A lidar imaging system, method, electronic device, and storage medium

Abstract

The application discloses a laser radar imaging system and method, an electronic device and a storage medium, relates to the technical field of laser radar, and is used for improving the imaging efficiency of the laser radar. The method comprises the following steps: acquiring a plurality of electrical signal groups, the electrical signal groups being generated based on M*N reflected light signals of a target to-be-measured object and a binary random template; wherein the M*N reflected light signals are light signals received by M*N pixel units respectively; the binary random template comprises M*N elements, the M*N elements corresponding to the M*N pixel units one by one; the M*N elements are used for randomly enabling the M*N pixel units, generating random electrical signals, and the random electrical signals form the electrical signal groups; and the target to-be-measured object is imaged based on the electrical signal groups generated by different binary random templates and a compressed sensing algorithm.

Description

Technical Field

[0001] This application relates to the field of lidar technology, and in particular to a lidar imaging system, method, electronic device and storage medium. Background Technology

[0002] Currently, lidar technology primarily determines the distance between a target object and the measurement system by measuring the time it takes for the laser light to travel from emission to reflection from the target object and then to the measurement system. When the laser pulses continuously illuminate the target object, data on all target points can be obtained. This data can then be processed to produce a precise 3D image. However, scanning all target points for imaging is slow, which is not conducive to practical applications. Currently, by using binary random templates for a smaller number of random samplings and employing compressed sensing algorithms, images with near-original resolution can be obtained, significantly improving the lidar imaging rate. Specifically, a digital micromirror device (DMD) is used. The control system adjusts the reflection angles of different mirrors within the DMD to achieve random sampling. The optical path is then aligned with a single-pixel detector to convert the optical signal to an electrical signal, outputting a random sampling signal. The image result is then obtained through compressed sensing algorithms. Figure 1 As shown. Due to the high cost of DMD deployment and the need for additional optical path alignment devices before imaging processing, the system is highly complex and not conducive to practical applications. Summary of the Invention

[0003] This application provides a lidar imaging system, method, electronic device, and storage medium to reduce system complexity and improve lidar imaging efficiency.

[0004] To achieve the above objectives, the embodiments of this application provide the following technical solutions:

[0005] In a first aspect, a lidar imaging system is provided, comprising: a pixel array detector and a signal processor; the pixel array detector includes M×N pixel units, where M and N are integers; the pixel array detector is used to receive M×N reflected light signals from a target object; wherein the reflected light signals are light signals emitted by a light source and reflected back after illuminating the target object; the pixel array detector is also used to generate an electrical signal group based on the reflected light signals and a binary random template; wherein the binary random template includes M×N elements, and the M×N elements correspond one-to-one with the M×N pixel units; the M×N elements are used to randomly enable the M×N pixel units, generating random electrical signals, and the random electrical signals form an electrical signal group; the first part of the M×N elements takes a first value, and the second part of the elements takes a second value, the first value is used to enable the corresponding pixel unit, and the second value is used to disable the corresponding pixel unit; the electrical signal group includes the electrical signals generated by the pixel units corresponding to the first part of the elements; the signal processor is used to image the target object based on the electrical signal groups generated by different binary random templates and a compressed sensing algorithm.

[0006] Currently, to improve the efficiency of lidar imaging, a method using a DMD (Digital Micrometer Device) to generate random reflected light signals, which in turn produce random electrical signals, is proposed. Compressed sensing algorithms are then used to achieve imaging based on these random electrical signals. However, the high deployment cost of DMDs hinders practical applications. To address this, the laser imaging system proposed in this application uses a pixel array detector to convert light signals into electrical signals. Random electrical signals generated by each pixel unit in the pixel array detector are then used to achieve imaging using a compressed sensing algorithm. By randomly enabling the pixel array, the system acquires the random imaging template required for the compressed sensing algorithm, thus replacing the DMD device used in traditional methods, reducing system costs, and improving system integration.

[0007] In one possible implementation, the system further includes: a sampling controller for generating multiple binary random templates, each of which generates at least one group of electrical signals; and a signal processor specifically for using the multiple binary random templates as random sampling parameters, acquiring the arrival time of at least one group of electrical signals generated by each binary random template, counting the frequency of the arrival times as random sampling results, and imaging the target object using a compressed sensing algorithm based on the random sampling parameters and random sampling results.

[0008] This paper provides a specific method for controlling the random generation of electrical signals by a pixel array detector, as well as a specific method for achieving imaging upon receiving random electrical signals. Specifically, a sampling controller generates a binary random template to control the generation of electrical signals by the pixel units in the pixel array, thereby improving the efficiency of random electrical signal generation and ultimately enhancing the efficiency of imaging based on compressed sensing algorithms.

