Radar data processing method and device, equipment, vehicle, medium and product

Abstract

The embodiment of the invention provides a radar data processing method and device, equipment, a vehicle, a medium and a product. The method comprises the following steps: receiving point cloud data collected by a radar; determining a central data point from the point cloud data of the current frame; and according to the first attribute information of the central data point, determining a filtering threshold value of the central data point, and according to the filtering threshold value, carrying out drag point filtering on the central data point. According to the embodiment of the invention, the method achieves the drag point filtering of the radar data, and improves the accuracy and reliability of the radar data.

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and in particular to methods, devices, equipment, vehicles, media, and products for processing radar data. Background Art

[0002] At present, radar is a key sensor for vehicles to achieve environmental perception. By emitting laser pulses and measuring the time and intensity of their reflection, it can accurately obtain the position and shape information of the target object, thereby providing high-precision three-dimensional spatial data for vehicle functions such as autonomous driving.

[0003] However, since the laser pulse has a certain divergence angle, when the laser beam simultaneously illuminates the boundaries of two objects at a close distance, two reflected echoes may be generated. Since the two objects are close to each other, the two reflected echoes are superimposed in the time dimension. The superimposed waves cause errors in the pulse width leading edge and pulse width measurement, which in turn affects the accuracy of the distance measurement. Finally, there is a false point between the two objects in the point cloud, that is, a drag point. This phenomenon is called "sticky point cloud", which greatly affects the accuracy and reliability of radar data, especially for complex scenes at close distances. Summary of the Invention

[0004] In view of the above problems, a radar data processing method, apparatus, device, vehicle, medium, and product are proposed to overcome or at least partially solve the above problems, including:

[0005] A method for processing radar data, the method comprising:

[0006] Receive point cloud data collected by radar;

[0007] Determine the center data point from the current frame point cloud data;

[0008] A filtering threshold of the central data point is determined according to the first attribute information of the central data point, and the central data point is subjected to drag-point filtering according to the filtering threshold.

[0009] Optionally, determining a filtering threshold of the central data point according to the first attribute information of the central data point includes:

[0010] Determine the segment interval where the first attribute information of the central data point is located;

[0011] A filtering threshold of the central data point is determined according to the segmented interval.

[0012] Optionally, the first attribute information is polar diameter.

[0013] Optionally, performing drag-point filtering on the central data point according to the filtering threshold includes:

[0014] determining a plurality of adjacent data points of the central data point;

[0015] Determining the polar distance difference between the plurality of adjacent data points and the central data point;

[0016] The central data point is subjected to drag point filtering according to the polar radius distance difference and the filtering threshold.

[0017] Optionally, the filtering threshold includes a distance difference threshold and a statistical threshold, and performing drag point filtering on the central data point according to the polar distance difference and the filtering threshold includes:

[0018] Determine target adjacent data points whose distance difference is less than or equal to the distance difference threshold;

[0019] When the number of the target adjacent data points is less than the statistical threshold, the central data point is filtered out by dragging.

[0020] Optionally, determining a central data point from the current frame point cloud data includes:

[0021] According to the second attribute information of the data points in the current frame point cloud data, a central data point is determined from the data points in the current frame point cloud data.

[0022] Optionally, the second attribute information is pulse width and polar diameter, and determining the central data point from the data points in the current frame point cloud data according to the second attribute information of the data points in the current frame point cloud data includes:

[0023] When the pulse width of a data point in the current frame point cloud data is greater than or equal to a preset pulse width, and the polar diameter is less than or equal to a first preset polar diameter, the data point is determined to be a central data point.

[0024] Optionally, determining a central data point from the current frame point cloud data includes:

[0025] A central data point is determined from the data points of the current frame point cloud data according to the third attribute information of the data points in the current frame point cloud data and the ground height of the current frame point cloud data.

[0026] Optionally, the third attribute information is pulse width, polar diameter, and height, and determining the center data point from the data points of the current frame point cloud data according to the third attribute information of the data points in the current frame point cloud data and the ground height of the current frame point cloud data includes:

[0027] When a pulse width of a data point in the current frame point cloud data is greater than or equal to a preset pulse width, and a polar diameter of the data point in the current frame point cloud data is within a range of a first preset polar diameter and a second preset polar diameter, determining a central data point from the data points in the current frame point cloud data according to a height of the data point in the current frame point cloud data and a ground height of the current frame point cloud data;

[0028] Wherein, the second preset polar diameter is larger than the first preset polar diameter.

[0029] Optionally, determining a central data point from the data points of the current frame point cloud data according to the height of the data points in the current frame point cloud data and the ground height of the current frame point cloud data includes:

[0030] Determine the reference height based on the ground height of the current frame point cloud data;

[0031] When the height of a data point in the current frame point cloud data is greater than or equal to the reference height, the data point is determined to be a central data point.

[0032] Optionally, the reference height is determined based on the ground height of the current frame point cloud data, including:

[0033] The slope coefficient is determined according to the ground slope, and the reference height is determined according to the ground height of the current frame point cloud data, the slope coefficient and the polar diameter.

[0034] Optionally, before determining the central data point from the data points of the current frame point cloud data according to the third attribute information of the data points in the current frame point cloud data and the ground height of the current frame point cloud data, the method further includes:

[0035] Determine the ground height of the current frame of point cloud data based on the ground height of the previous frame of point cloud data.

