Area array laser radar distance imaging method and system

Abstract

The invention discloses an area array laser radar distance imaging method and system. According to the method, effective clustering points are extracted from point cloud data acquired by a surface array laser radar sensor, and rotation transformation and quadratic curve fitting are carried out by using geometric parameters of the points, so that geometric features of an object are accurately calculated. Furthermore, geometric parameters are processed through Savitz-goaller filtering and low-pass filtering, and key feature points are extracted. And finally, the separation rate of the point clouds is calculated according to the feature points, the point clouds are grouped and subjected to curved surface fitting, and a distance image of the object is obtained through projection. According to the method, a complex convergence algorithm is avoided, the real-time performance and stability of data processing are ensured, and meanwhile, the accuracy of distance imaging is improved.

Description

Technical Field

[0001] The present invention relates to the field of range imaging technology, and in particular to a range imaging method and system for a planar laser radar. Background Art

[0002] LiDAR is a sensor that emits laser light and then receives reflected waves from obstacles to measure the distance to the obstacle through the propagation time of the laser light.

[0003] Area array lidar does not require a scanning device and can quickly detect the target as a whole, becoming a research hotspot that has attracted widespread attention in the lidar field in recent years.

[0004] In recent years, with the development of advanced driver assistance systems (ADAS), the demand for more reliable and sophisticated vehicle detection has increased. Radar and radar can detect the environment around the vehicle, so they are very important in this framework. In this context, you must master these detection systems and understand their similarities and differences. Due to the basic real-time performance requirements of ADAS, it is almost essential to understand the processing algorithms required for lidar, understand the items that can be optimized and the measures taken to meet the real-time requirements.

[0005] Typically, LiDAR scans the 3D structure of the object of interest and repairs it after point cloud scanning, aiming, segmentation, and reconstruction. Due to measurement errors, the original point cloud data (PCD) is bound to be contaminated by various noises, which damages the accuracy of distance imaging.

[0006] Researchers have proposed many methods to obtain distance images from noisy PCDs. Representative methods include VannaSam's stable skeleton line extraction method in 3D PCD and WENPINGWANG's B-spline curve extraction method based on minimization of squared distance of curvature. These methods actually show high performance under various noise conditions in PCD, but have the disadvantage of increased computational time. We usually sacrifice efficiency and accuracy in filter design. Because more accurate filtering performance means more computational cost and more time, and more efficient algorithms mean that more details of PCD give up the loss of rendering accuracy. Summary of the invention

[0007] Based on this, the embodiments of the present application provide a method and system for surface laser radar distance imaging, which obtains accurate distance images while ensuring stability and real-time performance, and solves the problems existing in the prior art.

[0008] In a first aspect, a method for range imaging of a planar laser radar is provided, the method comprising:

[0009] S10: Obtain cluster points, surface array lidar range imaging numbers and geometric parameters from point cloud data;

[0010] S20: putting the geometric parameters into a buffer, filtering the buffer data, and converting the filtered data into feature points;

[0011] S30: Determine the distance image of the object using the feature point and cluster point information.

[0012] Optionally, the S10 specifically includes:

[0013] S101: Obtain cluster points from point cloud data;

[0014] S102: using the midpoints of the cluster points to obtain a rotation matrix, and performing a rotation transformation on the cluster point data;

[0015] S103: Perform quadratic curve fitting on the rotation transformation data to calculate the polynomial coefficients; then obtain a new Z coordinate based on the polynomial coefficients and the X coordinate;

[0016] S104: Update the computational geometry parameters using the newly acquired X coordinate and Z coordinate.

[0017] Optionally, the S20 specifically includes:

[0018] S201: putting geometric parameters into a buffer;

[0019] S202: performing Savitsky-Golay filtering and low-pass filtering on the buffer data;

[0020] S203: Convert the filter output data into three spatial points; and use the converted three spatial points as feature points of the projected laser radar point cloud.

