of some degree of inaccuracy (some approach to the exactness of the value, roughly or reasonably close to the value). And, where the inaccuracy provided by “about” is not otherwise understood in the art in this ordinary sense, “about” as used herein indicates at least variation that may arise from the ordinary methods of measuring and using such parameters. For example, “about” may include variation of 5% or less, optionally 4% or less, optionally 3% or less, optionally 2% or less, optionally 1% or less, optionally 0.5% or less, and in certain embodiments optionally 0.1% or less.

[0049] In addition, the scope of the disclosure includes the disclosure of all values ​​within the entire range and further subdivided ranges, including the endpoints and subranges given for the range.

[0050] Next, exemplary embodiments will be described in more detail with reference to the accompanying drawings.

[0051] This disclosure provides a LiDAR system and method configured to detect not only the distance of an object but also the material composition of the object. In some examples, material composition classification may be achieved by polarization analysis processed using a machine learning algorithm.

[0052] The optical elements of the systems described herein may be configured to bring all light emitted from a light source (e.g., a laser) into a known polarization state so that the shift in polarization can be accurately measured later. This light travels to an object interface (an object composed of one or more materials), where some of the light is diffusely reflected back.

[0053] This disclosure describes, in particular, a novel method of perceiving the surroundings by adding material and surface texture (MST) classification to each point in a LIDAR cluster to create a semantic map of 3D space. The MST classification, inferred from the polarization signatures of returned photons, may reduce ambiguity in the 3D point cloud and facilitate the recognition of various objects (metallic points, glass points, rough dielectric points, etc.). Polarization classification may precede estimation of the tangent plane of the surface, and thus objects may be pre-identified by grouping points with similar polarization signatures. A LIDAR with MST classification is referred to herein as M-LIDAR.

[0054] According to one example of this disclosure, M-LIDAR technology may be configured to be lightweight and compact by using a paper-cut optical system rather than conventional bulky optical systems such as near-infrared optical systems. For example, the M-LIDAR systems and methods described herein may be used for detecting thin ice for vehicles with varying degrees of automation.

[0055] Referring here to Figure 1, a typical simplified M-LIDAR system 100 is provided. The M-LIDAR system 100 may include a laser 102, a beam maneuvering unit 106, a first polarizer 114, a second polarizer 116, a first polarization detector 122, a second polarization detector 124, and a processor 126. Figure 1 illustrates several implementations of the first and second polarizers 114, 116 and the first and second polarization detectors 122, 124, but only a single polarizer (e.g., the first polarizer 114) and a single polarization detector (e.g., the first polarization detector 122) may be included as part of the system 100 without departing from the teachings of this disclosure. Furthermore, according to certain examples, three or more polarizers and / or three or more polarization detectors may be included as part of the system 100 without departing from the teachings of this specification.

[0056] For the purposes of simplification and illustration, throughout the remainder of this disclosure, the first polarizer 114 will be treated as an s-polarized linear polarizer 114. Similarly, for the purposes of simplification and illustration, the second polarizer 116 will be treated as a p-polarized linear polarizer 116. Furthermore, the first polarization detector 122 will be treated as a p-polarized detector 122, and the second polarization detector will be treated as an s-polarized detector 124.

[0057] However, as will be understood by those skilled in the art, polarizers 114, 116 may be configured for various different types of polarization without departing from the teachings herein. For example, a given polarizer may be configured to perform linear polarization (e.g., s or p-type linear polarization), right-circular polarization, left-circular polarization, elliptic polarization, or any other suitable type of polarization known in the art. Similarly, a given detector may be configured to detect linear polarization (e.g., s or p-type linear polarization), right-circular polarization, left-circular polarization, elliptic polarization, or any other type of polarization known in the art. In some examples, the polarization of a light beam (i.e., a combination of two or more light pulses) may be modulated pulse by pulse to obtain additional information about one or more objects under consideration.

[0058] As will be discussed in more detail below, system 100 may be configured to detect one or more materials that make up object 110 and to classify object 110 based at least partially on the detected materials. In some examples, object classification may be performed using one or more artificial intelligence algorithms, including but not limited to neural network-based artificial intelligence.

[0059] During operation, system 100 may function as follows: The laser 102 may be configured to generate (i.e., emit) one or more polarized or unpolarized light pulses, which together form a polarized / unpolarized light beam 104. As shown in the example in Figure 1, each pulse includes an s-polarization component (represented by dots along beam 104 in Figure 1) and a transverse p-polarization component (represented by double-headed arrows running perpendicularly through beam 104 in Figure 1). Alternatively (and in conjunction with the preceding discussion of different types of polarized light), the pulses may include, for example, left and right circular polarization sequences, elliptic polarization sequences, any combination of the above, or any other suitable polarization sequences.

[0060] In some examples, laser 102 may be configured to generate any of 1 to over 1 million pulses per second. Furthermore, in some implementations, laser 102 may be configured as a pulsed laser of 550 nanometers (nm), 808 nm, 905 nm, or 1550 nm (or any other suitable wavelength) without departing from the teachings of this disclosure. For example, implementations for home robotics, autonomous vehicles, and machine vision may use lasers having eye-safe frequencies above 800 nm. For outdoor applications, a light beam with a water-permeable window, e.g., a light beam of about 900 nm to 1550 nm, may be appropriately used. In some implementations, once laser 102 has generated a given pulse, processor 126 (which executes executable instructions) may record the first time the pulse is generated. This “time of flight” information may then be used to calculate the distance to object 110 by using the speed of light.

[0061] The beam 104 may be directed by the laser 102 through the beam manipulator 106. The beam manipulator 106 may be configured to generate polarization-adjusted light pulses. In certain embodiments, the polarization of each polarization / unpolarized pulse of the polarized / unpolarized light beam 104 is adjusted by the beam manipulator 106. As used herein, adjusting polarization may include imparting or changing polarization. As a result, the beam manipulator 106 may adjust the polarization of each polarization / unpolarized pulse of the polarized / unpolarized light beam 104 to generate one or more linearly polarized light pulses (where linearly polarized light pulses collectively form a linearly polarized light beam 108). Although the above examples intend linear polarization, according to some examples, the beam manipulator 106 may polarize the beam 104 circularly (e.g., left or right) or elliptally. According to another example, the beam manipulator 106 may not apply any polarization to the beam at all. For example, if beam 104 is already polarized when it enters beam manipulator 106, beam manipulator 106 may further modify the characteristics of the resulting polarization-adjusted optical pulse (e.g., by splitting or modulating the pulse), but it may not be necessary to adjust the polarity of the previously polarized optical pulse. Furthermore, according to some examples, beam manipulator 106 may polarize a first pulse of the beam according to a first type of polarization and a second pulse of the same beam according to a second different type of polarization. In addition to performing polarization of beam 104, or as an alternative thereto, beam manipulator 106 may also control the direction of any beam emitted therefrom (e.g., beam 108). Furthermore, beam manipulator 106 may split a beam (e.g., beam 104) into several different beams in order to manipulate multiple beams at once, thereby emitting one or more beams at defined angles. This concept is illustrated in Figure 1 with respect to many divergent arrows emanating from beam manipulator 106.

[0062] In addition, or alternatively, in some examples, the beam control unit 106 may be configured to modulate a linearly polarized light beam 108. In one example, the beam control unit 106 may include a paper-cut nanocomposite beam control unit, etc. According to this example, as will be discussed in more detail below, the beam control unit 106 may be configured to linearly polarize and / or modulate the linearly polarized light beam 108 by increasing or decreasing the amount of strain applied to the paper-cut nanocomposite beam control unit.

