Laser radar data processing method, device, electronic device and storage medium

Abstract

The present application belongs to the field of radar data processing technology, and discloses a laser radar data processing method, device, electronic device and storage medium, which performs noise reduction processing on the laser radar full waveform data; generates an initialized data structure matrix so that the number of its rows is equal to the number of waveform intervals; allocates each row space of the data structure matrix to each waveform interval; performs waveform detection to obtain the first characteristic parameter of each waveform interval to update the data of each row space; performs waveform fitting on each single echo waveform interval to obtain the corresponding second characteristic parameter to update the data of the corresponding row space; performs waveform parameter estimation and waveform decomposition on each superimposed echo waveform interval in turn to obtain the corresponding third characteristic parameter to update the data of the corresponding row space; performs waveform moment calculation according to the data structure matrix to obtain a distance detection value; thereby reducing the data interaction time of the laser radar full waveform data processing process, which is beneficial to improving the ranging efficiency.

Description

Technical Field

[0001] The present application relates to the field of radar data processing technology, and more specifically, to a laser radar data processing method, device, electronic device and storage medium. Background Art

[0002] 3D laser scanners are widely used in surveying and mapping, geological disaster monitoring, bridge and tunnel construction monitoring, and assisted driving because they can quickly and efficiently obtain high-precision point cloud data of the scanned object. 3D laser scanners mainly use laser radar to measure distance to obtain high-precision point cloud data. During the scanning process, there are relatively strict time requirements for the entire data processing process. The time required to complete a distance measurement process is generally required to reach milliseconds.

[0003] In a full-waveform lidar ranging, the process of processing the lidar full-waveform data mainly includes waveform denoising, waveform detection, waveform parameter estimation, waveform fitting, waveform decomposition, waveform time calculation, etc. In order to complete a ranging in a short time, in addition to having specific requirements for the processing time and program calling method of each process, the data transmission method between each process is also particularly critical. At present, when transferring data between various processes, generally at the beginning of each process, a new memory space is opened for the data generated by the previous process, and the data generated by the previous process is copied to the newly opened memory space, and then the data of the current process is processed according to the data in the newly opened memory space. As a result, the interaction time required for data transmission between the processes is relatively long, which is not conducive to improving the ranging efficiency. Summary of the invention

[0004] The purpose of this application is to provide a laser radar data processing method, device, electronic device and storage medium, which can reduce the interaction time required for data transmission during the laser radar full waveform data processing, thereby facilitating improving the ranging efficiency.

[0005] In a first aspect, the present application provides a laser radar data processing method for processing laser radar full waveform data to achieve ranging, comprising the following steps:

[0006] A1. Perform noise reduction on the original laser radar full waveform data;

[0007] A2. Generate an initialized data structure matrix according to the laser radar full waveform data, so that the number of rows of the data structure matrix is ​​equal to the number of waveform intervals of the laser radar full waveform data;

[0008] A3. Allocate each row space of the data structure matrix to each of the waveform intervals;

[0009] A4. Performing waveform detection on the full waveform data of the laser radar to obtain a first characteristic parameter of each of the waveform intervals, and updating the data of each of the line spaces according to the first characteristic parameter;

[0010] A5. If at least one of the waveform intervals is a single echo waveform interval, waveform fitting is performed on each of the single echo waveform intervals to obtain a second characteristic parameter of each of the single echo waveform intervals, and the corresponding row space data is updated according to the second characteristic parameter;

[0011] A6. If at least one of the waveform intervals is a superimposed echo waveform interval, waveform parameter estimation and waveform decomposition are performed on each of the superimposed echo waveform intervals in turn to obtain a third characteristic parameter of each of the superimposed echo waveform intervals, and the corresponding row space data is updated according to the third characteristic parameter;

[0012] A7. Calculate the waveform time according to the data structure matrix to obtain the distance detection value.

[0013] The laser radar data processing method uses a data structure matrix to transfer data between various processing processes, simplifies the data interaction between various processing processes when processing the laser radar full waveform data, avoids the problem of multiple opening of new memory space and multiple data copying in the traditional processing process, which affects the processing efficiency, and greatly reduces the interaction time required for data transfer in the laser radar full waveform data processing process, which is beneficial to improving the ranging efficiency.

[0014] Preferably, step A2 comprises:

[0015] Identify the number of waveform intervals of the laser radar full waveform data as the number of rows of the data structure matrix;

[0016] Calculating the number of columns of the data structure matrix according to a preset rule;

[0017] A matrix in which all element values ​​are zero is generated according to the number of rows and the number of columns, to obtain the initialized data structure matrix.

[0018] Since the number of rows of the data structure matrix is ​​equal to the number of waveform intervals of the laser radar full waveform data, after subsequent allocation, it can be ensured that each row space of the data structure matrix corresponds to each waveform interval one by one, which is conducive to ensuring that the data structure matrix can record all important feature data of the laser radar full waveform data.

[0019] Preferably, the first characteristic parameter includes the start time, end time and number of effective peaks of the waveform interval;

[0020] Step A4 includes:

[0021] Performing waveform detection on the laser radar full waveform data to obtain a first characteristic parameter of each waveform interval;

[0022] The start time, the end time and the number of valid peaks of each waveform interval are used to update the first element value, the second element value and the third element value of the corresponding row space.

[0023] Preferably, the single echo waveform interval is the waveform interval in which the number of effective peaks is 1;

[0024] The second characteristic parameters include a first peak time, a first standard deviation and a first expected value of the single echo waveform interval;

[0025] Step A5 includes:

[0026] Performing waveform fitting on each of the single echo waveform intervals to obtain the second characteristic parameter of each of the single echo waveform intervals;

[0027] The first peak time, the first standard deviation and the first expected value of each single echo waveform interval are used to update the corresponding 4th element value, 5th element value and 6th element value of the row space respectively.

[0028] Preferably, the step of performing waveform fitting on each of the single echo waveform intervals to obtain the second characteristic parameter of each of the single echo waveform intervals includes:

[0029] The waveform fitting is performed on each single echo waveform interval according to the following model:

[0030] ;

[0031] in, is the time value, is the waveform amplitude fitting function, is the first standard deviation, is the first expected value.

