Charge configuration method, electronic equipment and computer readable storage medium

Abstract

The invention discloses an energy charging configuration method, electronic equipment and a computer readable storage medium, the energy charging configuration method is applied to a laser radar, the laser radar comprises a transmitting device, the energy charging configuration method comprises the following steps: obtaining N frames of target point clouds collected by the laser radar, the N frames of target point clouds are in one-to-one correspondence with N energy charging durations of the transmitting device, and the N frames of target point clouds are in one-to-one correspondence with N energy charging durations of the transmitting device; n is an integer greater than 1; calculating N evaluation function values corresponding to the N frames of target point clouds according to the reflectivity information of the N frames of target point clouds; the time gradient curve of the N evaluation function values is determined, and the time gradient curve is used for representing the change rate of the evaluation function values relative to the charging duration; based on the time gradient curve, obtaining a target energy charging duration in the N energy charging durations; and configuring laser radar energy charging according to the target energy charging duration. Through the above mode, active alignment can be carried out with small transmitting power on the premise of keeping a high detection rate, thereby improving the precision of the laser radar in long-distance detection.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of emission charging, and in particular, to a charging configuration method, an electronic device, and a computer-readable storage medium. Background Art

[0002] AA (Active Alignment) is a technology used in optical systems, especially during the assembly of lidar (Light Detection and Ranging) and sensors, to ensure the accurate alignment of optical components. This technology typically adjusts the position and angle of optical elements through precise mechanical adjustment and real-time feedback to achieve optimal beam alignment or precise configuration of the optical system. In lidar systems, AA technology is commonly used for the alignment of laser emitters and receivers to ensure the accurate emission and reception of laser beams, thereby improving measurement accuracy and system performance.

[0003] Currently, due to the limitations of machines and sites, the distance between the laser emitter and the target object in the AA scenario is relatively close, which may lead to errors when lidar is applied to long-distance detection. Summary of the Invention

[0004] The embodiments of the present application provide a charging configuration method, an electronic device, and a computer-readable storage medium, which can simulate the situation of long-distance detection of lidar during the AA process, and at the same time solve the problem of different charging configurations caused by the consistency of transmitters among multiple transmitter boards, thereby improving the detection accuracy of lidar.

[0005] In a first aspect, the embodiments of the present application provide a charging configuration method applied to a lidar. The lidar includes a transmitting device. The charging configuration method includes: obtaining N frames of target point clouds collected by the lidar, where the N frames of target point clouds correspond one-to-one to N charging durations of the transmitting device, and N is an integer greater than 1; calculating N evaluation function values corresponding to the N frames of target point clouds according to the reflectivity information of the N frames of target point clouds; determining a time gradient curve of the N evaluation function values, where the time gradient curve is used to represent the change rate of the evaluation function value with respect to the charging duration; obtaining a target charging duration from the N charging durations based on the time gradient curve; and configuring the lidar charging according to the target charging duration.

[0006] By obtaining the N evaluation function values corresponding to N frames of target point clouds and the time gradient curve representing the change rate of the evaluation function value with respect to the charging duration, it is possible to find a relatively small charging power while maintaining a high detection rate. Performing AA based on this charging power can correspond to the scenario of long-range detection by a lidar, which is conducive to improving the detection accuracy of the lidar in practical applications. Secondly, by taking the difference of the N evaluation function values, it is possible to determine the change of the point cloud state, which in turn helps to balance a better point cloud state and a shorter charging duration.

[0007] In one or more embodiments, determining the time gradient curve of the N evaluation function values includes: taking the first-order difference of the N evaluation function values with the unit charging duration as the time step to obtain the time gradient curve, where the abscissa of the time gradient curve represents the charging time, and the ordinate of the time gradient curve represents the change amount of the evaluation function value, and the change amount of the evaluation function value is the absolute value of the difference between the evaluation function values of the target point clouds corresponding to two adjacent charging durations.

[0008] In one or more embodiments, based on the time gradient curve, obtaining the target charging duration among the N charging durations includes: obtaining the charging duration corresponding to the peak point on the time gradient curve; and obtaining the target charging duration according to the charging duration corresponding to the peak point and a preset evaluation function threshold.

