Charging configuration method, electronic device and computer-readable storage medium

Abstract

The present application discloses a charging configuration method, an electronic device, and a computer-readable storage medium. The charging configuration method is applied to a laser radar, which includes a transmitting device. The charging configuration method includes: obtaining N frames of target point clouds collected by the laser radar, wherein 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 based on the reflectivity information of the N frames of target point clouds; determining a time gradient curve of the N evaluation function values, wherein the time gradient curve is used to represent the rate of change of the evaluation function value relative to the charging duration; based on the time gradient curve, obtaining the target charging duration in the N charging durations; and configuring the laser radar charging according to the target charging duration. In the above manner, active alignment can be performed with a smaller transmitting power while maintaining a higher detection rate, thereby improving the accuracy of the laser radar during long-distance detection.

Description

Technical Field

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

[0002] Active Alignment (AA) is a technology used in optical systems, particularly during the assembly of LiDAR and sensors, to ensure accurate alignment of optical components. This technology typically uses precise mechanical adjustments with real-time feedback to adjust the position and angle of optical components to achieve optimal beam alignment or precise configuration of the optical system. In LiDAR systems, AA is often used to align laser transmitters and receivers to ensure accurate transmission and reception of laser beams, thereby improving measurement accuracy and system performance.

[0003] Currently, due to the limitations of the machine and the site, the distance between the laser emitter and the target object in the AA scenario is relatively close, which may lead to errors when the lidar is used for 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 long-distance detection of a laser radar during the AA process, and at the same time solve the problem of different charging configurations between multiple transmitting boards due to the consistency of the transmitters, thereby improving the detection accuracy of the laser radar.

[0005] In a first aspect, an embodiment of the present application provides a charging configuration method, which is applied to a laser radar, wherein the laser radar includes a transmitting device, and the charging configuration method includes: obtaining N frames of target point clouds collected by the laser radar, wherein 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 based on the reflectivity information of the N frames of target point clouds; determining a time gradient curve of the N evaluation function values, wherein the time gradient curve is used to represent the rate of change of the evaluation function value relative to the charging duration; based on the time gradient curve, obtaining a target charging duration among the N charging durations; and configuring the laser radar charging according to the target charging duration.

[0006] By obtaining N evaluation function values ​​corresponding to N frames of target point cloud and a time gradient curve representing the rate of change of the evaluation function value relative to the charging duration, it is possible to find a smaller charging power while maintaining a high detection rate. AA based on this charging power can be used in scenarios where the LiDAR performs long-range detection, thereby improving the detection accuracy of the LiDAR in practical applications. Secondly, by differentiating the N evaluation function values, the change in the point cloud state can be determined, which helps to balance the optimal point cloud state and the minimum charging duration.

[0007] In one or more embodiments, determining a time gradient curve of N evaluation function values ​​includes: taking a unit charging time as a time step, performing a first-order difference on the N evaluation function values ​​to obtain a time gradient curve, wherein the abscissa of the time gradient curve represents the charging time, and 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 cloud corresponding to two adjacent charging time periods.

[0008] In one or more embodiments, based on the time gradient curve, a target charging duration is obtained from N charging durations, including: obtaining the charging duration corresponding to the peak point on the time gradient curve; and obtaining the target charging duration based on 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 where the evaluation function value changes the most. This means that after the peak point on the gradient curve, the rate of change of the evaluation function value gradually decreases, which helps to find the point corresponding to the optimal point cloud state on the time gradient curve. Then, based on the charging duration corresponding to the peak point and the preset evaluation function threshold, the point corresponding to the minimum charging duration can be found among the points corresponding to the optimal point cloud state on the time gradient curve. This allows the AA process to more closely match the conditions of long-range detection and improve the detection accuracy of the lidar.