[0036] Optionally, determining the ground height of the current frame of point cloud data based on the ground height of the previous frame of point cloud data includes:

[0037] When the ground height of the previous frame of point cloud data is within the preset height range, the ground height of the previous frame of point cloud data is determined as the ground height of the current frame of point cloud data;

[0038] When the ground height of the previous frame of point cloud data is not within the preset height range, the default value is determined as the ground height of the current frame of point cloud data.

[0039] Optionally, before determining the ground height of the current frame of point cloud data based on the ground height of the previous frame of point cloud data, the method further includes:

[0040] Determine the height of the target data point in the previous frame of point cloud data;

[0041] The ground height of the previous frame of point cloud data is determined according to the height of the target data point.

[0042] Optionally, determining the ground height of the previous frame of point cloud data according to the height of the target data point includes:

[0043] Determining a plurality of height partitions according to the height of the target data point;

[0044] A target height partition is determined from the multiple height partitions, and the ground height of the previous frame of point cloud data is determined according to the height in the target height partition.

[0045] Optionally, the target height partition is a height partition containing the most height data among the multiple height partitions.

[0046] Optionally, the target data point is a data point at a specified position in the previous frame of point cloud data.

[0047] Optionally, after receiving the point cloud data collected by the radar, the method further includes: performing vertical cavity surface emitting laser alignment on the point cloud data.

[0048] Optionally, the radar is a laser radar.

[0049] Optionally, the radar is a vehicle-mounted radar.

[0050] A radar data processing device, the device being configured to:

[0051] Receive point cloud data collected by radar;

[0052] Determine the center data point from the current frame point cloud data;

[0053] A filtering threshold of the central data point is determined according to the first attribute information of the central data point, and the central data point is subjected to drag-point filtering according to the filtering threshold.

[0054] An electronic device includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the method described above when executed by the processor.

[0055] A vehicle comprises the apparatus as described above, or comprises the equipment as described above.

[0056] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0057] A computer program product comprises a computer program, which implements the method described above when executed by a processor.

[0058] The embodiments of the present invention have the following advantages:

[0059] In an embodiment of the present invention, by receiving point cloud data collected by a radar, a central data point is determined from the current frame point cloud data, a filtering threshold of the central data point is determined based on the first attribute information of the central data point, and the central data point is subjected to drag point filtering based on the filtering threshold, thereby realizing drag point filtering of the radar data and improving the accuracy and reliability of the radar data. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the description of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 is a flowchart of the steps of a radar data processing method provided by some embodiments of the present invention;

[0062] Figure 2 This is a flowchart of the steps of starting a drag point algorithm provided by some embodiments of the present invention;

[0063] Figure 3 is a flowchart of the steps of a filtering threshold setting process provided by some embodiments of the present invention;

[0064] Figure 4 is a flowchart of the steps of a drag point filtering process provided by some embodiments of the present invention;

[0065] Figure 5 is a flowchart of the steps of a ground height calculation process provided by some embodiments of the present invention;

[0066] Figure 6 is a schematic diagram of the space requirement of a drag point algorithm provided by some embodiments of the present invention;

[0067] Figure 7 is a schematic diagram of a drag point provided by some embodiments of the present invention;

[0068] Figure 8 is a schematic diagram of point cloud data before drag point filtering provided by some embodiments of the present invention;

[0069] Figure 9 is a schematic diagram of point cloud data after drag point filtering provided by some embodiments of the present invention;

[0070] Figure 10This is a flowchart of the steps of another radar data processing method provided by some embodiments of the present invention. DETAILED DESCRIPTION

[0071] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments described are only a portion of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0072] The existence of tow points greatly affects the accuracy and reliability of radar data, as follows:

[0073] 1. Reduced ranging accuracy: The dragging point interferes with the accurate measurement of the pulse width leading edge and pulse width of the target object, thereby affecting the calculation of the flight time and resulting in reduced ranging accuracy.

[0074] 2. Increased target recognition complexity: Dragging creates a non-existent line between two objects that are close to each other in space, causing other targets to be mistakenly identified between the two objects, thereby interfering with the recognition and analysis of the actual objects.

[0075] 3. Damage to point cloud visualization quality: Dragging introduces outliers in the point cloud image. These outliers destroy the visualization effect of the point cloud and affect the intuitive understanding and analysis of the scene.

[0076] 4. Impact on point cloud clustering: The presence of dragged points may mislead point cloud clustering algorithms, as clustering algorithms generally rely on the relative positional relationships between point cloud points. The erroneous spatial relationships introduced by dragged points will reduce the performance and accuracy of the clustering algorithm.

[0077] Some related technologies use ranging information and echo signal information as the basis for determining dragged points. However, in actual data collection, it has been found that variations in ranging information and echo amplitude can lead to the inadvertent deletion of ground lines, which can result in the deletion of ground line outlines or even the entire ground line. In contrast, in the embodiments of the present invention, a ground point cloud protection mechanism uses the nearest ground height, ground slope, and distance segmentation to effectively filter out dragged points while preserving the ground point cloud.

[0078] Some related technologies use lidar's ranging characteristics and data fluctuations to determine whether each point is a drag point. However, in the embodiments of the present invention, by utilizing pulse width anomaly characteristics, the pulse width anomaly caused by the superposition of drag point echoes is used as a feature. Ranging information between each point is then calculated to accurately identify and effectively filter out drag points, improving the accuracy of drag point removal while reducing algorithm complexity.