[0032] Preferably, the superimposed echo waveform interval is the waveform interval in which the number of effective peaks is greater than 1;

[0033] The third characteristic parameter includes the second peak time of each peak in the superimposed echo waveform interval, and the weight, second standard deviation and second expected value of each sub-waveform in the superimposed echo waveform interval;

[0034] Step A6 includes:

[0035] Estimating waveform parameters of each of the superimposed echo waveform intervals to obtain the third characteristic parameter of each of the superimposed echo waveform intervals;

[0036] Performing waveform decomposition on each of the superimposed echo waveform intervals to optimize the third characteristic parameter;

[0037] Use the second peak time of each peak in each superimposed echo waveform interval to update the corresponding fourth element value of the row space to the element values, is the number of effective peaks in the corresponding superimposed echo waveform interval;

[0038] The weight of the same sub-waveform, the second standard deviation and the second expected value are used as an array, and the arrays of each sub-waveform in each superimposed echo waveform interval are used to update the corresponding row space. element values ​​to element value.

[0039] Preferably, the step of estimating waveform parameters of each of the superimposed echo waveform intervals to obtain the third characteristic parameter of each of the superimposed echo waveform intervals includes:

[0040] The waveform parameters of each of the superimposed echo waveform intervals are estimated according to the following model:

[0041] ;

[0042] ;

[0043] in, is the time value, is the waveform amplitude estimation function, is the first The sub-waveform The waveform amplitude at the time, is the first The weights of the sub-waveforms, is the first The second standard deviation of the sub-waveform, is the first The second expected value of the sub-waveform.

[0044] The data structure matrix finally obtained through the above process completely records the characteristic parameters required for the subsequent waveform moment calculation, and effectively reflects the overall distribution of the lidar echo. In the subsequent waveform moment calculation, various characteristic parameters can be extracted from it conveniently and quickly, which is conducive to improving calculation efficiency and further improving ranging efficiency.

[0045] In a second aspect, the present application provides a laser radar data processing device for processing laser radar full waveform data to achieve ranging, including:

[0046] A noise reduction processing module is used to perform noise reduction on the original laser radar full waveform data;

[0047] A generating module, used for generating an initialized data structure matrix according to the laser radar full waveform data, so that the number of rows of the data structure matrix is ​​equal to the number of waveform intervals of the laser radar full waveform data;

[0048] An allocation module, used for allocating each row space of the data structure matrix to each waveform interval;

[0049] A waveform detection module, used for performing waveform detection on the laser radar full waveform data to obtain a first characteristic parameter of each waveform interval, and updating the data of each row space according to the first characteristic parameter;

[0050] a waveform fitting module, for performing waveform fitting on each of the single echo waveform intervals when at least one of the waveform intervals is a single echo waveform interval, so as to obtain a second characteristic parameter of each of the single echo waveform intervals, and updating the data of the corresponding row space according to the second characteristic parameter;

[0051] A waveform parameter estimation and decomposition module, used for, when at least one of the waveform intervals is a superimposed echo waveform interval, sequentially performing waveform parameter estimation and waveform decomposition on each of the superimposed echo waveform intervals to obtain a third characteristic parameter of each of the superimposed echo waveform intervals, and updating the corresponding data of the row space according to the third characteristic parameter;

[0052] The distance calculation module is used to calculate the waveform time according to the data structure matrix to obtain the distance detection value.

[0053] The laser radar data processing device uses a data structure matrix to transfer data between various processing processes, simplifies the data interaction between various processing processes when processing the laser radar full waveform data, avoids the problem of multiple opening of new memory space and multiple data copying in the traditional processing process, which affects the processing efficiency, and greatly reduces the interaction time required for data transfer in the laser radar full waveform data processing process, which is beneficial to improving the ranging efficiency.

[0054] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the lidar data processing method described above are performed.

[0055] In a fourth aspect, the present application provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the lidar data processing method described above are performed.

[0056] Beneficial Effects

[0057] The laser radar data processing method, device, electronic device and storage medium provided in the present application perform noise reduction processing on the original laser radar full waveform data; generate an initialized data structure matrix based on the laser radar full waveform data, so that the number of rows of the data structure matrix is ​​equal to the number of waveform intervals of the laser radar full waveform data; allocate each row space of the data structure matrix to each waveform interval; perform waveform detection on the laser radar full waveform data to obtain a first characteristic parameter of each waveform interval, and update the data of each row space based on the first characteristic parameter; if at least one of the waveform intervals is a single echo waveform interval, The echo waveform interval is waveform fitted to obtain the second characteristic parameter of each single echo waveform interval, and the corresponding data of the row space is updated according to the second characteristic parameter; if at least one of the waveform intervals is a superimposed echo waveform interval, waveform parameter estimation and waveform decomposition are performed on each of the superimposed echo waveform intervals in turn to obtain the third characteristic parameter of each superimposed echo waveform interval, and the corresponding data of the row space is updated according to the third characteristic parameter; waveform moment calculation is performed according to the data structure matrix to obtain the distance detection value; thereby reducing the interaction time required for data transmission in the process of laser radar full waveform data processing, which is beneficial to improving the ranging efficiency.

[0058] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flowchart of a laser radar data processing method provided in an embodiment of the present application.

[0060] Figure 2 A schematic diagram of the structure of a laser radar data processing device provided in an embodiment of the present application.

[0061] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0062] Figure 4 This is a waveform diagram corresponding to the full waveform data of the LiDAR after exemplary noise reduction processing. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0064] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0065] Please refer to Figure 1 In some embodiments of the present application, a laser radar data processing method is used to process laser radar full waveform data to achieve ranging, including the following steps:

[0066] A1. Perform noise reduction on the original laser radar full waveform data;

[0067] A2. Generate an initialized data structure matrix based on the laser radar full waveform data (referring to the laser radar full waveform data after noise reduction processing), so that the number of rows of the data structure matrix is ​​equal to the number of waveform intervals of the laser radar full waveform data;

[0068] A3. Allocate each row space of the data structure matrix to each waveform interval;

[0069] A4. Perform waveform detection on the full waveform data of the laser radar to obtain the first characteristic parameter of each waveform interval, and update the data of each row space according to the first characteristic parameter;

[0070] A5. If at least one waveform interval is a single-echo waveform interval, waveform fitting is performed on each single-echo waveform interval to obtain a second characteristic parameter of each single-echo waveform interval, and the data of the corresponding row space is updated according to the second characteristic parameter;

[0071] A6. If at least one waveform interval is a superimposed echo waveform interval, waveform parameter estimation and waveform decomposition are performed on each superimposed echo waveform interval in turn to obtain a third characteristic parameter of each superimposed echo waveform interval, and the corresponding row space data is updated according to the third characteristic parameter;

[0072] A7. Calculate the waveform time according to the data structure matrix to obtain the distance detection value.