[0063] Furthermore, in one example, the beam manipulator 106 may be configured to linearly polarize each unpolarized pulse of the unpolarized light beam 104 by linearly polarizing each unpolarized pulse of the unpolarized light beam 104 to p-polarization. This example is illustrated in Figure 1, where it can be seen that the beam 104 no longer contains an s-polarization component after passing through the beam manipulator 106 (i.e., the “dot” component shown in beam 104 is not present in the linearly polarized light beam 108). In an alternative embodiment, the linearly polarized light beam 108 may instead be p-polarized. Furthermore, in certain embodiments, the beam manipulator 106 may modify, control, and maneuver the linearly polarized light beam 108 emitted toward the object 110, as will be discussed further herein. The beam manipulator 106 may enable dynamic wavelength-dependent beam maneuvering and amplitude modulation of the electromagnetic wave.

[0064] Continuing to refer to Figure 1, the linearly polarized light beam 108 may be diffusely reflected from the object 110. One or more pulses of light collectively form a beam 112 that constitutes a reflected version of the linearly polarized light beam 108. According to some examples, the reflected linearly polarized light beam 112 may have a different polarization than the linearly polarized light beam 108 (i.e., the pre-reflection of the beam from the object 110). The difference in this state is illustrated by beam 112 containing both p-polarization and s-polarization components (reflected by dots and double-headed arrows along the path of beam 112, respectively), while beam 108 is shown to contain only a p-polarization component. Furthermore, the object 110 may include any suitable object (or target) composed of one or more different materials for which detection is desired. As discussed in the above and following sections, the reflected “linearly” polarized light beam 112 is, but according to certain examples, the reflected beam 112 may be polarized in a variety of different ways, including circular or elliptical, without departing from the teachings herein.

[0065] A reflected linearly polarized light beam 112 diffusely reflected, scattered, or otherwise emitted by object 112 may pass through the s-polarized linear polarizer 114 and / or p-polarized linear polarizer 116 of system 100. In certain embodiments, each portion of the reflected, scattered, or otherwise emitted linearly polarized light beam 112 passes through both the s-polarized linear polarizer 114 and / or p-polarized linear polarizer 116 of system 100. The s-polarized linear polarizer 114 is configured to linearly polarize one or more light pulses constituting the beam 112 to s-polarization in order to generate one or more reflected s-polarized light pulses (one or more reflected s-polarized light pulses collectively form a reflected s-polarized light beam 118). Similarly, the p-polarized linear polarizer 116 is configured to linearly polarize one or more light pulses constituting the beam 112 to p-polarization in order to generate one or more reflected p-polarized light pulses (one or more reflected p-polarized light pulses collectively form a reflected p-polarized light beam 120). In some examples, the s-polarized linear polarizer 114 and / or the p-polarized linear polarizer 116 may include paper-cut nanocomposites, such as those discussed above with respect to the beam maneuvering unit 106 and / or discussed below with respect to Figures 4a-4d and 5a-5c. However, those skilled in the art will recognize that, without departing from the teachings herein, non-paper-cut nanocomposites or other optical devices may be used as part of the system 100 in some examples.

[0066] In some examples, similar configurations of polarizers 114, 116 may be used for polarizing left and right circularly polarized or elliptically polarized light reflected, scattered, or otherwise emitted from object 110.

[0067] The s-polarization detector 122 may be configured to detect the intensity of each of one or more reflected s-polarized light pulses that form the reflected s-polarized light beam 118. In addition, according to some implementations, the s-polarization detector 122 may be configured to detect the incident angle related to the reflected s-polarized light beam 118. The detected intensities of one or more reflected s-polarized light pulses that form the reflected s-polarized light beam 118 and / or the detected incident angle related to the reflected s-polarized light beam 118 may be utilized by the processor 126 to perform material type detection (e.g., using MST classification), as will be discussed in more detail below.

[0068] Similarly, the p-polarization detector 124 may be configured to detect the intensity of each of one or more reflected p-polarized light pulses that form the reflected p-polarized light beam 120. In addition, according to some implementations, the p-polarization detector 124 may be configured to detect the incident angle related to the reflected p-polarized light beam 120. The detected intensities of one or more reflected p-polarized light pulses that form the reflected p-polarized light beam 120 and / or the detected incident angle related to the reflected p-polarized light beam 120 may also be utilized by the processor 126 to perform material type detection, as will be discussed in more detail below.

[0069] The processor 126 is configured to detect at least one material of object 110 based on (i) the detected intensity of one or more light pulses forming beams 118 and / or 120 and / or (ii) the detected incident angle related to the reflected s-polarized light beam 118 and / or the reflected p-polarized light beam 120. More specifically, according to some examples, the processor 126 is configured to apply a machine learning algorithm to detect one or more materials constituting object 110. As used herein, “applying a machine learning algorithm” may include, but is not limited to, executing executable instructions stored in memory and accessible by the processor. In addition, according to one example, a particular machine learning algorithm used for material detection may include an artificial neural network. However, other machine learning algorithms known in the art may be appropriately used without departing from the teachings of this disclosure.

[0070] Furthermore, according to some examples, the processor 126 may be configured to classify objects 110 based on the detected material of the objects 110 by applying a machine learning algorithm. In this case as well, the machine learning algorithm used for object classification may include an artificial neural network. However, other machine learning algorithms known in the art may be used appropriately without departing from the teachings of this disclosure.

[0071] Before moving on to Figure 2, the following outlines the process for detecting the material of an object using an M-LIDAR system such as system 100 shown in Figure 1.

[0072] As noted above, one objective of this disclosure is to enable material detection of objects and reduce the data processing required for modern LIDAR devices by acquiring more data at each point in the point cloud. This additional polarization data, when combined with machine learning algorithms, enables material detection and simplifies object recognition for a variety of applications, including but not limited to autonomous vehicles, machine vision, medical applications (e.g., devices to assist the visually impaired), and advanced robotics.

[0073] An M-LIDAR system according to an exemplary implementation of this disclosure may operate as follows: A pair of detectors (e.g., detectors 122, 124) having vertically oriented linear polarizers (e.g., polarizers 114, 116) may be used to measure the reflected light (e.g., one or more pulses of light constituting the reflected version of the linearly polarized light beam 112). A portion of the diffuse backscattered light (e.g., reflected light 112) may be directed towards the detectors (e.g., detectors 122, 124) and pass through a narrowband interference filter (e.g., linear polarizers 114, 116) placed in front of each pair of detectors (e.g., pair of detectors 122 / 124). The narrowband interference filter may allow only a narrow range of wavelengths (e.g., 1-2 nm) to pass through and may reduce unwanted noise from ambient illumination or an external source.

[0074] As in other examples of the system described above, the system may be configured to detect circularly polarized and / or elliptically polarized light reflected, scattered, or otherwise emitted by an object and to perform machine learning processing.

[0075] Due to this selectivity, the M-LIDAR system according to this disclosure (e.g., system 100) may be configured to measure multiple wavelengths simultaneously and completely independently. The coherent light may be polarized, for example, using a copolarizer and / or cross-polarizer. The intensity of the light may decrease to some extent as it passes through the polarizer, depending on the polarization shift when it is reflected by an object (e.g., object 110). A beam focusing optical system (e.g., polarizers 114, 116) may direct the coherent polarized light (e.g., beams 118, 120) toward a detection surface (e.g., the surface of detectors 122, 124), and the angle at which the reflected light travels (i.e., the angle of incidence) can be detected based on where the light strikes the detection screen.