[0021] Optionally, the S30 specifically includes:

[0022] S301: Calculate the separation rate of the point cloud;

[0023] S302: grouping the point cloud according to the separation rate: using the calculated separation rate, the point cloud data is divided into different intervals;

[0024] S303: Obtain a fitting surface according to the interval, then project the points in the interval onto the fitting surface, and collect the projected points to obtain a range image.

[0025] Optionally, the geometric parameters of the point cloud data include a left limit point, a right limit point, a pole, a vector and a length between the left limit point and the pole, and a vector and a length between the limit point and a vertex.

[0026] Optionally, calculating the separation rate of the point cloud in S301 includes:

[0027] Use three feature points to get two vectors, then calculate the angle between the two vectors, and calculate the separation rate based on the angle between the two vectors.

[0028] In a second aspect, a planar laser radar range imaging system is provided, the system comprising:

[0029] The acquisition module obtains the cluster point, surface column laser radar range imaging number and geometric parameters from the point cloud data;

[0030] A conversion module, which puts the geometric parameters into a buffer, filters the buffer data, and converts the filtered data into feature points;

[0031] The determination module determines the distance image of the object using the feature point and cluster point information.

[0032] In a third aspect, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the planar laser radar distance imaging method described in any one of the first aspects is implemented.

[0033] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the planar laser radar range imaging method described in any one of the first aspects is implemented.

[0034] In a fifth aspect, a computer program product is provided, comprising a computer program / instruction, which, when executed by a processor, implements the planar laser radar distance imaging method described in any one of the first aspects above.

[0035] The technical solution provided by the embodiment of the present application is based on importing the point cloud of the surface laser radar sensor, calculating its geometric parameters for filtering, and then calculating the feature points. The distance image of the target can be obtained from the calculated feature points and point cloud. The present invention does not use convergence algorithms such as B-spline fitting or skeleton line extraction algorithm in the point cloud for obtaining the distance image of the target object, so the real-time nature of data processing can be fully guaranteed, and the stability of the distance image can be improved by using Savitsky-Ray filtering and low-pass filtering. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0037] Figure 1The following is a flowchart of a laser radar range imaging method in one embodiment.

[0038] Figure 2 Flowchart for obtaining cluster points, numbers and geometric parameters from point cloud data.

[0039] Figure 3 Flowchart for putting geometric parameters into a buffer and filtering the buffer data, thereby converting the filtered data into feature points.

[0040] Figure 4 Flowchart for obtaining the distance image of an object using cluster points and numbered,feature points. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0042] In the description of the present invention, the terms "comprises", "has" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may also include other steps or units that are not explicitly listed but are inherent to these processes, methods, products or apparatuses, or steps or units added based on further optimization schemes conceived by the present invention.

[0043] The present invention proposes a method for obtaining accurate distance imaging while ensuring stability and real-time performance by using PCD measured by a planar laser radar. Here, distance imaging discusses objects with relatively simple structures and shapes, and the basic focus is on stability and real-time performance, as well as the accuracy of distance measurement.

[0044] Please refer to Figure 1 , which shows a flow chart of a method for range imaging of a planar laser radar provided in an embodiment of the present application, the method may include the following steps:

[0045] S10: Get cluster points, numbers and geometric parameters from point cloud data.

[0046] Point cloud data is the output data of the area laser radar sensor, which includes the point cloud size and number, X, Y, Z coordinates, etc.

[0047] For example, in the present invention, an 8×64 array lidar sensor is used, so the point cloud size is 512, numbered (0, 0), (0, 1), ..., (7, 63) or 0, 1, 2, ..., 511, and the X, Y, Z coordinates are marked as (-200.0, 10.30, 600.4), (-198.1, 10.29, 600.3), ..., (200.34, -10.30, 600.5), etc.

[0048] The present invention uses a line of point cloud data (64 points) to obtain geometric parameters. The geometric parameters of the point cloud data include the left limit point, the right limit point, the pole, the vector and length between the left limit point and the pole, and the vector and length between the limit point and the vertex.