[0073] The laser radar data processing method uses a data structure matrix to transfer data between various processing processes, simplifies the data interaction between various processing processes when processing the laser radar full waveform data, avoids the problem of multiple opening of new memory space and multiple data copying in the traditional processing process, which affects the processing efficiency, and greatly reduces the interaction time required for data transfer in the laser radar full waveform data processing process, which is beneficial to improving the ranging efficiency.

[0074] Among them, the existing noise reduction processing method can be used to reduce the noise of the original laser radar full waveform data, which is not limited here.

[0075] Figure 4 A waveform graph corresponding to an exemplary laser radar full waveform data after noise reduction processing is shown, wherein the horizontal axis is the time axis (the horizontal axis value in the graph represents the number of sampling cycles corresponding to the sampling time. For example, the horizontal axis value of 100 in the graph represents the moment corresponding to the 100th sampling cycle. The specific corresponding time is related to the sampling cycle size of the system. If the sampling cycle of the system is 1μs, then the 100 represents 100μs), and the vertical axis is the waveform amplitude axis (the waveform amplitude value represents the strength of the waveform signal).

[0076] Specifically, step A2 includes:

[0077] Identify the number of waveform intervals of the lidar full waveform data as the number of rows in the data structure matrix;

[0078] Calculate the number of columns of the data structure matrix according to preset rules;

[0079] According to the number of rows and columns, a matrix with all element values ​​being zero is generated to obtain an initialized data structure matrix.

[0080] Since the number of rows of the data structure matrix is ​​equal to the number of waveform intervals of the laser radar full waveform data, after subsequent allocation, it can be ensured that each row space of the data structure matrix corresponds to each waveform interval one by one, which is conducive to ensuring that the data structure matrix can record all important feature data of the laser radar full waveform data.

[0081] by Figure 4 For example, the figure includes four waveform intervals, namely waveform interval a, waveform interval b, waveform interval c and waveform interval d. When identifying the waveform interval, it is possible to check whether each waveform amplitude value is greater than a preset amplitude threshold (which can be set according to actual needs) in chronological order, and divide the continuous waveform amplitude values ​​greater than the amplitude threshold into the same waveform interval, thereby obtaining the number of waveform intervals.

[0082] In some embodiments, the preset rule is: n=P; wherein n is the number of columns of the data structure matrix, and P is a preset positive integer greater than 3. That is, the number of columns of the data structure matrix is ​​set to a preset value, and the preset value (P) can be set according to actual needs. Generally, when the value of P is too small to cause the row space (the space of a row of data) of the data structure matrix to be insufficient to record all the characteristic parameters calculated subsequently, the number of columns can be expanded when it is detected that the row space is insufficient to record all the characteristic parameters calculated subsequently (specifically, inserting several columns of data whose element values ​​are all zero at the end of the data structure matrix).

[0083] In other embodiments, the preset rule is: n=3+4*M; wherein M is the maximum waveform superposition number of the superimposed echo waveform that can be identified by the data processing device. The waveform superposition number of the superimposed echo waveform refers to the number of sub-waveforms of the superimposed echo waveform (the superimposed echo waveform is composed of multiple sub-waveforms superimposed), and the maximum waveform superposition number of the superimposed echo waveform that can be identified by the data processing device is related to the performance of the data processing device. The number of columns calculated by the preset rule is generally sufficient to record all characteristic parameters obtained by subsequent calculations, so that there is no need to expand the data structure matrix in subsequent steps, which can further save time and improve ranging efficiency.

[0084] by Figure 4 For example, the full waveform data of the laser radar includes four waveform intervals. Therefore, the number of rows of its data structure matrix is ​​4. Assuming M=2, the number of columns of the data structure matrix is ​​3+4*2=11, thus generating the initialized data structure matrix for:

[0085] .

[0086] In some embodiments, the first characteristic parameter includes a start time, an end time, and a number of valid peaks of a waveform interval;

[0087] Step A4 includes:

[0088] Performing waveform detection on the laser radar full waveform data to obtain the first characteristic parameter of each waveform interval;

[0089] The first element value, the second element value, and the third element value of the corresponding row space are updated respectively using the start time, end time, and number of valid peaks of each waveform interval.

[0090] The start time of the waveform interval refers to the time corresponding to the first waveform amplitude data of the waveform interval, the end time of the waveform interval refers to the time corresponding to the last waveform amplitude data of the waveform interval, and the number of effective peaks of the waveform interval refers to the number of maximum values ​​of the waveform amplitude data of the waveform interval (corresponding to the number of peaks of the waveform interval in the waveform graph, for example Figure 4 , the number of valid peaks in waveform interval a, waveform interval b, waveform interval c, and waveform interval d are 1, 2, 2, and 1, respectively).

[0091] For the full waveform data of the laser radar, the data of the first waveform interval is the seed waveform data, and the data of each waveform interval thereafter represents an echo waveform data. When allocating the row space, the row space of the data structure matrix can be allocated to each waveform interval from top to bottom in sequence according to the order of the start time of each waveform interval; Figure 4 For example, the first row space of the data structure matrix is ​​allocated to waveform interval a, the second row space is allocated to waveform interval b, the third row space is allocated to waveform interval c, and the fourth row space is allocated to waveform interval d. For the subsequent waveform moment calculation, it is necessary to calculate the time difference between the echo waveform and the seed waveform according to the characteristic parameters of the echo waveform and the characteristic parameters of the seed waveform, and then calculate the distance data. At this time, it is only necessary to extract the other rows of data and the first row of data for waveform moment calculation, and the distance data corresponding to the corresponding echo waveform can be obtained. In practical applications, the row space allocation is not limited to this method.