[0079] Some related technologies use distance information from a target point to multiple auxiliary points to determine whether it is a drag point. However, in actual data collection, point cloud data is often dense at the beginning and sparse at the end. Distance information can also vary depending on the point cloud's polar radius, leading to the algorithm-set threshold missing drag points within certain distance segments and mistakenly deleting target objects. In the embodiments of the present invention, by rationally utilizing the point cloud density variation characteristics, the point cloud data is segmented by distance, and a different filtering threshold is set for each segment. This allows the removal of drag points while avoiding the mistaken deletion of target objects, ensuring the integrity of the ground line.

[0080] In this embodiment of the present invention, given that the LiDAR operates at a 10Hz frequency, meaning that the point cloud image is updated every 100ms, the nearest ground height calculated in the previous frame can be used as the nearest ground height in the next frame. This approach not only enhances the robustness of the dragging point algorithm in complex scenes but also minimizes the removal of erroneous ground lines. Testing on a large number of urban roads, highways, and country roads has ensured that ground lines are fully preserved while effectively filtering out dragged points.

[0081] like Figure 1 , the embodiment of the present invention mainly includes the following steps:

[0082] Step 101: Obtain a two-dimensional matrix of point clouds and perform Vcsel (Vertical Cavity Surface Emitting Laser) alignment.

[0083] Step 102, traverse the point cloud two-dimensional matrix to obtain the center data point information;

[0084] Step 103: determine whether the central data point meets the spatial requirements of the drag point algorithm to determine whether to start the drag point algorithm to perform drag point filtering;

[0085] Step 104, determining the relevant threshold of the drag point algorithm according to the polar radius of the point;

[0086] Step 105: construct a 5×5 sliding window with the central data point to perform drag point algorithm judgment;

[0087] Step 106, calculating the nearest ground height of the current frame as the radar reference height of the next frame;

[0088] Step 107: Filter out points that do not meet the requirements, that is, filter out dragged points.

[0089] Through the embodiments of the present invention, the phenomenon of dragging points is eliminated or reduced, the accidental deletion of ground point clouds is effectively avoided, the overall quality and application effect of point cloud data are improved, and the quality and reliability of point cloud data processing are effectively improved, providing a more accurate and efficient solution for lidar applications.

[0090] The present invention will be further described below:

[0091] Reference Figure 10 , which shows a flowchart of a radar data processing method according to some embodiments of the present invention. In some examples, the radar may be a lidar. In some examples, in the field of vehicle applications, the radar may be an onboard radar, and the radar data may be used for autonomous driving.

[0092] In some examples, the lidar may be a dToF (Direct Time of Flight) lidar, which calculates distance by directly measuring the time difference between the emission and return of a light pulse. It has the advantages of accurate measurement, fast response, and low power consumption.

[0093] Specifically, the following steps may be included:

[0094] Step 1001: Receive point cloud data collected by radar.

[0095] In some embodiments of the present invention, after receiving the point cloud data collected by the radar, the method further includes: performing vertical cavity surface emitting laser alignment on the point cloud data.

[0096] In practical applications, point cloud data can be a two-dimensional matrix, and the data points in the point cloud data are the data points in the two-dimensional matrix. For example, a two-dimensional matrix with a format of 96×1208 has 96×1208 data points, where 96 is the number of lines and 1208 is the number of slots (elements).

[0097] In some examples, the attribute information of each data point in the point cloud data includes, but is not limited to: polar diameter D, three-dimensional coordinates XYZ, intensity I, and reflectivity R.

[0098] Since Vcsel has four partitions, and there is a deviation of 8 slots between partitions 1 and 3 and partitions 2 and 4, Vcsel alignment can be performed on the two-dimensional matrix to avoid affecting subsequent algorithms.

[0099] Step 1002: Determine the central data point from the current frame point cloud data.

[0100] In some embodiments of the present invention, determining the central data point from the current frame point cloud data includes: determining the central data point from the data points of the current frame point cloud data based on second attribute information of the data points in the current frame point cloud data.

[0101] In some examples, the second attribute information may be pulse width and polar diameter.

[0102] In some embodiments of the present invention, a central data point is determined from the data points of the current frame point cloud data based on the second attribute information of the data points in the current frame point cloud data, including: when the pulse width of the data point in the current frame point cloud data is greater than or equal to the preset pulse width, and the polar diameter is less than or equal to the first preset polar diameter, determining the data point as the central data point.

[0103] In some examples, the preset pulse width is 14 ms and the first preset pole diameter is 3 m.

[0104] For each data point in the current frame point cloud data, its pulse width and polar diameter can be obtained respectively.

[0105] like Figure 6 Based on the data collected by the current radar, it is found that drag points cause abnormal pulse widths. This feature can be used to accurately identify and filter out drag points, effectively improving the accuracy and efficiency of the algorithm. Specifically, it can determine whether the pulse width is greater than or equal to the preset pulse width. If the pulse width is less than the preset pulse width, it indicates that the data point is not a drag point and is excluded. If the pulse width is greater than or equal to the preset pulse width, it indicates that the data point is likely a drag point and further analysis can be performed.

[0106] For data points whose pulse width is greater than or equal to the preset pulse width, we can further analyze whether their polar diameter is less than or equal to the first preset polar diameter. If the polar diameter is less than or equal to the first preset polar diameter, it indicates that the data point may be a drag point, and the data point can be determined as the central data point for subsequent drag point filtering.

[0107] In some embodiments of the present invention, determining the central data point from the current frame point cloud data includes: determining the central data point from the data points of the current frame point cloud data based on the third attribute information of the data points in the current frame point cloud data and the ground height of the current frame point cloud data.

[0108] In some examples, the third attribute information may be pulse width, polar diameter, or height.