[0049] S101: Obtain cluster points from point cloud data.

[0050] Delete invalid values ​​(no distance value or above the threshold) from the output data of the area array lidar sensor and select valid values ​​to configure the cluster points.

[0051] S102: Obtain a rotation matrix using the midpoints of the cluster points, and perform a rotation transformation on the cluster point data.

[0052] The present invention takes into account the small number of lines of the surface thermal laser radar and only uses the X coordinate and the Z coordinate to perform a two-dimensional rotation transformation, that is, to perform a rotation transformation on the cluster points on the XOZ plane.

[0053] S103: Perform quadratic curve fitting on the rotation transformation data to calculate the polynomial coefficients, and then obtain a new Z coordinate based on the polynomial coefficients and the X coordinate.

[0054] S104: Calculate geometric parameters using the newly acquired X coordinate and Z coordinate.

[0055] S20: putting the geometric parameters into a buffer, filtering the buffer data, and converting the filtered data into feature points.

[0056] S20 basically filters the geometric parameters imported from S10 and converts them into spatial points.

[0057] S201: Put the geometric parameters into a buffer.

[0058] Because the present invention performs 21 filtering operations on the geometric parameters, a buffer is required for storing the parameter data.

[0059] The geometry parameters have a 1×12 matrix and the buffer data has a 21×12 matrix.

[0060] S202: Put the geometric parameters into a buffer, and then perform Savitzky-Golay filtering and low-pass filtering on the buffer data.

[0061] The Savitzky-Golay filter smoothes the incoming data and the low-pass filter improves the stability of the signal by removing high-frequency noise from the incoming data.

[0062] S203: The filter output data is converted into three spatial points.

[0063] The three points obtained here can be used as feature points of the projected lidar point cloud.

[0064] S30: Obtain a distance image of the object using the clustering points, numbers, and feature points.

[0065] S301: Calculate the separation rate of the point cloud.

[0066] Use three feature points to get two vectors, then calculate the angle between the two vectors, and calculate the separation rate based on the angle between the two vectors.

[0067] S302: using the calculated separation rate and cluster points, numbering to obtain a distance image of the object.

[0068] Using the calculated separation rate, the point cloud data is divided into different intervals;

[0069] S303: Obtain a fitting surface according to the interval, then project the points in the interval onto the fitting surface, and collect the projected points to obtain a range image.

[0070] In addition, a recommended method for range imaging of surface laser radar is also provided. Figure 2 A flowchart of obtaining cluster points, numbers and geometric parameters from the point cloud data according to an embodiment of the present disclosure is shown.

[0071] like Figure 2 As shown, the step S10 includes the following steps S101-S104:

[0072] In step S101 , cluster points are obtained from point cloud data.

[0073] Remove invalid values ​​(no distance value or above the threshold) from the output data of the area array lidar sensor and select valid values ​​to configure the classification points.

[0074] In step S102, a rotation matrix is ​​calculated and a rotation transformation is performed on the cluster point data.

[0075] The cluster points are represented as

[0076] Among them, x i is the coordinate X value of cluster point i, y i is the Y value of the cluster point i, z iis the coordinate Z value of cluster point i, i belongs to the interval [0, N-1].

[0077] The third row of data of the selected area laser radar sensor is expressed as:

[0078]

[0079] Among them, j belongs to the interval [0, M-1].

[0080] In P C The point group on the XOZ plane is represented by

[0081] Calculate the midpoint and rotation matrix of the cluster points. The calculation formula is:

[0082]

[0083] Among them, P m =(x m , z m ) is the midpoint of the cluster.

[0084]

[0085] Among them, θ is the rotation angle and M is the rotation matrix.

[0086] Then for P C_XOZ To perform a rotation transformation, the calculation formula is:

[0087] P ROT =P C_XOZ *M' (4)

[0088] In step S103, quadratic curve fitting is performed on the rotation transformation data to obtain new cluster points. ROT and Z ROT P ROT The coordinate X value group and the coordinate Z value group.