[0092] For example, Figure 4 In the figure, the start time, end time and number of valid peaks of waveform interval a are 67, 85 and 1 respectively; the start time, end time and number of valid peaks of waveform interval b are 444, 474 and 2 respectively; the start time, end time and number of valid peaks of waveform interval c are 483, 516 and 2 respectively; the start time, end time and number of valid peaks of waveform interval d are 537, 553 and 1 respectively. Thus, the data structure matrix updated according to the first characteristic parameter is:

[0093] .

[0094] Among them, the single echo waveform interval is a waveform interval with a valid peak number of 1;

[0095] The second characteristic parameters include the first peak time, the first standard deviation and the first expected value of the single echo waveform interval;

[0096] Step A5 includes:

[0097] Performing waveform fitting on each single echo waveform interval to obtain a second characteristic parameter of each single echo waveform interval;

[0098] The first peak time, the first standard deviation and the first expected value of each single echo waveform interval are used to update the 4th element value, the 5th element value and the 6th element value of the corresponding row space respectively.

[0099] The data distribution of the single echo waveform interval generally conforms to the one-dimensional Gaussian distribution law. Therefore, the step of performing waveform fitting on each single echo waveform interval to obtain the second characteristic parameter of each single echo waveform interval includes:

[0100] The waveform of each single echo waveform interval is fitted according to the following model:

[0101] ;

[0102] in, is the time value, is the waveform amplitude fitting function, is the first standard deviation, is the first expected value.

[0103] The first peak time is the time corresponding to the peak of the single echo waveform interval, for example, Figure 4 In the figure, waveform interval a and waveform interval d are both single echo waveform intervals, and their first peak times are 76 and 545 respectively. Assume that the first standard deviation and the first expected value of waveform interval a are , , the first standard deviation and the first expected value of the waveform interval d are , , then the data structure matrix updated according to the second characteristic parameter is:

[0104] .

[0105] The superimposed echo waveform interval is a waveform interval in which the number of effective peaks is greater than 1;

[0106] The third characteristic parameters include the second peak time of each peak in the superimposed echo waveform interval, and the weight, second standard deviation and second expected value of each sub-waveform in the superimposed echo waveform interval;

[0107] Step A6 includes:

[0108] Estimating waveform parameters of each superimposed echo waveform interval to obtain a third characteristic parameter of each superimposed echo waveform interval;

[0109] Decomposing each superimposed echo waveform interval to optimize the third characteristic parameter;

[0110] Use the second peak time of each peak in each superimposed echo waveform interval to update the corresponding row space's 4th element value to the 5th element value. element values, is the number of effective peaks in the corresponding superimposed echo waveform interval;

[0111] The weight, second standard deviation and second expected value of the same sub-waveform are used as an array, and the arrays of each sub-waveform in each superimposed echo waveform interval are used to update the first element values ​​to element value.

[0112] The superimposed echo waveform is a waveform obtained by weighted superposition of multiple sub-waveforms, and the data distribution of each sub-waveform generally conforms to the one-dimensional Gaussian distribution law. Therefore, the step of estimating waveform parameters of each superimposed echo waveform interval to obtain the third characteristic parameter of each superimposed echo waveform interval includes:

[0113] The waveform parameters of each superimposed echo waveform interval are estimated according to the following model:

[0114] ;

[0115] ;

[0116] in, is the time value, is the waveform amplitude estimation function, is the first interval of the superimposed echo waveform. The sub-waveform The waveform amplitude at the time, is the first interval of the superimposed echo waveform. The weight of each sub-waveform, is the first interval of the superimposed echo waveform. The second standard deviation of the sub-waveforms, is the first interval of the superimposed echo waveform. The second expected value of the sub-waveform is calculated by an optimization algorithm (the prior art). The one that is closest to the superimposed echo waveform (for example, the one with the smallest Euclidean distance, but not limited to this) , and The value of .

[0117] Among them, the existing waveform decomposition processing method can be used to perform waveform decomposition on each superimposed echo waveform interval, so as to obtain the optimized second characteristic parameter. The specific waveform decomposition processing method is not limited here.

[0118] For example, Figure 4In the figure, waveform interval b and waveform interval c are both superimposed echo waveform intervals. The second peak times of the two peaks in waveform interval b are 453 and 470 respectively, and the second peak times of the two peaks in waveform interval c are 487 and 506 respectively. Assume that the optimized weight, second standard deviation, and second expected value of the first sub-waveform in waveform interval b are , , The optimized weight, second standard deviation, and second expected value of the second sub-waveform of waveform interval b are , , , the optimized weight, second standard deviation, and second expected value of the first sub-waveform of waveform interval c are , , The optimized weight, second standard deviation, and second expected value of the second sub-waveform of waveform interval c are , , , then the data structure matrix updated according to the third characteristic parameter is:

[0119] .

[0120] The data structure matrix finally obtained through the above process completely records the characteristic parameters required for the subsequent waveform moment calculation, effectively reflects the overall distribution of the laser radar echo, and can be conveniently and quickly extracted from it when performing the subsequent waveform moment calculation, which is conducive to improving the calculation efficiency and further improving the ranging efficiency. In addition, each valid data in the matrix (for the row space where the third data is 1, its valid data includes the first 6 data; for the row space where the third data is greater than 1, its valid data includes the first data), all represent an important feature of the lidar echo waveform. The definition of the matrix format can effectively reflect the overall distribution of the lidar echo and improve the efficiency of the ranging process.

[0121] For this data structure matrix, when extracting valid data from each waveform interval for subsequent calculation, the target data can be extracted according to the arrangement rules of various characteristic data, which is convenient and quick.

[0122] Among them, the waveform time calculation is mainly to calculate the time difference between each echo waveform and the seed waveform, and then calculate the corresponding distance data based on the time difference. The specific calculation process is the existing technology and will not be described in detail here. The parameters required in the calculation process can be directly extracted from the data structure matrix according to the arrangement rules of various characteristic data.