[0009] Secondly, a lidar imaging method is provided, comprising: acquiring multiple electrical signal groups, which are generated based on M×N reflected light signals of a target object and a binary random template; wherein, the reflected light signals are the light signals returned after the light signal emitted by the light source illuminates the target object, and the M×N reflected light signals are the light signals received by M×N pixel units respectively; the binary random template includes M×N elements, and the M×N elements correspond one-to-one with the M×N pixel units; the M×N elements are used to randomly enable the M×N pixel units to generate random electrical signals, and the random electrical signals form an electrical signal group; the first part of the M×N elements takes a first value, and the second part of the elements takes a second value, the first value is used to enable the corresponding pixel unit, and the second value is used to disable the corresponding pixel unit; the electrical signal group includes the electrical signals generated by the pixel units corresponding to the first part of the elements; and imaging the target object based on the electrical signal groups generated by different binary random templates and a compressed sensing algorithm.

[0010] The laser imaging method proposed in this application acquires the random imaging template required by the compressed sensing algorithm by randomly enabling pixel units, thereby replacing the DMD device used in traditional methods, reducing system cost, and helping to improve system integration.

[0011] In one possible implementation, before acquiring multiple groups of electrical signals, the method further includes: generating multiple binary random templates, each of the multiple binary random templates generating at least one group of electrical signals.

[0012] One possible implementation provides a specific method for controlling the pixel unit. By generating a binary random template, the pixel unit is controlled to generate electrical signals, thereby improving the efficiency of random electrical signal generation and thus improving the efficiency of imaging based on the compressed sensing algorithm.

[0013] In one possible implementation, imaging the target object based on multiple electrical signal groups and a compressed sensing algorithm includes: using multiple binary random templates as random sampling parameters, obtaining the arrival time of at least one electrical signal group generated by each binary random template, counting the frequency of the arrival time as the random sampling result, and using a compressed sensing algorithm to image the target object based on the random sampling parameters and the random sampling result.

[0014] In this possible implementation, the frequency of the time when the optical signal is converted into an electrical signal and received is used as the random sampling result, providing a specific implementation method for imaging based on compressed sensing algorithm.

[0015] In one possible implementation, the first value is specifically used to: control the corresponding pixel unit to convert the light signal into an electrical signal to enable the corresponding pixel unit; the second value is specifically used to: control the corresponding pixel unit not to convert the light signal into an electrical signal to disable the corresponding pixel unit; or, the first value is specifically used to control the electrical signal output terminal of the corresponding pixel unit to output an electrical signal to enable the corresponding pixel unit; the second value is specifically used to: control the electrical signal output terminal of the corresponding pixel unit not to output an electrical signal to disable the corresponding pixel unit.

[0016] This possible implementation provides two ways to generate random electrical signals: controlling the process of converting optical signals into electrical signals, or controlling the process of outputting electrical signals, thereby improving the feasibility of the solution.

[0017] Thirdly, an electronic device is provided, comprising: a functional unit for performing any of the methods provided in the second aspect, wherein the actions performed by each functional unit are implemented by hardware or by executing corresponding software through hardware. For example, the signal processor may include: an acquisition unit and an imaging unit; the acquisition unit is used to acquire multiple electrical signal groups, which are generated based on M×N reflected light signals of the target object and a binary random template; wherein, the reflected light signal is the light signal returned after the light signal emitted by the light source illuminates the target object, and the M×N reflected light signals are the light signals received by the M×N pixel units respectively; the binary random template includes M×N elements, and the M×N elements correspond one-to-one with the M×N pixel units; the M×N elements are used to randomly enable the M×N pixel units to generate random electrical signals, and the random electrical signals form an electrical signal group; the first part of the M×N elements takes a first value, and the second part of the elements takes a second value, the first value is used to enable the corresponding pixel unit, and the second value is used to disable the corresponding pixel unit; an electrical signal group includes an electrical signal generated by the target pixel unit based on the reflected light signal, and the target pixel unit is the pixel unit corresponding to the first value in the binary random template; the imaging unit is used to image the target object based on the electrical signal groups generated by different binary random templates and a compressed sensing algorithm.

[0018] Fourthly, an electronic device is provided, comprising: a processor and a memory. The processor is connected to the memory, the memory being used to store computer-executable instructions, and the processor executing the computer-executable instructions stored in the memory, thereby implementing any of the methods provided in the second aspect.

[0019] Fifthly, a chip is provided, comprising: a processor and an interface circuit; the interface circuit for receiving code instructions and transmitting them to the processor; and the processor for executing the code instructions to perform any of the methods provided in the second aspect.

[0020] In a sixth aspect, a computer-readable storage medium is provided, comprising computer-executable instructions that, when executed on a computer, cause the computer to perform any of the methods provided in the second aspect.

[0021] In a seventh aspect, a computer program product is provided, including computer execution instructions that, when executed on a computer, cause the computer to perform any of the methods provided in the second aspect.

[0022] The technical effects of any of the implementation methods in aspects three through seven can be found in the technical effects of the corresponding implementation methods in aspect two, and will not be repeated here. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of a lidar imaging system based on DMD for random sampling.

