A Laser Ranging Method and Laser Ranging Chip for Reducing Glare

Abstract

The present invention discloses a laser ranging method and laser ranging chip for reducing glare. The method includes: obtaining a photon signal reflected by a target object and constructing a histogram based on the photon signal; obtaining a histogram of each pixel, calculating the reflectivity of each pixel based on the histogram, and generating a first vector; calculating the value of the time bin corresponding to each pixel peak to generate a second vector set, the second vector set including multiple subvectors; performing a similarity calculation between the first vector and each subvector of the second vector set, obtaining the second subvector corresponding to the value with the smallest similarity, and recording the time bin corresponding to the second subvector as the target time; and generating the target distance of the object based on the target time. In an embodiment of the present invention, by calculating the similarity between the pixel reflectivity and the time bin corresponding to the pixel peak, the time bin corresponding to the pixel peak with the smallest similarity is used as the target time to calculate the distance to the target object, effectively reducing the multi-peak phenomenon caused by glare and improving ranging accuracy.

Description

Technical Field

[0001] The present invention relates to the field of distance measurement technology, and in particular to a laser distance measurement method and a laser distance measurement chip for reducing glare. Background Art

[0002] LiDAR calculates the distance to an object by measuring the flight time of a light beam in space. Due to its advantages such as high precision and large measurement range, it is widely used in consumer electronics, autonomous driving, remote sensing, AR / VR and other fields.

[0003] In the laser radar ranging method, SPAD (single photon avalanche diode) is usually used to receive the returned light signal. The ranging is based on the TCSPC (Time Correlated Single Photon Counting) method, which is a dTOF (DirectTOF) ranging method.

[0004] During the ranging process, LiDAR needs to form a histogram and then perform signal processing on the histogram to obtain target information. However, due to interference such as glare, reflections from the module and cover, multiple peaks will be introduced in the histogram during the ranging process of some pixels. In the case of single peaks, the common method is to select the peak with the highest signal-to-noise ratio as the target distance information. However, if multiple peaks appear and the signal-to-noise ratio of the peak corresponding to the target is weak, it will lead to errors in distance perception and measurement.

[0005] In the prior art, when the laser radar is measuring distance and the received photon signal has multiple peaks, an image filtering method is used. However, the filtering effect is poor, and distance calculation errors are more likely to occur, resulting in large distance measurement errors.

[0006] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0007] In view of the above-mentioned deficiencies in the prior art, the present invention provides a laser ranging method and a laser ranging chip that reduce glare, aiming to solve the problem in the prior art that when the laser radar is measuring distance, the received photon signal has multiple peaks and the image filtering method is used for the measurement. However, the filtering effect is poor, and distance calculation errors are more likely to occur, resulting in large distance measurement errors.

[0008] The technical solutions of the present invention are as follows:

[0009] A first embodiment of the present invention provides a laser ranging method for reducing glare, the method comprising:

[0010] Obtain the photon signal reflected by the target object and construct a histogram based on the photon signal;

[0011] Obtaining a histogram of each pixel, calculating the reflectivity of each pixel according to the histogram, and generating a first vector;

[0012] Calculating a value of a time bin corresponding to each pixel peak to generate a second vector set, where the second vector set includes a plurality of sub-vectors;

[0013] Calculate the similarity between the first vector and each subvector of the second vector set, obtain the second subvector corresponding to the minimum similarity value, and record the time bin corresponding to the second subvector as the target time;

[0014] Generates the target distance of the object based on the target time.

[0015] Furthermore, the step of obtaining a histogram of each pixel, calculating the reflectivity of each pixel according to the histogram, and generating a first vector includes:

[0016] Obtain a histogram corresponding to each pixel SPAD in the irradiated area, wherein the histogram is a histogram after noise removal;

[0017] The photon counts of the histogram of each pixel are superimposed to obtain the reflectivity corresponding to each pixel and generate a first vector.

[0018] Furthermore, the value of the time bin corresponding to each pixel peak is calculated to generate a second vector set, and the second vector set includes multiple sub-vectors, including:

[0019] Parse each histogram to obtain all pixel data, including m unimodal pixels and n multimodal pixels, where each multimodal pixel corresponds to values of at least two time bins;

[0020] Calculate the value of each pixel peak corresponding to the time bin and generate a second vector set, which includes at least 2 n sub-vector sets, each of which includes the values of all pixel peaks corresponding to the time bin.