[0123] As can be seen from the above, the laser radar data processing method performs noise reduction processing on the original laser radar full waveform data; generates an initialized data structure matrix according to the laser radar full waveform data, so that the number of rows of the data structure matrix is ​​equal to the number of waveform intervals of the laser radar full waveform data; allocates each row space of the data structure matrix to each waveform interval; performs waveform detection on the laser radar full waveform data to obtain the first characteristic parameter of each waveform interval, and updates the data of each row space according to the first characteristic parameter; if at least one waveform interval is a single echo waveform interval, then performs waveform fitting on each single echo waveform interval to obtain the second characteristic parameter of each single echo waveform interval, and updates the data of the corresponding row space according to the second characteristic parameter; if at least one waveform interval is a superimposed echo waveform interval, then performs waveform parameter estimation and waveform decomposition on each superimposed echo waveform interval in turn to obtain the third characteristic parameter of each superimposed echo waveform interval, and updates the data of the corresponding row space according to the third characteristic parameter; performs waveform moment calculation according to the data structure matrix to obtain a distance detection value; thereby reducing the interaction time required for data transmission in the laser radar full waveform data processing process, which is beneficial to improving the ranging efficiency.

[0124] refer to Figure 2 The present application provides a laser radar data processing device for processing laser radar full waveform data to achieve ranging, including:

[0125] Noise reduction processing module 1, used for performing noise reduction processing on the original laser radar full waveform data;

[0126] Generating module 2, used for generating an initialized data structure matrix according to the full waveform data of the laser radar, so that the number of rows of the data structure matrix is ​​equal to the number of waveform intervals of the full waveform data of the laser radar;

[0127] Allocation module 3, used for allocating each row space of the data structure matrix to each waveform interval;

[0128] The waveform detection module 4 is used to perform waveform detection on the laser radar full waveform data to obtain the first characteristic parameter of each waveform interval, and update the data of each row space according to the first characteristic parameter;

[0129] A waveform fitting module 5 is used for performing waveform fitting on each single echo waveform interval when at least one waveform interval is a single echo waveform interval to obtain a second characteristic parameter of each single echo waveform interval, and updating the data of the corresponding row space according to the second characteristic parameter;

[0130] A waveform parameter estimation and decomposition module 6 is used to perform waveform parameter estimation and waveform decomposition on each superimposed echo waveform interval in turn when at least one waveform interval is a superimposed echo waveform interval, so as to obtain a third characteristic parameter of each superimposed echo waveform interval, and update the data of the corresponding row space according to the third characteristic parameter;

[0131] The distance calculation module 7 is used to calculate the waveform time according to the data structure matrix to obtain the distance detection value.

[0132] The laser radar data processing device uses a data structure matrix to transfer data between various processing processes, simplifies the data interaction between various processing processes when processing the laser radar full waveform data, avoids the problem of multiple opening of new memory space and multiple data copying in the traditional processing process, which affects the processing efficiency, and greatly reduces the interaction time required for data transfer in the laser radar full waveform data processing process, which is beneficial to improving the ranging efficiency.

[0133] Among them, the existing noise reduction processing method can be used to reduce the noise of the original laser radar full waveform data, which is not limited here.

[0134] Figure 4 An exemplary waveform diagram corresponding to the full waveform data of the laser radar after noise reduction processing is shown, wherein the horizontal axis is the time axis and the vertical axis is the waveform amplitude axis (the waveform amplitude value represents the strength of the waveform signal).

[0135] Specifically, the generating module 2 is used to generate an initialized data structure matrix according to the laser radar full waveform data so that the number of rows of the data structure matrix is ​​equal to the number of waveform intervals of the laser radar full waveform data, and then execute:

[0136] Identify the number of waveform intervals of the lidar full waveform data as the number of rows in the data structure matrix;

[0137] Calculate the number of columns of the data structure matrix according to preset rules;

[0138] According to the number of rows and columns, a matrix with all element values ​​being zero is generated to obtain an initialized data structure matrix.

[0139] Since the number of rows of the data structure matrix is ​​equal to the number of waveform intervals of the laser radar full waveform data, after subsequent allocation, it can be ensured that each row space of the data structure matrix corresponds to each waveform interval one by one, which is conducive to ensuring that the data structure matrix can record all important feature data of the laser radar full waveform data.

[0140] by Figure 4For example, the figure includes four waveform intervals, namely waveform interval a, waveform interval b, waveform interval c and waveform interval d. When identifying the waveform interval, it is possible to check whether each waveform amplitude value is greater than a preset amplitude threshold (which can be set according to actual needs) in chronological order, and divide the continuous waveform amplitude values ​​greater than the amplitude threshold into the same waveform interval, thereby obtaining the number of waveform intervals.

[0141] In some embodiments, the preset rule is: n=P; wherein n is the number of columns of the data structure matrix, and P is a preset positive integer greater than 3. That is, the number of columns of the data structure matrix is ​​set to a preset value, and the preset value (P) can be set according to actual needs. Generally, when the value of P is too small to cause the row space (the space of a row of data) of the data structure matrix to be insufficient to record all the characteristic parameters calculated subsequently, the number of columns can be expanded when it is detected that the row space is insufficient to record all the characteristic parameters calculated subsequently (specifically, inserting several columns of data whose element values ​​are all zero at the end of the data structure matrix).

[0142] In other embodiments, the preset rule is: n=3+4*M; wherein M is the maximum waveform superposition number of the superimposed echo waveform that can be identified by the data processing device. The waveform superposition number of the superimposed echo waveform refers to the number of sub-waveforms of the superimposed echo waveform (the superimposed echo waveform is composed of multiple sub-waveforms superimposed), and the maximum waveform superposition number of the superimposed echo waveform that can be identified by the data processing device is related to the performance of the data processing device. The number of columns calculated by the preset rule is generally sufficient to record all characteristic parameters obtained by subsequent calculations, so that there is no need to expand the data structure matrix in subsequent steps, which can further save time and improve ranging efficiency.

