Method and device for displaying evaluation of radar scan quality, electronic equipment and medium

Abstract

This application provides a method, apparatus, electronic device, and medium for displaying and evaluating radar scan quality. It receives 3D point cloud data streams transmitted back from lidar scans in real time; divides the point cloud data according to a preset spatial resolution based on the spatial coordinate information of at least one point cloud data, determining at least one voxel unit and a voxel identifier for each voxel unit; determines the point cloud data transmission frequency for each voxel unit based on its voxel identifier; and determines the display parameters for the spatial region corresponding to each voxel unit based on its point cloud data transmission frequency and scan confidence value, and renders each spatial region according to the display parameters. In this way, the radar scan quality of a region is intuitively displayed through real-time rendered spatial regions, helping to promptly identify scanning blind spots and data anomalies, thereby improving the timeliness and accuracy of radar scan quality evaluation.

Description

Technical Field

[0001] This application relates to the field of radar scanning processing technology, and in particular to display evaluation methods, apparatus, electronic devices and media for radar scanning quality. Background Technology

[0002] With the development of LiDAR technology, 3D data acquisition based on the principle of active laser ranging is increasingly being applied to large-scene modeling. In practical applications, scanning quality directly affects the integrity and accuracy of the point cloud model, including indicators such as point cloud density, spatial coverage, data continuity, and the degree of structural detail restoration, all of which have a significant impact on the quality of the final result. Therefore, effectively evaluating the scanning effect during the data acquisition stage has become a crucial step in ensuring data reliability and reducing repetitive work.

[0003] In existing technologies, the assessment of scan quality usually relies on statistical information generated by the device or offline analysis of the output point cloud file (such as LAS format) after scanning. Data quality is evaluated by calculating parameters such as point density, overlap, or coverage. However, the above assessment methods cannot intuitively and promptly detect scan defects, scan blind spots, and abnormal data areas, resulting in low timeliness and accuracy of radar scan quality assessment. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method, apparatus, electronic device and medium for displaying and evaluating radar scan quality. The method involves spatially dividing the real-time acquired point cloud data stream, determining multiple voxel units, and determining the display parameters for the spatial region corresponding to each voxel unit based on the frequency of point cloud data backhaul in each voxel unit. The method then renders the spatial region and intuitively displays the radar scan quality of the region through the real-time rendered spatial region. This helps to promptly identify scanning blind spots and data anomaly areas, thereby improving the timeliness and accuracy of radar scan quality evaluation.

[0005] In a first aspect, embodiments of this application provide a display evaluation method for radar scanning quality, the display evaluation method comprising: The system receives a 3D point cloud data stream from a LiDAR scanner in real time; wherein the 3D point cloud data stream includes spatial coordinate information of at least one point cloud data. Based on the spatial coordinate information of at least one point cloud data, the data is divided according to a preset spatial resolution to determine at least one voxel unit after the division and the voxel identifier of each voxel unit. Based on the voxel identifier of each voxel unit, the point cloud data backhaul frequency of each voxel unit is determined; the point cloud data backhaul frequency changes dynamically with the change of scanning time. Based on the point cloud data backhaul frequency and scan confidence value of each voxel unit, the display parameters of the spatial region corresponding to each voxel unit are determined, and each spatial region is rendered according to the display parameters; wherein, the display parameters are used to characterize the dynamic changes in the radar scan quality of the corresponding spatial region as the scan time changes.

[0006] In one possible implementation, the voxel identifier for each voxel unit is determined through the following steps: For each voxel unit, determine the index value corresponding to the voxel unit when performing spatial partitioning; After offsetting the index value, it is encoded using bitwise operations. The encoded values ​​are then shifted and concatenated to determine the voxel identifier of the voxel unit.

[0007] In one possible implementation, determining the point cloud data backhaul frequency for each voxel unit based on its voxel identifier includes: For each voxel unit, the point cloud data backhaul frequency corresponding to that voxel unit is determined based on the voxel identifier of that voxel unit and the number of times the point cloud data falls into that voxel unit during the radar scan.

[0008] In one possible implementation, the display evaluation method further includes: In response to reaching a preset statistical time interval, the number of point cloud data contained in each voxel unit is determined; Remove voxel units containing less than a preset threshold number of point cloud data.

