Echo selection method and device for laser radar and storage medium

Abstract

The invention relates to an echo selection method and device for a laser radar and a storage medium. In one embodiment, the echo selection method for the laser radar comprises the following steps: acquiring echo data at a pixel point; determining the spatial similarity between the target pixel point and a similar pixel point; determining the time similarity between each echo at the target pixel point and each reference echo at the similar pixel point; calculating an echo score of each echo at the target pixel point based on the spatial similarity, the time similarity and a peak value of each echo at the target pixel point; and determining an object echo at the target pixel point based on the echo score of each echo at the target pixel point.

Description

Technical Field

[0001] The present disclosure relates generally to lidar, and particularly to echo extraction in lidar. Background Art

[0002] LiDAR (Light Detection and Ranging) is a distance measurement technology. The basic principle of LiDAR is that the transmitter emits a laser beam, which will be reflected and returned to the receiver after hitting the object in front. The distance to the object is measured based on the time difference between emission and reception (i.e., time-of-flight, ToF). The receiver will record the number of photons received at different times, which is called echo data. After the laser is reflected back by the object, an echo will be formed on the echo data, and the distance to the object can be determined by the position of the echo.

[0003] However, in actual scenarios, it is a challenge to accurately select the real object echo. Generally speaking, echo data contains not only the echo from the real object, but also the echo from the ambient light (also called noise). In current technology, the echo with the largest peak value is usually selected as the real object echo. However, in the presence of strong ambient light or when measuring distant objects, the peak value of the ambient light echo may be higher than the peak value of the real object echo. This will lead to the wrong selection of echoes, resulting in a decrease in the accuracy of ranging.

[0004] Therefore, developing a more accurate and robust echo selection (also known as peak selection) method is an urgent problem to be solved in current lidar technology. Summary of the invention

[0005] One aspect of the present disclosure relates to an echo selection method for a laser radar. According to an embodiment of the present disclosure, the method includes: acquiring echo data at a pixel point; determining the spatial similarity between a target pixel point and a nearby pixel point; determining the temporal similarity between each echo at the target pixel point and each reference echo at a nearby pixel point; calculating the echo score of each echo at the target pixel point based on the spatial similarity, the temporal similarity and the peak value of each echo at the target pixel point; and determining the object echo at the target pixel point based on the echo score of each echo at the target pixel point.

[0006] Another aspect of the present disclosure relates to an electronic device. The electronic device includes one or more processors and one or more memories having one or more instructions stored thereon. When the one or more instructions are executed by the one or more processors, the one or more processors execute the steps of the echo selection method for laser radar according to an embodiment of the present disclosure.

[0007] Another aspect of the present disclosure relates to a computer-readable storage medium having one or more instructions stored thereon. When the one or more instructions are executed by a processor, the processor executes the steps of the echo selection method for laser radar according to an embodiment of the present disclosure.

[0008] Another aspect of the present disclosure relates to a computer program product comprising one or more instructions. When the one or more instructions are executed by a processor, the processor performs the steps of the echo selection method for laser radar according to an embodiment of the present disclosure.

[0009] The above summary is provided to summarize some exemplary embodiments to provide a basic understanding of various aspects of the subject matter described herein. Therefore, the above features are merely examples and should not be interpreted as narrowing the scope or spirit of the subject matter described herein in any way. Other features, aspects and advantages of the subject matter described herein will become clear from the specific embodiments described below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] A better understanding of the present disclosure may be obtained when the following detailed description of the embodiments is considered in conjunction with the accompanying drawings. The same or similar reference numerals are used in the various drawings to represent the same or similar components. The accompanying drawings, together with the following detailed description, are included in and form a part of this specification and are used to illustrate embodiments of the present disclosure and to explain the principles and advantages of the present disclosure. Among them:

[0011] Figure 1A is a schematic diagram illustrating an application scenario of an echo selection method for a laser radar according to one or more embodiments of the present disclosure; and Figure 1B is exemplified in Figure 1A Schematic diagram of echo data obtained in an application scenario.

[0012] Figure 2 It is a flowchart illustrating the steps of an echo selection method for a laser radar according to one or more embodiments of the present disclosure.

[0013] Figure 3 is a flow chart illustrating an example of sub-steps of the step of calculating an echo score according to one or more embodiments of the present disclosure.

[0014] Figure 4 is a flowchart illustrating an example of sub-steps of the step of determining an object echo according to one or more embodiments of the present disclosure.

[0015] Figure 5 is a schematic diagram illustrating the steps of determining an object echo according to one or more embodiments of the present disclosure.

[0016] Figure 6It is a schematic diagram illustrating an echo selection device for a laser radar according to one or more embodiments of the present disclosure, which illustrates the main functional modules and information interactions that constitute the device.

[0017] Fig. 7A is a schematic diagram illustrating an application scenario of performing laser radar ranging and ambient light data therein; and Figure 7B-Figure 7C are examples of Fig. 7A A schematic diagram of the ranging effects of using the echo selection method according to one or more embodiments of the present disclosure and the echo selection method of the prior art when performing lidar ranging in an exemplified scenario.

[0018] Figure 8 is an example block diagram illustrating an electronic device according to one or more embodiments of the present disclosure.

[0019] Although the embodiments described in the present disclosure may be susceptible to various modifications and alternative forms, specific embodiments thereof are shown as examples in the drawings and described in detail herein. However, it should be understood that the drawings and detailed description thereof are not intended to limit the embodiments to the particular forms disclosed, but on the contrary, the purpose is to cover all modifications, equivalents and alternatives within the spirit and scope of the claims. DETAILED DESCRIPTION

[0020] Representative applications of various aspects such as the apparatus and method of the present disclosure are described below. The description of these examples is only to increase the context and help understand the described embodiments. Therefore, it is clear to those skilled in the art that the embodiments described below can be implemented without some or all of the specific details. In other cases, well-known process steps are not described in detail to avoid unnecessarily obscuring the described embodiments. Other applications are also possible, and the solutions of the present disclosure are not limited to these examples.

[0021] In the prior art of laser radar, the echo signal with the highest peak value at each pixel point is usually selected as the real object echo signal at the pixel point. That is, the peak selection of each pixel point is independent of each other. However, the inventors of the present application have realized that this peak selection method cannot cope with complex situations such as strong ambient light illumination or measuring distant objects.

[0022] The inventors of the present application also recognize that the objects to be measured usually present a certain degree of continuity in space, so the object echoes at the pixel points measuring the same object will be relatively concentrated. For example, assuming that the distance measured by the target pixel point is D, if the similar pixel points measure the same object, then the distance measured by the similar pixel points may be close to D. In this way, the flight time of the object echo of the target pixel point and this similar pixel point will be very close, or even overlap. In contrast, the echo of ambient light noise is random in time.