[0021] Furthermore, the positions of the internal elements of the first vector correspond one-to-one to the positions of the internal elements of each sub-vector in the second vector set.

[0022] Furthermore, the calculation of the value of each pixel peak corresponding to the time bin generates a second vector set, and the second vector set includes multiple sub-vectors and further includes:

[0023] Obtaining preset partitions, performing exposure according to the preset partitions, and calculating similarity between the first vector and each sub-vector of the second vector set in each partition;

[0024] A second sub-vector corresponding to the value with the minimum similarity is obtained, and a time bin corresponding to the second sub-vector is recorded as a target time of each partition.

[0025] Furthermore, the calculating of similarity between the first vector and each sub-vector of the second vector set includes:

[0026] A similarity calculation is performed on each sub-vector of the first vector and the second vector set based on a preset similarity formula, wherein the preset similarity formula is a cosine distance calculation formula or a Pearson correlation coefficient calculation formula.

[0027] Another embodiment of the present invention provides a laser ranging chip for reducing glare, the chip comprising:

[0028] The receiving module is used to obtain the photon signal reflected by the target object.

[0029] A storage module, for constructing a histogram based on the photon signal;

[0030] The controller is configured to obtain a histogram of each pixel, calculate the reflectivity of each pixel based on the histogram, and generate a first vector; calculate the value of a time bin corresponding to a peak value of each pixel to generate a second vector set, wherein the second vector set includes multiple subvectors; perform similarity calculations on the first vector and each subvector of the second vector set, obtain a second subvector corresponding to the value with the minimum similarity, and record the time bin corresponding to the second subvector as a target time; and generate a target distance of the object based on the target time.

[0031] Furthermore, the main controller is specifically used for:

[0032] Obtain a histogram corresponding to each pixel SPAD in the irradiated area, wherein the histogram is a histogram after noise removal;

[0033] The photon counts of the histogram of each pixel are superimposed to obtain the reflectivity corresponding to each pixel and generate a first vector.

[0034] Furthermore, the main controller is also used for:

[0035] Parse each histogram to obtain all pixel data, including m unimodal pixels and n multimodal pixels, where each multimodal pixel corresponds to values of at least two time bins;

[0036] Calculate the value of each pixel peak corresponding to the time bin and generate a second vector set, which includes at least 2 n sub-vector sets, each of which includes the values of all pixel peaks corresponding to the time bin.

[0037] Furthermore, the positions of the internal elements of the first vector correspond one-to-one to the positions of the internal elements of each sub-vector in the second vector set.

[0038] Beneficial effect: The embodiment of the present invention obtains the photon signal reflected by the target object, calculates the similarity between the reflectivity of the pixel and the value of the time bin corresponding to the pixel peak, takes the time bin corresponding to the pixel peak with the smallest similarity as the target time, and calculates the distance of the target object, which can effectively reduce the multi-peak phenomenon caused by glare and improve the ranging accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0040] Figure 1 A flow chart of a preferred embodiment of a laser ranging method of the present invention;

[0041] Figure 2 The figure is a hardware structure diagram of a preferred embodiment of a laser ranging chip of the present invention. DETAILED DESCRIPTION

[0042] To make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0043] The embodiments of the present invention are described below with reference to the accompanying drawings.

[0044] To address the above issues, an embodiment of the present invention provides a laser ranging method for reducing glare. Figure 1 , Figure 1 This is a flow chart of a preferred embodiment of a laser ranging method for reducing glare according to the present invention. Figure 1 As shown, it includes:

[0045] Step S100: Acquire a photon signal reflected by a target object, and construct a histogram based on the photon signal;

[0046] Step S200: Obtain a histogram of each pixel, calculate the reflectivity of each pixel according to the histogram, and generate a first vector;

[0047] Step S300: Calculate the value of each pixel peak corresponding to the time bin to generate a second vector set, where the second vector set includes multiple sub-vectors;

[0048] Step S400: Calculate the similarity between the first vector and each sub-vector of the second vector set, obtain the second sub-vector corresponding to the value with the minimum similarity, and record the time bin corresponding to the second sub-vector as the target time;

[0049] Step S500: Generate a target distance of the object according to the target time.

[0050] In specific implementation, the ranging method of the embodiment of the present invention can be used to solve the multi-peak phenomenon that is not on the same ray path, that is, on the same pixel, target reflection signals from multiple paths are received, and the target reflection signal may be caused by multiple reflection signals in the lens.