[0143] by Figure 4 For example, the full waveform data of the laser radar includes four waveform intervals. Therefore, the number of rows of its data structure matrix is ​​4. Assuming M=2, the number of columns of the data structure matrix is ​​3+4*2=11, thus generating the initialized data structure matrix for:

[0144] .

[0145] In some embodiments, the first characteristic parameter includes a start time, an end time, and a number of valid peaks of a waveform interval;

[0146] The waveform detection module 4 is used to perform waveform detection on the full waveform data of the laser radar to obtain the first characteristic parameter of each waveform interval, and when updating the data of each row space according to the first characteristic parameter, execute:

[0147] Performing waveform detection on the laser radar full waveform data to obtain the first characteristic parameter of each waveform interval;

[0148] The first element value, the second element value, and the third element value of the corresponding row space are updated respectively using the start time, end time, and number of valid peaks of each waveform interval.

[0149] The start time of the waveform interval refers to the time corresponding to the first waveform amplitude data of the waveform interval, the end time of the waveform interval refers to the time corresponding to the last waveform amplitude data of the waveform interval, and the number of effective peaks of the waveform interval refers to the number of maximum values ​​of the waveform amplitude data of the waveform interval (corresponding to the number of peaks of the waveform interval in the waveform graph, for example Figure 4 , the number of valid peaks in waveform interval a, waveform interval b, waveform interval c, and waveform interval d are 1, 2, 2, and 1, respectively).

[0150] For the full waveform data of the laser radar, the data of the first waveform interval is the seed waveform data, and the data of each waveform interval thereafter represents an echo waveform data. When allocating the row space, the row space of the data structure matrix can be allocated to each waveform interval from top to bottom in sequence according to the order of the start time of each waveform interval; Figure 4 For example, the first row space of the data structure matrix is ​​allocated to waveform interval a, the second row space is allocated to waveform interval b, the third row space is allocated to waveform interval c, and the fourth row space is allocated to waveform interval d. For the subsequent waveform moment calculation, it is necessary to calculate the time difference between the echo waveform and the seed waveform according to the characteristic parameters of the echo waveform and the characteristic parameters of the seed waveform, and then calculate the distance data. At this time, it is only necessary to extract the other rows of data and the first row of data for waveform moment calculation, and the distance data corresponding to the corresponding echo waveform can be obtained. In practical applications, the row space allocation is not limited to this method.

[0151] For example, Figure 4 In the figure, the start time, end time and number of valid peaks of waveform interval a are 67, 85 and 1 respectively; the start time, end time and number of valid peaks of waveform interval b are 444, 474 and 2 respectively; the start time, end time and number of valid peaks of waveform interval c are 483, 516 and 2 respectively; the start time, end time and number of valid peaks of waveform interval d are 537, 553 and 1 respectively. Thus, the data structure matrix updated according to the first characteristic parameter is:

[0152] .

[0153] Among them, the single echo waveform interval is a waveform interval with a valid peak number of 1;

[0154] The second characteristic parameters include the first peak time, the first standard deviation and the first expected value of the single echo waveform interval;

[0155] The waveform fitting module 5 is used to perform waveform fitting on each single echo waveform interval to obtain the second characteristic parameter of each single echo waveform interval, and update the data of the corresponding row space according to the second characteristic parameter, and execute:

[0156] Performing waveform fitting on each single echo waveform interval to obtain a second characteristic parameter of each single echo waveform interval;

[0157] The first peak time, the first standard deviation and the first expected value of each single echo waveform interval are used to update the 4th element value, the 5th element value and the 6th element value of the corresponding row space respectively.

[0158] The data distribution of the single echo waveform interval generally conforms to the one-dimensional Gaussian distribution law. Therefore, when the waveform fitting module 5 performs waveform fitting on each single echo waveform interval to obtain the second characteristic parameter of each single echo waveform interval, it executes:

[0159] The waveform of each single echo waveform interval is fitted according to the following model:

[0160] ;

[0161] in, is the time value, is the waveform amplitude fitting function, is the first standard deviation, is the first expected value.

[0162] The first peak time is the time corresponding to the peak of the single echo waveform interval, for example, Figure 4 In the figure, waveform interval a and waveform interval d are both single echo waveform intervals, and their first peak times are 76 and 545 respectively. Assume that the first standard deviation and the first expected value of waveform interval a are , , the first standard deviation and the first expected value of the waveform interval d are , , then the data structure matrix updated according to the second characteristic parameter is:

[0163] .

[0164] The superimposed echo waveform interval is a waveform interval in which the number of effective peaks is greater than 1;

[0165] The third characteristic parameters include the second peak time of each peak in the superimposed echo waveform interval, and the weight, second standard deviation and second expected value of each sub-waveform in the superimposed echo waveform interval;

[0166] The waveform parameter estimation and decomposition module 6 is used to perform waveform parameter estimation and waveform decomposition on each superimposed echo waveform interval in turn to obtain the third characteristic parameter of each superimposed echo waveform interval, and update the data of the corresponding row space according to the third characteristic parameter, and execute:

[0167] Estimating waveform parameters of each superimposed echo waveform interval to obtain a third characteristic parameter of each superimposed echo waveform interval;

[0168] Decomposing each superimposed echo waveform interval to optimize the third characteristic parameter;

[0169] Use the second peak time of each peak in each superimposed echo waveform interval to update the corresponding row space's 4th element value to the 5th element value. element values, is the number of effective peaks in the corresponding superimposed echo waveform interval;

[0170] The weight, second standard deviation and second expected value of the same sub-waveform are used as an array, and the arrays of each sub-waveform in each superimposed echo waveform interval are used to update the first element values ​​to element value.

[0171] The superimposed echo waveform is a waveform obtained by weighted superposition of multiple sub-waveforms, and the data distribution of each sub-waveform generally conforms to the one-dimensional Gaussian distribution law. Therefore, when the waveform parameter estimation and decomposition module 6 estimates the waveform parameters of each superimposed echo waveform interval to obtain the third characteristic parameters of each superimposed echo waveform interval, it executes:

[0172] The waveform parameters of each superimposed echo waveform interval are estimated according to the following model:

[0173] ;

[0174] ;

[0175] in, is the time value, is the waveform amplitude estimation function, is the first interval of the superimposed echo waveform. The sub-waveform The waveform amplitude at the time, is the first interval of the superimposed echo waveform. The weight of each sub-waveform, is the first interval of the superimposed echo waveform. The second standard deviation of the sub-waveforms, is the first interval of the superimposed echo waveform. The second expected value of the sub-waveform is calculated by an optimization algorithm (the prior art). The one that is closest to the superimposed echo waveform (for example, the one with the smallest Euclidean distance, but not limited to this) , and The value of .