[0023] Therefore, the present invention proposes a new and effective echo selection method for laser radar. Compared with the limitation of the prior art that only focuses on a single pixel point, the present invention also considers the surrounding pixels. By utilizing the spatial continuity of the object distance, the accuracy of selecting the real object echo is improved.

[0024] Specifically, the echo selection method for laser radar proposed in the present disclosure screens out pixel points that are likely to measure the same object based on the angular resolution of the laser radar, ambient light and other information. On this basis, echoes that are close in time are aggregated together to better select the echoes of real objects, thereby improving the accuracy of selecting the echoes of real objects and optimizing the measurement accuracy of the laser radar, bringing higher efficiency and practicality to the laser radar.

[0025] The solution of the present disclosure is applicable to the scenario of performing laser radar ranging based on flight time. For example, the solution of the present disclosure can be applied to various types of direct time of flight (DTOF) laser radars.

[0026] Moreover, since it can avoid the interference of noise and thus more accurately select the real object echo, the scheme of the present disclosure is particularly suitable for laser radar ranging scenarios that are sensitive to noise. For example, single photon avalanche diode (SPAD) laser radars with high detection sensitivity and flash laser radars with low emission energy density. In addition, as mentioned above, the scheme of the present disclosure is also particularly suitable for scenarios with strong ambient light or the need to measure distant objects.

[0027] Figure 1A A schematic diagram illustrating an application scenario of an echo selection method for a laser radar according to one or more embodiments of the present disclosure. Figure 1B Illustrated in Figure 1A A schematic diagram of echo data obtained in an application scenario of FIG. A person skilled in the art can easily understand that Figure 1A and Figure 1B The application scenario shown in is only an example, and the present disclosure is not limited thereto.

[0028] like Figure 1AAs shown in Figure 1, in the LiDAR ranging scheme, a transmitter is used to emit laser light and a receiver is used to receive the reflected signal. The receiver records the number of photons received at different times, thereby generating echo data (such as Figure 1B ). Figure 1A In , the solid line represents the laser emitted by the transmitter and the real object echo, while the dotted line represents the ambient light. Figure 1A The laser radar system shown in FIG. 1 has a total of three pixel points P1, P2, and P3, which have a corresponding relationship with the measurement object in the field of view as shown in the figure.

[0029] Here, the meaning of pixels in LiDAR is similar to that of pixels in image sensing, but the data recorded for pixels is not RGB color components, but the number of received photons (echo data). Pixels can correspond one-to-one with the field of view of LiDAR, so they can be represented and distinguished by the field of view. In addition, the density of pixels can reflect the angular resolution of LiDAR.

[0030] Figure 1B (1), (2), and (3) in FIG. 1 show the echo data at pixel points P1, P2, and P3, respectively. The horizontal axis indicates time, so the echo flight time can be shown; the vertical axis indicates the number of received photons, so the echo intensity can be shown. Figure 1B In the echo data in , the solid line echoes (such as a, b, c) represent the real object echoes, and the dotted line echoes (such as d, e, f) represent the ambient light echoes. In addition, there are some lower peaks in the echo data, but they can be ignored because they do not meet the echo judgment criteria (such as intensity and duration conditions, etc.).

[0031] It is worth noting that in Figure 1B In the example shown in , the real object echoes a and b are not the maximum echoes at the corresponding pixel points P1 and P2, while the real object echo c at the pixel point P3 has similar intensity to the ambient light echo f. In these cases, it is difficult to correctly select the real object echo using the existing technology.

[0032] Figure 2 A flowchart illustrating the steps of a method 200 for selecting an echo for a laser radar according to one or more embodiments of the present disclosure is provided. Figure 3-Figure 4 A flowchart illustrating an example of sub-steps of some steps of the echo selection method for laser radar according to an embodiment of the present disclosure. Figure 5 A schematic diagram illustrating the steps of determining an object echo according to one or more embodiments of the present disclosure is illustrated. The method according to an embodiment of the present disclosure may be performed by any device including a processing device. For example, the method according to an embodiment of the present disclosure may be performed by a controller in a laser radar device, any processing device communicating with the laser radar device, or a combination thereof.

[0033] like Figure 2 As shown, according to an embodiment of the present disclosure, the echo selection method 200 for laser radar may mainly include the following steps:

[0034] In step 210, echo data at a pixel point is acquired;

[0035] In step 220, the spatial similarity between the target pixel and the adjacent pixels is determined;

[0036] At step 230, determining the temporal similarity between each echo at the target pixel and each reference echo at a nearby pixel;

[0037] At step 240, the echo score of each echo at the target pixel is calculated based on the spatial similarity, the temporal similarity, and the peak value of each echo at the target pixel; and

[0038] In step 250 , the object echo at the target pixel is determined based on the echo score of each echo at the target pixel.

[0039] Here, the target pixel point may refer to the pixel point that is currently being processed to determine its object echo. In addition, a close pixel point may refer to a pixel point that is located near the target pixel point and provides reference information for determining the object echo. Therefore, the target pixel point and the close pixel point are relative concepts, and can even be interchanged in calculations for different pixels. As an example, all pixels in a laser radar can be target pixels. However, the present disclosure is not limited to this. For example, in some embodiments, only pixels that meet specific conditions can be used as target pixels. In the following description, the main Figure 1A-1B The description is made by taking the case where the pixel point P2 shown in FIG. 1 is the target pixel point as an example, but the present disclosure is not limited thereto.

[0040] First, if Figure 2 As shown, in step 210, echo data at a pixel point is acquired.

[0041] In some embodiments, the step 210 of acquiring echo data at a pixel point may include acquiring all echo data used in subsequent steps. However, the present disclosure is not limited thereto. For example, in some embodiments, the echo data of all pixels may be acquired in step 210. Alternatively, in some embodiments, only the echo data for one or more target pixels and their adjacent pixels may be acquired in step 210.

[0042] Next, if Figure 2 As shown, in step 220, the spatial similarity between the target pixel and the adjacent pixels is determined. Figure 1A-1BIn the example scenario shown, in step 220 , the spatial similarities between the target pixel point P2 and the adjacent pixels P1 and P3 are respectively determined.

[0043] Here, the spatial similarity between pixels can be used to represent the similarity of the spatial position of the field of view corresponding to the pixel or the similarity of the measured object in the field of view corresponding to the pixel, thereby representing the possibility that the pixel measures the same object in space. Therefore, calculating the spatial similarity between the target pixel and the adjacent pixels is conducive to screening out the pixels that measure the same object as the target pixel. It is worth noting that, in general, the pixels that measure the same object will be relatively close, so the present disclosure mainly considers screening the pixels near the target pixel to reduce unnecessary calculations.