[0009] In one possible implementation, determining the display parameters of the spatial region corresponding to each voxel unit based on the point cloud data backhaul frequency and scan confidence value of each voxel unit includes: The scan confidence value of each voxel is determined based on the point cloud data backhaul frequency of each voxel; wherein the scan confidence value is determined based on the point cloud data backhaul frequency of each voxel and the region backhaul frequency corresponding to each voxel. For each voxel, the display parameters of the corresponding spatial region are determined based on the scan confidence value of that voxel.

[0010] In one possible implementation, determining the display parameters of the spatial region corresponding to each voxel unit based on the scan confidence value of that voxel unit includes: For each voxel unit, the display parameters of the spatial region corresponding to that voxel unit are determined based on the scan confidence value of that voxel unit and the mapping relationship between the preset scan confidence value and the display parameters. The display parameters include at least the display color parameters of the spatial area.

[0011] In one possible implementation, the display evaluation method further includes: For each voxel unit, if the scan confidence value corresponding to the voxel unit is greater than the preset scan confidence threshold, the scan confidence value corresponding to the voxel unit is determined as the preset scan confidence threshold.

[0012] Secondly, embodiments of this application also provide a display evaluation device for radar scanning quality, the display evaluation device comprising: A point cloud data receiving module is used to receive the three-dimensional point cloud data stream transmitted back by LiDAR scanning in real time; wherein the three-dimensional point cloud data stream includes the spatial coordinate information of at least one point cloud data. The voxel identifier determination module is used to divide the spatial coordinate information of at least one point cloud data according to a preset spatial resolution, and determine at least one voxel unit after division and the voxel identifier of each voxel unit. The return frequency determination module is used to determine the point cloud data return frequency of each voxel unit based on the voxel identifier of each voxel unit; the point cloud data return frequency changes dynamically with the change of scanning time. The quality assessment module is used to determine the display parameters of the spatial region corresponding to each voxel unit based on the frequency of point cloud data backhaul and the scan confidence value of each voxel unit, and to render each spatial region according to the display parameters; wherein, the display parameters are used to characterize the dynamic changes of the radar scan quality of the corresponding spatial region as the scan time changes.

[0013] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the display evaluation method for radar scan quality as described in any of the first aspects.

[0014] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the display evaluation method for radar scan quality as described in any of the first aspects.

[0015] The radar scanning quality display evaluation method, apparatus, electronic device, and medium provided in this application embodiment receive a three-dimensional point cloud data stream transmitted back from a lidar scan in real time. The three-dimensional point cloud data stream includes spatial coordinate information of at least one point cloud data. Based on the spatial coordinate information of at least one point cloud data, the data is divided according to a preset spatial resolution to determine at least one voxel unit and a voxel identifier for each voxel unit. Based on the voxel identifier of each voxel unit, the point cloud data transmission frequency of each voxel unit is determined. The point cloud data transmission frequency dynamically changes with the scanning time. Based on the point cloud data transmission frequency and scanning confidence value of each voxel unit, display parameters for the spatial region corresponding to each voxel unit are determined, and each spatial region is rendered according to the display parameters. The display parameters characterize the dynamic changes in the radar scanning quality of the corresponding spatial region as the scanning time changes. In this way, the real-time acquired point cloud data stream is spatially divided to determine multiple voxel units. Based on the frequency of point cloud data backhaul in each voxel unit, the display parameters for the spatial region corresponding to the voxel unit are determined, and the spatial region is rendered. Then, through the real-time rendered spatial region, the radar scanning quality of the region is intuitively displayed, which helps to promptly detect scanning blind spots and data anomaly areas, thereby improving the timeliness and accuracy of radar scanning quality assessment.

[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a radar scanning quality display evaluation method provided in an embodiment of this application; Figure 2 A schematic diagram illustrating the spatial partitioning principle provided as an example in this application; Figure 3 This is a schematic diagram illustrating the scanning effect presentation process provided in the embodiments of this application; Figure 4 A schematic diagram of the structure of a radar scanning quality display and evaluation device provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0020] First, the applicable scenarios for this application will be introduced. This application can be applied to the field of radar scanning processing technology.

[0021] With the development of LiDAR technology, 3D data acquisition based on the principle of active laser ranging is gradually being applied to the field of large-scene modeling. LiDAR obtains spatial distance information of target objects by emitting laser pulses and receiving echo signals, thereby directly generating 3D point cloud data. Combined with inertial measurement units (IMUs) and simultaneous localization and mapping (SLAM) algorithms, mobile LiDAR devices can achieve spatial positioning and point cloud stitching during movement, possessing a certain real-time mapping capability.