[0009] The peak point on the time gradient curve is the point with the largest change rate of the evaluation function value, which means that after the peak point on the gradient curve, the change rate of the evaluation function value gradually decreases, helping to find the point corresponding to a better point cloud state on the time gradient curve. Then, according to the charging duration corresponding to the peak point and the preset evaluation function threshold, it is possible to find the point corresponding to the smallest charging duration among the points corresponding to the better point cloud state on the time gradient curve, so as to be closer to the situation during long-range detection during the AA process and improve the detection accuracy of the lidar.

[0010] In one or more embodiments, obtaining the target charging duration according to the charging duration corresponding to the peak point and a preset evaluation function threshold includes: determining the point with the smallest corresponding charging duration among the points on the time gradient curve where the charging duration is greater than the charging duration corresponding to the peak point and the change amount of the evaluation function value is less than the evaluation function threshold as the target data point; and obtaining the target charging duration according to the charging duration corresponding to the target data point. According to the charging duration being greater than the charging duration corresponding to the peak point and the change amount of the evaluation function value being less than the evaluation function threshold, it can be determined that the point cloud state tends to be stable and the point cloud state is better. In a better point cloud state, determining the point with the smallest charging duration as the target data point and charging the emitting device with the charging duration corresponding to the target data point can achieve AA with the smallest charging duration on the basis of achieving a high detection rate, which is conducive to improving the accuracy during long-range detection.

[0011] In one or more embodiments, the reflectivity information includes gray values, the evaluation function value is the two-dimensional distribution feature value of the target point cloud, and according to the reflectivity information of N frames of target point clouds, N evaluation function values corresponding to the N frames of target point clouds are calculated, including: for each frame of target point cloud, obtaining the probability density value of the two-dimensional Gaussian distribution of each point cloud point; calculating the product of the gray value of each point cloud point and the probability density value to obtain the weighted gray value of each point cloud point; summing the weighted gray values of each point cloud point to obtain the two-dimensional distribution feature value of the target point cloud.

[0012] In one or more embodiments, the reflectivity information includes gray values, the evaluation function value is the weighted average gray value of the target point cloud, and according to the reflectivity information of N frames of target point clouds, N evaluation function values corresponding to the N frames of target point clouds are calculated, including: for each frame of target point cloud, obtaining the weight value of each point cloud point according to the distance between each point cloud point and the center point of the target point cloud and the normal distribution function; calculating the weighted average gray value of the target point cloud according to the weight value of each point cloud point and the gray value of each point cloud point.

[0013] In one or more embodiments, the reflectivity information includes gray values, the evaluation function value is the average gray value of the target point cloud, and according to the reflectivity information of N frames of target point clouds, N evaluation function values corresponding to the N frames of target point clouds are calculated, including: for each frame of target point cloud, calculating the average value of the gray values of all point cloud points to obtain the average gray value of the target point cloud.

[0014] In one or more embodiments, obtaining the target charging duration according to the charging duration corresponding to the target data point includes: determining the shortest charging duration among the charging durations corresponding to the target data points as the target charging duration.

[0015] In a second aspect, an embodiment of the present application provides an electronic device, including: at least one processor and a memory; the memory is coupled to the processor, and the memory is used to store instructions or programs. When the instructions or programs are executed by the at least one processor, the at least one processor executes the charging configuration method as described above.

[0016] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, which when executed, implements the charging configuration method as described above.

[0017] The beneficial effects of this application are as follows: The charging configuration method according to the embodiments of this application can find a relatively small charging power while maintaining a high detection rate by obtaining N evaluation function values corresponding to N frames of target point clouds and a time gradient curve representing the change rate of the evaluation function value with respect to the charging duration. Based on this charging power, AA can be performed to simulate the scenario of long-distance detection by lidar, thereby improving the detection accuracy of lidar in actual applications. On the other hand, in the case where the lidar includes multiple transmitting devices, the charging configuration method according to the embodiments of this application can find the optimal charging duration for each transmitting device by traversing the charging of each transmitting device, and solve the problem of different transmitting charging configurations caused by the inconsistency of the transmitting devices. Description of the Drawings

[0018] One or more embodiments are illustrated by way of example in the accompanying drawings, and these illustrative descriptions are not intended to limit the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements.