[0044] The inventors of the present application have recognized that, since objects are generally continuous in space, the possibility of measuring the same object can be determined based on the spatial position of the field of view.

[0045] As an example, in some embodiments, the spatial similarity can be determined by the difference in field of view angles. For example, if the difference in field of view angles corresponding to two pixels is small, the spatial similarity of the two pixels is considered to be relatively high; otherwise, the spatial similarity of the two pixels is considered to be relatively low.

[0046] Therefore, in some embodiments, determining the spatial similarity between the target pixel and the similar pixel in step 220 may include: based on the difference in the field of view angles corresponding to the target pixel and the similar pixel, using a preset spatial threshold to determine the spatial similarity between the target pixel and the similar pixel.

[0047] It is worth noting that the difference in the field of view angle of two adjacent pixels generally corresponds to the field of view angle resolution in the arrangement direction. For example, the difference in the horizontal field of view angle of two adjacent pixels arranged in the horizontal direction can correspond to the field of view angle resolution in the horizontal direction; and the difference in the vertical field of view angle of two adjacent pixels arranged in the vertical direction can correspond to the field of view angle resolution in the vertical direction. Therefore, for two adjacent pixels, the difference in the field of view angle in the corresponding direction can be determined based on the field of view angle resolution and the pixel number interval in each direction.

[0048] Generally speaking, once the installation and debugging is completed, the field of view angle resolution of the laser radar is fixed. Therefore, the corresponding relationship between the pixel number interval and the field of view angle difference can be calculated based on the field of view angle resolution and a predetermined spatial threshold. In some embodiments, the spatial similarity of two pixels can be determined based on the pixel number interval of the two pixels.

[0049] For example, if it is believed based on calculation or experience that the objects measured within a horizontal field of view angle of ±1° are likely to be the same object, that is, the predetermined spatial threshold is ±1°, and the field of view angle resolution in the horizontal direction is designed to be 0.5°, then it can be determined that the spatial similarity between the target pixel point and the pixel points arranged within ±2 in the horizontal direction is high.

[0050] Here, the number of preset space thresholds may be one or more.

[0051] For example, when the number of preset spatial thresholds is one, the spatial similarity determined using the preset spatial thresholds may have two values; when the number of preset spatial thresholds is multiple, the spatial similarity determined using the preset spatial thresholds may have more than three values.

[0052] Here, the spatial similarity may be a discrete set of values. In particular, for ease of calculation, the spatial similarity may be a discrete set of values ​​falling within the interval [0, 1]. However, the present disclosure is not limited thereto, for example, the spatial similarity may be a continuous value, in particular a continuous value falling within the interval [0, 1].

[0053] The inventors of the present application also recognize that the material of an object is usually consistent, so the similarity of the field of view can be determined based on the consistency of the material of the measured object within the field of view. In addition, the material of the measured object (such as a wall, a metal sign, a piece of clothing fabric, etc.) generally affects the reflectivity, and thus affects the corresponding ambient light intensity. Therefore, the difference in ambient light intensity can reflect the difference in the material of the measured object to a certain extent.

[0054] Therefore, in some embodiments, the material consistency of the measured object can be evaluated by the difference in ambient light intensity, thereby determining the spatial similarity. For example, if the difference in ambient light intensity corresponding to two pixels is small, the spatial similarity of the two pixels is considered to be relatively high; otherwise, the spatial similarity of the two pixels is considered to be relatively low.

[0055] Therefore, in some embodiments, determining the spatial similarity between the target pixel and the similar pixels in step 220 may include: using a similarity measurement function to calculate the spatial similarity between the target pixel and the similar pixels based on ambient light information within the field of view corresponding to the target pixel and the similar pixels.

[0056] Here, the ambient light information may be any information reflecting the intensity of ambient light. For example, the ambient light information may include but is not limited to at least one of the following: ambient light data or echo data baseline.

[0057] In some embodiments, the ambient light data may be obtained by directly measuring the ambient light intensity. For example, in some embodiments, the ambient light intensity may be determined based on the received signal in a time period when the laser is not emitted to obtain the ambient light data. Alternatively, in some embodiments, a weak echo in the echo data in a time period when the laser is emitted may be selected to represent the ambient light intensity to obtain the ambient light data.

[0058] In some embodiments, the echo data baseline can be obtained by averaging the collected echo data. It is worth noting that the acquisition time of the echo data is generally much longer than the duration of the laser pulse, so even if the echo data includes real object echoes, it can still be used to represent the ambient light intensity. However, since the echo data within the acquisition time period may include real object echoes, the echo data baseline is generally slightly larger than the ambient light data.

[0059] In some embodiments, the similarity measurement function used to calculate the spatial similarity between the target pixel and the similar pixels may include but is not limited to at least one of the following: a similarity measurement function based on mean square error distance, a similarity measurement function based on absolute value distance, a Gaussian kernel function, and a similarity measurement function obtained based on machine learning, etc.

[0060] For example, for Figure 1A-1B In the example scenario shown in FIG. 1 , if the Gaussian kernel function is used to calculate the spatial similarity between the target pixel point P2 and the adjacent pixels P1 and P3, the spatial similarity sim between the target pixel point P2 and the pixel point P1 is s,21 The spatial similarity sim between the target pixel P2 and the pixel P3 s,23 Can be expressed as:

[0061] [Formula 1]

[0062]

[0063] Among them, x1, x2, and x3 are the ambient light intensities of pixel points P1, P2, and P3 respectively, and r1 is the variance parameter of the Gaussian kernel function.

[0064] Although the method of determining spatial similarity using a preset spatial threshold and the method of calculating spatial similarity using a similarity measurement function are illustrated above, these are only examples and the present disclosure is not limited thereto. Moreover, the two methods illustrated above can also be used in combination.

[0065] like Figure 2 As shown, in step 230, the temporal similarity between each echo at the target pixel and each reference echo at the adjacent pixel is determined. Figure 1A-1BIn the example scenario shown, in step 230 , the temporal similarity between each echo (e, b) at the target pixel point P2 and each reference echo (a, d, c, f) at the adjacent pixel points P1 and P3 is determined respectively.

[0066] Here, the time similarity between echoes can be used to indicate the similarity between the flight times corresponding to the echoes, and thus indicate the possibility that the echoes come from the same object. Since objects are generally continuous in space, the measured distances of the same object are usually close, so the flight times of the corresponding echoes are also close or even the same.

[0067] In some embodiments, the temporal similarity may be determined by the difference in flight time. For example, if the difference in flight time between two echoes is small, the temporal similarity between the two echoes is considered to be relatively high; otherwise, the temporal similarity between the two echoes is considered to be relatively low.