[0076] Once the detector identifies light, a time-of-flight sensor (e.g., a time-of-flight sensor implemented in processor 126) may record the travel time of that light pulse. Each light pulse may have its intensity measured for both a copolarizing detector and a cross-polarizing detector. The combined values ​​of these two allow for the quantification of the polarization effect caused during reflection.

[0077] Following this process, the following parameters may be detected: (i) the initial angle at which the beam was directed, (ii) the angle at which the backscattered light returns, (iii) the time of flight from emission to detection, and (iv) the intensity at each detector.

[0078] It should be noted that, due to the different locations of the detectors, a single pulse of light may take slightly different amounts of time to reach each detector. By understanding the system's shape and the relationship between intensity and distance, this difference can be compensated for, and the intensity at each detector can be precisely adjusted. Time-of-flight data may also be used to determine the distance between a light source (e.g., laser 102) and an object (e.g., object 110), and (in conjunction with the initial and return angles) the specific location of that point in space relative to the M-LIDAR system can be determined. These compensated intensity values ​​may contain information indicating the material to which the light pulse is reflecting. These values ​​may be used by machine learning algorithms to provide robust and comprehensive material recognition capabilities.

[0079] The process described above, which involves emitting pulses of light, diffusely reflecting light from an object, measuring the reflected light by a detector, and determining the object's location relative to the light source, may be repeated on the order of 1 to several million times per second. Points are generated each time, and these points are mapped to the same coordinate system to create a point cloud.

[0080] Once the point cloud is generated, one or more machine learning algorithms may be used to cluster the points into objects and ultimately characterize the respective materials of each object (e.g., where multiple objects are detected). The points may be clustered, for example, based on one or more measured intensity values, and in some examples also based on similar proximity between points.

[0081] Once clusters are determined using intensity values, machine learning algorithms may be used to classify the material of the cluster at that point by correlating the measured values ​​with a database of known materials. This process may be repeated for all clusters in the system. Understanding the surrounding materials allows the system (e.g., implemented in cars, robots, drones, etc.) to make faster, more knowledgeable decisions about what an object itself is. Then, factors such as associated risks can be evaluated, and decisions can be made subsequently (e.g., when the system detects thin ice in front of a vehicle). As the process continues over time, more information can be extracted from perceived changes, and the surroundings are better understood.

[0082] MST classification technology is also applicable to the detection of objects where, for example, a surface painted or textured with a macroscale, microscale, nanoscale, or molecular pattern has been modified to enhance detection by producing reflected beams with a specific optical response adapted to high-speed MST classification by LiDAR. Examples of such surface treatments include paints containing additives that produce reflected, scattered, or otherwise emitted light of a specific linear, circular, or elliptical polarization. In one example, metallic nano / micro / macro wires or axial carbon nanomaterials are added to the base paint. The alignment pattern can be random, linear, spiral, herringbone, or any other pattern that produces a specific polarization signature enabling high-speed identification of a particular object. In non-limiting examples, this may be used to create markers on roads, road signs, barriers, gates, guardrails, vehicles, bicycles, clothing, and other objects.

[0083] Another implementation of surface treatment to facilitate MST classification may involve the addition of chiral inorganic nanoparticles to a base paint used to coat such objects described above, such as road signs, vehicles, bicycles, clothing, etc. Chiral nanoparticles can exhibit a specific, very strong circular polarization response to the beam used by LIDAR. They can be mixed into the paint in specific ratios to create a polarization signature (e.g., a "barcode") for a particular object.

[0084] Another example of object polarization tagging may include using surface texturing to create a specific polarization response. One example of such texturing may include fabricating nanoscale patterns of metallic, semiconductor, insulator, or ceramic nanoparticles having specific geometric properties that result in a defined polarization response to a laser in a LiDAR. Two examples of such patterns include (a) linear nanoscale or microscale surface features that result in linearly polarized light reflected, scattered, or emitted from the object, and (b) out-of-plane chiral patterns on a metallic surface that result in specific chirality, and therefore circularly polarized light reflected, scattered, or emitted from the object.

[0085] According to some examples, the systems and methods described above may be used to accurately recognize materials for use in autonomous vehicles, machine learning, medical applications, and advanced robotics.

[0086] Existing conventional LIDAR systems typically operate primarily by measuring the distance between an object and a laser source, usually using either time-of-flight data or phase shifts. In such cases, objects are classified based on their shape and pattern in the arrangement of points within a point cloud. Some more advanced LIDAR point cloud classification methods utilize an additional parameter: overall intensity.

[0087] Based on the strength of the reflected light pulse signal, the system can effectively detect color differences. This additional data makes it easier to recognize the boundaries of objects within the point cloud and reduces the processing load required to classify all points. However, applications such as autonomous vehicles may require higher certainty, and overall strength may not be achievable. Furthermore, detection of long-range objects may be achieved with single-point detection utilizing MST classification, rather than the multi-point detection and processing type used in conventional LiDAR systems.

[0088] Therefore, the method described herein alters the approach currently taken by machine vision for object recognition. Instead of relying solely on shape, motion, and color to determine the uniqueness of an object, the system described herein takes another parameter, polarization. When reflected at a material interface, light experiences a change in polarization. This change in polarization is quantified by measuring the intensity of the light after it has passed through both a copolarizing filter and a cross-polarizing filter. This additional data may be combined with a machine learning approach to significantly improve clustering and, consequently, object recognition capabilities. Conventional object recognition methods are computationally very expensive. The approach described herein may significantly reduce the processing power required by LIDAR by using a material-based approach instead of the current shape-based approach.

[0089] In addition to conventional distance measurement, the polarization data collected by this system allows machine learning algorithms to determine the materials that make up an object. Current LIDAR systems lack perception or information about the materials in the surrounding environment. If realized, such information would provide context for a deeper understanding of the situation and smarter decision-making. In the case of autonomous vehicles, accurate and timely detection of potential hazards improves decision-making capabilities, and this deeper understanding of the environment may lead to improved safety for the occupants.

[0090] Moving on to Figures 2a and 2b, scanning electron microscope (SEM) images of nano-kirigami sheets that may be used to form nano-kirigami nanocomposite optical components incorporated into an M-LIDAR system. Optically active kirigami, as shown in Figures 2a and 2b, may be manufactured from ultra-strong nanoscale composites having cut patterns of 0.5 to 5 μm in length, according to some examples of this disclosure. In certain embodiments, composite materials (including highly conductive composite materials) can be modified by using the concept from the ancient Japanese art of paper cutting, known as "kirigami." As a result, this disclosure provides a kirigami approach to achieve adaptability by using multiple cuts or notches to create a reticular structure in planar polymer materials such as composites or nanocomposites. Such cuts (e.g., extending from one side of a material to the other in a polymer or composite) can be made by top-down patterning techniques such as photolithography to uniformly distribute stress within the polymer or nanocomposite and suppress singularities of uncontrolled high stress. As a non-limiting example, this approach can prevent unpredictable localized failure and increase the final strain of a rigid sheet from 4% to 370%.