[0010] In one or more embodiments, a target charging duration is obtained based on the charging duration corresponding to the peak point and a preset evaluation function threshold, including: determining the point on the time gradient curve with the smallest charging duration among the points where the charging duration is greater than the charging duration corresponding to the peak point and the change in the evaluation function value is less than the evaluation function threshold as the target data point; and obtaining the target charging duration based on the charging duration corresponding to the target data point.

[0011] Based on the charging duration being greater than the charging duration corresponding to the peak point and the change in the evaluation function value being less than the evaluation function threshold, it can be determined that the point cloud state is stable and optimal. In an optimal point cloud state, the point with the minimum charging duration is identified as the target data point, and the transmitting device is charged using the charging duration corresponding to the target data point. This allows AA to be achieved with the minimum charging duration while maintaining a high detection rate, which is beneficial for improving the accuracy of long-range detection.

[0012] In one or more embodiments, the reflectivity information includes a grayscale value, and the evaluation function value is a two-dimensional distribution characteristic value of the target point cloud. Based on 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 grayscale value of each point cloud point and the probability density value to obtain the weighted grayscale value of each point cloud point; summing the weighted grayscale values ​​of each point cloud point to obtain the two-dimensional distribution characteristic value of the target point cloud.

[0013] In one or more embodiments, the reflectivity information includes a grayscale value, and the evaluation function value is the weighted average grayscale value of the target point cloud. Based on the reflectivity information of N frames of target point clouds, N evaluation function values ​​corresponding to N frames of target point clouds are calculated, including: for each frame of target point cloud, the weight of each point cloud point is obtained based on the distance between each point cloud point and the center point of the target point cloud and the normal distribution function; the weighted average grayscale value of the target point cloud is calculated based on the weight of each point cloud point and the grayscale value of each point cloud point.

[0014] In one or more embodiments, the reflectivity information includes a grayscale value, and the evaluation function value is the average grayscale value of the target point cloud. Based on the reflectivity information of N frames of target point clouds, N evaluation function values ​​corresponding to N frames of target point clouds are calculated, including: for each frame of target point cloud, the average grayscale value of all point cloud points is calculated to obtain the average grayscale value of the target point cloud.

[0015] In one or more embodiments, obtaining the target charging duration according to the charging durations corresponding to the target data points includes: determining the shortest charging duration among the charging durations corresponding to the target data points as the target charging duration.

[0016] In a second aspect, an embodiment of the present application provides an electronic device comprising: at least one processor and a memory; the memory is coupled to the processor, and the memory is used to store instructions or programs, and 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.

[0017] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed, the charging configuration method as described above is implemented.

[0018] The beneficial effects of the present application are as follows: the charging configuration method of the embodiment of the present application can find a smaller charging power while maintaining a high detection rate by obtaining N evaluation function values ​​corresponding to N frames of target point cloud and a time gradient curve used to represent the rate of change of the evaluation function value relative to the charging time. AA is performed based on the charging power, which can simulate the scenario of long-distance detection by the laser radar, thereby improving the detection accuracy of the laser radar in actual application. On the other hand, in the case where the laser radar includes multiple transmitting devices, the charging configuration method of the embodiment of the present application can find the optimal charging time corresponding to each transmitting device by traversing the charging of each transmitting device, thereby solving the problem of different transmitting charging configurations due to the consistency of the transmitting devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] One or more embodiments are exemplarily described by the figures in the accompanying drawings, which are not intended to limit the embodiments. Elements with the same reference numerals in the drawings represent similar elements.

[0020] Figure 1 This is a flow chart of the charging configuration method provided in an embodiment of the present application;

[0021] Figure 2 Schematic diagram of an implementation of the charging configuration method provided in an embodiment of the present application;

[0022] Figure 3 Schematic diagram of an implementation of the charging configuration method provided in an embodiment of the present application;

[0023] Figure 4 Schematic diagram of an implementation of the charging configuration method provided in an embodiment of the present application;

[0024] Figure 5 Schematic diagram of an implementation of the charging configuration method provided in an embodiment of the present application;

[0025] Figure 6 Schematic diagram of an implementation of the charging configuration method provided in an embodiment of the present application;

[0026] Figure 7 Schematic diagram of the corresponding relationship between the evaluation function value and the charging time provided in the embodiment of the present application;

[0027] Figure 8 is with Figure 7 The schematic diagram shown corresponds to a schematic diagram of the time gradient curve;

[0028] Figure 9 It is a structural diagram of an electronic device provided in an embodiment of the present application.