[0068] Therefore, in some embodiments, determining the temporal similarity between each echo at the target pixel and each reference echo at a similar pixel in step 230 may include: determining the temporal similarity using a preset time threshold based on the difference between the flight time of each echo and each reference echo.

[0069] In some embodiments, the number of preset time thresholds may be one or more.

[0070] For example, when the number of preset time thresholds is one, the time similarity determined using the preset time thresholds may have two values; when the number of preset time thresholds is multiple, the time similarity determined using the preset time thresholds may have more than three values.

[0071] The temporal similarity determined based on the temporal threshold may be a discrete set of values. In particular, for ease of calculation, the temporal similarity may be a discrete set of values ​​falling within the interval [0, 1]. However, the present disclosure is not limited thereto, for example, the temporal similarity may be a continuous value, in particular, a continuous value falling within the interval [0, 1].

[0072] In some embodiments, determining the temporal similarity between each echo at the target pixel and each reference echo at a nearby pixel in step 230 may include: calculating the temporal similarity using a similarity metric function based on the difference between the flight time of each echo and each reference echo.

[0073] In some embodiments, the similarity measurement function used to calculate the temporal similarity between each echo at the target pixel point and each reference echo at the similar pixel point includes but is not limited to at least one of the following: a similarity measurement function based on mean square error distance, a similarity measurement function based on absolute value distance, a Gaussian kernel function, and a similarity measurement function obtained based on machine learning, etc.

[0074] The inventor of the present application realizes that the measurement accuracy of the flight time of an echo is related to the peak value of the echo. Generally speaking, the larger the peak value of the echo, the smaller the error of the flight time of the measured echo. Therefore, when calculating the time similarity based on the flight time using the similarity metric function, the result of the calculation may be affected by the peak value of the echo.

[0075] Therefore, in some embodiments, the similarity metric function used to calculate the temporal similarity may be configured to dynamically adjust parameters according to the peak values ​​of the two echoes for which the temporal similarity is calculated.

[0076] For example, if the peak values ​​of the two echoes are higher and thus the measured flight time is more accurate, the time similarity between the two echoes is calculated with a stricter standard; otherwise, the time similarity between the two echoes is calculated with a looser standard.

[0077] Advantageously, by dynamically adjusting the parameters of the similarity metric function used to calculate the temporal similarity, the influence of the peak fluctuation of the echo on the calculation result of the temporal similarity can be reduced.

[0078] For example, for Figure 1A-1B In the example scenario shown in FIG. 1 , if a Gaussian kernel function with dynamically adjusted parameters is used to calculate the temporal similarity between each echo at the target pixel point P2 and each reference echo at the adjacent pixels P1 and P3, the temporal similarity sim between the echo b at the target pixel point P2 and the echo c at the pixel point P3 is t,bc It can be expressed as:

[0079] [Formula 2]

[0080]

[0081] Among them, t b ,t c are the flight times of echoes b and c, respectively, and z b 、z c are the peak values ​​of echoes b and c, respectively, N is the maximum photon count per unit time of the receiver, and r2 is the variance parameter of the Gaussian kernel function. As shown in Formula 2, the Gaussian kernel function used to calculate the temporal similarity is calculated based on the peak values ​​z of echoes b and c. b 、z c And dynamically adjust the parameters.

[0082] In the example, the temporal similarity sim between the echo b at the target pixel point P2 and the echoes a, d, and f at the pixel points P1 and P3 can be calculated based on a similar method. t,ba ,sim t,bd and sim t,bf .

[0083] In the examples given above, both the spatial similarity and the temporal similarity are calculated using a similarity measurement function, but these are merely examples and the present disclosure is not limited thereto.

[0084] In addition, although the method of determining the time similarity using a preset time threshold and the method of calculating the time similarity using a similarity measurement function are illustrated above, these are only examples and the present disclosure is not limited thereto. Moreover, the two methods illustrated above can also be used in combination.

[0085] Next, if Figure 2 As shown, in step 240, the echo score of each echo at the target pixel is calculated based on the spatial similarity, the temporal similarity and the peak value of each echo at the target pixel. Figure 1A-1B In the illustrated example scenario, the echo score of each echo (b, e) at the target pixel point P2 is calculated in step 240 .

[0086] The following will be combined Figure 3 The flowchart of FIG. 2 is used to illustrate in detail an example of calculating the echo score of each echo at the target pixel point (step 240). It is easy for a person skilled in the art to understand that Figure 3 The calculation method shown is only an example, and the present disclosure is not limited thereto. Those skilled in the art may also use various existing calculation methods in combination with the ideas disclosed in the present disclosure to obtain the echo score of each echo at the target pixel point.

[0087] like Figure 3 As shown, in some embodiments, step 240 of calculating the echo score of each echo at the target pixel point may include the following sub-steps.

[0088] In step 242, the inter-echo similarity between the echo and each reference echo is calculated based on the temporal similarity between the echo and each reference echo and the spatial similarity between the target pixel and the adjacent pixel to which the reference echo belongs.

[0089] In some embodiments, calculating the inter-echo similarity between the echo and each reference echo in step 242 may include: multiplying the temporal similarity between the echo and each reference echo and the spatial similarity between the target pixel point and the adjacent pixel point to which the reference echo belongs, so as to obtain the inter-echo similarity between the echo and each reference echo.

[0090] For example, for Figure 1A-1B In the example scenario shown, the similarity between the echo b at the target pixel point P2 and the echo c at the pixel point P3 can be expressed as:

[0091] [Formula 3]

[0092] sim bc =sim s,23 ·sim t,bc .

[0093] In addition, the similarity sim between the echo b at the target pixel point P2 and the echoes a, d, and f at the pixel points P1 and P3 can be calculated based on a similar method. ba ,sim bd and sim bf .

[0094] However, the above method for calculating the similarity between echoes is only an example, and the present disclosure is not limited thereto. For example, the similarity between echoes may also be calculated by taking a weighted average or the like.

[0095] In step 244, the similar echo peak value of the echo is calculated based on the inter-echo similarity between the echo and each reference echo and the peak value of each reference echo.

[0096] In some embodiments, calculating the similar echo peak value of the echo in step 244 may include: performing weighted summation on the peak values ​​of all reference echoes of the echo based on the inter-echo similarity between the echo and each reference echo to obtain the similar echo peak value of the echo.

[0097] For example, for Figure 1A-1B In the example scenario shown, the similar echo peak value of the echo b at the target pixel point P2 can be expressed as:

[0098] [Formula 4]

[0099] zδ b =sim ba z a +sim bd z d +sim bc z c +sim bf z f

[0100] =sim s,12 sim t,ba z a +sim s,12 sim t,bd z d +sim s,23 sim t,bc z c +

[0101] sim s,23 sim t,bf z f .

[0102] In addition, the similar echo peak value zδ of the echo e at the target pixel point P2 can be calculated based on a similar method: e .