[0024] Figure 2 A schematic diagram of a lidar imaging system provided in an embodiment of this application;

[0025] Figure 3 A schematic diagram of a lidar imaging system provided in an embodiment of this application;

[0026] Figure 4 A hardware schematic diagram of an electronic device provided in an embodiment of this application;

[0027] Figure 5 A schematic flowchart of a lidar imaging method provided in an embodiment of this application;

[0028] Figure 6 A schematic diagram illustrating the correspondence between a binary random template and a pixel array detector, provided for an embodiment of this application;

[0029] Figure 7 A time difference histogram based on binary random template statistics is provided for embodiments of this application;

[0030] Figure 8 This is a schematic diagram of the composition of an electronic device provided in an embodiment of this application. Detailed Implementation

[0031] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0032] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0033] The basic principle of current lidar imaging methods is to detect the propagation time of light from emission to detection after reflection from the target object, and then calculate the distance of the light from emission to reception based on the propagation time and the speed of light, thereby measuring the distance between the device and the target object. Assuming the surface of the target object is different, the propagation time of light after reflection at different points on the target object will vary. Therefore, the distance calculated based on different light signals will be different, and imaging is achieved based on this distance. Alternatively, the light signal can be converted into an electrical signal, and imaging is based on this electrical signal.

[0034] It is understandable that if the distances between multiple target points on a target object and the measuring device can be measured, and these multiple target points can reflect the characteristics of the target object, then the imaging result is relatively accurate. However, measuring unknown target points on an unknown target object is more difficult, requiring multiple samplings and involving a large amount of computation. Therefore, the current approach proposes using compressed sensing algorithms to reconstruct the imaging result of the target object by sampling the target object a relatively small number of times. One of the important prerequisites for reconstructing the imaging result using compressed sensing algorithms is that the signal of the target object's imaging result is sparsity in a certain transform domain. By utilizing the sparsity of the signal in this transform domain, the compressed sensing algorithm can reconstruct the original signal in this transform domain (also known as the sparse domain). Another important prerequisite for reconstructing the imaging result using compressed sensing algorithms is that the few samplings of the target object are random. Specifically, this is achieved through subsampling techniques that sample at a sampling rate lower than the Nyquist frequency. For example, for a two-dimensional signal, a two-dimensional Gaussian random measurement sampling matrix is ​​used to subsample the image. For a one-dimensional signal, random unequal-interval subsampling is used.

[0035] Currently, to achieve lidar imaging based on compressed sensing algorithms, it is assumed that the signal of the target object's imaging result is sparse in a certain transform domain. Sampling is then performed using a binary random sampling template (hereinafter referred to as a binary random template). The obtained random sampling results are combined with the sparsity characteristics to reconstruct the signal of the target object's imaging result, thereby achieving fast imaging with relatively accurate results. Figure 1 As shown, to achieve random sampling, a control system is currently used to control the flip angle of each mirror in the DMD. By randomly adjusting the flip angle of each mirror in the DMD, partially reflected light is obtained, reflected by the DMD, and output, thus obtaining a random signal. The DMD is a reflective mirror device composed of many small aluminum reflective mirrors. Each mirror in this device can switch between two states of ±12°, thereby reflecting the light beam to different positions. Figure 1 In this process, a light source emits a light signal, a target object reflects the light signal, and lens 1 focuses the light signal reflected by the target object onto the DMD. The control system, based on the one-to-one correspondence between a binary random matrix and each lens in the DMD, controls the lenses in the DMD to randomly flip, so that some lenses are at +12° and the rest at -12°. Lens 2 then focuses the light signals reflected by the +12° lenses or the -12° lenses into a single light signal, which is output to an avalanche photodiode (APD) point detector. The APD point detector converts this light signal into an electrical signal. Further, signal processing uses this electrical signal and the binary random matrix generated by the control system to reconstruct the image based on a compressed sensing algorithm, thus achieving imaging.

[0036] Because DMDs are costly to deploy and bulky, which is not conducive to miniaturization and integration; in addition, an additional optical path alignment device is required before imaging processing so that the reflected light signals can be combined into one output to the APD single-point detector to achieve photoelectric conversion. Therefore, current lidar imaging systems are highly complex and not conducive to practical applications.

[0037] In response, this application provides a lidar imaging system. For example... Figure 2 As shown, the lidar imaging system 20 includes at least a pixel array detector 201 and a signal processor 202.

[0038] The pixel array detector 201 is a photodetector used to receive optical signals and convert them into electrical signals for output. The pixel array detector comprises M×N pixel units, where M and N are both integers. For example, Figure 2 The pixel array detector 201 shown includes 8×8 pixel units, or 64 pixel units, wherein each pixel unit can receive an optical signal and output an electrical signal under a given voltage.

[0039] The signal processor 202 is used to receive the electrical signals output by the pixel array detector and perform data processing.