[0051] The embodiment of the present invention performs ranging based on a direct time-of-flight measurement method. The laser emission module includes but is not limited to one of an EEL, a VCSEL, and a picosecond laser. SPAD is used as a receiving module, wherein the EEL is an edge-emitting laser, and the light emitted by the edge-emitting laser is emitted parallel to the substrate surface, and the VCSEL is a surface-emitting laser, and the light emitting direction of the surface-emitting laser is perpendicular to the substrate surface. The SPAD ARRAY operates in Geiger mode. In theory, SPAD can realize single-photon detection with the highest detection sensitivity.

[0052] The transmitting module emits a pulsed laser toward the target object, obtains the photon signal reflected back by the target object through the receiving module, and constructs a histogram based on the photon signal.

[0053] No matter how much flare interference there is, the intensity reflectivity value after the interference does not change much from the intensity value before the interference, so the intensity value can be used as a reference for comparison. The bin close to the intensity value is the signal peak, and the bin with a large difference from the intensity value is caused by flare and should be eliminated.

[0054] Different pixels correspond to different histograms. The histogram of each pixel is obtained, and the reflectivity of each pixel is calculated according to the histogram to generate a first vector.

[0055] Obtain the value of each pixel peak corresponding to the time bin, and generate a second vector set based on the value of each pixel peak corresponding to the time bin. The second vector set contains multiple sub-vectors, and each sub-vector has the same vector dimension as the first vector.

[0056] Obtain each subvector in the second vector set, calculate the similarity between each subvector and the first vector, obtain the second subvector corresponding to the value with the minimum similarity, record the time bin corresponding to the second subvector as the target time, and calculate the target distance of the object based on the target time.

[0057] By comparing the distribution trends of the elements within two vectors, for example, the first vector is x(I1,...I21) and the second vector is y1(T1,...T21)...y64(T1,...T21), although x is the reflectance value and y is the bin value, the two values are different. However, the similarity formula can be used to compare the internal distributions of x and y. If the distributions are consistent, the similarity value of x and y is the smallest, and one of the 64 y values is selected as the target time.

[0058] The target distance is recorded as d, and the value of the time bin with the minimum similarity is recorded as t, then d = ct / 2, where c is the speed of light, which is generally recorded as 3*10 8 m / s.

[0059] In one embodiment, obtaining a histogram of each pixel, calculating the reflectivity of each pixel according to the histogram, and generating a first vector includes:

[0060] Obtain the histogram corresponding to each pixel SPAD in the irradiated area. The histogram is the histogram after removing noise.

[0061] The photon counts of the histogram of each pixel are superimposed to obtain the reflectivity corresponding to each pixel and generate a first vector.

[0062] In the specific implementation, the intensity method is used to deal with the multi-peak problem inside the irradiation area, as follows: the reflectivity is calculated using the hist distribution of each pixel inside the irradiation area

[0063] intensity(x,y)=sum(hist(x,y))-sum(noise(x,y)).

[0064] Thus, a histogram corresponding to each SPAD pixel in the illumination area is obtained, and this histogram is a histogram after noise removal. The photon counts of each pixel's histogram are superimposed to obtain the reflectivity corresponding to each pixel, generating a first vector.

[0065] In one embodiment, the value of each pixel peak corresponding to the time bin is calculated to generate a second vector set, which includes multiple sub-vectors, including:

[0066] Parse each histogram to obtain all pixel data, which includes m unimodal pixels and n multimodal pixels. Each multimodal pixel corresponds to the value of at least two time bins.

[0067] Calculate the value of each pixel peak corresponding to the time bin and generate a second vector set, which includes at least 2 n sub-vector sets, each of which includes the values of all pixel peaks corresponding to the time bin.

[0068] In specific implementation, after performing pixel filtering operation on each histogram, all pixel data are obtained. All pixel data include multi-modal and unimodal data. For example, all pixel data include m unimodal pixels and n multimodal pixels. Each multimodal pixel corresponds to the value of at least 2 time bins, and the coordinates of the unimodal pixels and the multimodal pixels are recorded.

[0069] Calculate the value of each pixel peak corresponding to the time bin and generate a second vector set, which includes at least 2 n The sub-vectors in the second vector have the same vector dimensions as the first vector.

