4D millimeter wave radar clustering method and device and electronic equipment

Abstract

The invention belongs to the technical field of point cloud data processing, and relates to a 4D millimeter wave radar clustering method and device and electronic equipment. The method comprises the following steps: inputting 4D millimeter wave radar point cloud data; setting the first neighborhood radius and the minimum point number of the point clouds in the first neighborhood radius; performing point cloud density clustering by using a DBSCAN algorithm to obtain a point cloud density clustering result of each target area; calculating the center point position of each point cloud density cluster, and counting the width of each point cloud density cluster; setting a second neighborhood radius according to the width of each point cloud density cluster to obtain a set of the second neighborhood radiuses; and performing DBSCAN clustering on each point cloud density cluster according to the set second neighborhood radius to complete the point cloud clustering operation. The method can adapt to target point cloud data of different sizes, and the neighborhood radius of large-size clustering is adaptively adjusted according to the point cloud width, so that the point cloud clustering accuracy of the 4D millimeter wave radar is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of point cloud data processing, and in particular, relates to a 4D millimeter wave radar clustering method, device and electronic equipment. Background Art

[0002] Due to the sparsity of millimeter-wave radar point clouds, the point cloud of the same target lacks continuity, resulting in poor target clustering and easy target splitting.

[0003] Millimeter wave radar can measure the distance and radial velocity of each target in a multi-target scene simultaneously by transmitting multiple cycles of frequency modulation continuous wave (FMCW) signals. The transmitted waveform is shown in the attached figure. Figure 1 As shown. The radio waves used by millimeter-wave radar have a longer wavelength and a lower resolution than the laser beams used by lidar. This means that at the same detection distance, millimeter-wave radar can capture fewer target details, resulting in sparse point cloud data. When millimeter waves encounter target objects, they will scatter. Due to the long wavelength and relatively large scattering angle, the distribution of reflected signals in space is relatively dispersed, making it difficult to form dense point cloud data, and the imaging ability of the target is not strong, and the target cannot be accurately identified.

[0004] When the millimeter-wave radar detects the same target, it can obtain multiple sparse point targets. When tracking the target, multiple point targets will cause the track to split. Therefore, before tracking the target, target clustering is required. The basic principle of the clustering algorithm is to aggregate a group of continuous point clouds into one category based on the continuity of the point cloud. If the point cloud of the same target is not continuous, it is usually not well clustered. Because the millimeter-wave radar point cloud is sparse and unevenly distributed, when using the DBSCAN algorithm for clustering, the fixed neighborhood radius and the minimum number of points in the neighborhood are usually not matched. When the neighborhood radius is set too large, it is easy to cluster the point clouds of different targets into one category. When the neighborhood radius is set too small, it is easy to cluster the point clouds of the same target into multiple categories. The clustering accuracy of the millimeter-wave radar point cloud is low.

[0005] Since 4D millimeter-wave radar has a high angular resolution of about 1 degree and its point cloud density is higher than that of traditional millimeter-wave radar, the lateral detection density of 4D millimeter-wave radar for targets is higher. At the same time, since 4D millimeter-wave radar cannot solve the strong scattering characteristics of millimeter-wave radar and front and rear occlusions, the longitudinal imaging effect of 4D millimeter-wave radar on targets is not significantly different from that of traditional millimeter-wave radar. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a 4D millimeter wave radar clustering method, device and electronic equipment.

[0007] In a first aspect, the present invention provides a 4D millimeter wave radar clustering method, comprising: Input 4D millimeter wave radar point cloud data; Set the first neighborhood radius and the minimum number of points in the point cloud within the first neighborhood radius, and use each first neighborhood radius area as the target area; Use the DBSCAN algorithm to perform point cloud density clustering and obtain the point cloud density clustering results of each target area; Calculate the center point position of each point cloud density cluster and count the width of each point cloud density cluster; The second neighborhood radius is set according to the width of each point cloud density cluster to obtain a set of second neighborhood radii; Perform DBSCAN clustering on each point cloud density cluster according to the set second neighborhood radius to complete the point cloud clustering operation.