[0089] A=[X ROT .*X ROT , X ROT , ones(length(X ROT ))]

[0090] Coef=(A′*A) -1 *A′*Z ROT (5)

[0091] Where Coef is the coefficient of the quadratic polynomial.

[0092] Z ROT_HAT =A*Coef (6)

[0093] Among them, Z ROT_HAT The coordinate Z value of the new cluster point.

[0094] In step S104, geometric parameters about the cluster points are calculated.

[0095] The calculation can be regarded as the reference point of the target feature point. The calculation process is:

[0096] xParam = -Coef[1] / (2*Coef[0])

[0097]

[0098] tempParamInUnit=(xParam-X ROT [0]) / (X ROT [end]-X ROT [0])

[0099]

[0100] Among them, m c is the mean and is the variance, and the present invention uses m c =0.5, xParamInUnit=tempParamInUnit*CurentRatio+lastDivideRatio*(1-currentRatio).

[0101] Among them, lastDivideRatio is the separation ratio with a mean of 0.5 and a standard deviation of 0.1.

[0102] xParam=XROT[0]+(XROT[end]-XROT[0])*xParamInUnit

[0103] zParamn=[xParam2xParam, 1]|*Coef

[0104] The reference point groups are:

[0105]

[0106] lastDivideRatio=xParamInUnit

[0107] The calculation formula for rotating refPnts0 is:

[0108] ref Pnts ROT =ref Pnts0*M

[0109] Finally, the geometric parameters are calculated.

[0110] geoParam = [refPnts ROT .row(2),refPnts ROT .row(0), V1, L1, ref Pnts ROT .row(1),V2,L1] (7)

[0111] Among them, geoParam is the geometric parameter:

[0112] L1=|||ref Pnts ROT .row(0)-refPnts ROT .row(2)||,

[0113] L2=|||refPnts ROT .row(1)-refPnts ROT .row(2)||,

[0114] V1=(refPnts ROT .row(0)-ref Pnts ROT .row(2)) / L1,

[0115] V2=(refPnts ROT .row(1)-ref Pnts ROT .row(2)) / L2, ||·‖ is the norm operator.

[0116] Figure 3 FIG. 4 shows a flow chart of the S20 according to an embodiment of the present disclosure. Figure 3 As shown, step S20 includes the following steps S201-S203:

[0117] In step S201, the geometric parameters are placed in a buffer. Because the present invention performs 21 filtering operations on the geometric parameters, a buffer is required for placing the parameter data.

[0118] The geometry parameter geoParam has a 1×12 matrix and the buffer data has a 21×12 matrix.

[0119] In step S202, the buffer data is filtered.

[0120] The filtering of buffered data is performed in two stages: Savitzky-Gory filtering and low-pass filtering. The Savitzky-Gory filter smoothes the input data, and the low-pass filter improves the stability of the signal by removing high-frequency noise from the input data.

[0121] The Savitsky-Gorlay filter calculation formula is:

[0122]

[0123] Among them, H s is the Savitzky-Golay filter coefficient, and B is the buffer data.

[0124] sgolayParam is the output data of the Savitzky-Gowlay filter.

[0125] The formula for calculating the low pass rate is:

[0126]

[0127] Among them, H low is the filter coefficient of the low-pass filter, and filterParam is the output data of the low-pass filter.

[0128] In step S203, the filtered data is converted into feature points.

[0129] The feature point conversion process is:

[0130] V C =(filterParam[0],filterParam[1])

[0131] V L =(filterParam[4],filterParam[5])

[0132] L L =filterParam[6]

[0133] V R =(filterParam[9],filterParam

[10] )

[0134] L R =filterParam

[11]

[0135]

[0136] paramPnts 3D =[paramPnts 2D .col(0) zeros(3,1) paramPnts 2D .col(1)] (10)

[0137] Among them, V C is the center point of the cluster, V L is the left vector of the cluster point, V R is the right vector of the cluster point.