[0070] In one embodiment, the position of the internal elements of the first vector corresponds one-to-one to the position of the internal elements of each sub-vector in the second vector set.

[0071] In specific implementation, in order to calculate the similarity between the first vector and each subvector in the second vector set, in order to prevent the distribution from being misremembered, it is necessary to ensure that the positions of the internal elements of the first vector correspond to the positions of the internal elements of each subvector in the second vector set, so as to accurately find the time bin with the minimum similarity.

[0072] In one embodiment, the value of each pixel peak corresponding to the time bin is calculated to generate a second vector set. The second vector set includes multiple sub-vectors and further includes:

[0073] Obtaining preset partitions, performing exposure according to the preset partitions, and calculating similarity between the first vector and each sub-vector of the second vector set in each partition;

[0074] The second subvector corresponding to the value with the smallest similarity is obtained, and the time bin corresponding to the second subvector is recorded as the target time of each partition.

[0075] In specific implementation, the use of partitioned illumination combined with partitioned reception can effectively reduce the multipath problems outside the lens and inside the lens. Therefore, the method of pre-setting the illumination area can be used to solve the glare phenomenon.

[0076] The preset irradiation area is recorded as a preset partition. After the preset partition is obtained, exposure is performed within the preset partition, and a histogram generated by receiving the photon signal returned by each pixel within the partition is generated.

[0077] The similarity between the first vector and each sub-vector of the second vector set in each partition is calculated to obtain the second sub-vector corresponding to the minimum similarity value. The time bin corresponding to the second sub-vector is recorded as the target time of each partition.

[0078] Taking partition illumination as an example, calculate the intensity of all SPADs in the partition. For example, if there are 21 SPAD pixels in the partition, calculate the intensity of 21 pixels, as shown in Table 1 below:

[0079] Table 1

[0080] I1 I2 I3 I4 I5 I6 I7 I8 I9 I10 I11 I12 I13 I14 I15 I16 I17 I18 I19 I20 I21

[0081] As shown in Table 1, intensity calculation is the sum of all counts in the histogram (noise subtracted). The counts are obtained from the histogram of each spad. Even if there is a double peak, the count values of the double peak must be summed. The bin values of the 21 pixels are calculated as shown in Table 2 below:

[0082] Table 2

[0083] T1 T2(a2, b2) T3 T4 T5(a5,b5) T6 T7 T8(a8,b8) T9(a9,b9) T10 T11 T12 T13(a13,b13) T14 T15 T16 T17 T18(a18,b18) T19 T20 T21

[0084] From Table 2, identify unimodal pixels, such as T1, T4, T7, and so on, totaling 15, and obtain the bin value for each peak. Identify multimodal pixels, such as T2, T8, T9, and so on, totaling 6, and obtain the bin value for each peak. From these multimodal pixels, we can obtain the bin value for each unimodal pixel, namely (a2_bin, W2) and (b2_bin, W2), where W2 refers to the position of the SPAD unit. This means that a multimodal pixel has at least two unimodal peaks, but it's unclear which one is the desired peak and which one is caused by flare. From the histogram of each pad, obtain the bin value corresponding to each peak.

[0085] The similarity values are calculated, and the calculation results are shown in Table 3. In the calculation process, Table 1 as a whole is regarded as a vector X, recorded as the first vector; Table 2 as a whole is regarded as a vector Y, recorded as the second vector set;

[0086] However, the selection of two peaks in the double peak will change the vector Y. For example, if there are 6 spads in Table 2 that are double peaks, then the vector composed of 21 spads may produce 64 Ys, namely Y1...Y64;

[0087] Substitute (X, Y1)(X, Y2)...(X, Y64) into formula F to calculate the similarity value, and get 64 Fs, namely F1, F2, .... F64; the smaller the F value, the more similar, so take the smallest value, such as F5 is the smallest, then the vector bin of Y5 corresponding to F5, that is, the flight time, is used to calculate the distance.

[0088] Finally, the combination bin corresponding to Y5 is left, as shown in Table 3;

[0089] Table 3

[0090] T1 T2(a2) T3 T4 T5(b5) T6 T7 T8(a8) T9(a9) T10 T11 T12 T13(b13) T14 T15 T16 T17 T18(b18) T19 T20 T21

[0091] As can be seen from Table 3, one peak has been removed from the double peak, and the bin corresponding to the remaining peak is the target time.