[0022] In practical applications, scanning quality directly affects the integrity and accuracy of point cloud models. Indicators such as point cloud density, spatial coverage, data continuity, and the degree of structural detail reproduction all significantly impact the quality of the final output. Therefore, effectively evaluating the scanning effect during the data acquisition phase is crucial for ensuring data reliability and reducing repetitive work.

[0023] In existing technologies, the assessment of scan quality typically relies on statistical information generated by the device or offline analysis of the output point cloud files after scanning, evaluating data quality by calculating parameters such as point density, overlap, or coverage. However, these methods are mostly based on static data post-processing analysis, lacking the ability to provide real-time dynamic feedback on the scanning process. This makes it difficult to reflect the stability and completeness of data acquisition in a timely manner, resulting in operators being unable to detect scan blind spots or data anomalies immediately, thus increasing the cost and time investment in rescanning. Therefore, how to achieve real-time assessment and intuitive feedback of point cloud data quality during the scanning process has become a pressing technical problem that needs to be solved in this field.

[0024] In another existing technology, in the field of outdoor large-scale 3D modeling, oblique photogrammetry is typically used to acquire multi-angle real-world images of the target area. After acquiring a large amount of high-resolution image data using drones or other mobile platforms equipped with multi-lens cameras, photogrammetric algorithms are used to perform feature matching, aerial triangulation, and dense point cloud generation to further construct a 3D model of the scene. This type of technical solution mainly relies on image data for 3D reconstruction. Typically, after data acquisition, the acquired image data is imported into post-processing software, which centrally calculates and generates point cloud models, texture models, or real-world 3D models, thereby achieving a digital representation of a large-scale scene. While the aforementioned offline reconstruction technology based on oblique photogrammetry can obtain a relatively complete 3D model, its data processing flow is complex, computationally time-consuming, and lacks a real-time quality feedback mechanism for the acquisition process. It cannot promptly determine scene coverage and data validity during the data acquisition stage. For applications with high timeliness requirements, requiring immediate on-site decision-making, or where the equipment's processing algorithms have robustness issues, this method struggles to meet the need for real-time viewing and dynamic evaluation of the 3D model quality of large scenes during scanning, exhibiting significant response lag.

[0025] Based on this, embodiments of this application provide a display evaluation method for radar scan quality to improve the timeliness and accuracy of radar scan quality evaluation.

[0026] Please see Figure 1 , Figure 1 This is a flowchart illustrating a radar scan quality display evaluation method provided in an embodiment of this application. Figure 1 As shown in the embodiments of this application, the radar scanning quality display evaluation method includes: S101. Receive the three-dimensional point cloud data stream transmitted back by the lidar in real time; wherein the three-dimensional point cloud data stream includes the spatial coordinate information of at least one point cloud data.

[0027] S102. Based on the spatial coordinate information of at least one point cloud data, divide it according to a preset spatial resolution, and determine at least one voxel unit after division and the voxel identifier of each voxel unit.

[0028] S103. Based on the voxel identifier of each voxel unit, determine the point cloud data backhaul frequency of each voxel unit; the point cloud data backhaul frequency changes dynamically with the change of scanning time.

[0029] S104. Based on the point cloud data backhaul frequency and scan confidence value of each voxel unit, determine the display parameters of the spatial region corresponding to each voxel unit, and render each spatial region according to the display parameters; wherein, the display parameters are used to characterize the dynamic change of the radar scan quality of the corresponding spatial region as the scan time changes.

[0030] The radar scanning quality display evaluation method provided in this application divides the real-time acquired point cloud data stream into spatial segments, determines multiple voxel units, and determines the display parameters for the spatial region corresponding to each voxel unit based on the frequency of point cloud data backhaul in each voxel unit. The method then renders the spatial region and intuitively displays the radar scanning quality of the region through the real-time rendered spatial region. This helps to promptly identify scanning blind spots and abnormal data areas, thereby improving the timeliness and accuracy of radar scanning quality evaluation.

[0031] The exemplary steps of the embodiments of this application are described below: S101. Receive the three-dimensional point cloud data stream transmitted back by the lidar in real time; wherein the three-dimensional point cloud data stream includes the spatial coordinate information of at least one point cloud data.