[0040] In addition, the system may also include a sampling controller 203. The sampling controller 203 enables a pixel unit to output an electrical signal, and disables a pixel unit to prevent it from outputting an electrical signal. The sampling controller 203 can control the pixel units in several ways. One implementation involves controlling the process of converting a light signal into an electrical signal. For example, the sampling controller controls the quenching circuit of a pixel unit to control this conversion. When a voltage is applied to the quenching circuit of a pixel unit, the pixel unit converts the light signal into an electrical signal and outputs it; when the quenching circuit does not apply voltage, the pixel unit cannot convert the light signal into an electrical signal and therefore cannot output an electrical signal. Another implementation involves controlling the electrical signal output terminal of each pixel unit. For example, a logic gate can be configured on the electrical signal output terminal of each pixel unit, and the sampling controller controls the output of the pixel unit's electrical signal by controlling the logic gate of each pixel unit. Figure 2 As shown, the pixel units corresponding to the blank parts are the pixel units that output electrical signals, and the pixel units corresponding to the shaded parts are the pixel units that do not output electrical signals.

[0041] It should be noted that the above-described method of controlling the output electrical signal of the pixel unit by the sampling controller is only an example, and other methods may also be included, which are not limited in this application.

[0042] In addition, the lidar imaging system 20 may also include a light source 204 and a lens 205, such as Figure 3 As shown.

[0043] Light source 204 refers to an object capable of emitting electromagnetic waves within a certain wavelength range. A laser light source is a special light source capable of emitting light using excited-state particles under stimulated emission. A device that emits laser light can be called a laser. Among them, a device capable of emitting pulsed laser light can be called a pulsed laser. In the embodiments of this application, multiple pulsed lasers emitted by a pulsed laser are used to calculate the distance between the reflected light signal of each pulsed laser and the target object, thereby achieving imaging via lidar.

[0044] Lens 205 is used to focus the light reflected from the target object onto pixel array detector 201.

[0045] The target object to be tested refers to the object to be tested, which can also be understood as the target object mentioned above.

[0046] It should be noted that the aforementioned lidar imaging system may include more or fewer devices, and this application does not impose any limitations on this. The aforementioned signal processor 202 and sampling controller 203 can be integrated into an electronic device.

[0047] In terms of hardware implementation, the imaging method based on the aforementioned lidar imaging system can be achieved through methods such as... Figure 4 The computer device shown is implemented as follows. Figure 4 The diagram shown is a hardware structure schematic of a computer device 40 provided in an embodiment of this application. The computer device 40 can be used to implement the functions of the signal processor and / or sampling controller described above.

[0048] Figure 4 The computer device 40 shown may include a processor 401, a memory 402, a communication interface 403, and a bus 404. The processor 401, the memory 402, and the communication interface 403 can be connected via the bus 404.

[0049] The processor 401 is the control center of the computer device 40. It can be a general-purpose central processing unit (CPU) or other general-purpose processors. The general-purpose processor can be a microprocessor or any conventional processor.

[0050] As an example, processor 401 may include one or more CPUs, for example Figure 4 CPU 0 and CPU 1 are shown in the diagram.

[0051] The memory 402 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0052] In one possible implementation, the memory 402 can exist independently of the processor 401. The memory 402 can be connected to the processor 401 via a bus 404 and is used to store data, instructions, or program code. When the processor 401 calls and executes the instructions or program code stored in the memory 402, it can implement the lidar imaging method provided in the embodiments of this application.

[0053] In another possible implementation, the memory 402 can also be integrated with the processor 401.

[0054] Communication interface 403 is used for connecting computer device 40 to other devices via a communication network, which may be Ethernet, radio access network (RAN), wireless local area network (WLAN), etc. Communication interface 403 may include a receiving unit for receiving data and a transmitting unit for transmitting data.

[0055] Bus 404 can be an industry standard architecture (ISA) bus, a peripheral component interconnect (PCI) bus, or an extended industry standard architecture (EISA) bus. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0056] It should be pointed out that, Figure 4 The structure shown does not constitute a limitation on computer device 40, except... Figure 4 In addition to the components shown, computer device 40 may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0057] This application does not limit the specific form of the computer device. For example, the computer device can specifically be an electronic device. The electronic device can be referred to as: terminal, user equipment (UE), terminal device, wireless communication device, or control device, etc. Specifically, the electronic device can be a mobile phone, monitor, augmented reality (AR) device, virtual reality (VR) device, tablet computer, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), etc.

[0058] like Figure 5The diagram shows a flowchart of a lidar imaging method provided in this application. The method is applied to electronic devices and includes steps S501-S502.

[0059] S501. The electronic device acquires multiple electrical signal groups, which are generated based on M×N reflected light signals of the target object and a binary random template. The M×N reflected light signals are the light signals received by the M×N pixel units respectively. The binary random template includes M×N elements, which correspond one-to-one with the M×N pixel units. The M×N elements are used to randomly enable the M×N pixel units, generating random electrical signals, which form electrical signal groups. The first part of the M×N elements takes a first value, and the second part takes a second value. The first value is used to enable the corresponding pixel unit, and the second value is used to disable the corresponding pixel unit. The electrical signal groups contain the electrical signals generated by the pixel units corresponding to the first part of the elements.

[0060] Among them, the reflected light signal is the light signal that returns after the light signal emitted by the light source shines on the target object.