[0029] Reference numerals:

[0030] 900. Electronic device; 901. Processor; 902. Memory. DETAILED DESCRIPTION

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of them. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0032] It should be noted that when an element is described as being "connected" to another element, it may be directly connected to the other element, or one or more intermediate elements may be present therebetween. In addition, the technical features involved in the various embodiments of the present application described below may be combined with each other as long as there is no structural conflict between them.

[0033] Please refer to Figure 1 , Figure 1 A flow chart of a charging configuration method provided in an embodiment of the present application. The charging configuration method is applied to a laser radar, which may be a solid-state laser radar, a semi-solid-state laser radar, etc., and the present application does not impose the sole limitation thereto. The laser radar can be applied to any device that requires laser detection, such as a mobile robot, a ship, or a vehicle. The laser radar includes a transmitting device for emitting a detection laser and a receiving device for receiving an echo signal. Specifically, the transmitting device is capable of emitting a pulsed detection laser, which is projected onto a target object, and a signal formed by reflection from the target object is an echo signal. The receiving device receives the echo signal and obtains relevant information about the target object based on the echo signal, such as the distance to the target object.

[0034] like Figure 1 As shown, the charging configuration method includes the following method steps:

[0035] Step 101: Obtain N frames of target point cloud collected by the laser radar, wherein the N frames of target point cloud correspond one-to-one to N charging durations of the transmitting device, and N is an integer greater than 1.

[0036] Specifically, the emitting device includes at least one emitting array, which includes at least one emitting unit. In some embodiments, the emitting device is an emitting board, the emitting array is a VCSEL (Vertical-Cavity Surface-Emitting Laser) array, and the emitting unit is a VCSEL. In some embodiments, the emitting array is an EEL (Edge-Emitting Laser) array, and the emitting unit is an EEL.

[0037] Before the emitting device emits the detection laser each time, the emitting unit in the emitting device needs to be charged based on a preset charging duration, wherein the charging duration of each emitting unit is the same each time the emitting device is charged, and is the charging duration of the detection laser emitted by the emitting device that time. The emitting device is configured to emit the detection laser N times, and before the emitting device emits the detection laser each time, the charging duration of the emitting unit is different, that is, the emitting unit is charged based on N charging durations. Based on the detection laser emitted by the emitting 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 emitting 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 in the process of collecting N frames of target point cloud, according to the collection sequence of N frames of target point cloud, N charging durations are increased at equal intervals, that is, according to the time sequence of collecting target point cloud, each frame is assigned a uniformly increasing charging duration in turn. For example, the charging duration corresponding to collecting the first frame of target point cloud is T1, the charging duration T2 corresponding to collecting the second frame of target point cloud is T2=T1+Δt, the charging duration T3 corresponding to collecting the third frame of target point cloud is T3=T2+Δt, ..., the charging duration T corresponding to collecting the Nth frame of target point cloud is T N T N =T N-1 +Δt.

[0038] It should be noted that in the embodiment of the present application, the charging time is positively correlated with the charging power, that is, the longer the charging time, the greater the charging power, and the greater the power of the detection laser emitted by the transmitting unit; conversely, the shorter the charging time, the smaller the charging power, and the smaller the power of the detection laser emitted by the transmitting unit.

[0039] In addition, the laser radar may include one or more transmitting devices. When the laser radar includes multiple transmitting devices, such as multiple transmitting arrays located on different transmitting boards, any transmitting device can implement the charging configuration method provided in the embodiment of the present application.