[0019] Figure 1 is a flowchart of the charging configuration method provided by the embodiments of this application; Figure 2 is a schematic diagram of an implementation manner of the charging configuration method provided by the embodiments of this application; Figure 3 is a schematic diagram of an implementation manner of the charging configuration method provided by the embodiments of this application; Figure 4 is a schematic diagram of an implementation manner of the charging configuration method provided by the embodiments of this application; Figure 5 is a schematic diagram of an implementation manner of the charging configuration method provided by the embodiments of this application; Figure 6 is a schematic diagram of an implementation manner of the charging configuration method provided by the embodiments of this application; Figure 7 is a schematic diagram of the correspondence between the evaluation function value and the charging duration provided by the embodiments of this application; Figure 8 is related to Figure 7 is a schematic diagram of the time gradient curve corresponding to the schematic diagram shown; Figure 9 is a schematic diagram of the structure of the electronic device provided by the embodiments of this application.

[0020] Reference Numerals: 900, electronic device; 901, processor; 902, memory. Detailed Embodiments

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and elaborately described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are some, but not all, of the embodiments of this application. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0022] It should be noted that when an element is expressed as "connected to" another element, it can be directly connected to the other element, or there can be one or more intermediate elements therebetween. In addition, the technical features involved in the various embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0023] Please refer to Figure 1 , Figure 1 which is a flowchart of the energy charging configuration method provided for the embodiments of this application. Among them, this energy charging configuration method is applied to a lidar, and the lidar can be a solid-state lidar, a semi-solid-state lidar, etc., and this application does not make a unique limitation on this. The lidar can be applied to any device that needs to perform laser detection, such as a mobile robot, a ship, or a vehicle. The lidar includes a transmitting device for emitting detection laser and a receiving device for receiving echo signals. Specifically, the transmitting device can emit pulsed detection laser, and the detection laser is projected onto the target object, and the signal formed by reflection from the target object is the echo signal; the receiving device receives the echo signal and obtains relevant information of the target object based on the echo signal, such as the distance from the target object.

[0024] As Figure 1 shown, the energy charging configuration method includes the following method steps: Step 101: Obtain N frames of target point clouds collected by the lidar, where the N frames of target point clouds correspond one-to-one to the N charging durations of the transmitting device, and N is an integer greater than 1.

[0025] Specifically, the transmitting device includes at least one transmitting array, and the transmitting array includes at least one transmitting unit. In some embodiments, the transmitting device is a transmitting board, the transmitting array is a VCSEL (Vertical-Cavity Surface-Emitting Laser) array, and the transmitting unit is a VCSEL. In some embodiments, the transmitting array is an EEL (Edge-Emitting Laser) array, and the transmitting unit is an EEL.

[0026] Before each emission of detection laser by the emission device, the emission units in the emission device need to be charged based on a preset charging duration. Among them, the charging duration of each emission unit is the same during each charging, and it is the charging duration for the emission device to emit detection laser this time. Configure the emission device to emit detection laser N times, and before each emission of detection laser by the emission device, the charging duration of the emission unit is different, that is, the emission unit is charged based on N charging durations. Based on the detection laser emitted by the emission device once, an echo signal can be obtained, and then a frame of target point cloud can be obtained; based on the detection laser emitted by the emission device N times, N echo signals can be obtained, and then N frames of target point cloud can be obtained. It can be understood that during the acquisition process of N frames of target point cloud, according to the acquisition timing of N frames of target point cloud, the N charging durations increase at equal intervals, that is, according to the time sequence of acquiring the target point cloud, a uniformly increasing charging duration is assigned to each frame in turn. For example, the charging duration corresponding to the acquisition of the first frame of target point cloud is T1, the charging duration T2 corresponding to the acquisition of the second frame of target point cloud is T2 = T1 + Δt, the charging duration T3 corresponding to the acquisition of the third frame of target point cloud is T3 = T2 + Δt,..., the charging duration T N is T N = T N-1 + Δt.

[0027] It should be noted that in the embodiments of the present application, the charging duration is positively correlated with the charging power, that is, the longer the charging duration, the greater the charging power, and the greater the power of the detection laser emitted by the emission unit; conversely, the shorter the charging duration, the smaller the charging power, and the smaller the power of the detection laser emitted by the emission unit.

[0028] In addition, the lidar may include one or more emission devices. When the lidar includes multiple emission devices, such as multiple emission arrays located on different emission boards, any emission device can implement the charging configuration method provided by the embodiments of the present application.

[0029] Step 102: Calculate N evaluation function values corresponding to N frames of target point cloud according to the reflectivity information of N frames of target point cloud.