[0061] Specifically, the M×N pixel units refer to the pixel array detector in the aforementioned lidar imaging system. In other words, the M×N pixel units in the pixel array detector receive M×N reflected light signals from the target object. Here, M and N are both integers. Each pixel unit receives one reflected light signal.

[0062] Optionally, if the lidar imaging system includes a light source, the method further includes, before step S501, the method comprising: an electronic device controlling the light source to emit a light signal.

[0063] It is understandable that the light signal is used to illuminate the target object, so that the light signal illuminating the target object is reflected to obtain a reflected light signal.

[0064] In this process, the electronic device enables the corresponding pixel unit randomly based on the values ​​of the elements in the binary random template, and obtains a group of electrical signals composed of random electrical signals.

[0065] Optionally, the binary random template can be an M×N matrix containing M×N elements.

[0066] Understandably, imaging based on compressed sensing algorithms requires random sampling of the target object. The electrical signal set generated based on each binary random template can be considered as a random sampling of the target object. Electronic devices generate binary random templates and determine whether to enable the corresponding pixel units based on the value of each element in the binary random template, so that some pixel units output electrical signals while others do not, thereby achieving the purpose of randomly generating electrical signals.

[0067] Optionally, the electronic device can implement randomly enabled pixel units based on binary random templates by randomly enabling the conversion of photoelectric signals of the pixel units or controlling the electrical signal output of the pixel units, including the following two possible implementation methods.

[0068] In one possible implementation, the first value is specifically used to control the corresponding pixel unit to convert the light signal into an electrical signal so as to enable the corresponding pixel unit, and the second value is specifically used to control the corresponding pixel unit not to convert the light signal into an electrical signal so as to disable the corresponding pixel unit.

[0069] In another possible implementation, the first value is specifically used to control the electrical signal output terminal of the corresponding pixel unit to output an electrical signal to enable the corresponding pixel unit, and the second value is specifically used to control the electrical signal output terminal of the corresponding pixel unit not to output an electrical signal to disable the corresponding pixel unit.

[0070] The two implementation methods described above correspond to the specific implementation methods of the sampling controller 203 controlling the pixel unit, and will not be repeated here.

[0071] For example, such as Figure 6 The diagram illustrates the correspondence between a binary random template and a pixel array detector. It assumes the first value is 1 and the second value is 0. When an element is 1, the corresponding pixel unit can convert the received reflected light signal into an electrical signal and output it; this pixel unit is represented by a blank space. When an element is 0, the corresponding pixel unit cannot output an electrical signal; this pixel unit is represented by a shaded area. Therefore, in... Figure 6 In the first row, the elements are {1,0,1,0,0,1,0,1}, which correspond one-to-one with the pixel units in the first row of the pixel array detector.

[0072] It is understandable that the M×N elements are randomly selected to randomly enable the corresponding pixel units. There is no limitation on the values ​​of the M×N elements, which may enable some pixel units or enable all pixel units.

[0073] S502, the electronic device images the target object based on electrical signal groups generated by different binary random templates and compressed sensing algorithms.

[0074] It is understandable that the current premise for reconstructing the imaging signal of the target object based on compressed sensing algorithms is to randomly sample the imaging signal of the target object and obtain random sampling results. The multiple electrical signal groups generated based on multiple binary random templates mentioned above are the random sampling results used in the compressed sensing algorithm after multiple random samplings.

[0075] Optionally, before step S501, the method further includes: the electronic device generating multiple binary random templates, each of which generates at least one group of electrical signals. This application does not limit the generation method of the multiple binary random templates. It is understood that the process of generating one group of electrical signals based on one binary random template for one optical signal is a single random sampling based on one binary random template; if the imaging result of the target object is to be reconstructed based on a compressed sensing algorithm, multiple random samplings of the target object based on multiple binary random templates are required.

[0076] Optionally, the number of binary random templates generated can be determined based on the number of pixel units in the pixel array detector. For example, when the pixel array detector contains 8×8, or 64 pixel units, the number of random samplings can be 10% to 30% of the number of pixel units, meaning 6 to 19 binary random templates need to be generated. This application does not limit the number of binary random templates generated.

[0077] Optionally, for a fixed pixel array detector, the matrices corresponding to the multiple binary random templates are fixed values. It is understandable that when the number of pixel units is fixed, these multiple binary random templates can also be fixed values. In this way, random sampling based on a fixed set of templates for different optical signals helps save processing resources in electronic devices.

[0078] Optionally, when the binary random template is a matrix, the matrix corresponding to multiple binary random templates is a preset matrix.

[0079] Optionally, the electronic device uses multiple binary random templates as random sampling parameters, acquires the arrival time of at least one group of electrical signals generated by each binary random template, and counts the frequency of occurrence of the time as the random sampling result. Based on the random sampling parameters and the random sampling result, the target object is imaged using a compressed sensing algorithm. Specifically, after the electronic device acquires multiple groups of electrical signals, the imaging of the target object based on the compressed sensing algorithm includes the following steps S11-S14.