[0138] L L and L R V L and V R The length of paramPnts 3D are the characteristic points of the clustering points.

[0139] like Figure 4 As shown, step S30 includes the following steps S301-S303:

[0140] In S30, two vectors are created using the feature points, and then the angle between them is calculated to calculate the separation rate. According to the following separation rate, the point set is divided into a left plane part, a curved surface part, and a right plane part, and projection is performed on these parts to obtain a distance image.

[0141] In step S301, two vectors are created using the feature points, and then the angle between them is calculated to calculate the separation rate.

[0142] The separation rate indicates the percentage of arcs in the cluster points.

[0143] leftVector=paramPnts 3D .row(0)-paramPnts 3D .row(2)

[0144] leftLength=||leftVector|

[0145] leftVector n =leftVector / leftLength

[0146] rightVector=paramPnts 3D .row(1)-paramPnts 3D .row (2)

[0147] rightLength=||rightVector||

[0148] rightVector n =rightVector / rightLength

[0149]

[0150] Among them, leftVector is the left vector of the feature point, and rightVector is the right vector of the feature point. n and rightVector nare the normalized vectors of leftVector and rightVector respectively, and leftLength and rightLength are the lengths of leftVector and rightVector respectively. is leftVector n and rightVector n The angle between.

[0151] Then, the separation rate is calculated as follows:

[0152]

[0153] Wherein, divideRatio is the separation ratio.

[0154] In step S302, the point set is divided into a left plane part, a curved surface part, and a right plane part according to the separation rate. The length of the plane part is calculated using the formula:

[0155]

[0156] Calculate the separation point using the following formula:

[0157] divP left =paramPnts 3D .row(2)+leftVector n *L move

[0158] divP right =paramPnts 3D .row(2)+rightVector n *L move

[0159] Then, determine each partial interval.

[0160] Left plane interval: [paramPnts 3D .row(0),divP left ]

[0161] Surface interval: [divP left ,divP right ]

[0162] Right plane interval: [dicP right , paramPnts 3D .row(1)]

[0163] In step S303, a fitting surface is obtained according to the interval, and then the points in the interval are projected here, and then the projected points are collected to obtain a range image.

[0164] The present invention uses the least square method (LSQ) for fitting, and its process is:

[0165] For plane interval The input point set is represented as

[0166] Among them, P plane There are K The point set.

[0167]

[0168] dvLen=||dirV||

[0169] dirV n =dirV / dvLen

[0170] Calculate the fitted plane coefficients:

[0171] A=-dirV[2]

[0172] B=0

[0173] C=dirV[0]

[0174]

[0175] Then get the projection point, the process is:

[0176] V plane =[ABC]| T

[0177] T=P plane *V plane

[0178]

[0179] in, for The projection point of , i = 1, 2, ..., K. Then, for If the following conditions are met, become The distance to the image point.

[0180]

[0181] For surface fitting, the best fitting surface can also be obtained by establishing a polynomial regression equation for the surface and minimizing the sum of squares of distances from data points to the fitting surface.

[0182] From the above, it can be seen that the focus of this application is:

[0183] A recommended method comprises the following steps:

[0184] Step 1: Get cluster points, numbers and geometric parameters from the point cloud data.

[0185] The specific steps are:

[0186] Get cluster points from point cloud data, calculate the rotation matrix and perform rotation transformation on the cluster point data.

[0187] Then, a quadratic curve fitting is performed on the rotation transformed data to obtain new clustering points.

[0188] Step 2: Put the geometric parameters into a buffer, filter the buffer data, and convert the filtered data into feature points.

[0189] The specific steps are:

[0190] Place the geometry parameters into a buffer.

[0191] The filtering of buffered data is carried out in two stages: Savitsky-Gorlay filtering and low-pass filtering.

[0192] Convert the filtered data into feature points.

[0193] Step 3: Use the cluster points and numbers, feature points to obtain the distance image of the object.