[0091] By using microscale paper-cutting patterns, rigid nanocomposite sheets can achieve high extensibility. Furthermore, paper-cut patterned composite sheets maintain their conductivity across the entire strain region, in stark contrast to many stretchable conductive materials. The paper-cutting structure may include composite materials such as nanocomposites. In certain embodiments, the paper-cutting structure may be a multilayer structure having at least two layers, with at least one layer being a polymer material. The polymer material may be a composite or a nanocomposite. The composite material includes a matrix material such as a polymer, a polyelectrolyte, or another matrix (e.g., cellulose paper), and at least one reinforcing material dispersed therein. In certain embodiments, the nanocomposite is a composite material particularly suitable for use in paper-cutting structures and including reinforcing nanomaterials such as nanoparticles. In certain modifications, the composite material may be in the form of a sheet or a film.

[0092] "Nanoparticles" are solid or semi-solid materials that can have various shapes or forms, but are generally understood by those skilled in the art to mean that the particles have at least one spatial dimension of about 10 μm (10,000 nm) or less. In certain embodiments, nanoparticles have a relatively low aspect ratio (AR) (defined as the length of the longest axis divided by the diameter of the component) of about 100 or less, optionally about 50 or less, optionally about 25 or less, optionally about 20 or less, optionally about 15 or less, optionally about 10 or less, optionally about 5 or less, and in certain modifications equal to 1. In other embodiments, nanoparticles having the shape of a tube or fiber have a relatively high aspect ratio (AR) of about 100 or more, optionally about 1000 or more, and in certain modifications optionally 10,000 or more.

[0093] In certain modifications, the longest dimension of the nanoparticles is about 100 nm or less. In certain embodiments, the nanoparticles selected for inclusion in the nanocomposite are conductive nanoparticles that make up a conductive nanocomposite material. The nanoparticles may be substantially circular nanoparticles having a low aspect ratio as defined above and having morphologies or shapes including spherical, spheroidal, hemispherical, disk, spherical, annular, donut-shaped, cylindrical, disc, dome-shaped, egg-shaped, elliptic, circular, oval, etc. In certain preferred modifications, the morphology of the nanoparticles is spherical. Alternatively, the nanoparticles may have alternative shapes such as filaments, fibers, rods, nanotubes, nanostars, or nanoshells. The nanocomposite material may also include any combination of such nanoparticles.

[0094] Furthermore, in certain embodiments, nanoparticles particularly suitable for use according to this teaching have a particle size (average diameter of multiple nanoparticles present) of about 10 nm or more and 100 nm or less. Conductive nanoparticles may be formed from a variety of conductive materials, including nanoscale particles of metals, semiconductors, ceramics, and / or polymers having multiple shapes. Nanoparticles may have magnetic or paramagnetic properties. Nanoparticles may include conductive materials such as carbon, graphene / graphite, graphene oxide, gold, silver, copper, aluminum, nickel, iron, platinum, silicon, cadmium, mercury, lead, molybdenum, iron, and alloys or compounds thereof. As a result, suitable nanoparticles include graphene oxide, graphene, gold, silver, copper, nickel, iron, carbon, platinum, silicon, metalloids (seedling metals), CdTe, CdSe, CdS, HgTe, HgSe, HgS, PbTe, PbSe, PbS, MoS2, FeS2, FeS, FeSe, WO 3-xGraphene oxide is a conductive material particularly well-suited for use as a reinforcing material in composites. In certain modifications, the nanoparticles may include carbon nanotubes such as single-walled nanotubes (SWNTs) or multi-walled nanotubes (MWNTs). SWNTs are formed from a single sheet of graphite or graphene, while MWNTs consist of multiple cylinders arranged concentrically. Typical diameters of SWNTs can range from about 0.8 nm to about 2 nm, while NWMTs can have diameters exceeding 100 nm.

[0095] In certain modifications, the nanocomposite may contain a total amount of multiple nanoparticles, ranging from approximately 1% to approximately 97% by weight of the total amount of nanoparticles in the nanocomposite, optionally from approximately 3% to approximately 95% by weight, optionally from approximately 5% to approximately 75% by weight, optionally from approximately 7% to approximately 60% by weight, and optionally from approximately 10% to approximately 50% by weight. Of course, the appropriate amount of nanoparticles in the composite material depends on the material properties, percolation threshold, and other parameters for a particular type of nanoparticle in a particular matrix material.

[0096] In certain modifications, the nanocomposite may contain a total amount of polymer matrix material of approximately 1% by weight or more and approximately 97% by weight or less of the total amount of matrix material in the nanocomposite, optionally approximately 10% by weight or more and approximately 95% by weight or less, optionally approximately 15% by weight or more and approximately 90% by weight or less, optionally approximately 25% by weight or more and approximately 85% by weight or less, optionally approximately 35% by weight or more and approximately 75% by weight or less, and optionally approximately 40% by weight or more and approximately 70% by weight or less.

[0097] In certain modifications, the nanocomposite material contains multiple conductive nanoparticles, approximately 1.5 × 10⁻⁶ 3 It has an conductivity of S / cm or higher. In certain other embodiments, the nanocomposite material may include multiple conductive nanoparticles as reinforcing nanomaterials, resulting in approximately 1 × 10⁻⁶ nanoparticles. -4It may have an electrical resistivity of Ω·m or less. In certain other modifications, the impedance (Z) of a conductive nanocomposite containing multiple nanoparticles is about 1 × 10⁻⁶. 4 It may be less than Ω (for example, measured using an AC sinusoidal signal with an amplitude of 25mV, with the impedance value measured at a frequency of 1kHz).

[0098] Polymers or nanocomposite materials may be in a planar form, such as a sheet, in their initial state (before cutting), but after the cutting process, they may be folded or molded into a three-dimensional structure, and as a result, may be used as structural components. As an example, a structure 220 including a portion of an exemplary nanocomposite material sheet 230 having a surface with a cut-in pattern is shown in Figure 10. The sheet 230 includes a first row 232 of first discontinuous cuts 242 (extended through the sheet 230 to create openings) in a pattern defining a first non-cut region 252 between discontinuous cuts 242. A discontinuous cut is a partial or individual cut formed in a sheet that does not divide the sheet into separate smaller sheets or portions, leaving the entire sheet at its original dimensions. If there are multiple discontinuous cuts 242, at least some of them are discontinuous and not connected to one another, such that at least one non-cut region remains in the sheet as a bridge between discontinuous sheets. While many cut patterns are possible, a simple cut paper pattern of straight lines in a grid pattern centered on the surface, as shown in Figure 10, is used as an exemplary pattern herein. The first non-cut region 252 has a length "x". Each discontinuous cut 242 has a length "L".

[0099] In certain embodiments, the length of each discontinuous cut (e.g., discontinuous cut 242) may be microscale, mesoscale, nanoscale, and / or macroscale. Macroscale is typically considered to have dimensions of about 500 μm (0.5 mm) or more, while mesoscale is about 1 μm (1,000 nm) or more and about 500 μm (0.5 mm) or less. Microscale is typically considered to be about 100 μm (0.5 mm) or less, while nanoscale is typically about 1 μm (1,000 nm) or less. As a result, conventional mesoscale, microscale, and nanoscale dimensions may be considered to overlap. In certain embodiments, the length of each discontinuous cut may be, for example, less than about 100 μm (i.e., 100,000 nm), optionally less than about 50 μm (i.e., 50,000 nm), optionally less than about 10 μm (i.e., 10,000 nm), optionally less than about 5 μm (i.e., 5,000 nm), and in certain embodiments, less than about 1 μm (i.e., 1,000 nm). In certain embodiments, the discontinuous cut 42 may have a length of less than about 50 μm (i.e., 50,000 nm), optionally less than about 10 μm (i.e., 10,000 nm), and optionally less than about 1 μm (i.e., less than about 1,000 nm).