[0080] S11. The electronic device counts the arrival time of each electrical signal in each group of electrical signals collected by each binary random template.

[0081] S12. The electronic device determines the time difference based on the arrival time of each electrical signal. This time difference is used to characterize the time from transmission to reception of the optical signal.

[0082] Specifically, electronic devices include a time-to-digital converter module used to convert the time difference of received electrical signals into digital values. For example, a time-to-digital converter (TDC).

[0083] It is understandable that the time difference mentioned above includes the time for the optical signal to be converted into an electrical signal. Since optical signals need to be converted into electrical signals before processing, this time can be ignored.

[0084] S13. The electronic device counts the frequency of occurrence of the time difference measured for each binary random template.

[0085] It is understandable that different light signals arrive at the pixel array detector at different times after reflection. Therefore, the time difference measured by the electrical signal output based on the binary random template is also different. The time difference of these multiple electrical signals is statistically analyzed and the frequency of occurrence is recorded.

[0086] It should be noted that, considering light scattering in the environment and the influence of other light sources, it is generally helpful to improve the accuracy of random sampling results by emitting multiple light signals based on a binary random template for statistical analysis. For example, 100 light signals are emitted for each binary random template, with each signal emitted at the same time interval (e.g., 1 millisecond). The pixel array detector, based on the emission time of each light signal, counts the reflected light signals within a preset time period (e.g., 0.5 milliseconds), calculates the time difference of the reflected light signals arriving within the preset time period, and performs statistical analysis. It is understood that setting the aforementioned preset time period helps to filter out more accurate reflected light signals from the received light signals, that is, assuming that the reflected light signals within 0.5 milliseconds after the emitted light signal are the reflected light signals of that light signal, rather than other light signals mixed with environmental factors.

[0087] Optionally, the frequency of time differences can be counted in the histogram. Specifically, the binary random template can be set to P... i This indicates that the time difference measured in each binary random template is expressed in terms of t. k If f represents f, then i (t k ) represents the time difference t in the i-th template. k The frequency of occurrence of . The maximum value of i is the number of binary random templates. For example, if there are p binary random templates, then i = 1, 2, ..., p. The maximum value of k is the number of time differences. For example, Figure 7 The figure shows a time difference histogram based on binary random template statistics. It represents multiple time differences calculated from the emission of the optical signal to the reception of the pixel array detector, including 10 microseconds, 20 microseconds, 30 microseconds, and 40 microseconds. That is, when k takes values ​​of 1, 2, 3, and 4, t... k The frequencies are 10, 20, 30, and 40, respectively. Among them, 10 microseconds occur 12 times, 20 microseconds occur 25 times, 30 microseconds occur 45 times, and 40 microseconds occur 18 times, i.e., f(t1) = 12, f(t2) = 25, f(t3) = 45, and f(t4) = 18.

[0088] S14. The electronic device uses the frequency of time difference occurrence as the processed random sampling result, and combines it with the corresponding binary random template to perform imaging of the target object based on the compressed sensing algorithm.

[0089] For example, the set of occurrence frequencies of time differences based on multiple binary random templates can be represented as follows:

[0090] F(t k )=[f1(t k ),f2(t k ),f3(t k ), ..., f p (t k )]

[0091] When using compressed sensing algorithm to calculate the imaging results of the target object, it is assumed that each time difference t k The wavefront distribution generated by the object at the corresponding distance is w k (x, y), and assume that this distribution is sparse in a certain transform domain (such as in the discrete Fourier transform basis, wavelet transform basis, etc.), that is, w k (x, y) = Bs k Where B is the basis matrix, s k is the sparsity coefficient under this basis.

[0092] At this time, regarding w k When performing random sampling on (x, y) and obtaining the random sampling result, the following formula should be satisfied:

[0093] f i (t k ) = P i ·w k (x, y) = P i ·Bs k =θ i s k ;

[0094] Where, θ i This is the sensing matrix.

[0095] Here, "·" indicates element-wise matrix multiplication, θ i =P i B is a row vector with N = m × n elements.

[0096] Then, if F(t) k )=[f1(t k ), f2(t k ), ..., f p (t k )],Θ=[θ1,θ2， ..., θ p Then, the result of the measurement based on p binary random templates can be expressed as:

[0097] F(t k )=Θs k ;

[0098] Although the number of rows in matrix Θ is less than the number of columns (p < N), resulting in the above equation being an underdetermined equation with fewer equations than unknowns, due to s k Sparsity can be addressed by minimizing the l1 norm, which is the sum of the absolute values ​​of the elements in a vector.

[0099] All of the above steps S11-S14 can be executed by the signal processor 202 described above.

[0100] The above method enables a random imaging template required by the compressed sensing algorithm by randomly enabling the pixel array, thereby replacing the DMD device used in traditional methods. Since the pixel array detector can directly output electrical signals, additional optical path alignment operations are no longer required, reducing system costs and helping to improve system integration.