[0030] The point cloud evaluation function is used to evaluate the state of the point cloud, such as evaluating the detection rate of the point cloud or the alignment degree between the transmitting device and the receiving device, and outputting the corresponding evaluation function value. Among them, in the LiDAR system, the detection rate of the point cloud refers to the proportion of the system that can successfully detect and identify the target object. From a mathematical perspective, the detection rate of the point cloud can be defined as the proportion of all actual existing targets that are correctly detected. For example, in a scene, there are 100 potential targets, and the lidar and the corresponding processing algorithm successfully identify 95 of them, then the detection rate of the system is 95%. A high detection rate means higher point cloud quality, the system can more accurately identify the target object, and the accuracy and reliability of AA are also higher. In some embodiments, the relationship between the point cloud state and the evaluation function value is positively correlated, that is, the larger the evaluation function value, the better the point cloud state; conversely, the lower the evaluation function value, the worse the point cloud state.

[0031] Specifically, first, for each frame of target point cloud, according to the reflectivity information of each point cloud point of the target point cloud, the gray value of each point cloud point is obtained. Among them, the reflectivity information, also known as the reflection intensity (Intensity), refers to the proportion of the energy reflected back by the surface when the laser beam emitted by the lidar irradiates the object surface. The gray value refers to the brightness level of each pixel point in the image. In digital image processing, the gray value is usually represented by an integer from 0 to 255. 0 represents black, 255 represents white, and the intermediate values represent different levels of gray. The point cloud data itself usually consists of a series of spatial coordinates (x, y, z) and other additional information (such as intensity, color, reflectivity, timestamp, etc.). For the point cloud data collected by the lidar or optical sensor, the gray value is usually used to represent the intensity of the reflected signal received by the sensor. In most LiDAR systems, the gray value of the point cloud corresponds to the reflectivity of each point. In some embodiments, the relationship between the reflectivity and the gray value is positively correlated, that is, when the reflectivity increases, the gray value will also increase accordingly; conversely, when the reflectivity decreases, the gray value will also decrease accordingly. Specifically, when the reflectivity is 100%, the gray value is 255; when the reflectivity is 0, the gray value is 0.

[0032] After that, for each frame of target point cloud, based on the gray value of each point cloud point and the point cloud evaluation function, the evaluation function value of the target point cloud is calculated, so that N evaluation function values can be obtained according to N frames of target point cloud.

[0033] In some embodiments, the reflectivity information includes the gray value, and the evaluation function value is the two-dimensional distribution feature value of the target point cloud. Then the specific implementation process of obtaining N evaluation function values can be as Figure 2 shown, that is, the specific implementation process of step 102 includes the following steps: Step 201: For each frame of target point cloud, obtain the probability density value of the two-dimensional Gaussian distribution of each point cloud point.

[0034] Specifically, define the mean and covariance matrix of the two-dimensional Gaussian distribution. For example, the covariance matrix is ​​a standard normal distribution [[500, 0], [0, 500]], with no correlation between the x-axis and y-axis directions; the mean is set at the origin [0, 0]. Using the determined mean and covariance matrix, calculate the probability density value of the two-dimensional Gaussian distribution corresponding to each point cloud point in each frame of the target point cloud.

[0035] Step 202: Calculate the product of the grayscale value of each point cloud point and the probability density value to obtain a weighted grayscale value of each point cloud point.

[0036] The weight value indicates the contribution of a data point to the entire weighted average. For example, if a data point has a larger value and a higher weight, it will have a greater impact on the weighted average. By using the probability density of a two-dimensional normal distribution as the weight, more emphasis can be placed on the central part of the target point cloud in each frame.

[0037] Step 203: summing the weighted grayscale value of each point in the point cloud to obtain the two-dimensional distribution feature value of the target point cloud.

[0038] Specifically, the two-dimensional distribution eigenvalue is the sum of the weighted grayscale values ​​of each point cloud point. The weighted average value calculates a representative average value by assigning different weights to different data points. Compared with the ordinary arithmetic mean, the weighted average value can reflect the importance of the position of the point cloud point. The grayscale characteristics of a specific image area are analyzed using the two-dimensional normal distribution weighted method. The calculated two-dimensional distribution eigenvalue can effectively reflect the grayscale distribution characteristics of the area of ​​interest. The method is efficient and universal, which is conducive to highlighting the central area of ​​the field of view and reducing the influence of edge noise, and can achieve better alignment of the light receiving and light receiving paths.