[0100] In certain other modifications, these dimensions can be reduced to at least about 1 / 100th of the nanoscale, for example, the cuts may have lengths of about 1 μm (1,000 nm) or less, optionally about 500 nm or less, and optionally about 100 nm or less in certain modifications.

[0101] It should be noted that "x" and "L" may vary within the row depending on the pattern being formed, but in a preferred embodiment, these dimensions remain constant.

[0102] The second column 234 of the second discontinuous cut 244 is also patterned on the sheet 230. The second discontinuous cuts 244 define a second non-cut area 254 between them. The third column 236 of the third discontinuous cut 246 is also patterned on the sheet 230. The third discontinuous cuts 246 define a third non-cut area 256 between them. The first column 232, the second column 234, and the third column 236 are used for illustrative and subjective purposes, but it should be noted that, as can be seen, the cut-and-fill pattern on the surface of the sheet 230 has more than three distinct columns. The first column 232 is spaced apart from the second column 234, as indicated by the designation "y". The second column 234 is similarly spaced apart from the third column 236. It should be noted that "y" may vary between columns, but in certain embodiments it remains constant between columns. Such spacings between columns may also be on the microscale, mesoscale, nanoscale, and / or macroscale, as described above.

[0103] In particular, the first discontinuous cut 242 in the first column 232 is offset laterally (along the dimension / axis indicated as "x") from the second discontinuous cut 244 in the second column 234, thereby forming a cut-and-fit pattern. Similarly, the second discontinuous cut 244 in the second column 234 is offset laterally from the third discontinuous cut 246 in the third column 236. As a result, the first non-cut region 252, the second non-cut region 254, and the third non-cut region 256 in each column cooperate to form a structural bridge 260 extending from the first column 232, across the second column 234, and into the third column 236.

[0104] In this regard, a sheet 230 having a patterned patchwork surface with multiple discontinuous cuts (e.g., 242, 244, and 246) can be stretched in at least one direction (e.g., along a dimension / axis indicated as "y" or "x"). As a result, a sheet 230 formed from the nanocomposite material exhibits certain advantageous properties, including enhanced strain.

[0105] In various embodiments, optical devices incorporating stretchable multilayer polymers or composite materials formed by a paper-cutting process are envisioned. "Stretchable" means that the material, structure, components, and device can withstand strain without fracture or other mechanical failure. A stretchable material is stretchable, and as a result, can be stretched and / or compressed to at least some extent without damage, mechanical failure, or significant performance degradation.

[0106] Young's modulus is a mechanical property that refers to the ratio of stress to strain for a given material. Young's modulus may also be given by the following formula:

number

[0107] In certain embodiments, tensile composite materials, structures, components, and devices may be subjected to a maximum tensile strain of at least about 50%, optionally about 75%, optionally about 100%, optionally about 150%, optionally about 200%, optionally about 250%, optionally about 300%, optionally about 350%, and in certain embodiments, optionally about 370% or more without breaking.

[0108] Extensible materials may also be flexible in addition to being extensible, as a result of being able to undergo significant stretching, bending, flexing, or other deformation along one or more axes. The term "flexible" can refer to the ability of a material, structure, or component to deform (e.g., into a curved shape) without undergoing permanent deformation that introduces significant strain, such as a strain that indicates a point of failure in the material, structure, or component.

[0109] As a result, the Disclosure provides, in certain embodiments, stretchable polymer materials. In further embodiments, the Disclosure provides stretchable composite materials comprising a polymer and a plurality of nanoparticles or other reinforcing materials. The polymer may be an elastomer or a thermoplastic polymer. As a non-limiting example, one suitable polymer includes polyvinyl alcohol (PVA).

[0110] For example, for a particular material, creating a surface having a patterned paper cut according to a particular aspect of the present disclosure can increase the ultimate strain of an initial rigid sheet by about 100% or more, optionally about 500% or more, optionally about 1,000% or more, and optionally about 9,000% or more in certain modifications, from the initial ultimate strain before any cut.

[0111] In particular, a wide range of maximum achievable strain or expansion levels can be achieved based on the shape of the cut pattern used. As a result, the ultimate strain is determined by the shape. Ultimate strain (%strain) is the ratio of the length that can ultimately be achieved when the structure is stretched to the point before it breaks, to the original or initial length (Li).

number

[0112] In certain embodiments, the paper-cut nanocomposite can form a tunable optical lattice structure that can maintain stable periodicity over macroscopic length scales even under 100% stretching. The transverse spacing in the diffraction pattern shows a negative correlation with the amount of stretching, which is consistent with the interrelationship between the dimensions in the diffraction pattern and the corresponding lattice spacing. The longitudinal spacing in the diffraction pattern is less dependent on the amount of stretching because the change in longitudinal periodicity with transverse stretching is relatively small. The diffraction pattern also shows a strong dependence on the wavelength of the incident laser. The stretchable and tunable optical lattice structure of the polymer exhibits elastic behavior with stretching and spontaneously recovers to a relaxed (i.e., unstretched) shape when the stretching is removed under periodic mechanical action. The diffracted beam forms a distinct pattern that changes consistently with the deformation of the stretchable and tunable optical lattice structure of the polymer. This behavior demonstrates excellent functionality for dynamic wavelength-dependent beam manipulation.

[0113] As a result, three-dimensional (3D) kirigami nanocomposites offer a new dimension to conventional reflective and refractive optical systems due to their out-of-plane surface features, as illustrated in Figures 2a and 2b. For example, reconfigurable fins and slits formed by cuts, as illustrated in the nano-kirigami sheets shown in Figures 2a and 2b, enable efficient modulation of light by reversible expansion (or strain level) of the kirigami cut sheet. Consequently, nano-kirigami sheets, such as those shown in Figures 2a and 2b, may be incorporated into one or more optical components of the M-LIDAR system described herein. More specifically, these thin and inexpensive optical components may be used, for example, to achieve beam maneuvering and / or polarization modulation of the red and infrared portions of the optical spectrum. According to some implementations, kirigami nanocomposites of the type shown in Figures 2a and 2b may be used to form the beam maneuvering section 106, the s-polarized linear polarizer 114, and / or the p-polarized linear polarizer 116 of the system illustrated in Figure 1.

[0114] In certain modifications, the paper-cut nanocomposites can form paper-cut optical modules fabricated from ultra-strong, multilayer (Layer-by-Layer) (LbL) assembled nanocomposites. These nanocomposites possess high strength, e.g., about 650 MPa, and an elastic modulus (E) of about 350 GPa, and offer outstanding mechanical properties, environmental resistance, and proven scalability, in addition to a wide operating temperature range (e.g., -40° to +40°C). The high elasticity of the LbL composites makes them reconfigurable, and their high-temperature elasticity enables integration with various types of actuators and CMOS compatibility. In certain embodiments, the nanocomposites may be coated with a plasmonic film such as titanium nitride, gold, etc., to enhance interaction with photons of a target wavelength, e.g., 1550 nm photons from a laser source having a wavelength of 1550 nm.