[0109] In some embodiments of the present invention, a central data point is determined from the data points of the current frame point cloud data based on the third attribute information of the data points in the current frame point cloud data and the ground height of the current frame point cloud data, including: when the pulse width of the data point in the current frame point cloud data is greater than or equal to the preset pulse width, and the polar diameter of the data point in the current frame point cloud data is within the range of the first preset polar diameter and the second preset polar diameter, the central data point is determined from the data points of the current frame point cloud data based on the height of the data point in the current frame point cloud data and the ground height of the current frame point cloud data.

[0110] Wherein, the second preset polar diameter is larger than the first preset polar diameter.

[0111] In some examples, the second preset diameter may be 20 m.

[0112] For each data point in the current frame point cloud data, its pulse width, polar diameter, and height can be obtained respectively.

[0113] like Figure 6 Based on the data collected by the current radar, it is found that drag points cause abnormal pulse widths. This feature can be used to accurately identify and filter out drag points, effectively improving the accuracy and efficiency of the algorithm. Specifically, it can determine whether the pulse width is greater than or equal to the preset pulse width. If the pulse width is less than the preset pulse width, it indicates that the data point is not a drag point and is excluded. If the pulse width is greater than or equal to the preset pulse width, it indicates that the data point is likely a drag point and further analysis can be performed.

[0114] For data points whose pulse width is greater than or equal to the preset pulse width, we can further analyze whether their polar diameter is less than or equal to the first preset polar diameter. If the polar diameter is greater than the first preset polar diameter, we can further determine whether its polar diameter is less than or equal to the second preset polar diameter. If the polar diameter is greater than the second preset polar diameter, it indicates that the data point is not a drag point and the data point is excluded. If the polar diameter is less than or equal to the second preset polar diameter, it indicates that the data point may be a drag point and further analysis can be performed.

[0115] For data points whose polar diameter is less than or equal to the second preset polar diameter, the height of the data point is further compared with the ground height of the current frame point cloud data, and then it can be determined whether the data point is the central data point based on the result of the height comparison to determine whether to perform subsequent drag point filtering.

[0116] In some embodiments of the present invention, a central data point is determined from the data points of the current frame point cloud data based on the height of the data point in the current frame point cloud data and the ground height of the current frame point cloud data, including: determining a reference height based on the ground height of the current frame point cloud data; when the height of the data point in the current frame point cloud data is greater than or equal to the reference height, determining the data point as the central data point.

[0117] Specifically, the reference height is determined based on the ground height of the current frame point cloud data, and the height of the data point is compared with the reference height. If the height of the data point is less than the reference height, it indicates that the data point is not a drag point and is excluded. If the height of the data point is greater than or equal to the reference height, it indicates that the data point may be a drag point and is determined as the central data point for subsequent drag point filtering.

[0118] In some embodiments of the present invention, the reference height is determined based on the ground height of the current frame point cloud data, including: determining the slope coefficient based on the ground slope, and determining the reference height based on the ground height of the current frame point cloud data, the slope coefficient and the polar diameter.

[0119] In some examples, the slope coefficient and the slope magnitude are positively correlated.

[0120] In practical applications, the slope coefficient can be determined by obtaining the current ground slope, and then the slope coefficient and the polar diameter can be multiplied and combined with the ground height of the current frame point cloud data to determine the reference height.

[0121] In this implementation case, since the computing power cannot support ground line segmentation of the point cloud and the data points processed each time are limited, it is impossible to obtain more information to determine whether the point is a ground point cloud. The ground height is combined with the ground slope to avoid the drag point algorithm from acting on the ground point cloud and avoid deleting the ground point cloud.

[0122] The following combination Figure 2 The following is an example of how to enable the drag point algorithm:

[0123] Step 201: Obtain the attribute information of the center data point, record the pulse width of the center data point as fwhm, the polar diameter as d, and the height as d z , the ground height of the current frame is height;

[0124] Step 202: Determine whether the central data point meets the space requirements of the drag point algorithm, specifically:

[0125] 1. The pulse width fwhm of the central data point ≥ fwhmThe, where fwhmThe is the preset pulse width;

[0126] 2. The polar diameter d of the central data point is less than or equal to dis1 (the first preset polar diameter), or the polar diameter d of the central data point is greater than dis1 and less than or equal to dis2 (the second preset polar diameter), and at the same time, d z ≥0.05×d+height.

[0127] Here, 0.05×d+height is the reference height, and 0.05 is an example of the slope coefficient.

[0128] Step 203: If the central data point meets the space requirement of the drag point algorithm, the drag point algorithm is started;

[0129] Step 204: If the central data point does not meet the space requirement of the drag point algorithm, the drag point algorithm is closed, and the internal loop is exited to find the next central data point.

[0130] In a specific implementation, the drag point algorithm operates on the data points from 0 to dis1, or from dis1 to dis2, whose heights meet the range of 5 degrees above the ground height and whose pulse width is greater than or equal to fwhmThe.

[0131] In some embodiments of the present invention, before determining the central data point from the data points of the current frame point cloud data based on the third attribute information of the data points in the current frame point cloud data and the ground height of the current frame point cloud data, it also includes: determining the ground height of the current frame point cloud data based on the ground height of the previous frame point cloud data.

[0132] In practical applications, the ground height of the current frame point cloud data is determined based on the ground height of the previous frame point cloud data, the ground height of the next frame point cloud data is determined based on the ground height of the current frame point cloud data, and so on.