[0092] In one embodiment, calculating the similarity between the first vector and each sub-vector of the second vector set includes:

[0093] The similarity between the first vector and each sub-vector of the second vector set is calculated based on a preset similarity formula. The preset similarity formula is a cosine distance calculation formula or a Pearson correlation coefficient calculation formula.

[0094] In specific implementation, when calculating the similarity of each sub-vector of the first vector and the second vector set, the cosine distance calculation formula or the Pearson correlation coefficient calculation formula can be used. When using the cosine distance calculation formula, the cosine value between the angle between two vectors in a vector space is used as a measure of the size of the difference between the two individuals. The closer the cosine value is to 1 and the angle tends to 0, the more similar the two vectors are. The closer the cosine value is to 0 and the angle tends to 90 degrees, the more dissimilar the two vectors are. The cosine distance calculation formula is a prior art and will not be illustrated here.

[0095] The Pearson correlation coefficient calculation formula first calculates the covariance of the numerator X and Y variables, then calculates the standard deviation of the denominator X and Y, ultimately obtaining the Pearson correlation coefficient. The Pearson correlation coefficient calculation formula is known in the art and will not be further illustrated here.

[0096] It should be noted that there is not necessarily a certain order between the above steps. A person skilled in the art can understand, based on the description of the embodiments of the present invention, that in different embodiments, the above steps may have different execution orders, that is, they may be executed in parallel, or may be executed interchangeably, etc.

[0097] Another embodiment of the present invention provides a laser ranging chip that reduces glare, such as Figure 2 As shown, chip 1 includes:

[0098] The receiving module 11 is used to obtain the photon signal reflected by the target object.

[0099] A storage module 12, configured to construct a histogram based on the photon signal;

[0100] The controller 13 is used to obtain a histogram of each pixel, calculate the reflectivity of each pixel based on the histogram, and generate a first vector; calculate the value of the time bin corresponding to each pixel peak to generate a second vector set, where the second vector set includes multiple sub-vectors; perform similarity calculations on the first vector and each sub-vector of the second vector set, obtain the second sub-vector corresponding to the value with the smallest similarity, and record the time bin corresponding to the second sub-vector as the target time; and generate a target distance of the object based on the target time.

[0101] During specific implementation, the receiving module obtains the photon signal reflected by the target object, and the storage module constructs a histogram based on the photon signal.

[0102] The controller obtains the histogram of each pixel, calculates the reflectivity of each pixel according to the square map, and generates a first vector;

[0103] Obtain the value of each pixel peak corresponding to the time bin, and generate a second vector set based on the value of each pixel peak corresponding to the time bin. The second vector set contains multiple sub-vectors, and each sub-vector has the same vector dimension as the first vector.

[0104] Obtain each subvector in the second vector set, calculate the similarity between each subvector and the first vector, obtain the second subvector corresponding to the value with the minimum similarity, record the time bin corresponding to the second subvector as the target time, and calculate the target distance of the object based on the target time.

[0105] By comparing the distribution trends of the elements within two vectors, for example, the first vector is x(I1,...I21) and the second vector is y1(T1,...T21)...y64(T1,...T21), although x is the reflectivity value and y is the bin value, the two values are different. However, the similarity formula can be used to compare the internal distributions of x and y. If the distributions are consistent, the similarity value of x and y is the smallest, and one of the 64 y values is selected as the target time.

[0106] The target distance is recorded as d, and the value of the time bin with the minimum similarity is recorded as t, then d = ct / 2, where c is the speed of light, which is generally recorded as 3*10 8 m / s.

[0107] In one embodiment, the main controller is specifically configured to:

[0108] Obtain the histogram corresponding to each pixel SPAD in the irradiated area. The histogram is the histogram after removing noise.

[0109] The photon counts of the histogram of each pixel are superimposed to obtain the reflectivity corresponding to each pixel and generate a first vector.

[0110] In the specific implementation, the intensity method is used to deal with the multi-peak problem inside the irradiation area, as follows: the reflectivity is calculated using the hist distribution of each pixel inside the irradiation area

[0111] intensity(x,y)=sum(hist(x,y))-sum(noise(x,y)).

[0112] Thus, a histogram corresponding to each SPAD pixel in the illumination area is obtained, and this histogram is a histogram after noise removal. The photon counts of each pixel's histogram are superimposed to obtain the reflectivity corresponding to each pixel, generating a first vector.