[0040] Step 102: Calculate N evaluation function values ​​corresponding to the N frames of target point clouds based on the reflectivity information of the N frames of target point clouds.

[0041] 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 degree of alignment between the transmitting device and the receiving device, and output the corresponding evaluation function value. In the LiDAR system, the detection rate of the point cloud refers to the proportion of target objects that the system can successfully detect and identify. From a mathematical point of view, the detection rate of the point cloud can be defined as the proportion of correctly detected targets among all actual targets. For example, there are 100 potential targets in a scene, and the LiDAR and the corresponding processing algorithm successfully identify 95 of them. The detection rate of the system is 95%. A high detection rate means higher point cloud quality, the system can more accurately identify target objects, 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.

[0042] Specifically, for each frame of the target point cloud, the grayscale value of each point is obtained based on the reflectivity information of each point in the target point cloud. Reflectivity information, also known as reflection intensity, refers to the proportion of energy reflected by the surface after the laser beam emitted by the lidar hits the surface. Grayscale refers to the brightness level of each pixel in the image. In digital image processing, grayscale values ​​are typically represented by integers from 0 to 255. 0 represents black, 255 represents white, and values ​​in between represent different levels of gray. Point cloud data itself typically consists of a series of spatial coordinates (x, y, z) and other additional information (such as intensity, color, reflectivity, timestamp, etc.). For point cloud data collected by lidar or optical sensors, grayscale values ​​are typically used to represent the intensity of the reflected signal received by the sensor. In most lidar systems, the grayscale value of the point cloud corresponds to the reflectivity of each point. In some embodiments, the relationship between reflectivity and grayscale is positively correlated, that is, when reflectivity increases, the grayscale increases accordingly; conversely, when reflectivity decreases, the grayscale decreases accordingly. Specifically, when the reflectivity is 100%, the grayscale is 255; when the reflectivity is 0, the grayscale is 0.

[0043] Afterwards, for each frame of target point cloud, the evaluation function value of the target point cloud is calculated based on the grayscale value of each point cloud point and the point cloud evaluation function, so that N evaluation function values ​​can be obtained according to N frames of target point cloud.

[0044] In some embodiments, the reflectivity information includes grayscale values, and the evaluation function value is a two-dimensional distribution characteristic value of the target point cloud. The specific implementation process of obtaining N evaluation function values ​​can be as follows: Figure 2 As shown, the specific implementation process of step 102 includes the following steps:

[0045] Step 201: For each frame of target point cloud, obtain the probability density value of the two-dimensional Gaussian distribution of each point in the point cloud.

[0046] Specifically, define the mean and covariance matrix of a 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 in the target point cloud for each frame.

[0047] Step 202: Calculate the product of the grayscale value of each point cloud point and the probability density value to obtain the weighted grayscale value of each point cloud point.

[0048] The weight value indicates the contribution of a data point to the overall weighted average. For example, a data point with a larger value and a higher weight will have a greater impact on the weighted average. By using the probability density of a two-dimensional normal distribution as the weight, we can place greater emphasis on the central portion of the target point cloud in each frame.

[0049] Step 203: Sum the weighted grayscale value of each point in the point cloud to obtain the two-dimensional distribution feature value of the target point cloud.

[0050] 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 a two-dimensional normal distribution weighted method. The calculated two-dimensional distribution eigenvalue can effectively reflect the grayscale distribution characteristics of the area of ​​interest. This method is efficient and universal, which helps to highlight the central area of ​​the field of view, reduce the influence of edge noise, and achieve better alignment of the light receiving and light receiving paths.

[0051] It is understandable that Figure 2 The embodiment only exemplifies the implementation method of obtaining N evaluation function values ​​based on one point cloud evaluation function. In other embodiments, N evaluation function values ​​may also be obtained based on other point cloud evaluation functions.