[0039] Understandably, Figure 2 The implementation method of obtaining N evaluation function values ​​based on one point cloud evaluation function is only exemplified. In other embodiments, N evaluation function values ​​may also be obtained based on other point cloud evaluation functions.

[0040] For example, in some embodiments, the reflectivity information includes a grayscale value, and the evaluation function value is a weighted average grayscale value of the target point cloud. According to the reflectivity information of N frames of target point clouds, the specific implementation process of obtaining N evaluation function values ​​can be as follows: Figure 3 As shown, the specific implementation process of step 102 includes the following steps: Step 301: For each frame of the target point cloud, the weight of each point cloud point is obtained according to the distance between each point cloud point and the center point of the target point cloud and the normal distribution function.

[0041] Specifically, calculate the distance from each point cloud point to the center point of the target point cloud, and then apply the normal function to calculate the weight of each point cloud point. The weight of the point cloud point decreases as the distance from the center point of its target point cloud increases.

[0042] In some embodiments, the coordinates of the center point of the target point cloud are the mean of the coordinates of all point cloud points in the target point cloud, emphasizing the importance of the central region of the field of view. In other embodiments, the coordinates of the center point of the target point cloud can be determined according to the actual situation. By defining the center point and performing weighted sampling on the gray values of the point cloud points, the point cloud points near the center point can obtain higher weights, effectively highlighting the region of interest (ROI), weakening the influence of noise points or irrelevant points far from the center point, improving the accuracy of local analysis, and the parameters (mean and standard deviation) of the normal distribution function can be adjusted according to the specific scenario, with high flexibility.

[0043] Step 302: Calculate the weighted average gray value of the target point cloud according to the weight of each point cloud point and the gray value of each point cloud point.

[0044] Specifically, using the weight of each point cloud point as the weight, calculate the weighted average of the gray values of all point cloud points in the target point cloud, which is the weighted average gray value of the target point cloud.

[0045] For another example, in some embodiments, the reflectivity information includes gray values, and the evaluation function value is the average gray value of the target point cloud. Then the specific implementation process of obtaining N evaluation function values can be as Figure 4 shown, that is, the specific implementation process of step 102 includes the following steps: Step 401: For each frame of the target point cloud, calculate the average value of the gray values of all point cloud points to obtain the average gray value of the target point cloud.

[0046] Specifically, in this embodiment, taking the mean of the gray values corresponding to the reflectivities of all point cloud points in the full map as the standard for judging the alignment degree of the light emitting and receiving paths can simply and effectively reflect the global characteristics and avoid the deviation that may be caused by only judging based on local data.

[0047] Step 103: Determine the time gradient curve of N evaluation function values, where the time gradient curve is used to represent the change rate of the evaluation function value with respect to the charging duration.

[0048] Among them, according to the time gradient curve of N evaluation function values, the change situation of the target point cloud can be determined, which helps to find the better point cloud state and the shorter charging duration at the same time.

[0049] In some embodiments, the specific implementation process of step 103 includes the following method steps: taking the unit charging duration as the time step, performing a first-order difference on N evaluation function values to obtain a time gradient curve, where the abscissa of the time gradient curve represents the charging time, the ordinate of the time gradient curve represents the change in the evaluation function value, and the change in the evaluation function value is the absolute value of the difference between the evaluation function values of the target point clouds corresponding to two adjacent charging durations.

[0050] Specifically, according to the ratio of the difference between the (K + 1)-th evaluation function value and the K-th evaluation function value to the unit charging duration, a time gradient curve is obtained, where K is an integer greater than or equal to 1 and less than or equal to N - 1. The K-th evaluation function value is the evaluation function value of the current charging duration, the (K + 1)-th evaluation function value is the evaluation function value of the next charging duration, and the difference between the (K + 1)-th evaluation function value and the K-th evaluation function value is the difference between the evaluation function value of the next charging duration and the evaluation function value of the current charging duration, thereby realizing the forward difference process and obtaining the time gradient curve, where the time step in this difference process is the difference between the next charging duration and the current charging duration (i.e., the unit charging duration). Based on the gradient of the time gradient curve, the change trend of the evaluation function value with the charging duration can be determined, and then the change situation of the point cloud state can be determined, which helps to simultaneously find a better point cloud state and a smaller charging duration. Specifically, if the gradient gradually decreases, it can be determined that the change in the evaluation function value with the charging duration decreases, and then the change in the point cloud state decreases, and the change in the detection rate of the point cloud decreases; if the gradient gradually increases, it can be determined that the change in the evaluation function value with the charging duration increases, and then the change in the point cloud state increases, and the change in the detection rate of the point cloud increases. Thus, according to the time gradient curve, on the basis of determining a better point cloud state, a smaller charging duration can be found, which can not only achieve a better light emission and reception optical path alignment effect at AA, but also improve the accuracy during long-distance detection. Among them, a better point cloud state can be determined by the detection rate of the point cloud being greater than the corresponding threshold, and the threshold can be set based on the actual application scenario, and the embodiments of the present application do not make specific limitations in this regard. For example, in a specific embodiment, the detection rate threshold can be set to 90%, that is, the detection rate of the point cloud should be greater than 90%.