[0113] In one embodiment, the main controller is further configured to:

[0114] Parse each histogram to obtain all pixel data, which includes m unimodal pixels and n multimodal pixels. Each multimodal pixel corresponds to the value of at least two time bins.

[0115] Calculate the value of each pixel peak corresponding to the time bin and generate a second vector set, which includes at least 2 n sub-vector sets, each of which includes the values of all pixel peaks corresponding to the time bin.

[0116] In specific implementation, after performing pixel filtering operation on each histogram, all pixel data are obtained. All pixel data include multi-modal and unimodal data. For example, all pixel data include m unimodal pixels and n multimodal pixels. Each multimodal pixel corresponds to the value of at least 2 time bins, and the coordinates of the unimodal pixels and the multimodal pixels are recorded.

[0117] Calculate the value of each pixel peak corresponding to the time bin and generate a second vector set, which includes at least 2 n The sub-vectors in the second vector have the same vector dimensions as the first vector.

[0118] In one embodiment, the position of the internal elements of the first vector corresponds one-to-one to the position of the internal elements of each sub-vector in the second vector set.

[0119] In specific implementation, in order to calculate the similarity between the first vector and each subvector in the second vector set, in order to prevent the distribution from being misremembered, it is necessary to ensure that the positions of the internal elements of the first vector correspond to the positions of the internal elements of each subvector in the second vector set, so as to accurately find the time bin with the minimum similarity.

[0120] The specific implementation method is shown in the method embodiment, which will not be repeated here.

[0121] Another embodiment of the present invention provides an electronic device comprising:

[0122] One or more processors, and a memory. Taking one processor as an example, the processor and the memory may be connected via a bus or other means.

[0123] The processor is used to implement various control logics of electronic devices. It can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a single-chip microcomputer, an ARM (Acorn RISC Machine) or other programmable logic device, discrete gate or transistor logic, discrete hardware controls, or any combination of these components. In addition, the processor can also be any traditional processor, microprocessor, or state machine. The processor can also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration.

[0124] The memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions corresponding to the glare-reducing laser ranging method in the embodiments of the present invention. The processor executes the non-volatile software programs, instructions, and modules stored in the memory to execute various functional applications and data processing of the device, thereby implementing the glare-reducing laser ranging method in the aforementioned method embodiments.

[0125] The memory may include a program storage area and a data storage area, wherein the program storage area may store applications required for operating the chip and at least one function; the data storage area may store data created based on the use of the device, etc. In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0126] One or more units are stored in the memory, and when executed by one or more processors, perform the laser ranging method for reducing glare in any of the above method embodiments, for example, perform the above described Figure 1 Method steps S100 to S600 in.

[0127] An embodiment of the present invention provides a non-volatile computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, which are executed by one or more processors, for example, to execute the above-described Figure 1 Method steps S100 to S600 in.

[0128] As examples, non-volatile storage media can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) as external cache memory. By way of illustration and not limitation, RAM can be obtained in many forms such as synchronous RAM (SRAM), dynamic RAM, (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The disclosed memory controls or memories of the operating environment described herein are intended to include one or more of these and / or any other suitable types of memory.

[0129] Another embodiment of the present invention provides a computer program product, which includes a computer program stored on a non-volatile computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the laser ranging method for reducing glare of the above method embodiment. For example, the above described method is executed. Figure 1 Method steps S100 to S600 in.

[0130] The embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected to achieve the objectives of the present embodiments as needed.

[0131] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a general hardware platform, and of course can also be implemented by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the relevant technology can be embodied in the form of a software product. This computer software product can be present in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for causing a computer chip (which can be a personal computer, a server, or a network chip, etc.) to execute the methods of each embodiment or certain parts of the embodiment.

[0132] Conditional language such as "can," "may," or "might," among others, unless specifically stated otherwise or otherwise understood within the context as used, is generally intended to convey that particular embodiments can include, while other embodiments do not, particular features, elements, and / or operations. Thus, such conditional language is also generally intended to imply that features, elements, and / or operations are anyway required for one or more embodiments or that one or more embodiments must include logic for determining, with or without input or prompting, whether such features, elements, and / or operations are included or to be performed in any particular embodiment.