[0176] Among them, the existing waveform decomposition processing method can be used to perform waveform decomposition on each superimposed echo waveform interval, so as to obtain the optimized second characteristic parameter. The specific waveform decomposition processing method is not limited here.

[0177] For example, Figure 4 In the figure, waveform interval b and waveform interval c are both superimposed echo waveform intervals. The second peak times of the two peaks in waveform interval b are 453 and 470 respectively, and the second peak times of the two peaks in waveform interval c are 487 and 506 respectively. Assume that the optimized weight, second standard deviation, and second expected value of the first sub-waveform in waveform interval b are , , The optimized weight, second standard deviation, and second expected value of the second sub-waveform of waveform interval b are , , , the optimized weight, second standard deviation, and second expected value of the first sub-waveform of waveform interval c are , , The optimized weight, second standard deviation, and second expected value of the second sub-waveform of waveform interval c are , , , then the data structure matrix updated according to the third characteristic parameter is:

[0178] .

[0179] The data structure matrix finally obtained through the above process completely records the characteristic parameters required for the subsequent waveform moment calculation, effectively reflects the overall distribution of the laser radar echo, and can be conveniently and quickly extracted from it when performing the subsequent waveform moment calculation, which is conducive to improving the calculation efficiency and further improving the ranging efficiency. In addition, each valid data in the matrix (for the row space where the third data is 1, its valid data includes the first 6 data; for the row space where the third data is greater than 1, its valid data includes the first data), all represent an important feature of the lidar echo waveform. The definition of the matrix format can effectively reflect the overall distribution of the lidar echo and improve the efficiency of the ranging process.

[0180] For this data structure matrix, when extracting valid data from each waveform interval for subsequent calculation, the target data can be extracted according to the arrangement rules of various characteristic data, which is convenient and quick.

[0181] Among them, the waveform time calculation is mainly to calculate the time difference between each echo waveform and the seed waveform, and then calculate the corresponding distance data based on the time difference. The specific calculation process is the existing technology and will not be described in detail here. The parameters required in the calculation process can be directly extracted from the data structure matrix according to the arrangement rules of various characteristic data.

[0182] As can be seen from the above, the laser radar data processing device performs noise reduction processing on the original laser radar full waveform data; generates an initialized data structure matrix based on the laser radar full waveform data, so that the number of rows of the data structure matrix is ​​equal to the number of waveform intervals of the laser radar full waveform data; allocates each row space of the data structure matrix to each waveform interval; performs waveform detection on the laser radar full waveform data to obtain the first characteristic parameter of each waveform interval, and updates the data of each row space according to the first characteristic parameter; if at least one waveform interval is a single echo waveform interval, then performs waveform fitting on each single echo waveform interval to obtain the second characteristic parameter of each single echo waveform interval, and updates the data of the corresponding row space according to the second characteristic parameter; if at least one waveform interval is a superimposed echo waveform interval, then performs waveform parameter estimation and waveform decomposition on each superimposed echo waveform interval in turn to obtain the third characteristic parameter of each superimposed echo waveform interval, and updates the data of the corresponding row space according to the third characteristic parameter; performs waveform time calculation according to the data structure matrix to obtain a distance detection value; thereby reducing the interaction time required for data transmission during the laser radar full waveform data processing process, which is beneficial to improving the ranging efficiency.

[0183] Please refer to Figure 3 , Figure 3This is a structural schematic diagram of an electronic device provided in an embodiment of the present application. The present application provides an electronic device, including: a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other through a communication bus 303 and / or other forms of connection mechanisms (not marked). The memory 302 stores a computer program executable by the processor 301. When the computing device is running, the processor 301 executes the computer program to execute the laser radar data processing method in any optional implementation of the above-mentioned embodiment to achieve the following functions: denoising the original laser radar full waveform data; generating an initialized data structure matrix based on the laser radar full waveform data, so that the number of rows of the data structure matrix is ​​equal to the number of waveform intervals of the laser radar full waveform data. ; Allocate each row space of the data structure matrix to each waveform interval; perform waveform detection on the full waveform data of the laser radar to obtain the first characteristic parameter of each waveform interval, and update the data of each row space according to the first characteristic parameter; if at least one waveform interval is a single echo waveform interval, perform waveform fitting on each single echo waveform interval to obtain the second characteristic parameter of each single echo waveform interval, and update the data of the corresponding row space according to the second characteristic parameter; if at least one waveform interval is a superimposed echo waveform interval, perform waveform parameter estimation and waveform decomposition on each superimposed echo waveform interval in turn to obtain the third characteristic parameter of each superimposed echo waveform interval, and update the data of the corresponding row space according to the second characteristic parameter; perform waveform time calculation according to the data structure matrix to obtain the distance detection value.