[0194] In one embodiment of the present application, a planar laser radar range imaging system is also provided, the system comprising:

[0195] The acquisition module obtains the cluster point, surface column laser radar range imaging number and geometric parameters from the point cloud data;

[0196] A conversion module, which puts the geometric parameters into a buffer, filters the buffer data, and converts the filtered data into feature points;

[0197] The determination module determines the distance image of the object using the feature point and cluster point information.

[0198] For the specific definition of the planar laser radar range imaging system, please refer to the definition of the planar laser radar range imaging method above, which will not be repeated here. Each module in the above-mentioned planar laser radar range imaging system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0199] In one embodiment, an electronic device is provided, which may be a computer. The electronic device includes a processor, a memory, and a network interface connected via a system bus. The processor of the device is used to provide computing and control capabilities. The memory of the device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to array laser radar range imaging data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for array laser radar range imaging is implemented.

[0200] In one embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned planar laser radar range imaging method are implemented.

[0201] In one embodiment of the present application, a computer program product is provided, including a computer program / instruction, which implements the steps of the above-mentioned planar laser radar range imaging method when executed by a processor.

[0202] The computer-readable storage medium and computer program product provided in this embodiment have similar implementation principles and technical effects to those of the above method embodiments, and are not described in detail here.

[0203] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in M ​​forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SyMchliMk) DRAM (SLDRAM), memory bus (RaMbus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0204] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0205] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A method for range imaging of a surface laser radar, characterized in that: The method comprises: S10: Obtain cluster points, surface array lidar range imaging numbers and geometric parameters from point cloud data; S20: putting the geometric parameters into a buffer, filtering the buffer data, and converting the filtered data into feature points; S30: Determine the distance image of the object using the feature point and cluster point information.

2. The method for range imaging of a planar laser radar according to claim 1, characterized in that: The S10 specifically includes: S101: Obtain cluster points from point cloud data; S102: using the midpoints of the cluster points to obtain a rotation matrix, and performing a rotation transformation on the cluster point data; S103: Perform quadratic curve fitting on the rotation transformation data to calculate the polynomial coefficients; then obtain a new Z coordinate based on the polynomial coefficients and the X coordinate; S104: Update the computational geometry parameters using the newly acquired X coordinate and Z coordinate.

3. The method for range imaging of a planar laser radar according to claim 1, characterized in that: The S20 specifically includes: S201: putting geometric parameters into a buffer; S202: performing Savitsky-Golay filtering and low-pass filtering on the buffer data; S203: Convert the filter output data into three spatial points; and use the converted three spatial points as feature points of the projected laser radar point cloud.

4. The method for range imaging of a planar laser radar according to claim 1, characterized in that: The S30 specifically includes: S301: Calculate the separation rate of the point cloud; S302: grouping the point cloud according to the separation rate: using the calculated separation rate, the point cloud data is divided into different intervals; S303: Obtain a fitting surface according to the interval, then project the points in the interval onto the fitting surface, and collect the projected points to obtain a range image.

5. The method for range imaging of a planar laser radar according to claim 1, characterized in that: The geometric parameters of point cloud data include the left limit point, the right limit point, the pole, the vector and length between the left limit point and the pole, and the vector and length between the limit point and the vertex.

6. The method for range imaging of a planar laser radar according to claim 1, characterized in that: The separation rate of the point cloud is calculated in S301, including: Use three feature points to get two vectors, then calculate the angle between the two vectors, and calculate the separation rate based on the angle between the two vectors.

7. A planar laser radar range imaging system, characterized in that: The system comprises: The acquisition module obtains the cluster point, surface column laser radar range imaging number and geometric parameters from the point cloud data; A conversion module puts the geometric parameters into a buffer, filters the buffer data, and converts the filtered data into feature points; The determination module determines the distance image of the object using the feature point and cluster point information.

8. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the planar laser radar distance imaging method as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the planar laser radar distance imaging method as described in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the planar laser radar distance imaging method as described in any one of claims 1 to 6 is implemented.

Images