[0103] At step 246, an echo score of the echo is calculated based on the peak value of the echo and the peak values ​​of similar echoes of the echo.

[0104] In some embodiments, calculating the echo score of the echo in step 246 may include: summing the peak value of the echo and the peak values ​​of similar echoes of the echo according to a preset proportionality coefficient to obtain the echo score of the echo.

[0105] For example, for Figure 1A-1B In the example scenario shown, the echo score of the echo b at the target pixel point P2 can be expressed as:

[0106] [Formula 5]

[0107] score b =kz b +jz′ b

[0108] Among them, k, j are preset proportional coefficients.

[0109] In addition, the echo score score of the echo e at the target pixel point P2 can be calculated based on a similar method: e .

[0110] In some embodiments, the above-mentioned proportionality coefficients k and j may be set to be constants. For example, the above-mentioned proportionality coefficients k and j may be set to satisfy 1:1.

[0111] Alternatively, in some embodiments, the above-mentioned proportionality coefficient may be set to be dynamically adjustable. For example, the above-mentioned proportionality coefficient may be set to be dynamically adjusted according to the magnitude relationship between the peak value of the echo and the amplitude of the ambient light.

[0112] In some embodiments, the above-mentioned proportionality coefficient between the peak value of the echo and the peak value of a similar echo may be set to satisfy: the larger the ratio of the peak value of the echo to the amplitude of the ambient light, the larger the above-mentioned proportionality coefficient.

[0113] Generally speaking, the larger the peak value of the echo is compared to the amplitude of the ambient light, the easier it is to directly determine the object echo based on the peak value, and the less dependent on the reference echo of the adjacent pixel points. Therefore, dynamically adjusting the above-mentioned proportionality coefficient according to the magnitude relationship between the peak value of the echo and the amplitude of the ambient light can advantageously prevent the reference echo of the adjacent pixel points from excessively affecting the selection of the object echo of the target pixel point, thereby improving the ranging accuracy.

[0114] It is easy for a person skilled in the art to understand that the above method for calculating the echo score is only an example, and the present disclosure is not limited thereto. For example, in some embodiments, the echo score of each echo may be calculated based only on the spatial similarity, the temporal similarity, and the peak value of each echo at the target pixel, without considering the peak value of the reference echo at the adjacent pixel.

[0115] Then, if Figure 2 As shown, in step 250, the object echo at the target pixel is determined based on the echo score of each echo at the target pixel. Figure 1A-1B In the example scenario shown, in step 250, based on the echo score of each echo b, e at the target pixel point P2 b and score e , determine the object echo at the target pixel point P2.

[0116] In some embodiments, determining the object echo at the target pixel in step 250 may include: comparing the echo scores of all echoes at the target pixel; and based on the comparison result, selecting the echo with the largest echo score from all echoes as the object echo at the target pixel.

[0117] For example, for Figure 1A-1B In the example scenario shown, in step 250, the echo scores of all echoes b and e at the target pixel point P2 are compared. b 、score e (like Figure 5 ), and based on the comparison result, the echo b with the largest echo score is selected from all the echoes as the object echo at the target pixel point P2.

[0118] Figure 4 The flowchart of FIG. 2 illustrates another example of determining the object echo at the target pixel point (step 250). Figure 4 The example is described in detail with reference to the flowchart of FIG.

[0119] like Figure 4 As shown, in some embodiments, step 250 of determining the object echo at the target pixel point may include the following sub-steps.

[0120] In step 252, it is determined whether the target pixel is a valid pixel or an invalid pixel according to the echo scores of each echo at the target pixel.

[0121] For example, for Figure 1A-1B In the example scenario shown, after calculating the echo scores of each echo with pixel points P1, P2, and P3 as target pixel points in sequence, if the highest echo score of pixel point P1 is still less than the preset echo score threshold, the pixel point P1 can be considered invalid. It is easy for those skilled in the art to understand that the above method for determining valid / invalid pixel points is only an example, and the present disclosure is not limited thereto.

[0122] In step 253, it is determined whether the target pixel is a valid pixel or an invalid pixel.

[0123] In step 254 , in response to determining that the target pixel is an invalid pixel (“No” in step 253 ), the target pixel is deleted.

[0124] The inventor of the present application realizes that if the echo scores of each echo at a certain pixel are very low, the pixel may not actually detect a real object. Deleting such pixels can advantageously avoid the influence of noise on the results and reduce the amount of calculation.

[0125] In step 256 , in response to determining that the target pixel is a valid pixel (“Yes” in step 253 ), the echo score of each echo at the target pixel is recalculated after deleting invalid pixels.

[0126] In some embodiments, the step of recalculating the echo score of each echo at the target pixel is similar to the above steps 220 - 240 , except that only valid pixels are used for calculation.

[0127] For example, recalculating the echo score of each echo at the target pixel in step 256 may include: determining the spatial similarity between the target pixel and the adjacent valid pixels; determining the temporal similarity between each echo at the target pixel and each reference echo at the adjacent valid pixels; and calculating the echo score of each echo at the target pixel based on the spatial similarity, the temporal similarity and the peak value of each echo at the target pixel.

[0128] At step 258, object echoes are determined based on the recalculated echo scores for each echo.

[0129] In some embodiments, in step 258, the recalculated echo scores of all echoes at the target pixel are compared; and based on the comparison result, the echo with the largest recalculated echo score is selected from all echoes as the object echo at the target pixel.

[0130] The boundaries between the various steps in the method described above are merely illustrative. In actual operation, the various steps can be combined arbitrarily, or even synthesized into a single step. In addition, the execution order of the various steps is not limited by the description order, and some steps can be omitted. The operation steps of each embodiment can also be combined with each other in any appropriate order, so as to similarly implement more or less operations than described.

[0131] For example, in some embodiments, step 220 of determining the spatial similarity of pixels and step 230 of determining the temporal similarity of echoes may be performed in parallel. Alternatively, in some embodiments, step 220 of determining the spatial similarity of pixels and step 230 of determining the temporal similarity of echoes may be performed in a specific order.

[0132] For example, in some embodiments, the spatial similarity calculation is performed first. If the spatial similarity of two pixels is low, for example, lower than a preset minimum spatial similarity threshold, the spatial similarity of the two pixels can be set to 0. In this case, the calculation of the temporal similarity of the echoes at the two pixels can even be omitted, and the corresponding temporal similarity can be directly set to 0, thereby advantageously improving the accuracy of peak selection and saving computing resources. Figure 1A-1B In the example scenario shown, if it is determined in step 220 that the spatial similarity between the pixel points P2 and P1 is low, or even lower than the preset minimum spatial similarity threshold, the spatial similarity between the pixel points P2 and P1 can be set to 0, and the calculation of the temporal similarity between the echoes of the pixel points P2 and P1 can be directly skipped. Therefore, although the temporal similarity between the echoes d and e may be high, it will not have a negative impact on the result.