[0133] In some embodiments of the present invention, the ground height of the current frame point cloud data is determined based on the ground height of the previous frame point cloud data, including: when the ground height of the previous frame point cloud data is within a preset height range, the ground height of the previous frame point cloud data is determined as the ground height of the current frame point cloud data; when the ground height of the previous frame point cloud data is not within the preset height range, the default value is determined as the ground height of the current frame point cloud data.

[0134] In specific implementation, the default value of the ground height is set according to the vehicle model that the lidar is compatible with. If there is no ground line in the current frame or most of the ground line is concentrated on higher or lower objects, the obtained ground height may be a positive number or an extremely outrageous negative value. This value cannot be used as the ground height. In this case, the obtained ground height can be updated to the default value for use in the next frame.

[0135] In some embodiments of the present invention, before determining the ground height of the current frame point cloud data based on the ground height of the previous frame point cloud data, it also includes: determining the height of the target data point in the previous frame point cloud data; and determining the ground height of the previous frame point cloud data based on the height of the target data point.

[0136] In some embodiments of the present invention, the ground height of the previous frame of point cloud data is determined based on the height of the target data point, including: determining multiple height partitions based on the height of the target data point; determining a target height partition from the multiple height partitions, and determining the ground height of the previous frame of point cloud data based on the height in the target height partition.

[0137] The target height partition is the height partition containing the most height data among the multiple height partitions.

[0138] The target data point is the data point at the specified position in the previous frame of point cloud data. If the point cloud data is a two-dimensional matrix, the target data point is the data point corresponding to the first row in the two-dimensional matrix. Taking a 96×1208 two-dimensional matrix as an example, the 1205 data points in the first row are used as target data points.

[0139] In practical applications, the height of the target data point in the previous frame of point cloud data is obtained, and then the height of the target data point is partitioned to obtain multiple height partitions. The height partition containing the most height data is then determined as the target height partition. The ground height of the previous frame of point cloud data can then be determined based on the height in the target height partition. In some examples, the height in the target height partition can be removed from the mean to obtain the ground height of the previous frame of point cloud data, or the height in the target height partition can be removed from the median to obtain the ground height of the previous frame of point cloud data.

[0140] The following combination Figure 5 The above ground height calculation process is illustrated as follows:

[0141] Step 501: Obtain the height information of the first pixel (i.e., the target data point) in the two-dimensional point cloud matrix and construct a 1208×1 height information matrix;

[0142] Step 502: defining the height interval boundary;

[0143] Step 503: traverse the height information matrix, and for each height information, check which interval (i.e., height partition) it falls into, and increase the corresponding interval count by one;

[0144] Step 504: Determine the mode interval (i.e., target height partition) based on the maximum count value, and average the height values in this interval. This represents the dominant height of the ground point cloud. This value can be approximately considered as the height value of the current frame radar from the nearest ground point cloud (i.e., ground height).

[0145] Step 1003: Determine a filtering threshold of the central data point according to the first attribute information of the central data point, and perform drag-point filtering on the central data point according to the filtering threshold.

[0146] In some examples, the first attribute information may be a polar diameter.

[0147] After the central data point is determined, a corresponding filtering threshold may be determined according to the first attribute information of the central data point, and then the central data point may be filtered out by dragging according to the filtering threshold.

[0148] In some embodiments of the present invention, determining the filtering threshold of the central data point based on the first attribute information of the central data point includes: determining the segmented interval where the first attribute information of the central data point is located; and determining the filtering threshold of the central data point based on the segmented interval.

[0149] In practical applications, the first attribute information may be segmented, and different filtering thresholds may be set for the first attribute information belonging to different segmented intervals.

[0150] In some examples, the first attribute information is the polar diameter, and the filtering threshold includes a distance difference threshold and a statistical threshold. The distance difference threshold is positively correlated with the polar diameter size, and the statistical threshold is negatively correlated with the polar diameter size.

[0151] In practice, the radar's actual point cloud density variation characteristics can be statistically analyzed to divide the point cloud data into different distance segments, each of which uses different distance difference thresholds and statistical thresholds. For example, a stricter threshold can be used in areas with higher point cloud density, while a looser threshold can be used in areas with lower point cloud density. This allows for the removal of dragged points while minimizing the risk of accidental deletion of target objects.

[0152] The following combination Figure 3 The above filtering threshold setting process is described as follows:

[0153] Step 301: Determine whether the center data point dragging algorithm is enabled. If enabled, proceed to the next step.

[0154] Step 302: Determine whether the polar radius d of the central data point is less than or equal to a distance threshold disThe1, such as the distance threshold disThe1 is 5m;

[0155] Step 303: If the conditions are met, set the distance difference threshold to diffThe1 and the statistical threshold to countThe1. For example, the distance difference threshold diffThe1 is 0.02m and the statistical threshold countThe1 is 10.

[0156] Step 304: If not, determine whether the polar radius d of the central data point is less than or equal to the distance threshold disThe2, such as the distance threshold disThe2 is 10m;

[0157] Step 305: If the conditions are met, set the distance difference threshold to diffThe2 and the statistical threshold to countThe2. The distance difference threshold diffThe2 is 0.03m, and the statistical threshold countThe2 is 8.

[0158] Step 306: If not, determine whether the polar radius d of the central data point is less than or equal to the distance threshold disThe3, such as the distance threshold disThe3 is 15m;

[0159] Step 307: If the conditions are met, set the distance difference threshold to diffThe3 and the statistical threshold to countThe3. The distance difference threshold diffThe3 is 0.04m, and the statistical threshold countThe3 is 6.