[0115] In certain other modifications, the kirigami nanocomposite sheet may include magnetic materials dispersed therein or coated thereon. For example, a layer of nickel may be deposited on the ultrastrong composite. The nickel layer can function as a magnetic and reflective layer, thereby providing a magnetically activated kirigami element. As a result, the kirigami unit can be directly integrated with a LIDAR component and function as a beam maneuver (e.g., using primary and secondary diffracted beams) or a polarizer (e.g., using a primary diffracted beam).

[0116] Referring to Figures 3a to 3c, images illustrating the laser diffraction patterns from nano-cut paper-based graphene composites are shown. For reference, the scale bar shown in the upper right corner of Figures 3a to 3c represents 25 mm. Figure 3a illustrates the laser diffraction pattern from nano-cut paper-based graphene composites for 0% strain (relaxed state). Figure 3b illustrates the laser diffraction pattern from nano-cut paper-based graphene composites for 50% strain. Finally, Figure 3c illustrates the laser diffraction pattern from nano-cut paper-based graphene composites for 100% strain.

[0117] Polarization modulation of LIDAR beams and polarization analysis of reflected photons enable the acquisition of information about the material of objects, which is currently lacking in applications such as automotive safety and robot vision devices. Machine learning (ML) algorithms may be trained to recognize different materials, and MST classification may be realized based on their unique polarization signatures. Among other advantages, MST classification of objects by material may accelerate object recognition and improve the accuracy of machine perception of the surroundings.

[0118] Before moving on to the details of Figures 4a to 4d, it should be noted that System 100 in Figure 1, and the corresponding material detection and object classification methods, may be implemented using non-nano-kiri optics, as shown in some examples of this disclosure. In fact, such non-nano-kiri optics-based M-LIDAR systems may be preferred for certain applications (e.g., where size and weight are not major issues). Thus, implementations of M-LIDAR systems currently disclosed that do not utilize nano-kiri optics, other conventional optical components such as (i) IR polarizers, (ii) beam splitters, (iii) lenses made from CdS, ZnS, silicon, and / or (iv) similarly suitable optics may be used equally without departing from the teachings herein. However, nano-kiri optics are generally suitable for M-LIDAR systems that benefit from their lightweight and small size.

[0119] Against this backdrop, Figures 4a to 4d illustrate stepwise lithography-type processes for manufacturing nano-cut-paper based optical elements such as beam splitters or linear polarizers. For example, the processes for manufacturing nano-cut-paper based optical elements described in Figures 4a to 4d may include the use of vacuum-assisted filtration (VAF), thereby allowing the nanocomposite material to be deposited as layers on a rigid (e.g., plastic) substrate suitable for lithographic patterning. As noted above, Patent Document 1 describes a method for manufacturing such nanocomposite materials, including vacuum-assisted filtration (VAF) and multilayer (LBL) deposition process techniques. Nanocomposite materials manufactured according to this process are known to exhibit high toughness and strong light absorption.

[0120] Figure 4a is a simplified diagram of the first step of process 400, in which the nanocomposite layer 404a is deposited on the substrate 402 via VAF, multilayer deposition (LBL), or any other suitable deposition method known in the art. Figure 4b illustrates the second step of process 400, after the nanocomposite material 404a of Figure 4 has been patterned to produce a cut-paper nanocomposite material 404b patterned through a selected region of the nanocomposite layer 404a on the substrate 402, for example, via a photolithography cutting process. Figure 4c illustrates the third step of process 400, in which the cut or patterned cut-paper nanocomposite material 404b is released (e.g., lifted) from the substrate 402. Finally, Figure 4d illustrates the final step of process 400, in which at least a portion of the patterned cut-paper nanocomposite material 408 is incorporated into a subassembly configured for beam manipulation and / or modulation, among other things.

[0121] The subassembly shown in Figure 4d includes a patterned kirigami nanocomposite portion 408, a housing of a microfabricated silicon layer 406, and one or more bending beam actuators 410. The double-headed arrows 412 illustrate potential directions of motion for the actuators 410. As will be discussed in more detail below, the bending beam actuators 410 may be configured to apply reversible strain to the kirigami nanocomposite portion 408, for example, to adjust the size and / or orientation of the various slits and / or fins that constitute the pattern of the kirigami nanocomposite portion 408. As a result, the kirigami nanocomposite portion 408 may be reversibly stretched at strain levels ranging from 0% to 100%.

[0122] A more detailed discussion of the patterning aspects of process 400 shown in Figures 4a to 4d follows. The fabrication of the paper-cut transmissive optical module may follow the stepwise diagrams shown in Figures 4a to 4d. Modulation of the LIDAR laser beam in the visible and IR ranges may require, for example, a 3 μm feature size fabricated over a width of 0.1 to 1 cm. The feasibility of such patterns has already been demonstrated. The 2D shape of the pattern may be selected based on computer simulations of their 2D-to-3D reconstruction when stretched or deformed. The 3D shape may be modeled for optical properties, e.g., polarization modulation in a desired wavelength range. Photolithography may be the primary patterning tool, made possible by the chemical properties of the VAF composite material described above. The patterning protocol may be substantially the same as those currently used for large-scale microfabrication. For example, the VAF composite material on a glass substrate may be coated with a standard SU8 photoresist following photopatterning using a commercially available conventional mask aligner. Examples of prepared paper cutting patterns are shown in Figures 5a to 5c, which will be discussed in more detail below.

[0123] Papercraft optical elements may be manufactured, for example, by integrating a papercraft nanocomposite sheet with a commercially available micro-electromechanical actuator, as shown in Figure 4d. Papercraft units of micro-electromechanical systems (MEMS) may be directly integrated with LIDAR components and function as beam maneuvers (e.g., using primary and secondary diffracted beams) and / or polarizers (e.g., using a primary diffracted beam). Given a nearly infinite number of papercraft patterns and a wide variety of 2D-to-3D reconstructions, papercraft optical elements possessing both beam maneuvering and polarization functions, as well as other optical functions, are contemplated within the teachings herein.

[0124] A brief reference to Figures 5a to 5c shows various images of exemplary paper-cut optical elements. For example, Figure 5a is an image of a paper-cut optical element, such as the one described herein, manufactured on a wafer according to process 400 described above with respect to Figures 4a to 4d. Figure 5b is an SEM image of the paper-cut optical element of Figure 5a under 0% strain. Finally, Figure 5c is an SEM image of the paper-cut optical element of Figure 5a under 100% strain. The scale bar depicted in the upper right corner of Figures 5b and 5c is 50 μm.

[0125] Photolithographic techniques can be used to manufacture paper-cut transmissive or reflective optical modules / elements. For example, a modulation of a LIDAR laser beam having a wavelength of approximately 1550 nm may have a feature size of approximately 1 μm to 2 μm, fabricated over a width in the range of approximately 0.1 cm to 1 cm, as illustrated by the current patterns in Figures 5a to 5c. The two-dimensional (2D) shape of the pattern can be selected based on computer simulations of their reconstruction from 2D to three-dimensional (3D) when stretched. The 3D shape can be modeled for optical properties, such as polarization modulation in a desired wavelength range.

[0126] Photolithography is a primary patterning technique that can be used in combination with LbL composites to form cut-paper optical elements. In one example, the patterning protocol may include providing an LbL composite on a glass substrate coated with standard SU-8 photoresist, following photopatterning using a commercially available mask aligner (UM Lurie Nanofabrication Facility, LNF). Such a process can form cut-paper elements as shown in Figures 5a-5c.