[0133] Combine the following Figure 6 The echo selection device 600 for laser radar according to the embodiment of the present disclosure is described exemplarily. For ease of understanding, Figure 6 The main functional modules and some information interactions of the device 600 are illustrated in FIG. According to an embodiment of the present disclosure, the echo selection device 600 for laser radar can be configured to perform each step of the echo selection method for laser radar according to an embodiment of the present disclosure. Figure 1A-1B , Figure 2-Figure 5 The described content may also be applicable to the corresponding features, and the description of some repeated content will be omitted.

[0134] In the embodiments of the present disclosure, Figure 6 As shown, the echo selection device 600 for laser radar may include:

[0135] The echo data acquisition module 602 is configured to acquire echo data at a pixel point;

[0136] A spatial similarity calculation module 604 is configured to determine the spatial similarity between the target pixel and the adjacent pixels;

[0137] A temporal similarity calculation module 606 is configured to determine the temporal similarity between each echo at a target pixel and each reference echo at a similar pixel;

[0138] an echo score calculation module 608; configured to calculate the echo score of each echo at the target pixel point based on the spatial similarity, the temporal similarity and the peak value of each echo at the target pixel point; and

[0139] The object echo selection module 610 is configured to determine the object echo at the target pixel point based on the echo score of each echo at the target pixel point.

[0140] In some embodiments, the spatial similarity calculation module 604 may be configured to determine the spatial similarity between the target pixel and the adjacent pixel using a preset spatial threshold based on the difference in field of view angles corresponding to the target pixel and the adjacent pixel.

[0141] Alternatively, in some embodiments, the spatial similarity calculation module 604 may be configured to calculate the spatial similarity between the target pixel and the adjacent pixels using a similarity measurement function based on ambient light information within the field of view corresponding to the target pixel and the adjacent pixels.

[0142] Although only two examples of the spatial similarity calculation module 604 performing corresponding functions are described here, these are only examples and the present disclosure is not limited thereto. Moreover, the above examples may also be combined.

[0143] In some embodiments, the time similarity calculation module 606 may be configured to determine the time similarity using a preset time threshold based on the difference between the flight time of each echo and each reference echo.

[0144] Alternatively, in some embodiments, the temporal similarity calculation module 606 may be configured to calculate the temporal similarity using a similarity metric function based on the difference between the flight time of each echo and each reference echo.

[0145] Optionally, in some embodiments, the time similarity calculation module 606 may be configured to dynamically adjust the parameters of the similarity metric function according to the peak values ​​of the two echoes for which the time similarity is calculated.

[0146] Although only two examples of the time similarity calculation module 606 performing corresponding functions are described here, these are only examples and the present disclosure is not limited thereto. Moreover, the above examples may also be combined.

[0147] In some embodiments, the echo score calculation module 608 may include an inter-echo similarity calculation submodule. The inter-echo similarity calculation submodule may be configured to calculate the inter-echo similarity between the echo and each reference echo based on the temporal similarity between the echo and each reference echo and the spatial similarity between the target pixel and the adjacent pixel to which the reference echo belongs.

[0148] Optionally, in some embodiments, the echo-to-echo similarity calculation submodule can be configured to multiply the temporal similarity between the echo and each reference echo and the spatial similarity between the target pixel and the adjacent pixel to which the reference echo belongs to obtain the echo-to-echo similarity between the echo and each reference echo.

[0149] In some embodiments, the echo score calculation module 608 may further include a similar echo peak value calculation submodule which may be configured to calculate the similar echo peak value of the echo according to the inter-echo similarity between the echo and each reference echo and the peak value of each reference echo.

[0150] Optionally, in some embodiments, the similar echo peak value calculation submodule may be configured to perform weighted summation on the peak values ​​of all reference echoes of an echo based on the inter-echo similarity between the echo and each reference echo to obtain a similar echo peak value of the echo.

[0151] In some embodiments, the echo score calculation module 608 may further include an echo score acquisition submodule. The echo score acquisition submodule may be configured to calculate the echo score of the echo according to the peak value of the echo and the peak value of a similar echo of the echo.

[0152] Optionally, in some embodiments, the echo score acquisition submodule may be configured to sum the peak value of the echo and the peak value of similar echoes of the echo according to a preset proportionality coefficient to obtain the echo score of the echo.

[0153] Optionally, in some embodiments, the above-mentioned proportionality coefficient is set to a constant.

[0154] Optionally, in some embodiments, the above-mentioned proportionality coefficient is set to be dynamically adjustable. For example, the above-mentioned proportionality coefficient can be set to be dynamically adjusted according to the magnitude relationship between the peak value of the echo and the amplitude of the ambient light.

[0155] In some embodiments, the object echo selection module 610 may include a comparison submodule and a selection submodule. The comparison submodule may be configured to compare the echo scores of all echoes at the target pixel. The selection submodule may be configured to select the echo with the largest echo score from all echoes as the object echo at the target pixel based on the comparison result.

[0156] Alternatively, in some embodiments, the object echo selection module 610 may include a validity judgment submodule. The validity judgment submodule may be configured to determine whether a target pixel is a valid pixel or an invalid pixel according to the echo scores of each echo at the target pixel.

[0157] In some embodiments, the object echo selection module 610 may further include a pixel point deletion submodule. The pixel point deletion submodule may be configured to delete the target pixel point in response to determining that the target pixel point is an invalid pixel point.

[0158] In some embodiments, the object echo selection module 610 may further include a recalculation submodule. The recalculation submodule may be configured to, in response to determining that the target pixel is a valid pixel, recalculate the echo score of each echo at the target pixel after deleting the invalid pixel. The object echo selection module 610 may further include a determination submodule. The determination submodule may be configured to determine the object echo based on the recalculated echo score of each echo.

[0159] As analyzed above, the echo selection method and device for laser radar proposed in the present disclosure can better select the echo of the real object by referring to similar echoes at pixel points that may measure the same object, thereby improving the accuracy of selecting the echo of the real object, improving the ranging performance of the laser radar in strong ambient light, low laser energy and long distance, optimizing the measurement accuracy of the laser radar, and bringing higher efficiency and practicality to the laser radar.

[0160] Fig. 7A A schematic diagram illustrating an application scenario of performing LiDAR ranging and the ambient light data therein. Figure 7B-7C The examples are respectively Fig. 7A A schematic diagram of the ranging effect (point cloud) of using the echo selection method according to one or more embodiments of the present disclosure and the echo selection method using the prior art when performing lidar ranging in an exemplified scene.