[0052] For example, in some embodiments, the reflectivity information includes grayscale values, and the evaluation function value is the weighted average grayscale value of the target point cloud. The specific implementation process of obtaining N evaluation function values ​​based on the reflectivity information of N frames of target point clouds can be as follows: Figure 3 As shown, the specific implementation process of step 102 includes the following steps:

[0053] Step 301: For each frame of target point cloud, obtain the weight 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.

[0054] Specifically, the distance from each point cloud point to the center point of the target point cloud is calculated, and then the normal function is applied 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 the target point cloud increases.

[0055] In some embodiments, the coordinates of the center point of the target point cloud are the mean of the coordinates of all the point cloud points in the target point cloud, emphasizing the importance of the central area of ​​the field of view. In other embodiments, the coordinates of the center point of the target point cloud can be determined based on actual conditions. By defining a center point and performing weighted sampling on the grayscale values ​​of the point cloud points, point cloud points near the center point are given higher weights. This effectively highlights the region of interest (ROI), weakens the influence of noise points or irrelevant points far from the center point, and improves the accuracy of local analysis. The parameters of the normal distribution function (mean and standard deviation) can be adjusted according to the specific scenario, providing high flexibility.

[0056] Step 302: Calculate the weighted average grayscale value of the target point cloud according to the weight of each point cloud point and the grayscale value of each point cloud point.

[0057] Specifically, the weight of each point cloud point is used as the weight, and the weighted average of the grayscale values ​​of all point cloud points in the target point cloud is calculated, which is the weighted average grayscale value of the target point cloud.

[0058] For example, in some embodiments, the reflectivity information includes grayscale values, and the evaluation function value is the average grayscale value of the target point cloud. The specific implementation process of obtaining N evaluation function values ​​can be as follows: Figure 4 As shown, the specific implementation process of step 102 includes the following steps:

[0059] Step 401: For each frame of the target point cloud, calculate the average grayscale value of all the point cloud points to obtain the average grayscale value of the target point cloud.

[0060] Specifically, in this embodiment, the grayscale values ​​corresponding to the reflectivity of the full-image point cloud points are averaged as the standard for judging the degree of alignment of the light-receiving and light-emitting paths, which can simply and effectively reflect the global characteristics and avoid the deviation that may be caused by judging only based on local data.

[0061] Step 103: Determine the time gradient curves of N evaluation function values, wherein the time gradient curve is used to represent the rate of change of the evaluation function value relative to the charging duration.

[0062] Among them, according to the time gradient curve of N evaluation function values, the change of the target point cloud can be determined, which helps to find the optimal point cloud state and the smallest charging time at the same time.

[0063] In some embodiments, the specific implementation process of step 103 includes the following method steps: taking the unit charging time as the time step, performing first-order difference on N evaluation function values ​​to obtain a time gradient curve, wherein the horizontal axis of the time gradient curve represents the charging time, and the vertical axis 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 time periods.

[0064] Specifically, a time gradient curve is derived based on the ratio of the difference between the K+1th and Kth evaluation function values ​​to the unit charging duration, where K is an integer greater than or equal to 1 and less than or equal to N-1. The Kth evaluation function value is the evaluation function value for the current charging duration, the K+1th evaluation function value is the evaluation function value for the next charging duration, and the difference between the K+1th and Kth evaluation function values ​​is the difference between the evaluation function values ​​for the next charging duration and the current charging duration. This implements a forward difference process and yields a 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 changing trend of the evaluation function value with charging duration can be determined, and thus the change in the point cloud state can be determined, helping to simultaneously find an optimal point cloud state and a smaller charging duration. Specifically, if the gradient gradually decreases, it can be determined that the evaluation function value decreases with the change of the charging time, and then it can be determined that the change of the point cloud state decreases and the change of the detection rate of the point cloud decreases; if the gradient gradually increases, it can be determined that the evaluation function value increases with the change of the charging time, and then it can be determined that the change of the point cloud state increases and the change of the detection rate of the point cloud increases. Thus, according to the time gradient curve, a smaller charging time can be found on the basis of determining the better point cloud state, which can achieve a better alignment effect of the light-receiving and light-emitting paths during AA and improve the accuracy of long-distance detection. Among them, the better point cloud state can be determined by the point cloud detection rate being greater than the corresponding threshold. The threshold can be set based on the actual application scenario. The embodiment of the present application does not impose specific restrictions on this. 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%.