[0127] Another representative, simplified, and compact M-LIDAR system 300 for use in vehicles such as autonomous vehicles is provided in Figure 11. For the sake of brevity, the components of the M-LIDAR system 300 are similar to those in the M-LIDAR system 100 of Figure 1, and for the sake of brevity, their functions are not repeated herein. The M-LIDAR system 300 may include a laser 310, a beam maneuvering unit 312, one or more polarizers (not shown, but similar to the first polarizer 114 and second polarizer 116 described in the context of Figure 1), and a processor (not shown, but similar to the processor 126 shown in Figure 1). In the M-LIDAR system 300, a pulse generator 312 is connected to the laser 310 to generate a first polarized or unpolarized optical pulse 314 and a second polarized or unpolarized optical pulse 316. The pulse generator 312 is connected to an oscilloscope 324. The first optical pulse 314 and the second optical pulse 316 generated by the laser 310 are directed to a beam manipulator 318, which in certain embodiments may be a paper-cut beam manipulator as discussed above. The beam manipulator 318 is connected to and controlled by a servo motor / Arduino 352. The servo motor / Arduino 352 is connected to a controller 350, which may be MATLAB® controlled in a non-limiting example. As noted above, the beam manipulator 318 may, in a non-limiting example, polarize, modify, split, and / or modulate one or both of the first optical pulse 314 and the second optical pulse 316 as discussed above. The first optical pulse 314 and the second optical pulse 316 are then directed to the object 340 to be detected.

[0128] The first light pulse 314 and the second light pulse 316 may be diffusely reflected from the object 110. One or more pulses of light collectively form a first reflected beam 342 and a second reflected beam 344, which constitute reflected versions of the first light pulse 314 and the second light pulse 316. In some examples, the first reflected beam 342 and the second reflected beam 344 may have different polarizations than the first light pulse 314 and the second light pulse 316 (i.e., before reflection from the object 340). After reflection from the object 340, the first reflected beam 342 and the second reflected beam 344 may be directed to an off-axis parabolic mirror / mirror 330 that redirects the first reflected beam 342 and the second reflected beam 344 to a beam splitter 360.

[0129] As a result, the first reflected beam 342 is split and directed towards both the first detector 362 and the second detector 364. The first detector 362 and the second detector 364 may be connected to an oscilloscope 324. The first detector 362 may be an s-polarization detector configured to detect the intensity of one or more reflected s-polarized light pulses forming the first reflected light beam 342. Similarly, the second detector 364 may be a p-polarization detector configured to detect the intensity of one or more reflected p-polarized light pulses forming the first reflected light beam 342. After passing through the beam splitter 360, the second reflected light beam 344 is directed towards both the first detector 362 and the second detector 364, and the intensities of s-polarized light pulses and / or p-polarized light pulses can be detected from the second reflected light beam 344. The first detector 362 and the second detector 364 may be connected to a processor (not shown) for further analysis of the information received from them, as previously described above. The M-LIDAR system 300 is compact and, in non-limiting examples, may have dimensions of approximately 7 inches x 12 inches, making it particularly suitable for mounting in a vehicle.

[0130] As described above, MST classification may be implemented through the use of light source-based MST classification having an optical polarization classifier added to the point cloud, according to the examples of this disclosure. In one example, the linear / circular polarization of the returned photons may be acquired for each 3D range measurement of the point cloud. In addition, the relationship between surface properties and polarization state may be noisy in some cases due to surface roughness, but local curvature and local scattering conditions may be directly based on the polarization state of the returned photons.

[0131] Referring here to Figure 6, the MST polarization analysis of the reflected laser light was performed using AI data processing with a neural network algorithm to generate the confusion matrix shown in Figure 6. More specifically, the confusion matrix was generated based on the analysis of s and p polarized light beams (such as s and p polarized light beams 118 and 120 shown in Figure 1). Along the x-axis, the predicted type of material for the test object subject to the M-LIDAR system and processing method described herein is identified. Along the y-axis, the actual type of material for the test object is identified. The accuracy of the various predictions of the AI ​​algorithm for various material types is reflected at the intersection of the predicted material type and the actual material type. As shown, the material detection capability of the M-LIDAR system may be achieved with high accuracy (including in some cases 99% or more) using these polarized light beams.

[0132] Referring to Figure 7, a confusion matrix for the detection of thin ice, simulated in comparison to other materials, is shown. In this case as well, the material detection capability of the M-LIDAR system may be achieved with high accuracy (including 100% in some cases).

[0133] Figure 8 illustrates an example of an M-LIDAR device 800 for use, for example, in thin ice detection (e.g., when installed in a vehicle). While this example focuses on black ice detection applications, those skilled in the art will recognize that the device 800 is not limited to black ice detection and may be appropriately used for a wide range of material detection and object classification applications, including autonomous vehicles. The device 800 includes a housing 802, an emitter 804 (i.e., an emitter for emitting light pulses that constitute a laser light beam), a first detector 806a, and a second detector 806b. In one example, one or more of the detectors 806a and 806b include an orthogonal polarization analyzer. Furthermore, in one example, one or more of the emitter 804, detector 806a, and / or detector 806b may be made of cut-paper optical elements. While the primary example of the device is use in automobiles, the device can also be used in aircraft, such as drones.

[0134] Referring here to Figure 9, a flowchart illustrating method 900 for performing object classification using an M-LIDAR system is provided. Method 900 begins in 902, where an unpolarized light pulse is generated. In 904, the unpolarized light pulse is linearly polarized to generate a linearly polarized light pulse. The linearly polarized light pulse is emitted toward an object and reflected back from the object to generate a reflected linearly polarized light pulse. In 906, the reflected linearly polarized light pulse may be linearly polarized to s-polarization to generate a reflected s-polarized light pulse.

[0135] In 908, the reflected linearly polarized light pulse may be linearly polarized to p-polarized light to produce a reflected p-polarized light pulse. In 910, the intensity of the reflected s-polarized light pulse may be detected. In 912, the intensity of the reflected p-polarized light pulse may be detected. In 914, at least one material of the object may be detected based on the intensity of the reflected s-polarized light pulse and the intensity of the reflected p-polarized light pulse. Finally, in 916, the object may be classified based on the detected at least one material. Following 916, method 900 ends.

[0136] Finally, according to some examples, paper cutout patterns may be used as MST tags for polarization-based detection of objects. Mass-produced paper cutout components may also be added to paints to give specific polarization responses to road signs, clothing, markers, vehicles, household items, or other suitable objects.

[0137] In certain modifications, the LiDAR system of this disclosure can provide modulation of transmitted and reflected beams. A paper-cut optical element can be added to the emitter side of the LiDAR to function as a beam maneuver, thereby replacing conventional bulky rotary or liquid crystal phase array beam maneuvers. A magnetic actuation module can be integrated with a 1550 nm laser light source. To reduce the majority of the beam maneuver, an optical fiber can be directly coupled to the module.