[0051] It can be understood that this embodiment takes the forward difference as an example. Since the forward difference only depends on the data of the current point and the next point, it is very suitable for occasions that require real-time calculation. Moreover, it is very intuitive and simple to implement, without the need for complex algorithms or a large amount of data storage. In other embodiments, backward differences or central differences can also be set. For the backward difference, the specific implementation is as follows: obtain the time gradient curve according to the ratio of the difference between the Jth evaluation function value and the (J - 1)th evaluation function value to the unit charging duration, where J is an integer greater than 1 and less than or equal to N + 1. The backward difference can be well used for retrospective data analysis because it uses the current point and the previous data points. Moreover, the backward difference is usually more stable than the forward difference because it does not depend on future data. For the central difference, the specific implementation is as follows: obtain the time gradient curve according to the ratio of half of the difference between the (I + 1)th evaluation function value and the (I - 1)th evaluation function value to the unit charging duration, where I is an integer greater than 1 and less than or equal to N - 1. Compared with the forward difference and the backward difference, the central difference provides higher precision, and its error is second-order, which means that it can provide a more accurate derivative estimate for small time steps. And because it uses the data in both the front and back directions at the same time, it is more sensitive to the changes of the function.

[0052] Step 104: Based on the time gradient curve, obtain the target charging duration among the N charging durations.

[0053] Among them, the target charging duration is the charging duration corresponding to a relatively high detection rate and a relatively small charging power, so as to simulate long-distance detection on the basis of ensuring the detection rate.

[0054] In some embodiments, as Figure 5 shown, the specific implementation process of Step 104 includes the following steps: Step 501: Obtain the charging duration corresponding to the peak point on the time gradient curve.

[0055] Step 502: Obtain the target charging duration according to the charging duration corresponding to the peak point and the preset evaluation function threshold.

[0056] Specifically, the peak point on the time gradient curve is the point with the largest change rate of the evaluation function value, which means that after the peak point on the gradient curve, the change rate of the evaluation function value gradually decreases, and the point cloud state gradually tends to be stable (that is, the detection rate of the point cloud has basically remained unchanged), which helps to find the point corresponding to the better point cloud state on the time gradient curve. Then, according to the charging duration corresponding to the peak point and the preset evaluation function threshold, the point corresponding to the smaller charging duration can be found among the points corresponding to the better point cloud state on the time gradient curve, which can not only achieve a better light emission and reception optical path alignment effect at AA, but also improve the accuracy during long-distance detection.

[0057] Among them, the evaluation function threshold is a preset threshold, which can be set based on the actual application scenario, and the embodiments of the present application do not make specific limitations in this regard. In a specific implementation manner, the evaluation function threshold can be determined in the following way: Obtain the first point cloud of T frames collected by the lidar, where the first point cloud of T frames corresponds one-to-one to the T charging durations of the emission plate, and T is an integer greater than 1; Calculate the T evaluation function values corresponding to the third point cloud of T frames according to the reflectivity information of the third point cloud of T frames and the point cloud evaluation function; Determine the normal distribution curve according to the T evaluation function values; Determine the evaluation function threshold according to the normal distribution curve (for example, according to the standard deviation of the normal distribution curve).

[0058] In some embodiments, as Figure 6 shown, the specific implementation process of step 502 includes the following method steps: Step 601: Determine the target data point as the point with the smallest corresponding charging duration among the points on the time gradient curve whose charging duration is greater than the charging duration corresponding to the peak point and the change amount of the evaluation function value is less than the evaluation function threshold.

[0059] Step 602: Obtain the target charging duration according to the charging duration corresponding to the target data point.