[0065] It is understandable that this embodiment takes the forward difference as an example, because 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 calculations, and it is very intuitive and simple to implement, without the need for complex algorithms or large amounts of data storage. In other embodiments, a backward difference or a central difference can also be set. For the backward difference, the specific implementation is: according to the ratio between the difference between the Jth evaluation function value and the J-1th evaluation function value and the unit charging time, a time gradient curve is obtained, 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 point, and the backward difference is usually more stable than the forward difference because it does not rely on future data. For the central difference, the specific implementation is: according to the ratio of half of the difference between the I+1th evaluation function value and the I-1th evaluation function value and the unit charging time, the time gradient curve is obtained, 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 accuracy, and its error is second-order, which means that it can provide more accurate derivative estimates for small time steps, and because it uses data in both the forward and backward directions at the same time, it is more sensitive to changes in the function.

[0066] Step 104: Based on the time gradient curve, a target charging duration is obtained from the N charging durations.

[0067] Among them, the target charging time is the charging time corresponding to a high detection rate and a low charging power, so as to simulate long-distance detection while ensuring the detection rate.

[0068] In some embodiments, as Figure 5 As shown, the specific implementation process of step 104 includes the following steps:

[0069] Step 501: Obtain the charging duration corresponding to the peak point on the time gradient curve.

[0070] Step 502: Obtain a target charging duration based on the charging duration corresponding to the peak point and a preset evaluation function threshold.

[0071] Specifically, the peak point on the time gradient curve is the point with the largest rate of change in the evaluation function value. This means that after the peak point on the gradient curve, the rate of change in the evaluation function value gradually decreases, and the point cloud state gradually stabilizes (i.e., the point cloud detection rate remains essentially unchanged), which helps to find the point on the time gradient curve that corresponds to the optimal point cloud state. Then, based on the charging duration corresponding to the peak point and the preset evaluation function threshold, the point with a smaller charging duration can be found among the points on the time gradient curve that correspond to the optimal point cloud state. This allows for better alignment of the light and light paths during AA, as well as improved accuracy during long-range detection.

[0072] The evaluation function threshold is a pre-set threshold that can be set based on the actual application scenario, and the embodiments of the present application do not impose specific restrictions on this. In a specific embodiment, the evaluation function threshold can be determined by: obtaining the first point cloud of T frames collected by the laser radar, wherein the first point cloud of T frames corresponds one-to-one to T charging durations of the launch plate, and T is an integer greater than 1; based on the reflectivity information of the third point cloud of T frames and the point cloud evaluation function, calculating T evaluation function values ​​corresponding to the third point cloud of T frames; based on the T evaluation function values, determining a normal distribution curve; and based on the normal distribution curve (for example, based on the standard deviation of the normal distribution curve), determining the evaluation function threshold.

[0073] In some embodiments, as Figure 6 As shown, the specific implementation process of step 502 includes the following method steps:

[0074] Step 601: Determine the point with the smallest charging time among the points on the time gradient curve whose charging time is greater than the charging time corresponding to the peak point and whose evaluation function value change is less than the evaluation function threshold as the target data point.

[0075] Step 602: Obtain a target charging duration according to the charging duration corresponding to the target data point.