[0161] In the example, a SPAD lidar can be used to Fig. 7A The scene shown in the figure is measured and a point cloud is drawn based on the distance measured for each pixel. Fig. 7A The scene shown has reflectivity panels set up. Figure 7B The schematic diagram of the distance measurement effect (point cloud) using the echo selection method of the prior art is illustrated. Figure 7B As shown in the figure, due to the strong ambient light, the real object echo of the laser is difficult to distinguish from the random ambient light echo, resulting in the peak selection method based on the maximum peak value selecting a large number of wrong echoes, which in turn affects the final distance measurement and point cloud effect. For example, a 10% reflectivity plate can hardly be seen on the point cloud. Figure 7C The schematic diagram illustrates the ranging effect (point cloud) of the echo selection method according to one or more embodiments of the present disclosure. Figure 7C As shown in , the echo selection method according to one or more embodiments of the present disclosure can significantly improve the peak selection effect, thereby improving the distance measurement and point cloud quality. For example, the point cloud of a 10% reflectivity plate is basically correct.

[0162] The embodiment of the present disclosure also provides an electronic device. The electronic device includes one or more processors and one or more memories storing one or more instructions. When the one or more instructions are executed by the one or more processors, the one or more processors execute the steps of the echo selection method for laser radar according to the embodiment of the present disclosure.

[0163] The embodiment of the present disclosure also provides a computer-readable storage medium having one or more instructions stored thereon. When the one or more instructions are executed by a processor, the processor executes the steps of the echo selection method for laser radar according to the embodiment of the present disclosure.

[0164] The embodiment of the present disclosure also provides a computer program product including one or more instructions. When the one or more instructions are executed by a processor, the processor executes the steps of the echo selection method for laser radar according to the embodiment of the present disclosure.

[0165] It should be understood that the instructions in the computer-readable storage medium according to the embodiments of the present disclosure can be configured to perform operations corresponding to the above-mentioned system and method embodiments. When referring to the above-mentioned system and method embodiments, the embodiments of the computer-readable storage medium are clear to those skilled in the art, so they are not described repeatedly. Computer-readable storage media for carrying or including the above-mentioned instructions also fall within the scope of the present disclosure. Such computer-readable storage media may include, but are not limited to, floppy disks, optical disks, magneto-optical disks, memory cards, memory sticks, and the like.

[0166] The embodiments of the present disclosure also provide various devices including components or units for executing the steps of the echo selection method for laser radar in the above-mentioned embodiments.

[0167] It should be noted that the above-mentioned various components or units are only logical modules divided according to the specific functions implemented by them, rather than being used to limit the specific implementation mode, for example, they can be implemented in the form of software, hardware or a combination of software and hardware. In actual implementation, the above-mentioned various components or units can be implemented as independent physical entities, or can also be implemented by a single entity (for example, a processor (CPU or DSP, etc.), an integrated circuit, etc.). For example, a plurality of functions included in a unit in the above embodiments can be implemented by separate devices. Alternatively, a plurality of functions implemented by a plurality of units in the above embodiments can be implemented by separate devices respectively. In addition, one of the above functions can be implemented by a plurality of units.

[0168] In addition, it should be understood that the above series of processes and devices can also be implemented by software and / or firmware. In the case of being implemented by software and / or firmware, from a storage medium or a network to a computer with a dedicated hardware structure, such as Figure 8 The general-purpose computer 800 shown installs the programs constituting the software, and when the various programs are installed, the computer can execute various functions and the like. Figure 8 An example block diagram of a computer that can be implemented as an echo selection device, an application device, and a system for a laser radar according to an embodiment of the present disclosure is shown.

[0169] exist Figure 8 In the embodiment, a central processing unit (CPU) 801 executes various processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage section 808 to a random access memory (RAM) 803. In the RAM 803, data required when the CPU 801 executes various processes and the like is also stored as needed.

[0170] The CPU 801, the ROM 802, and the RAM 803 are connected to one another via a bus 804. An input / output interface 805 is also connected to the bus 804.

[0171] The following components are connected to the input / output interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet.

[0172] A drive 810 is also connected to the input / output interface 805 as needed. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 810 as needed so that a computer program read therefrom is installed into the storage section 808 as needed.

[0173] In the case where the above-described series of processing is realized by software, a program constituting the software is installed from a network such as the Internet or a storage medium such as the removable medium 811 .

[0174] Those skilled in the art should understand that such storage media is not limited to Figure 8 The removable medium 811 shown has a program stored therein and is distributed separately from the device to provide the program to the user. Examples of the removable medium 811 include magnetic disks (including floppy disks (registered trademark)), optical disks (including compact disk read-only memory (CD-ROM) and digital versatile disks (DVD)), magneto-optical disks (including minidiscs (MD) (registered trademark)), and semiconductor memories. Alternatively, the storage medium may be a ROM 802, a hard disk included in the storage portion 808, or the like, in which the program is stored and distributed to the user together with the device containing them.

[0175] The exemplary embodiments of the present disclosure are described above with reference to the accompanying drawings, but the present disclosure is certainly not limited to the above examples. Those skilled in the art may obtain various changes and modifications within the scope of the appended claims, and it should be understood that these changes and modifications will naturally fall within the technical scope of the present disclosure.

[0176] Although the present disclosure and its advantages have been described in detail, it should be understood that various changes, substitutions and transformations can be made without departing from the spirit and scope of the present disclosure as defined by the appended claims. Moreover, the terms "including", "comprising" or any other variants of the embodiments of the present disclosure are intended to cover non-exclusive inclusions, so that the process, method, article or equipment including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of further restrictions, the elements defined by the statement "including one..." do not exclude the presence of other identical elements in the process, method, article or equipment including the elements.

[0177] Embodiments of the present disclosure also include the following.

[0178] 1. A method for selecting echoes for a laser radar, comprising:

[0179] Acquire echo data at a pixel point;

[0180] Determine the spatial similarity between the target pixel and the adjacent pixels;

[0181] Determine the temporal similarity between each echo at the target pixel and each reference echo at a nearby pixel;

[0182] Calculating an echo score of each echo at the target pixel based on the spatial similarity, the temporal similarity, and the peak value of each echo at the target pixel; and

[0183] The object echo at the target pixel is determined based on the echo score of each echo at the target pixel.

[0184] 2. The method according to item 1, wherein determining the spatial similarity between the target pixel and the adjacent pixels comprises:

[0185] Based on the difference in the field of view angles corresponding to the target pixel and the adjacent pixel, a preset spatial threshold is used to determine the spatial similarity between the target pixel and the adjacent pixel.

[0186] 3. The method according to item 1, wherein determining the spatial similarity between the target pixel and the adjacent pixels comprises:

[0187] Based on the ambient light information in the field of view corresponding to the target pixel and the adjacent pixels, a similarity measurement function is used to calculate the spatial similarity between the target pixel and the adjacent pixels.