[0060] Specifically, when the charging duration is greater than the charging duration corresponding to the peak point and the change amount of the evaluation function value is less than the evaluation function threshold, the charging duration with better point cloud state and more stable point cloud state can be determined. Thus, while ensuring a better point cloud state, select the point with the smallest charging duration (i.e., the target data point). Charging the emitting device according to the target charging duration determined by the charging duration corresponding to the target data point can achieve the alignment of the optical path with the smallest possible emission power on the basis of ensuring a high detection rate, which is beneficial to improving the detection accuracy of the lidar.

[0061] Referring to Figure 7 and Figure 8 , Figure 7 exemplarily shows the correspondence between the evaluation function value and the charging duration in a specific embodiment, where the abscissa is the charging duration in nanoseconds and the ordinate is the evaluation function value; Figure 8 exemplarily shows the time gradient curve obtained on the basis of the correspondence between the evaluation function value and the charging duration shown in Figure 7 , where the abscissa is the time series number of the data points in the data set obtained by taking the first-order difference of N evaluation function values, and the ordinate is the difference value of the evaluation function value.

[0062] As Figure 7 shown, the evaluation function value is positively correlated with the charging duration, and the increasing trend of the evaluation function value gradually flattens out as the charging duration increases. AsFigure 8 As shown, the time series number of the peak point is 2, the preset threshold is 15, and the points with a charging duration greater than the charging duration corresponding to the peak point and a change amount of the evaluation function value less than the evaluation function threshold include all points starting from the time series number 3 (including the point with the time series number 3). Among these points, the point with the smallest corresponding charging duration (i.e., the target data point) is the point with the time series number 3. As Figure 7 shown, if the charging duration corresponding to the point with the time series number 3 is 60 nanoseconds and 70 nanoseconds, then 60 nanoseconds or 70 nanoseconds can be selected as the target charging duration. A charging duration greater than the charging duration corresponding to the peak point indicates that the evaluation function value meets the requirements for the point cloud detection rate, and a change amount of the evaluation function value less than the evaluation function threshold indicates that the change in the charging duration has a gradually decreasing impact on the evaluation function value. Therefore, selecting a charging duration that meets the above conditions to charge the emitting device can perform AA with the minimum emission power on the basis of ensuring a high detection rate, effectively simulate the application scenario of long-distance detection, and improve the accuracy during lidar detection.

[0063] In some embodiments, the specific implementation process of step 602 includes the following steps: determining the shortest charging duration among the charging durations corresponding to the target data point as the target charging duration. Still taking Figure 7 and Figure 8 as an example, according to the above description, the charging durations corresponding to the target data point are 60 nanoseconds and 70 nanoseconds respectively, then 60 nanoseconds is taken as the target charging duration. For a lidar, the shorter the charging duration of the emitting device, the smaller the emission power, and the smaller the divergence angle of the detection laser. At this time, the point cloud obtained is more sensitive to the alignment degree of the optical path transceiver. That is, in the case of the same offset, the smaller the emission power of the emitting device, the more obvious the deterioration of the point cloud. During AA, it is hoped that the deterioration of the point cloud caused by the offset is as obvious as possible. Therefore, it is necessary to select as small a charging duration as possible on the basis of ensuring the detection rate, that is, select as small an emission power as possible to effectively improve the accuracy during long-distance detection.

[0064] Please refer to Figure 9 , Figure 9 which is a schematic structural diagram of the electronic device provided by the embodiment of the present application. As Figure 9 shown, the electronic device 900 includes at least one processor 901 and a memory 902. Among them, the memory 902 can be built into the electronic device 900, can also be external to the electronic device 900, and the memory 902 can also be a remotely set memory, connected to the electronic device 900 through a network.

[0065] The memory 902, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The memory 902 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the terminal and the like. In addition, the memory 902 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 902 optionally includes a memory remotely disposed relative to the processor 901, and these remote memories can be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0066] The processor 901 executes various functions of the terminal and processes data by running or executing software programs and / or modules stored in the memory 902, and by calling data stored in the memory 902, so as to perform overall monitoring of the terminal, for example, implementing the charging energy configuration method described in any embodiment of the present application.

[0067] The processor 901 may be one or more. Figure 9 Taking one processor 901 as an example. The processor 901 and the memory 902 can be connected through a bus or other means. The processor 901 may include a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a controller, a field programmable gate array (FPGA) device, etc. The processor 901 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.

[0068] The embodiment of the present application also provides a non-volatile computer-readable storage medium, and the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by one or more processors. For example, execute the Figures 1 - 6 method steps described above.