[0076] Specifically, if the charging duration is greater than the charging duration corresponding to the peak point and the change in the evaluation function value is less than the evaluation function threshold, the charging duration at which the point cloud state is optimal and stable can be determined. Thus, while ensuring optimal point cloud state, the point with the minimum charging duration (i.e., the target data point) is selected. Charging the transmitting device according to the target charging duration determined based on the charging duration corresponding to the target data point allows optical path alignment to be achieved with the lowest possible transmit power while maintaining a high detection rate, thereby improving LiDAR detection accuracy.

[0077] Reference Figure 7 and Figure 8 , Figure 7The corresponding relationship between the evaluation function value and the charging time in a specific embodiment is exemplified, wherein the horizontal axis is the charging time in nanoseconds, and the vertical axis is the evaluation function value; Figure 8 An example is shown in Figure 7 The time gradient curve is obtained based on the correspondence between the evaluation function value and the charging time shown, where the horizontal axis is the time series number of the data point in the data set obtained after the first-order difference of N evaluation function values, and the vertical axis is the difference value of the evaluation function value.

[0078] like Figure 7 As shown in , the evaluation function value is positively correlated with the charging time, and the increasing trend of the evaluation function value gradually becomes flat as the charging time increases. Figure 8 As shown, the time series number of the peak point is 2, the preset threshold is 15, and the points whose charging duration is greater than the charging duration corresponding to the peak point and whose evaluation function value change is less than the evaluation function threshold include all points starting from time series number 3 (including the point with 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 time series number 3. Figure 7 As shown in the figure, the charging duration corresponding to point number 3 in the time series is 60 nanoseconds or 70 nanoseconds. Therefore, 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. A change in the evaluation function value less than the evaluation function threshold indicates that the impact of the change in the charging duration on the evaluation function value gradually decreases. Therefore, selecting a charging duration that meets these conditions for charging the transmitting device allows for AA with minimal transmit power while maintaining a high detection rate. This effectively simulates long-range detection application scenarios and improves lidar detection accuracy.

[0079] 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 points as the target charging duration. Figure 7 and Figure 8 For example, according to the above description, the charging time corresponding to the target data points is 60 nanoseconds and 70 nanoseconds respectively, so 60 nanoseconds is used as the target charging time. For lidar, the shorter the charging time of the transmitting device, the smaller the transmission power, and the smaller the divergence angle of the detection laser. At this time, the point cloud obtained is more sensitive to the degree of alignment of the optical path transmission and reception. That is, under the same offset, the smaller the transmission power of the transmitting device, the more obvious the point cloud degradation. In the AA process, it is hoped that the point cloud degradation caused by the offset is as obvious as possible. Therefore, it is necessary to select the smallest possible charging time while ensuring the detection rate, that is, to select the smallest possible transmission power to effectively improve the accuracy of long-distance detection.

[0080] Please refer to Figure 9, Figure 9 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 9 As shown, the electronic device 900 includes at least one processor 901 and a memory 902, wherein the memory 902 can be built into the electronic device 900 or externally located outside the electronic device 900. The memory 902 can also be a remotely set memory connected to the electronic device 900 via a network.

[0081] The memory 902 is a non-volatile computer-readable storage medium that 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, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal, etc. In addition, the memory 902 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 902 may optionally include a memory remotely located relative to the processor 901, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0082] The processor 901 performs various functions of the terminal and processes data by running or executing software programs and / or modules stored in the memory 902, and calling data stored in the memory 902, thereby monitoring the terminal as a whole, for example, implementing the charging configuration method described in any embodiment of the present application.

[0083] The processor 901 may be one or more, Figure 9 In the figure, a processor 901 is used as an example. The processor 901 and the memory 902 may be connected via 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), etc. The processor 901 may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration.

[0084] The embodiment of the present application further provides a non-volatile computer-readable storage medium, which stores computer-executable instructions. The computer-executable instructions are executed by one or more processors, for example, to execute the above-described Figures 1-6 method steps.

[0085] The present application also provides a computer program product, including a computer program stored on a non-volatile computer-readable storage medium, wherein the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the charging configuration method in any of the above method embodiments, for example, executing the above described Figures 1-6 method steps.