[0188] 4. The method according to item 3, wherein the ambient light information includes at least one of the following:

[0189] Ambient light data; or

[0190] Echo data baseline.

[0191] 5. The method according to item 1, wherein determining the temporal similarity between each echo at the target pixel and each reference echo at a similar pixel comprises:

[0192] The temporal similarity is determined using a preset time threshold based on the difference between the flight time of each echo and each reference echo.

[0193] 6. The method according to item 1, wherein determining the temporal similarity between each echo at the target pixel and each reference echo at a similar pixel comprises:

[0194] The time similarity is calculated using a similarity metric function based on the difference between the flight time of each echo and each reference echo.

[0195] 7. The method according to item 6, wherein the similarity measurement function is configured to dynamically adjust parameters according to the peak values ​​of the two echoes for calculating the temporal similarity.

[0196] 8. The method according to item 1, wherein the step of calculating the echo score of each echo at the target pixel comprises:

[0197] Calculating the inter-echo similarity between the echo and each of the reference echoes according to the temporal similarity between the echo and each of the reference echoes and the spatial similarity between the target pixel and the adjacent pixel to which the reference echo belongs;

[0198] Calculating a similar echo peak value of the echo according to the echo-echo similarity between the echo and each reference echo and the peak value of each reference echo;

[0199] An echo score of the echo is calculated according to a peak value of the echo and a peak value of a similar echo of the echo.

[0200] 9. The method according to item 8, wherein the calculation of the inter-echo similarity between the echo and each reference echo includes: multiplying the temporal similarity between the echo and each reference echo and the spatial similarity between the target pixel point and the adjacent pixel point to which the reference echo belongs, so as to obtain the inter-echo similarity between the echo and each reference echo.

[0201] 10. The method according to item 8, wherein the calculating of the similar echo peak value of the echo comprises: based on the echo similarity between the echo and each reference echo, performing weighted summation on the peak values ​​of all reference echoes of the echo to obtain the similar echo peak value of the echo.

[0202] 11. The method according to item 8, wherein the calculating the echo score of the echo comprises: summing the peak value of the echo and the peak value of similar echoes of the echo according to a preset proportionality coefficient to obtain the echo score of the echo.

[0203] 12. The method according to item 11, wherein the proportionality factor is set to a constant.

[0204] 13. The method according to item 11, wherein the proportionality factor is set to be dynamically adjustable.

[0205] 14. The method according to item 13, wherein the proportionality coefficient is configured to be dynamically adjusted according to the magnitude relationship between the peak value of the echo and the amplitude of the ambient light.

[0206] 15. According to the method of item 1, determining the object echo at the target pixel point comprises:

[0207] comparing the echo scores of all echoes at the target pixel; and

[0208] Based on the comparison result, the echo with the largest echo score is selected from all echoes as the object echo at the target pixel.

[0209] 16. According to the method of item 1, determining the object echo at the target pixel point comprises:

[0210] According to the echo scores of each echo at the target pixel point, it is determined whether the target pixel point is a valid pixel point or an invalid pixel point.

[0211] 17. The method according to item 16, wherein, in response to determining that the target pixel is an invalid pixel, the target pixel is deleted.

[0212] 18. The method according to item 17, wherein, in response to determining that the target pixel is a valid pixel,

[0213] After deleting the invalid pixels, recalculating the echo score of each echo at the target pixel; and

[0214] The object echo is determined based on the recalculated echo score of each echo.

[0215] 19. An electronic device comprising:

[0216] one or more processors; and

[0217] One or more memories having one or more instructions stored thereon;

[0218] When the one or more instructions are executed by the one or more processors, the one or more processors are caused to perform the steps of the method according to any one of items 1-18.

[0219] 20. A computer-readable storage medium having one or more instructions stored thereon, which, when executed by a processor, cause the processor to perform the steps of the method according to any one of items 1-18.

[0220] 21. A computer program product comprising one or more instructions, which, when executed by a processor, cause the processor to perform the steps of the method according to any one of items 1-18.

Claims

1. A method for selecting echoes for a laser radar, comprising: Acquire echo data at the pixel point; Determine the spatial similarity between the target pixel and the adjacent pixels; Determine the temporal similarity between each echo at the target pixel and each reference echo at a nearby pixel; Calculating an echo score of each echo at the target pixel based on the spatial similarity, the temporal similarity, and the peak value of each echo at the target pixel; as well as The object echo at the target pixel is determined based on the echo score of each echo at the target pixel.

2. The method of claim 1, wherein: Determining the spatial similarity between the target pixel and the adjacent pixels includes: Based on the difference in the field of view angles corresponding to the target pixel and the adjacent pixel, a preset spatial threshold is used to determine the spatial similarity between the target pixel and the adjacent pixel.

3. The method of claim 1, wherein: Determining the spatial similarity between the target pixel and the adjacent pixels includes: Based on the ambient light information in the field of view corresponding to the target pixel and the adjacent pixels, a similarity measurement function is used to calculate the spatial similarity between the target pixel and the adjacent pixels.

4. The method of claim 3, wherein: The ambient light information includes at least one of the following: Ambient light data; or Echo data baseline.

5. The method of claim 1, wherein: Determining the time similarity between each echo at the target pixel and each reference echo at a similar pixel includes: The temporal similarity is determined using a preset time threshold based on the difference between the flight time of each echo and each reference echo.

6. The method of claim 1, wherein: Determining the time similarity between each echo at the target pixel and each reference echo at a similar pixel includes: The temporal similarity is calculated using a similarity metric function based on the difference between the flight time of each echo and each reference echo.

7. The method of claim 1, wherein: The calculating the echo score of each echo at the target pixel point comprises: Calculating the inter-echo similarity between the echo and each of the reference echoes according to the temporal similarity between the echo and each of the reference echoes and the spatial similarity between the target pixel and the adjacent pixel to which the reference echo belongs; Calculating a similar echo peak value of the echo according to the echo-echo similarity between the echo and each reference echo and the peak value of each reference echo; An echo score of the echo is calculated according to a peak value of the echo and a peak value of a similar echo of the echo.

8. An electronic device comprising: one or more processors; as well as One or more memories having one or more instructions stored thereon; When the one or more instructions are executed by the one or more processors, the one or more processors are caused to perform the steps of the method according to any one of claims 1-7.

9. A computer-readable storage medium having one or more instructions stored thereon, which, when executed by a processor, cause the processor to perform the steps of the method according to any one of claims 1 to 7.

10. A computer program product comprising one or more instructions which, when executed by a processor, cause the processor to perform the steps of the method according to any one of claims 1 to 7.

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