[0160] Step 308: If not satisfied, set the distance difference threshold to diffThe4 and the statistical threshold to countThe4. For example, the distance difference threshold diffThe4 is 0.05m and the statistical threshold countThe4 is 4.

[0161] In some embodiments of the present invention, the central data point is subjected to drag-point filtering according to the filtering threshold, including: determining multiple adjacent data points of the central data point; determining the polar distance difference between the multiple adjacent data points and the central data point; and the central data point is subjected to drag-point filtering according to the polar distance difference and the filtering threshold.

[0162] In practical applications, a sliding window can be defined, and multiple adjacent data points of a central data point can be determined based on the sliding window. For example, a 5*5 sliding window can determine 24 adjacent data points centered on the central data point. After determining the adjacent data points, since the corresponding filtering threshold for the central data point has been determined, the radial distance difference between each adjacent data point and the central data point can be calculated. Based on the radial distance difference and the filtering threshold, a judgment is made to determine whether to perform point filtering based on the judgment result.

[0163] In some embodiments of the present invention, the filtering threshold includes a distance difference threshold and a statistical threshold. According to the polar distance difference and the filtering threshold, the center data point is subjected to drag point filtering, including: determining the target adjacent data points whose distance difference is less than or equal to the distance difference threshold; when the number of the target adjacent data points is less than the statistical threshold, the center data point is subjected to drag point filtering.

[0164] In practical applications, target adjacent data points whose distance difference is less than or equal to the distance difference threshold can be screened out from adjacent data points, and then the number of target adjacent data points can be counted to determine whether the number of target adjacent data points is less than the statistical threshold. When the number of target adjacent data points is less than the statistical threshold, the central data point is characterized as a drag point, and the central data point is filtered out. When the number of target adjacent data points is greater than or equal to the statistical threshold, the central data point is characterized as not a drag point and is retained.

[0165] In an embodiment of the present invention, the drag point algorithm determines whether the central data point is a drag point by comparing the difference in polar distance between the central data point and the surrounding adjacent data points, and counting the number of adjacent data points that meet the conditions. By rationally utilizing the discrete characteristic of drag points in space, the drag points can be effectively deleted while reducing the accidental deletion of target objects, thereby improving the quality of point cloud data.

[0166] The following combination Figure 4 The above drag point filtering process is exemplified as follows:

[0167] Step 401: Select a 5×5 grid area of a central data point and its 24 adjacent data points, and process the upper, lower, left, and right boundaries. Assume that the distance difference threshold corresponding to the central data point is diffThe1 and the statistical threshold is countThe1;

[0168] Step 402: traverse the 24 adjacent points in the neighborhood of the 5×5 grid and determine whether the traversal is complete. If the traversal is complete, exit the drag point algorithm.

[0169] Step 403: If the traversal is not complete, select a neighboring data point and record the difference between the polar diameter of the neighboring data point and the polar diameter of the central data point as diff;

[0170] Step 404: Determine whether the diameter difference diff is less than or equal to the distance difference threshold diffThe1. If not, return to step 402.

[0171] Step 405: If satisfied, the statistical value count = count + 1, and return to step 402;

[0172] Step 406: After traversing all adjacent data points, determine whether the statistical value count is greater than or equal to the statistical threshold countThe1;

[0173] Step 407: If the condition is satisfied, the attribute Valid of the center data point is set to true, indicating that the center data point is not a drag point and the point is retained;

[0174] Step 408: If not satisfied, the central data point attribute Valid is set to false, indicating that the central data point is a drag point, and the central data point is filtered out.

[0175] In an embodiment of the present invention, by receiving point cloud data collected by a radar, a central data point is determined from the current frame point cloud data, a filtering threshold of the central data point is determined based on the first attribute information of the central data point, and the central data point is subjected to drag point filtering based on the filtering threshold, thereby realizing drag point filtering of the radar data and improving the accuracy and reliability of the radar data.

[0176] like Figure 7 The laser radar's laser beam hits the edges of the front and rear objects at the same time. Part of the light spot of the front and rear objects is diffusely reflected, and the reflected echo is captured by the receiver. The two echoes generated are superimposed to form an echo with a larger pulse width, which in turn forms a drag point, resulting in an error in the distance calculation of the point. The point cloud shows that there are other targets in the front and rear objects. Figure 8 This is the point cloud data before drag point filtering, such as Figure 9 This is the point cloud data after drag point filtering using the embodiment of the present invention. The front object is 5 meters away from the lidar, and the rear object is about 0.9 meters away from the front object. It can be clearly seen that a series of continuous point clouds are generated between the two objects. After the drag point filtering is performed using the embodiment of the present invention, the drag points are completely filtered out.

[0177] It should be noted that for the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0178] Some embodiments of the present invention provide a radar data processing device, which can be specifically used for:

[0179] Receive point cloud data collected by radar;

[0180] Determine the center data point from the current frame point cloud data;

[0181] A filtering threshold of the central data point is determined according to the first attribute information of the central data point, and the central data point is subjected to drag-point filtering according to the filtering threshold.

[0182] Optionally, determining a filtering threshold of the central data point according to the first attribute information of the central data point includes:

[0183] Determine the segment interval where the first attribute information of the central data point is located;

[0184] A filtering threshold of the central data point is determined according to the segmented interval.

[0185] Optionally, the first attribute information is polar diameter.