[0101] The foregoing primarily describes the solutions of the embodiments of this application from a methodological perspective. It is understood that, in order to achieve the aforementioned functions, the electronic device includes at least one of the hardware structures and software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0102] This application embodiment can divide the electronic device into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0103] When dividing each function into modules according to its corresponding function. Figure 8 A schematic diagram of a possible structure of the electronic device involved in the above embodiments is shown. For example... Figure 8 As shown, the electronic device 80 includes an acquisition unit 801 and a processing unit 802.

[0104] The acquisition unit 801 acquires multiple electrical signal groups, which are generated based on M×N reflected light signals of the target object and a binary random template. The reflected light signals are the light signals emitted by the light source and reflected back after illuminating the target object. The M×N reflected light signals are the light signals received by the M×N pixel units respectively. The binary random template includes M×N elements, each corresponding to one of the M×N pixel units. The M×N elements are used to randomly enable the M×N pixel units, generating random electrical signals, which form the electrical signal group. The first part of the M×N elements takes a first value, and the second part takes a second value. The first value enables the corresponding pixel unit, and the second value disables the corresponding pixel unit. The electrical signal group contains the electrical signals generated by the pixel units corresponding to the first part of the elements, as described in step S501 above.

[0105] The processing unit 802 is used to image the target object based on the electrical signal groups generated by different binary random templates and the compressed sensing algorithm, such as in step S502 above.

[0106] In one example, the electronic device 80 further includes a generation unit 803 for generating a plurality of binary random templates, each of the plurality of binary random templates generating at least one group of electrical signals.

[0107] In one example, the processing unit 802 is specifically used to: use multiple binary random templates as random sampling parameters, obtain the arrival time of at least one group of electrical signals generated by each binary random template, count the frequency of the arrival time as the random sampling result, and image the target object using a compressed sensing algorithm based on the random sampling parameters and the random sampling result.

[0108] In one example, the first value is specifically used to: control the corresponding pixel unit to convert the light signal into an electrical signal to enable the corresponding pixel unit; the second value is specifically used to: control the corresponding pixel unit not to convert the light signal into an electrical signal to disable the corresponding pixel unit; or, the first value is specifically used to control the electrical signal output terminal of the corresponding pixel unit to output an electrical signal to enable the corresponding pixel unit; the second value is specifically used to: control the electrical signal output terminal of the corresponding pixel unit not to output an electrical signal to disable the corresponding pixel unit.

[0109] All relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0110] Of course, the electronic devices provided in this application embodiment include, but are not limited to, the above-described units. For example, the electronic device may also include a storage unit 804.

[0111] Storage unit 804 can be used to store the program code and data of the video decoding device.

[0112] The acquisition unit 801 and processing unit 802 in the electronic device 80 can be executed accordingly. Figure 2 or Figure 3 The signal processor 202 functions, and the generation unit 803 can execute the corresponding functions. Figure 3 Functions of the sampling controller 203.

[0113] This application also provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to perform the methods executed by any of the electronic devices described above.

[0114] For explanations of the relevant content and descriptions of the beneficial effects in any of the computer-readable storage media provided above, please refer to the corresponding embodiments described above, which will not be repeated here.

[0115] This application also provides a chip. This chip integrates a control circuit for implementing the functions of the aforementioned lidar imaging system and one or more ports. Optionally, the functions supported by this chip can be referred to above, and will not be repeated here. Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium. The aforementioned storage medium can be a read-only memory, random access memory, etc. The aforementioned processing unit or processor can be a central processing unit, a general-purpose processor, an application-specific integrated circuit (ASIC), a microprocessor (digital signal processor, DSP), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof.

[0116] This application also provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform any of the methods described in the above embodiments. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or may include one or more data storage devices such as servers or data centers that can be integrated with the medium. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), etc.

[0117] It should be noted that the devices for storing computer instructions or computer programs provided in the embodiments of this application, such as but not limited to the memory, computer-readable storage medium and communication chip, are all non-transitory.

[0118] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks, SSDs).

[0119] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, the disclosure, and the appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0120] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A lidar imaging system, characterized in that, It includes a pixel array detector and a signal processor; the pixel array detector includes M×N pixel units, where M and N are both integers; The pixel array detector is used to receive M×N reflected light signals from the target object; wherein, the reflected light signals are light signals emitted by the light source at the same preset interval and returned after illuminating the target object; For each light signal emitted by the light source, the pixel array detector is specifically used to determine that the reflected light signal received within a preset time after the light signal is emitted is the reflected light signal corresponding to the light signal; the preset time is less than the preset interval; The pixel array detector is further configured to generate an electrical signal group based on the reflected light signal and a binary random template; wherein the binary random template comprises M×N elements, each of which corresponds one-to-one with one of the M×N pixel units; the M×N elements are used to randomly enable the M×N pixel units, generating random electrical signals, which constitute the electrical signal group; the first part of the M×N elements takes a first value, and the second part takes a second value, the first value being used to enable the corresponding pixel unit, and the second value being used to disable the corresponding pixel unit, wherein the enabled pixel unit can convert the light signal into an electrical signal, and the disabled pixel unit cannot convert the light signal into an electrical signal; the electrical signal group includes the electrical signals generated by the pixel units corresponding to the first part of the elements. The signal processor is configured to use multiple binary random templates as random sampling parameters, obtain the arrival time of at least one group of electrical signals generated by each binary random template, count the frequency of occurrence of the arrival time as the random sampling result, and image the target object using a compressed sensing algorithm based on the random sampling parameters and the random sampling result.