[0008] In a second aspect, the present invention provides a 4D millimeter wave radar clustering device, comprising an input unit, a first setting unit, a first clustering unit, a statistical unit, a second setting unit and a second clustering unit; Input unit, used to input 4D millimeter wave radar point cloud data; A first setting unit is used to set a first neighborhood radius and a minimum number of points in a point cloud within the first neighborhood radius, and to use each first neighborhood radius area as a target area; The first clustering unit is used to perform point cloud density clustering using the DBSCAN algorithm to obtain point cloud density clustering results for each target area; The statistical unit is used to calculate the center point position of each point cloud density cluster and count the width of each point cloud density cluster; A second setting unit, used to set a second neighborhood radius according to the width of each point cloud density cluster to obtain a set of second neighborhood radii; The second clustering unit is used to perform DBSCAN clustering on each point cloud density cluster according to the set second neighborhood radius to complete the point cloud clustering operation.

[0009] In a third aspect, the present invention provides an electronic device, comprising: Processor and memory; The memory is used to store computer operation instructions; The processor is used to execute the 4D millimeter wave radar clustering method by calling the computer operation instruction.

[0010] Based on the above technical solution, the present invention can also be improved as follows.

[0011] Furthermore, the DBSCAN algorithm is used to perform point cloud density clustering to obtain the point cloud density clustering results of each target area, including: Let the radius of the first neighborhood be ε, and the minimum number of points within the radius of the first neighborhood be MinPts; Find core points: traverse each point in the data set and calculate the number of points within the first neighborhood radius; If the number of points within the first neighborhood radius of a point is greater than or equal to the minimum number of points within the first neighborhood radius, the point is marked as a core point; Construct clusters: Start from an unvisited core point, add the core point to the current cluster, and mark it as visited; find all points within the first neighborhood radius of the core point, if there is a core point, recursively add the points within the first neighborhood radius to the current cluster, and mark them as visited; if the point within the first neighborhood radius is a non-core point but is within the first neighborhood radius of a core point, mark the point within the first neighborhood radius as a boundary point and add it to the same cluster as the core point; repeatedly find all points within the first neighborhood radius of the core point until there are no new points that can be added within the first neighborhood radius of the current cluster; Repeatedly build new clusters: Continue to traverse the unvisited points in the data set and repeatedly search for core points until all data points have been visited and classified into various clusters.

[0012] Furthermore, the points in the dataset that have not been visited and do not belong to any cluster are marked as noise points.

[0013] Furthermore, DBSCAN clustering is performed on each point cloud density cluster according to the set second neighborhood radius, including: Set up The point cloud density is clustered as , the nth cloud density cluster is , the point cloud density clustering set is , No. Point cloud density clustering The second neighborhood radius of , for The clusters, for The center point of For the Point cloud density clustering The center point coordinates, For the Point cloud density clustering The center point coordinates and Point cloud density clustering The distance difference between the center coordinates of from Start building clusters and set Point cloud density clustering The second neighborhood radius , set the Point cloud density clustering The second neighborhood radius The minimum number of points is 1; Search Nearby center points, traverse the point cloud density clustering set ,calculate ,if ,but for Neighborhood of no Neighborhood of; from Starting from each neighboring point of Until the nearest center point is found All neighboring points of Use when the center point is close to Neighborhood radius ; Clustering of point cloud density not included , repeat the search Nearby center points until all point cloud density clustering sets are traversed .

[0014] Furthermore, the first neighborhood radius is smaller than the second neighborhood radius.

[0015] Further, when the second neighborhood radius is set according to the width of each point cloud density cluster, the second neighborhood radius is set according to the relationship between the length and width of the target.

[0016] The beneficial effect of the present invention is that after completing the traditional clustering, the present invention counts the width of each high-density horizontal clustering result, performs a large-scale clustering operation from each high-density clustering result, and the second neighborhood radius ε during large-scale clustering is determined according to the size of the horizontal clustering width, that is, the larger the current horizontal clustering width, the larger the second neighborhood radius ε is set, until all point cloud clustering is completed. Therefore, the present invention can adapt to target point cloud data of different sizes, and adaptively adjust the neighborhood radius of large-scale clustering according to the point cloud width, thereby improving the point cloud clustering accuracy of 4D millimeter wave radar. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a 4D millimeter wave radar clustering method provided in Example 1 of the present invention; Figure 2 A schematic diagram of a single target point cloud provided in Example 1 of the present invention; Figure 3 A schematic diagram of the processing results of the horizontal clustering results provided in Example 1 of the present invention; Figure 4 A schematic diagram of a 4D millimeter wave radar clustering device provided in Example 2 of the present invention; Figure 5 A schematic diagram of an electronic device provided in Example 3 of the present invention.