[0186] Optionally, performing drag-point filtering on the central data point according to the filtering threshold includes:

[0187] determining a plurality of adjacent data points of the central data point;

[0188] Determining the polar distance difference between the plurality of adjacent data points and the central data point;

[0189] The central data point is subjected to drag point filtering according to the polar radius distance difference and the filtering threshold.

[0190] Optionally, the filtering threshold includes a distance difference threshold and a statistical threshold, and performing drag point filtering on the central data point according to the polar distance difference and the filtering threshold includes:

[0191] Determine target adjacent data points whose distance difference is less than or equal to the distance difference threshold;

[0192] When the number of the target adjacent data points is less than the statistical threshold, the central data point is filtered out by dragging.

[0193] Optionally, determining a central data point from the current frame point cloud data includes:

[0194] According to the second attribute information of the data points in the current frame point cloud data, a central data point is determined from the data points in the current frame point cloud data.

[0195] Optionally, the second attribute information is pulse width and polar diameter, and determining the central data point from the data points in the current frame point cloud data according to the second attribute information of the data points in the current frame point cloud data includes:

[0196] When the pulse width of a data point in the current frame point cloud data is greater than or equal to a preset pulse width, and the polar diameter is less than or equal to a first preset polar diameter, the data point is determined to be a central data point.

[0197] Optionally, determining a central data point from the current frame point cloud data includes:

[0198] A central data point is determined from the data points of the current frame point cloud data according to the third attribute information of the data points in the current frame point cloud data and the ground height of the current frame point cloud data.

[0199] Optionally, the third attribute information is pulse width, polar diameter, and height, and determining the center data point from the data points of the current frame point cloud data according to the third attribute information of the data points in the current frame point cloud data and the ground height of the current frame point cloud data includes:

[0200] When a pulse width of a data point in the current frame point cloud data is greater than or equal to a preset pulse width, and a polar diameter of the data point in the current frame point cloud data is within a range of a first preset polar diameter and a second preset polar diameter, determining a central data point from the data points in the current frame point cloud data according to a height of the data point in the current frame point cloud data and a ground height of the current frame point cloud data;

[0201] Wherein, the second preset polar diameter is larger than the first preset polar diameter.

[0202] Optionally, determining a central data point from the data points of the current frame point cloud data according to the height of the data points in the current frame point cloud data and the ground height of the current frame point cloud data includes:

[0203] Determine the reference height based on the ground height of the current frame point cloud data;

[0204] When the height of a data point in the current frame point cloud data is greater than or equal to the reference height, the data point is determined to be a central data point.

[0205] Optionally, the reference height is determined based on the ground height of the current frame point cloud data, including:

[0206] The slope coefficient is determined according to the ground slope, and the reference height is determined according to the ground height of the current frame point cloud data, the slope coefficient and the polar diameter.

[0207] Optionally, before determining the central data point from the data points of the current frame point cloud data according to the third attribute information of the data points in the current frame point cloud data and the ground height of the current frame point cloud data, the method further includes:

[0208] Determine the ground height of the current frame of point cloud data based on the ground height of the previous frame of point cloud data.

[0209] Optionally, determining the ground height of the current frame of point cloud data based on the ground height of the previous frame of point cloud data includes:

[0210] When the ground height of the previous frame of point cloud data is within the preset height range, the ground height of the previous frame of point cloud data is determined as the ground height of the current frame of point cloud data;

[0211] When the ground height of the previous frame of point cloud data is not within the preset height range, the default value is determined as the ground height of the current frame of point cloud data.

[0212] Optionally, before determining the ground height of the current frame of point cloud data based on the ground height of the previous frame of point cloud data, the method further includes:

[0213] Determine the height of the target data point in the previous frame of point cloud data;

[0214] The ground height of the previous frame of point cloud data is determined according to the height of the target data point.

[0215] Optionally, determining the ground height of the previous frame of point cloud data according to the height of the target data point includes:

[0216] Determining a plurality of height partitions according to the height of the target data point;

[0217] A target height partition is determined from the multiple height partitions, and the ground height of the previous frame of point cloud data is determined according to the height in the target height partition.

[0218] Optionally, the target height partition is a height partition containing the most height data among the multiple height partitions.

[0219] Optionally, the target data point is a data point at a specified position in the previous frame of point cloud data.

[0220] Optionally, after receiving the point cloud data collected by the radar, the method further includes: performing vertical cavity surface emitting laser alignment on the point cloud data.

[0221] Optionally, the radar is a laser radar.

[0222] Optionally, the radar is a vehicle-mounted radar.

[0223] Some embodiments of the present invention further provide an electronic device, comprising a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the above method when executed by the processor.

[0224] Some embodiments of the present invention further provide a vehicle comprising the apparatus as described above, or comprising the equipment as described above.

[0225] Some embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above method is implemented.

[0226] Some embodiments of the present invention further provide a computer program product, including a computer program, which implements the above method when executed by a processor.

[0227] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0228] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0229] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0230] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus, or computer program products. Thus, embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0231] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the process in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0232] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0233] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0234] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0235] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the above elements.

[0236] The above describes in detail the radar data processing methods, devices, equipment, vehicles, media, and products provided. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only intended to help understand the methods and core concepts of the present invention. At the same time, for those skilled in the art, based on the concepts of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A radar data processing method, characterized in that: The method comprises: Receive point cloud data collected by radar; Determine the center data point from the current frame point cloud data; A filtering threshold of the central data point is determined according to the first attribute information of the central data point, and the central data point is subjected to drag-point filtering according to the filtering threshold.