[0086] The above description is merely an embodiment of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

[0087] The above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them. Based on the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, and the steps may be implemented in any order. A person skilled in the art should understand that the technical solutions described in the above embodiments may be modified, or some of the technical features may be replaced by equivalents. However, such modifications or replacements do not deviate the essence of the corresponding technical solutions 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 laser radar, the laser radar includes a transmitting device, and the charging configuration method includes: Obtaining N frames of target point clouds collected by the laser radar, wherein the N frames of the target point clouds correspond one-to-one to N charging durations of the transmitting device, where N is an integer greater than 1; Calculate N evaluation function values ​​corresponding to the target point cloud of the N frames according to the reflectivity information of the target point cloud of the N frames; Determining N time gradient curves of the evaluation function values, wherein the time gradient curves are used to represent the rate of change of the evaluation function value relative to the charging duration; Based on the time gradient curve, obtaining a target charging duration from the N charging durations; Configuring the LiDAR charging according to the target charging duration; The reflectivity information includes a grayscale value, the evaluation function value is a two-dimensional distribution characteristic value of the target point cloud, and the calculation of N evaluation function values ​​corresponding to the target point cloud of N frames according to the reflectivity information of the target point cloud of N frames includes: For each frame of the target point cloud, obtaining a probability density value of a two-dimensional Gaussian distribution of each point in the point cloud; Calculating 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; Summing the weighted grayscale value of each point in the point cloud to obtain a two-dimensional distribution feature value of the target point cloud; or, The reflectivity information includes a grayscale value, the evaluation function value is a weighted average grayscale value of the target point cloud, and calculating N evaluation function values ​​corresponding to the target point cloud in N frames according to the reflectivity information of the target point cloud in N frames includes: For each frame of the target point cloud, a weight of each point in the point cloud is obtained according to the distance between each point in the point cloud and the center point of the target point cloud and a normal distribution function; Calculating a weighted average grayscale value of the target point cloud according to the weight of each point in the point cloud and the grayscale value of each point in the point cloud; or, The reflectivity information includes a grayscale value, the evaluation function value is an average grayscale value of the target point cloud, and calculating N evaluation function values ​​corresponding to the target point cloud in N frames according to the reflectivity information of the target point cloud in N frames includes: For each frame of the target point cloud, the average grayscale value of all the point cloud points is calculated to obtain the average grayscale value of the target point cloud.

2. The energy charging configuration method according to claim 1, characterized in that: Determining the time gradient curves of N evaluation function values ​​includes: Taking the unit charging time as the time step, the N evaluation function values ​​are first-order differentiated to obtain the time gradient curve, wherein the abscissa of the time gradient curve represents the charging time, and 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 time periods.

3. The charging configuration method according to claim 2, characterized in that: Obtaining a target charging duration from the N charging durations based on the time gradient curve includes: Obtaining the charging time corresponding to the peak point on the time gradient curve; The target charging duration is obtained according to the charging duration corresponding to the peak point and a preset evaluation function threshold.

4. The charging configuration method according to claim 3, characterized in that: Obtaining the target charging duration according to the charging duration corresponding to the peak point and a preset evaluation function threshold includes: The point on the time gradient curve where the charging time is greater than the charging time corresponding to the peak point and the change in the evaluation function value is less than the evaluation function threshold and the corresponding charging time is the smallest is determined as the target data point; The target charging duration is obtained according to the charging duration corresponding to the target data point.

5. The energy 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: The shortest charging duration among the charging durations corresponding to the target data points is determined as the target charging duration.

6. An electronic device, characterized in that: include: processor and memory; The memory is coupled to the processor, and the memory is used to store instructions or programs. When the instructions or the program are executed by the processor, the processor executes the charging configuration method according to any one of claims 1 to 5.

7. 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 to 5 is implemented.

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