[0018] Icons: 30 - electronic device; 310 - processor; 320 - bus; 330 - memory; 340 - transceiver. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0020] Example 1 As an example, Figure 1 As shown, in order to solve the above technical problems, this embodiment provides a 4D millimeter wave radar clustering method, including: Input 4D millimeter wave radar point cloud data; Set the first neighborhood radius and the minimum number of points in the point cloud within the first neighborhood radius, and use each first neighborhood radius area as the target area; Use the DBSCAN algorithm to perform point cloud density clustering and obtain the point cloud density clustering results of each target area; Calculate the center point position of each point cloud density cluster and count the width of each point cloud density cluster; The second neighborhood radius is set according to the width of each point cloud density cluster to obtain a set of second neighborhood radii; Perform DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering on each point cloud density cluster according to the set second neighborhood radius to complete the point cloud clustering operation.

[0021] When clustering targets, first, use the DBSCAN algorithm to count the horizontal width of the target point cloud, set the neighborhood radius ε to a smaller value (generally less than 0.2 meters), and set the minimum number of points MinPts to a larger value (generally greater than 50 points); after clustering is completed, the high-density area horizontal clustering results of each target are obtained ; Then, count the width of each high-density horizontal clustering result; Start to perform large-size clustering operation; the first neighborhood radius ε during large-size clustering is determined by the horizontal clustering width, that is, the larger the current horizontal clustering width, the larger the first neighborhood radius ε is set, until all point clouds are clustered.

[0022] Input a frame of 4D millimeter wave radar point cloud data. The schematic diagram of a single target point cloud is as shown in the attached figure. Figure 2 As shown in Figure 2, radar point clouds are characterized by non-uniformity and sparsity.

[0023] Optionally, points in the dataset that have not been visited and do not belong to any cluster are marked as noise points.

[0024] The schematic diagram of the horizontal clustering result processing is shown in the attached figure. Figure 3 As shown, the x-axis is the horizontal distance, unit: m, and the y-axis is the vertical distance, unit: m.

[0025] No. The point cloud density is clustered as , point cloud density clustering The width is , the second neighborhood radius is , clustering according to the density of each point cloud Width Set the neighborhood radius ,get When setting the neighborhood radius, based on the comparison of common target width and length, such as the length and width of human targets do not exceed 1*0.5m, the length and width of motorcycles do not exceed 3*1m, the length and width of small cars do not exceed 6*2m, and the length and width of large cars do not exceed 10*3m, the length and width of typical targets are used to set the second neighborhood radius for clustering, then: .

[0026] Optionally, DBSCAN clustering is performed on each point cloud density cluster according to the set second neighborhood radius, including: Set up The point cloud density is clustered as , the nth cloud density cluster is , the point cloud density clustering set is , No. Point cloud density clustering The second neighborhood radius of , for The clusters, for The center point of For the Point cloud density clustering The center point coordinates, For the Point cloud density clustering The center point coordinates and Point cloud density clustering The distance difference between the center coordinates of from Start building clusters and set Point cloud density clustering The second neighborhood radius , set the Point cloud density clustering The second neighborhood radius The minimum number of points is 1; Search Nearby center points, traverse the point cloud density clustering set ,calculate ,if ,but for Neighborhood of no Neighborhood of; from Starting from each neighboring point of Until the nearest center point is found All neighboring points of Use when the center point is close to Neighborhood radius ; Clustering of point cloud density not included , repeat the search Nearby center points until all point cloud density clustering sets are traversed .

[0027] As attached Figure 4 As shown, all point clouds of the same target are clustered into one category.

[0028] Optionally, the first neighborhood radius is smaller than the second neighborhood radius.

[0029] Optionally, when setting the second neighborhood radius according to the width of each point cloud density cluster, the second neighborhood radius is set according to the relationship between the length and width of the target.