[0184] The embodiment of the present application provides a storage medium. When the computer program is executed by a processor, the laser radar data processing method in any optional implementation method of the above embodiment is executed to achieve the following functions: denoising the original laser radar full waveform data; generating an initialized data structure matrix based on the laser radar full waveform data, so that the number of rows of the data structure matrix is ​​equal to the number of waveform intervals of the laser radar full waveform data; allocating each row space of the data structure matrix to each waveform interval; performing waveform detection on the laser radar full waveform data to obtain a first characteristic parameter of each waveform interval, and updating the data of each row space according to the first characteristic parameter; if at least one waveform interval is a single echo waveform interval, waveform fitting is performed on each single echo waveform interval to obtain a second characteristic parameter of each single echo waveform interval, and the data of the corresponding row space is updated according to the second characteristic parameter; if at least one waveform interval is a superimposed echo waveform interval, waveform parameter estimation and waveform decomposition are performed on each superimposed echo waveform interval in turn to obtain a third characteristic parameter of each superimposed echo waveform interval, and the data of the corresponding row space is updated according to the second characteristic parameter; waveform time calculation is performed according to the data structure matrix to obtain a distance detection value. Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0185] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0186] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0187] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0188] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0189] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A laser radar data processing method for processing laser radar full waveform data to achieve ranging, characterized in that: The following steps are involved: A1. Perform noise reduction on the original laser radar full waveform data; A2. Generate an initialized data structure matrix according to the laser radar full waveform data, so that the number of rows of the data structure matrix is ​​equal to the number of waveform intervals of the laser radar full waveform data; A3. Allocate each row space of the data structure matrix to each of the waveform intervals; A4. Performing waveform detection on the full waveform data of the laser radar to obtain a first characteristic parameter of each of the waveform intervals, and updating the data of each of the line spaces according to the first characteristic parameter; A5. If at least one of the waveform intervals is a single echo waveform interval, waveform fitting is performed on each of the single echo waveform intervals to obtain a second characteristic parameter of each of the single echo waveform intervals, and the corresponding row space data is updated according to the second characteristic parameter; A6. If at least one of the waveform intervals is a superimposed echo waveform interval, waveform parameter estimation and waveform decomposition are performed on each of the superimposed echo waveform intervals in turn to obtain a third characteristic parameter of each of the superimposed echo waveform intervals, and the corresponding row space data is updated according to the third characteristic parameter; A7. Calculate the waveform time according to the data structure matrix to obtain the distance detection value.

2. The laser radar data processing method according to claim 1, characterized in that: Step A2 includes: Identify the number of waveform intervals of the laser radar full waveform data as the number of rows of the data structure matrix; Calculating the number of columns of the data structure matrix according to a preset rule; A matrix in which all element values ​​are zero is generated according to the number of rows and the number of columns, to obtain the initialized data structure matrix.

3. The laser radar data processing method according to claim 2, characterized in that: The first characteristic parameter includes the start time, end time and number of effective peaks of the waveform interval; Step A4 includes: Performing waveform detection on the laser radar full waveform data to obtain a first characteristic parameter of each waveform interval; The start time, the end time and the number of valid peaks of each waveform interval are used to update the first element value, the second element value and the third element value of the corresponding row space.

4. The laser radar data processing method according to claim 3, characterized in that: The single echo waveform interval is the waveform interval in which the number of effective peaks is 1; The second characteristic parameters include a first peak time, a first standard deviation and a first expected value of the single echo waveform interval; Step A5 includes: Performing waveform fitting on each of the single echo waveform intervals to obtain the second characteristic parameter of each of the single echo waveform intervals; The first peak time, the first standard deviation and the first expected value of each single echo waveform interval are used to update the corresponding 4th element value, 5th element value and 6th element value of the row space respectively.

5. The laser radar data processing method according to claim 4, characterized in that: The step of performing waveform fitting on each of the single echo waveform intervals to obtain the second characteristic parameter of each of the single echo waveform intervals includes: The waveform fitting is performed on each single echo waveform interval according to the following model: ； in, is the time value, is the waveform amplitude fitting function, is the first standard deviation, is the first expected value.

6. The laser radar data processing method according to claim 3, characterized in that: The superimposed echo waveform interval is the waveform interval in which the number of effective peaks is greater than 1; The third characteristic parameter includes the second peak time of each peak in the superimposed echo waveform interval, and the weight, second standard deviation and second expected value of each sub-waveform in the superimposed echo waveform interval; Step A6 includes: Estimating waveform parameters of each of the superimposed echo waveform intervals to obtain the third characteristic parameter of each of the superimposed echo waveform intervals; Performing waveform decomposition on each of the superimposed echo waveform intervals to optimize the third characteristic parameter; Use the second peak time of each peak in each superimposed echo waveform interval to update the corresponding fourth element value of the row space to the element values, is the number of effective peaks in the corresponding superimposed echo waveform interval; The weight of the same sub-waveform, the second standard deviation and the second expected value are used as an array, and the arrays of each sub-waveform in each superimposed echo waveform interval are used to update the corresponding row space. element values ​​to element value.

7. The laser radar data processing method according to claim 6, characterized in that: The step of estimating waveform parameters of each of the superimposed echo waveform intervals to obtain the third characteristic parameter of each of the superimposed echo waveform intervals includes: The waveform parameters of each of the superimposed echo waveform intervals are estimated according to the following model: ； ； in, is the time value, is the waveform amplitude estimation function, is the first The sub-waveform The waveform amplitude at time, is the first The weights of the sub-waveforms, is the first The second standard deviation of the sub-waveform, is the first The second expected value of the sub-waveform.

8. A laser radar data processing device for processing laser radar full waveform data to achieve ranging, characterized in that: include: A noise reduction processing module is used to perform noise reduction on the original laser radar full waveform data; A generating module, used for generating an initialized data structure matrix according to the laser radar full waveform data, so that the number of rows of the data structure matrix is ​​equal to the number of waveform intervals of the laser radar full waveform data; An allocation module, used for allocating each row space of the data structure matrix to each waveform interval; A waveform detection module, used for performing waveform detection on the laser radar full waveform data to obtain a first characteristic parameter of each waveform interval, and updating the data of each row space according to the first characteristic parameter; a waveform fitting module, for performing waveform fitting on each of the single echo waveform intervals when at least one of the waveform intervals is a single echo waveform interval, so as to obtain a second characteristic parameter of each of the single echo waveform intervals, and updating the data of the corresponding row space according to the second characteristic parameter; A waveform parameter estimation and decomposition module, used for, when at least one of the waveform intervals is a superimposed echo waveform interval, sequentially performing waveform parameter estimation and waveform decomposition on each of the superimposed echo waveform intervals to obtain a third characteristic parameter of each of the superimposed echo waveform intervals, and updating the corresponding data of the row space according to the third characteristic parameter; The distance calculation module is used to calculate the waveform time according to the data structure matrix to obtain the distance detection value.

9. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the laser radar data processing method as described in any one of claims 1 to 7 are executed.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the lidar data processing method as described in any one of claims 1 to 7 are executed.

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