[0133] What has been described herein in this specification and the accompanying drawings includes examples of laser ranging methods and chips that can provide glare reduction. Of course, it is not possible to describe every conceivable combination of elements and / or methods for the purpose of describing the various features of the present disclosure, but it will be appreciated that many additional combinations and permutations of the disclosed features are possible. Therefore, it will be apparent that various modifications can be made to the present disclosure without departing from the scope or spirit of the present disclosure. In addition, or in the alternative, other embodiments of the present disclosure may be apparent from consideration of this specification and the accompanying drawings and from the practice of the present disclosure as presented herein. It is intended that the examples set forth in this specification and the accompanying drawings be considered in all respects to be illustrative and not restrictive. Although specific terms are employed herein, they are used in a general and descriptive sense and not for purposes of limitation.

Claims

1. A laser ranging method for reducing glare, characterized in that , the method comprises: Obtain the photon signal reflected by the target object and construct a histogram based on the photon signal; Obtaining a histogram of each pixel, calculating the reflectivity of each pixel according to the histogram, and generating a first vector; Calculating a value of a time bin corresponding to each pixel peak to generate a second vector set, where the second vector set includes a plurality of sub-vectors; Calculate the similarity between the first vector and each subvector of the second vector set, obtain the second subvector corresponding to the minimum similarity value, and record the time bin corresponding to the second subvector as the target time; Generate target distance of the object based on target time; The obtaining of a histogram of each pixel, calculating the reflectivity of each pixel according to the histogram, and generating a first vector includes: Obtain a histogram corresponding to each pixel SPAD in the irradiated area, wherein the histogram is a histogram after noise removal; The photon counts of the histogram of each pixel are superimposed to obtain the reflectivity corresponding to each pixel and generate a first vector; The value of the time bin corresponding to each pixel peak is calculated to generate a second vector set, where the second vector set includes multiple sub-vectors, including: Parse each histogram to obtain all pixel data, including m unimodal pixels and n multimodal pixels, where each multimodal pixel corresponds to values of at least two time bins; Calculate the value of each pixel peak corresponding to the time bin and generate a second vector set, which includes at least 2 n sub-vector sets, each of which includes the values of all pixel peaks corresponding to the time bin.

2. The method according to claim 1, characterized in that The positions of the internal elements of the first vector correspond one-to-one to the positions of the internal elements of each sub-vector in the second vector set.

3. The method according to claim 1, characterized in that The calculation of each image The value of the time bin corresponding to the prime peak is used to generate a second vector set. The second vector set includes multiple sub-vectors and further includes: Get the preset partitions, perform exposure according to the preset partitions, and Calculate the similarity between a vector and each sub-vector of the second vector set; Get the second sub-vector corresponding to the value with the smallest similarity, and assign the second sub-vector to The time bins are recorded as the target time for each partition.

4. The method according to claim 1, wherein The first vector and The similarity calculation is performed on each sub-vector of the second vector set, including: A similarity calculation is performed on each sub-vector of the first vector and the second vector set based on a preset similarity formula, wherein the preset similarity formula is a cosine distance calculation formula or a Pearson correlation coefficient calculation formula.

5. A laser ranging chip, characterized in that: include: The receiving module is used to obtain the photon signal reflected by the target object. A storage module, for constructing a histogram based on the photon signal; a controller, configured to obtain a histogram of each pixel, calculate a reflectivity of each pixel according to the histogram, and generate a first vector; Calculating the value of the time bin corresponding to each pixel peak to generate a second vector set, the second vector set including multiple subvectors; performing similarity calculation on the first vector and each subvector of the second vector set, obtaining the second subvector corresponding to the value with the smallest similarity, recording the time bin corresponding to the second subvector as the target time; and generating a target distance of the object based on the target time; The controller is further configured to obtain a histogram corresponding to each SPAD pixel in the illumination area, wherein the histogram is a noise-removed histogram; superimpose the number of photons in the histogram of each pixel to obtain a reflectivity corresponding to each pixel, thereby generating a first vector; The controller is further configured to parse each histogram to obtain all pixel data, wherein the all pixel data includes m single-peak pixels and n multi-peak pixels, and each multi-peak pixel corresponds to the value of at least two time bins; calculate the value of each pixel peak corresponding to the time bin, and generate a second vector set, wherein the second vector set includes at least 2 n sub-vector sets, each of which includes the values of all pixel peaks corresponding to the time bin.

6. The laser ranging chip according to claim 5, characterized in that: The positions of the internal elements of the first vector correspond one-to-one to the positions of the internal elements of each sub-vector in the second vector set.

Images