[0030] After completing traditional clustering, the present invention counts the width of each high-density horizontal clustering result; performs large-scale clustering operations starting from each high-density clustering result; the second neighborhood radius ε during large-scale clustering is determined according to the width of the horizontal clustering, that is, the larger the current horizontal clustering width, the larger the second neighborhood radius ε is set, until all point cloud clustering is completed. Therefore, the present invention can adapt to target point cloud data of different sizes, and adaptively adjust the neighborhood radius of large-scale clustering according to the point cloud width, thereby improving the point cloud clustering accuracy of 4D millimeter wave radar.

[0031] Example 2 Based on the same principle as the method shown in Embodiment 1 of the present invention, a 4D millimeter wave radar clustering device is also provided in an embodiment of the present invention, including an input unit, a first setting unit, a first clustering unit, a statistical unit, a second setting unit and a second clustering unit; Input unit, used to input 4D millimeter wave radar point cloud data; A first setting unit is used to set a first neighborhood radius and a minimum number of points in a point cloud within the first neighborhood radius, and to use each first neighborhood radius area as a target area; The first clustering unit is used to perform point cloud density clustering using the DBSCAN algorithm to obtain point cloud density clustering results for each target area; The statistical unit is used to calculate the center point position of each point cloud density cluster and count the width of each point cloud density cluster; A second setting unit, used to set a second neighborhood radius according to the width of each point cloud density cluster to obtain a set of second neighborhood radii; The second clustering unit is used to perform DBSCAN clustering on each point cloud density cluster according to the set second neighborhood radius to complete the point cloud clustering operation.

[0032] Optionally, points in the dataset that have not been visited and do not belong to any cluster are marked as noise points.

[0033] Optionally, DBSCAN clustering is performed on each point cloud density cluster according to the set second neighborhood radius, including: Set up The point cloud density is clustered as , the nth cloud density cluster is , the point cloud density clustering set is , No. Point cloud density clustering The second neighborhood radius of , for The clusters, for The center point of For the Point cloud density clustering The center point coordinates, For the Point cloud density clustering The center point coordinates and Point cloud density clustering The distance difference between the center coordinates of from Start building clusters and set Point cloud density clustering The second neighborhood radius , set the Point cloud density clustering The second neighborhood radius The minimum number of points is 1; Search Nearby center points, traverse the point cloud density clustering set ,calculate ,if ,but for Neighborhood of no Neighborhood of; from Starting from each neighboring point of Until the nearest center point is found All neighboring points of Use when the center point is close to Neighborhood radius ; Clustering of point cloud density not included , repeat the search Nearby center points until all point cloud density clustering sets are traversed .

[0034] Optionally, the first neighborhood radius is smaller than the second neighborhood radius.

[0035] Optionally, when setting the second neighborhood radius according to the width of each point cloud density cluster, the second neighborhood radius is set according to the relationship between the length and width of the target.

[0036] Example 3 Based on the same principle as the method shown in the embodiment of the present invention, an electronic device is also provided in the embodiment of the present invention, as shown in the attached Figure 5 As shown, the electronic device may include but is not limited to: a processor and a memory; the memory is used to store a computer program; the processor is used to execute a 4D millimeter wave radar clustering method shown in an embodiment of the present invention by calling the computer program.

[0037] In an optional embodiment, an electronic device is provided. Figure 5 The electronic device 30 shown includes: a processor 310 and a memory 330. The processor 310 and the memory 330 are connected, for example, via a bus 320.

[0038] Optionally, the electronic device 30 may further include a transceiver 340, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 340 is not limited to one, and the structure of the electronic device 30 does not constitute a limitation on the embodiments of the present invention.

[0039] The processor 310 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. The processor 310 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0040] The bus 320 may include a path to transmit information between the above components. The bus 320 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 320 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0041] The memory 330 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0042] The memory 330 is used to store application code (computer program) for executing the solution of the present invention, and is controlled to be executed by the processor 310. The processor 310 is used to execute the application code stored in the memory 330 to implement the contents shown in the above method embodiment.