[0138] In certain modifications, the LiDAR systems provided by this disclosure may provide enhanced detection in precipitation and / or humid atmospheric conditions. For example, the LiDAR systems contemplated by this disclosure may be particularly suited for use in low-visibility conditions by using a laser with a wavelength of approximately 1550 nm, as a non-limiting example, to improve detection and performance in adverse weather conditions, including low-visibility conditions associated with fog, rain, and snow. Such LiDAR systems can enable long-range warnings up to 200 meters, for example, and are particularly useful for highway driving situations. Conventional LiDARs use lasers with a wavelength of approximately 900 nm, which is convenient for silicon-based detectors. However, these conventional laser beams experience relatively strong scattering in humid atmospheric conditions. LiDAR operating at 1550 nm can take advantage of the high transparency of humid air, which is advantageous for various levels of autonomy, from proximity warning to assisted driving and fully autonomous driving modalities. However, such LiDARs can be bulky and expensive due to the heavy weight and high cost of near-infrared optics. According to certain aspects of this disclosure, paper-cutting-based optical elements can solve this problem by utilizing the space charge and subwavelength effects possible for patterned paper-cutting sheets (see, for example, Figures 2a and 2b). As shown in Figures 3a and 3b, such paper-cutting sheets can effectively modulate and beam-maneuver a near-infrared laser using the reconfigurable out-of-plane pattern of the paper-cutting sheet. A 1550 nm beam-maneuvering device incorporating such a paper-cutting-based optical element can be used as a thin, lightweight, and inexpensive solid-state LiDAR. Furthermore, the versatility of paper-cutting technology allows for the customization of LiDAR systems for specific applications, for example, to a particular vehicle, potentially adapting the pattern and / or adapting it to the surfaces of automotive parts with various curvatures.

[0139] In certain embodiments, the Disclosure can provide relatively fast detection to a LiDAR system by using a two-stage object proposition and detection method without sacrificing accuracy for latency. For example, improving model accuracy and generalizability for classification models can include enhancing static object classifiers by adding material dimensions to the data. Objects with material fingerprints containing plastic, wood, and brick are very unlikely to be moving, while objects with metal or fabric fingerprints are more likely to be pedestrians and vehicles. Moreover, since material dimensions are more robust to variations in scenarios, these models generalize to rare and complex cases such as construction sites or streets with intricate festival decorations. As a result, material fingerprints significantly improve model accuracy for point cloud-related models, impacting tracking and autonomous vehicle maps. For example, a pedestrian walking while pushing a bicycle can be picked up as a point cloud with metal material on the underside and fabric or skin features from the pedestrian, so the material dimensions of the point cloud can make detection and classification much more reliable, and make it much easier for an autonomous driving system to distinguish it from a pure pedestrian. Furthermore, the material fingerprint of an object facilitates the system's ability to associate point clouds with the correct object classification, helping to maintain accurate and consistent composite object classification.

[0140] As a result, this disclosure provides an inexpensive and compact LIDAR system with enhanced object recognition, including the ability to distinguish between material types, a faster detection and warning system, including the ability to identify objects within milliseconds, and, among other advantages, high effectiveness in low-visibility conditions.

[0141] The above description of embodiments is provided for illustrative and explanatory purposes only. It is not intended to be exhaustive or to limit the disclosure. Individual elements or features of a particular embodiment are generally interchangeable, where applicable, and can be used in selected embodiments even if not specifically shown or described. Similarly, they may be modified in many ways. Such modifications should not be considered departures from the disclosure, and all such modifications are intended to be within the scope of the disclosure.

Claims

1. A laser configured to generate light pulses that are emitted toward an object, At least one polarizer configured to polarize light reflected, scattered, or emitted from the object, A processor configured to detect at least one material of an object based on the intensity, polarization, and return angle of polarized reflected, scattered, or emitted light from the object, using a machine learning algorithm to perform material and surface texture (MST) classification, At least one polarizer and at least one polarizing detector connected to the processor, wherein the at least one polarizing detector is configured to detect the intensity of the polarized reflected, scattered, or emitted light from the object and the return angle of the polarized reflected, scattered, or emitted light from the object, based on the location where the polarized reflected, scattered, or emitted light from the object strikes at least one detection surface of the at least one polarizing detector, the return angle being the angle at which the polarized reflected, scattered, or emitted light from the object travels relative to the at least one detection surface. A system equipped with these features.

2. The system according to claim 1, further comprising a beam manipulator configured to adjust the polarization of the light pulses in order to generate polarization-adjusted light pulses emitted toward the object.

3. The system according to claim 2, wherein the beam manipulator is configured to adjust the polarization of the light pulse by changing the polarization of the light pulse.

4. The system according to claim 2, wherein the polarization of the light pulse is at least one of linear polarization, circular polarization, and elliptic polarization.

5. The system according to claim 1, wherein the processor is further configured to classify the object based on the at least one material of the object that has been detected.

6. The system according to claim 5, wherein the processor is configured to classify the object based on the at least one detected material of the object by applying a machine learning algorithm.

7. The system according to claim 6, wherein the machine learning algorithm includes an artificial neural network algorithm.

8. The system according to claim 1, wherein the at least one polarizer is configured to polarize the reflected, scattered, or emitted light returned from the object by applying at least one of linearly polarized, circularly polarized, and elliptically polarized light.

9. The system according to claim 8, wherein the at least one polarizer is configured to apply linear polarization by applying at least one of s-type linear polarization and p-type linear polarization.

10. The system according to claim 1, wherein the at least one polarizer is a plurality of polarizers.

11. The system according to claim 1, wherein the at least one polarization detector is a plurality of polarization detectors.

12. The steps include generating light pulses that are emitted toward an object using a laser, The steps of polarizing the reflected, scattered, or emitted light returned from the object with at least one polarizer, A step of detecting the intensity of the polarized reflected, scattered, or emitted light from an object and the return angle of the polarized reflected, scattered, or emitted light from an object, based on the location where the polarized reflected, scattered, or emitted light from the object strikes at least one detection surface of the at least one polarizer, wherein the return angle is the angle at which the polarized reflected, scattered, or emitted light from the object travels relative to the at least one detection surface, The processor performs material and surface texture (MST) classification using a machine learning algorithm to detect at least one material of the object based on the intensity, polarization, and return angle of the polarized reflected, scattered, or emitted light from the object. A method that includes this.

13. The method according to claim 12, further comprising the step of adjusting the polarization of the light pulses in a beam manipulator to generate polarization-adjusted light pulses emitted toward an object.

14. The method according to claim 12, further comprising the step of classifying the object based on the at least one material of the object that has been detected.

15. The method according to claim 14, wherein the step of classifying the objects includes the step of classifying the objects by applying a machine learning algorithm.

16. The method according to claim 15, wherein the machine learning algorithm includes an artificial neural network algorithm.

17. A laser configured to generate polarized light pulses that are emitted toward an object, At least one polarizer configured to polarize light reflected, scattered, or emitted from the object, At least one polarization detector connected to the at least one polarizer, wherein the at least one polarization detector is configured to detect the intensity of the polarized reflected, scattered, or emitted light from the object and the return angle of the polarized reflected, scattered, or emitted light from the object, based on the location where the polarized reflected, scattered, or emitted light from the object strikes at least one detection surface of the at least one polarization detector, the return angle being the angle at which the polarized reflected, scattered, or emitted light from the object travels relative to the at least one detection surface, A processor connected to the at least one polarization detector, configured to detect at least one material of the object based on the intensity, polarization, and return angle of the polarized reflected, scattered, or emitted light from the object, using a machine learning algorithm to perform material and surface texture (MST) classification. A system equipped with these features.

18. The system according to claim 17, wherein the at least one polarizer is configured to polarize the reflected, scattered, or emitted light returned from the object by applying at least one of linearly polarized, circularly polarized, and elliptically polarized light.

19. The system according to claim 18, wherein the at least one polarizer is configured to apply linear polarization by applying at least one of s-type linear polarization and p-type linear polarization.

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