2. The method according to claim 1, characterized in that Determining a filtering threshold of the central data point according to the first attribute information of the central data point includes: Determine the segment interval where the first attribute information of the central data point is located; A filtering threshold of the central data point is determined according to the segmented interval.

3. The method according to claim 2, characterized in that The first attribute information is the polar diameter.

4. The method according to claim 1, wherein Performing drag-point filtering on the central data point according to the filtering threshold includes: determining a plurality of adjacent data points of the central data point; Determining the polar distance difference between the plurality of adjacent data points and the central data point; The central data point is subjected to drag point filtering according to the polar radius distance difference and the filtering threshold.

5. The method according to claim 4, characterized in that The filtering threshold includes a distance difference threshold and a statistical threshold. According to the polar distance difference and the filtering threshold, the center data point is subjected to drag point filtering, including: Determine target adjacent data points whose distance difference is less than or equal to the distance difference threshold; When the number of the target adjacent data points is less than the statistical threshold, the central data point is filtered out by dragging.

6. The method according to any one of claims 1 to 5, characterized in that Determine the center data point from the current frame point cloud data, including: According to the second attribute information of the data points in the current frame point cloud data, a central data point is determined from the data points in the current frame point cloud data.

7. The method according to claim 6, characterized in that The second attribute information is pulse width and polar diameter. Determining a central data point from the data points in the current frame point cloud data according to the second attribute information of the data points in the current frame point cloud data includes: When the pulse width of a data point in the current frame point cloud data is greater than or equal to a preset pulse width, and the polar diameter is less than or equal to a first preset polar diameter, the data point is determined to be a central data point.

8. The method according to any one of claims 1 to 5, characterized in that Determine the center data point from the current frame point cloud data, including: A central data point is determined from the data points of the current frame point cloud data according to the third attribute information of the data points in the current frame point cloud data and the ground height of the current frame point cloud data.

9. The method according to claim 8, characterized in that The third attribute information is pulse width, polar diameter, and height. According to the third attribute information of the data point in the current frame point cloud data and the ground height of the current frame point cloud data, the center data point is determined from the data points of the current frame point cloud data, including: When a pulse width of a data point in the current frame point cloud data is greater than or equal to a preset pulse width, and a polar diameter of the data point in the current frame point cloud data is within a range of a first preset polar diameter and a second preset polar diameter, determining a central data point from the data points in the current frame point cloud data according to a height of the data point in the current frame point cloud data and a ground height of the current frame point cloud data; Wherein, the second preset polar diameter is larger than the first preset polar diameter.

10. The method according to claim 9, characterized in that According to the height of the data point in the current frame point cloud data and the ground height of the current frame point cloud data, the center data point is determined from the data points of the current frame point cloud data, including: Determine the reference height based on the ground height of the current frame point cloud data; When the height of a data point in the current frame point cloud data is greater than or equal to the reference height, the data point is determined to be a central data point.

11. The method according to claim 10, characterized in that Determine the reference height based on the ground height of the current frame point cloud data, including: The slope coefficient is determined according to the ground slope, and the reference height is determined according to the ground height of the current frame point cloud data, the slope coefficient and the polar diameter.

12. The method according to claim 8, characterized in that Before determining the center data point from the data points of the current frame point cloud data according to the third attribute information of the data points in the current frame point cloud data and the ground height of the current frame point cloud data, the method further includes: Determine the ground height of the current frame of point cloud data based on the ground height of the previous frame of point cloud data.

13. The method according to claim 12, characterized in that Determine the ground height of the current frame of point cloud data based on the ground height of the previous frame of point cloud data, including: When the ground height of the previous frame of point cloud data is within the preset height range, the ground height of the previous frame of point cloud data is determined as the ground height of the current frame of point cloud data; When the ground height of the previous frame of point cloud data is not within the preset height range, the default value is determined as the ground height of the current frame of point cloud data.

14. The method according to claim 12, characterized in that Before determining the ground height of the current frame of point cloud data based on the ground height of the previous frame of point cloud data, the following steps are also included: Determine the height of the target data point in the previous frame of point cloud data; The ground height of the previous frame of point cloud data is determined according to the height of the target data point.

15. The method according to claim 14, characterized in that Determine the ground height of the previous frame of point cloud data according to the height of the target data point, including: Determining a plurality of height partitions according to the height of the target data point; A target height partition is determined from the multiple height partitions, and the ground height of the previous frame of point cloud data is determined according to the height in the target height partition.

16. The method according to claim 15, characterized in that The target height partition is a height partition containing the most height data among the multiple height partitions.

17. The method according to claim 14, characterized in that The target data point is a data point at a specified position in the previous frame of point cloud data.

18. The method according to claim 1, wherein After receiving the point cloud data collected by the radar, the method further includes: performing vertical cavity surface emitting laser alignment on the point cloud data.

19. The method according to claim 1, wherein The radar is a laser radar.

20. The method according to claim 1, wherein The radar is a vehicle-mounted radar.

21. A radar data processing device, characterized in that: The device is used to: Receive point cloud data collected by radar; Determine the center data point from the current frame point cloud data; A filtering threshold of the central data point is determined according to the first attribute information of the central data point, and the central data point is subjected to drag-point filtering according to the filtering threshold.

22. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the method according to any one of claims 1 to 20 when executed by the processor.

23. A vehicle, characterized in that: Includes the device as claimed in claim 21, or includes the equipment as claimed in claim 22.

24. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 20 is implemented.

25. A computer program product, characterized in that A computer program is included which, when executed by a processor, implements the method according to any one of claims 1 to 20.

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