[0043] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A 4D millimeter wave radar clustering method, characterized in that: include: Input 4D millimeter wave radar point cloud data; Set the first neighborhood radius and the minimum number of points in the point cloud within the first neighborhood radius, and use each first neighborhood radius area as the target area; Use the DBSCAN algorithm to perform point cloud density clustering and obtain the point cloud density clustering results of each target area; Calculate the center point position of each point cloud density cluster and count the width of each point cloud density cluster; The second neighborhood radius is set according to the width of each point cloud density cluster to obtain a set of second neighborhood radii; Perform DBSCAN clustering on each point cloud density cluster according to the set second neighborhood radius to complete the point cloud clustering operation.

2. A 4D millimeter wave radar clustering method according to claim 1, characterized in that: Use the DBSCAN algorithm to perform point cloud density clustering and obtain the point cloud density clustering results of each target area, including: Let the radius of the first neighborhood be ε, and the minimum number of points within the radius of the first neighborhood be MinPts; Find core points: traverse each point in the data set and calculate the number of points within the first neighborhood radius; If the number of points within the first neighborhood radius of a point is greater than or equal to the minimum number of points within the first neighborhood radius, the point is marked as a core point; Construct clusters: Start from an unvisited core point, add the core point to the current cluster, and mark it as visited; find all points within the first neighborhood radius of the core point, if there is a core point, recursively add the points within the first neighborhood radius to the current cluster, and mark them as visited; if the point within the first neighborhood radius is a non-core point but is within the first neighborhood radius of a core point, mark the point within the first neighborhood radius as a boundary point and add it to the same cluster as the core point; repeatedly find all points within the first neighborhood radius of the core point until there are no new points that can be added within the first neighborhood radius of the current cluster; Repeatedly build new clusters: Continue to traverse the unvisited points in the data set and repeatedly search for core points until all data points have been visited and classified into various clusters.

3. A 4D millimeter wave radar clustering method according to claim 2, characterized in that: The points in the dataset that have not been visited and do not belong to any cluster are marked as noise points.

4. The 4D millimeter wave radar clustering method according to claim 1, characterized in that: Perform DBSCAN clustering on each point cloud density cluster according to the set second neighborhood radius, including: Set up The point cloud density is clustered as , the nth cloud density cluster is , the point cloud density clustering set is , No. Point cloud density clustering The second neighborhood radius of , for The clusters, for The center point of For the Point cloud density clustering The center point coordinates, For the Point cloud density clustering The center point coordinates and Point cloud density clustering The distance difference between the center coordinates of from Start building clusters and set Point cloud density clustering The second neighborhood radius , set the Point cloud density clustering The second neighborhood radius The minimum number of points is 1; Search The nearby center point traverses the point cloud density clustering set ,calculate ,if ,but for Neighborhood of no Neighborhood of; from Starting from each neighboring point of Until the nearest center point is found All neighboring points of Use when the center point is close to Neighborhood radius ; Clustering of point cloud density not included , repeat the search Nearby center points until all point cloud density clustering sets are traversed .

5. The 4D millimeter wave radar clustering method according to claim 1, characterized in that: The first neighborhood radius is smaller than the second neighborhood radius.

6. The 4D millimeter wave radar clustering method according to claim 1, characterized in that: When the second neighborhood radius is set according to the width of each point cloud density cluster, the second neighborhood radius is set according to the relationship between the length and width of the target.

7. A 4D millimeter wave radar clustering device, characterized in that: It includes an input unit, a first setting unit, a first clustering unit, a statistical unit, a second setting unit and a second clustering unit; Input unit, used to input 4D millimeter wave radar point cloud data; A first setting unit is used to set a first neighborhood radius and a minimum number of points in a point cloud within the first neighborhood radius, and to use each first neighborhood radius area as a target area; The first clustering unit is used to perform point cloud density clustering using the DBSCAN algorithm to obtain point cloud density clustering results for each target area; The statistical unit is used to calculate the center point position of each point cloud density cluster and count the width of each point cloud density cluster; A second setting unit, used to set a second neighborhood radius according to the width of each point cloud density cluster to obtain a set of second neighborhood radii; The second clustering unit is used to perform DBSCAN clustering on each point cloud density cluster according to the set second neighborhood radius to complete the point cloud clustering operation.

8. An electronic device, characterized in that: include: Processor and memory; The memory is used to store computer operation instructions; The processor is used to execute a 4D millimeter-wave radar clustering method according to any one of claims 1 to 6 by calling the computer operation instruction.

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