[0069] The embodiment of the present application also provides a computer program product, including a computing program stored on a non-volatile computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the charging energy configuration method in any of the above method embodiments. For example, execute the Figures 1 - 6 method steps described above.

[0070] The above are only embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present application.

[0071] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting them; under the idea of the present application, the technical features in the above embodiments or different embodiments can also be combined, and the steps can be implemented in any order. Those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A charging configuration method, characterized in that, Applied to a lidar, the lidar includes a transmitting device, and the charging configuration method includes: Obtain N frames of target point clouds collected by the lidar, where the N frames of the target point clouds correspond one-to-one to N charging durations of the transmitting device, and N is an integer greater than 1; Calculate N evaluation function values corresponding to the N frames of the target point clouds according to the reflectivity information of the N frames of the target point clouds; Determine the time gradient curve of the N evaluation function values, where the time gradient curve is used to represent the change rate of the evaluation function value with respect to the charging duration; Based on the time gradient curve, obtain the target charging duration among the N charging durations; Configure the charging of the lidar according to the target charging duration.

2. The charging configuration method according to claim 1, wherein The determining the time gradient curve of the N evaluation function values includes: Taking the unit charging duration as the time step, perform a first-order difference on the N evaluation function values to obtain the time gradient curve, where the abscissa of the time gradient curve represents the charging time, the ordinate of the time gradient curve represents the change amount of the evaluation function value, and the change amount of the evaluation function value is the absolute value of the difference between the evaluation function values of the target point clouds corresponding to two adjacent charging durations.

3. The charging configuration method according to claim 2, wherein The obtaining the target charging duration among the N charging durations based on the time gradient curve includes: Obtain the charging duration corresponding to the peak point on the time gradient curve; According to the charging duration corresponding to the peak point and a preset evaluation function threshold, obtain the target charging duration.

4. The charging configuration method according to claim 3, characterized in that The obtaining the target charging duration according to the charging duration corresponding to the peak point and a preset evaluation function threshold includes: Determine the point with the smallest corresponding charging duration among the points on the time gradient curve where the charging duration is greater than the charging duration corresponding to the peak point and the change amount of the evaluation function value is less than the evaluation function threshold as the target data point; According to the charging duration corresponding to the target data point, obtain the target charging duration.

5. The charging configuration method according to claim 1, characterized in that The reflectivity information includes gray values, the evaluation function value is the two-dimensional distribution feature value of the target point cloud, and the calculating the N evaluation function values corresponding to the N frames of the target point clouds according to the reflectivity information of the N frames of the target point clouds includes: For each frame of the target point cloud, obtain the probability density value of the two-dimensional Gaussian distribution of each point cloud point; Calculate the product of the gray value and the probability density value of each point cloud point to obtain the weighted gray value of each point cloud point; Sum the weighted gray values of each point cloud point to obtain the two-dimensional distribution feature value of the target point cloud.

6. The charging configuration method according to claim 1, characterized in that, The reflectivity information includes gray values, the evaluation function value is the weighted average gray value of the target point cloud, and the calculating the N evaluation function values corresponding to the N frames of the target point clouds according to the reflectivity information of the N frames of the target point clouds includes: For each frame of the target point cloud, obtain the weight value of each point cloud point according to the distance between each point cloud point and the center point of the target point cloud and the normal distribution function; Calculate the weighted average gray value of the target point cloud according to the weight value of each point cloud point and the gray value of each point cloud point.

7. The charging configuration method according to claim 1, characterized in that The reflectivity information includes grayscale values, the evaluation function value is the average grayscale value of the target point cloud, and calculating N evaluation function values corresponding to the N frames of the target point cloud according to the reflectivity information of the N frames of the target point cloud includes: For each frame of the target point cloud, calculate the average value of the grayscale values of all the point cloud points to obtain the average grayscale value of the target point cloud.

8. The charging configuration method according to claim 4, characterized in that Obtaining the target charging duration according to the charging duration corresponding to the target data point includes: Determine the shortest charging duration among the charging durations corresponding to the target data points as the target charging duration.

9. An electronic device, characterized in that, Including: A processor and a memory; The memory is coupled to the processor, and the memory is used to store instructions or programs. When the instructions or the programs are executed by the processor, the processor is caused to execute the charging configuration method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed, the charging configuration method according to any one of claims 1-8 is implemented.

Images