2. The system according to claim 1, characterized in that, The system also includes: A sampling controller is configured to generate a plurality of said binary random templates, each of said binary random templates generating at least one of said electrical signal groups.

3. A lidar imaging method, characterized in that, include: Multiple electrical signal groups are acquired, each group being generated based on M×N reflected light signals from a target object and a binary random template. The reflected light signals are light signals emitted by a light source at equal preset intervals and reflected back after illuminating the target object. The M×N reflected light signals are light signals received by M×N pixel units. The binary random template includes M×N elements, each corresponding one-to-one with one of the M×N pixel units. These M×N elements are used to randomly enable the M×N pixel units, generating random electrical signals that form the electrical signal groups. A first portion of the M×N elements takes a first value, and a second portion takes a second value. The first value enables the corresponding pixel unit, and the second value disables it. The electrical signal groups contain the electrical signals generated by the pixel units corresponding to the first portion of the elements. For each light signal emitted by the light source, the reflected light signal received within a preset time after the light signal is emitted is determined to be the reflected light signal corresponding to the light signal; the preset time is less than the preset interval. The first value is specifically used to: control the corresponding pixel unit to convert the optical signal into an electrical signal, so as to enable the corresponding pixel unit; The second value is specifically used to: control the corresponding pixel unit to not convert the light signal into an electrical signal, so as to disable the corresponding pixel unit; or, The first value is specifically used to control the electrical signal output terminal of the corresponding pixel unit to output an electrical signal, so as to enable the corresponding pixel unit; The second value is specifically used to: control the electrical signal output terminal of the corresponding pixel unit to not output an electrical signal, so as to disable the corresponding pixel unit; Multiple binary random templates are used as random sampling parameters. The arrival time of at least one group of electrical signals generated by each binary random template is obtained. The frequency of occurrence of the arrival time is counted as the random sampling result. Based on the random sampling parameters and the random sampling result, the target object is imaged using a compressed sensing algorithm.

4. The method according to claim 3, characterized in that, Prior to acquiring multiple groups of electrical signals, the method further includes: A plurality of binary random templates are generated, and each of the plurality of binary random templates generates at least one of the electrical signal groups.

5. An electronic device, characterized in that, include: An acquisition unit is used to acquire multiple electrical signal groups, which are generated based on M×N reflected light signals of a target object and a binary random template. The reflected light signals are light signals emitted by a light source at equal preset intervals and reflected back after illuminating the target object. The M×N reflected light signals are light signals received by M×N pixel units respectively. The binary random template includes M×N elements, each corresponding one-to-one with one of the M×N pixel units. The M×N elements are used to randomly enable the M×N pixel units, generating random electrical signals, which form the electrical signal groups. A first portion of the M×N elements takes a first value, and a second portion takes a second value. The first value enables the corresponding pixel unit, and the second value disables the corresponding pixel unit. The electrical signal groups contain the electrical signals generated by the pixel units corresponding to the first portion of the elements. The acquisition unit is further configured to, for each light signal emitted by the light source, determine that the reflected light signal received within a preset time after the light signal is emitted is the reflected light signal corresponding to the light signal; the preset time is less than the preset interval; The first value is specifically used to: control the corresponding pixel unit to convert the optical signal into an electrical signal, so as to enable the corresponding pixel unit; The second value is specifically used to: control the corresponding pixel unit to not convert the light signal into an electrical signal, so as to disable the corresponding pixel unit; or, The first value is specifically used to control the electrical signal output terminal of the corresponding pixel unit to output an electrical signal, so as to enable the corresponding pixel unit; The second value is specifically used to: control the electrical signal output terminal of the corresponding pixel unit to not output an electrical signal, so as to disable the corresponding pixel unit; An imaging unit is used to use multiple binary random templates as random sampling parameters, obtain the arrival time of at least one group of electrical signals generated by each binary random template, count the frequency of the arrival times as random sampling results, and image the target object using a compressed sensing algorithm based on the random sampling parameters and the random sampling results.

6. The electronic device according to claim 5, characterized in that, The electronic device also includes: The generation unit is used to generate a plurality of said binary random templates, each of the plurality of binary random templates generating at least one of said electrical signal groups.

7. An electronic device, characterized in that, It includes a memory and a processor; the memory is used to store program code; the processor is used to invoke the program code to perform the method as described in any one of claims 3-4.

8. A computer-readable storage medium, characterized in that, Includes program code that, when run on a computer or processor, causes the computer or processor to perform the method as described in any one of claims 3-4.

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