[0032] Here, lidar obtains spatial distance information of the target object by emitting laser pulses and receiving echo signals, thereby directly generating three-dimensional point cloud data.

[0033] Point cloud refers to a data set consisting of a large number of three-dimensional points in space.

[0034] In this embodiment of the application, in order to evaluate the quality of the LiDAR scan in real time during the LiDAR scanning process, the three-dimensional point cloud data stream transmitted back by the LiDAR scan can be received directly during the scanning process. The three-dimensional point cloud data stream includes multiple point cloud data and the spatial coordinate information of each point cloud data.

[0035] Furthermore, after obtaining the spatial coordinate information of each point cloud data, it is possible to divide the data based on the spatial coordinate information of at least one point cloud data, and determine at least one voxel unit and the voxel identifier corresponding to each voxel unit.

[0036] S102. Based on the spatial coordinate information of at least one point cloud data, divide it according to a preset spatial resolution, and determine at least one voxel unit after division and the voxel identifier of each voxel unit.

[0037] In one possible implementation, at least one voxel unit can be determined by quantizing the spatial coordinate information of each point cloud data according to a preset spatial resolution.

[0038] Among them, a voxel refers to a regular discrete unit used to represent volumetric data in a three-dimensional space. Each unit corresponds to a fixed spatial resolution and can store position, density, color, confidence level, or other attribute information.

[0039] Here, the preset spatial resolution can be adjusted according to the subdivision ratio parameter. However, due to the increase in resolution, the working index magnitude will also increase by a factor of (1 << DivideScale) on a single axis and by a factor of (1 << DivideScale) cubed in the three-dimensional world. Therefore, although the confidence level of the detailed objects feedback during scanning can be improved, the actually set preset spatial resolution should not be too high.

[0040] Furthermore, after determining multiple voxel units, the corresponding index values can be processed to determine the corresponding voxel identifiers.

[0041] Specifically, the voxel identifiers of each voxel unit are determined through the following steps: a1: For each voxel unit, determine the index value corresponding to this voxel unit when performing spatial division.

[0042] a2: After performing an offset processing on the index value, encode it through bit operations, and perform displacement splicing on the encoded value to determine it as the voxel identifier of this voxel unit.

[0043] In a possible implementation manner,示例性, please refer to Figure 2 , Figure 2 which is the schematic diagram of the spatial division principle provided by this application example. As Figure 2 shown, in the example, it is a method of uniformly dividing a unit space of 1×1×1 cubic meter into smaller voxel units based on DivideScale (subdivision level). Specifically, the world coordinate range is (0m, 1m); X is the spatial coordinate (unit: meter) of the point cloud data in the three-dimensional physical space (In World); X ’ is the coordinate of the point cloud data mapped to the index space (IndexWorld). DivideScale = n means dividing each side of the unit cube into 2 n segments. Specifically, in IndexWorld, the index value is the integer number of the voxel unit where the point cloud data is located; each voxel unit covers a continuous physical space. Exemplary, when the voxel unit size is 1×1×1 cubic meter, X = 0.4 -> X ’ = 0.4 -> [0.4] = 0, that is, the index value (index) is 0; X = 0.6 -> X ’=0.6->[0.6]=0, that is, the index value is 0; the entire three-dimensional space is a voxel unit; when the size of the voxel unit is 0.5×0.5×0.5 cubic meters, X=0.4->X ’ =0.8>[0.8]=0, that is, the index value is 0 (corresponding to the three-dimensional physical interval [0,0.5)); X=0.6->X ’ =1.2->[1.2]=1, that is, the index value is 1 (corresponding to the three-dimensional physical interval [0.5,1.0)); the indices are two voxel units, 0 and 1; when the voxel unit size is 0.25×0.25×0.25 cubic meters, X=0.4->X ’ =1.6>[1.6]=1, that is, the index value is 1 (corresponding to the three-dimensional physical interval [0.25,0.5)); X=0.6->X ’ =2.4->[2.4]=2, that is, the index value is 2 (corresponding to the three-dimensional physical interval [0.5,0.75)); the indexes are four voxel units: 0, 1, 2, and 3.

[0044] Furthermore, after determining the index value corresponding to each voxel unit and performing offset processing to eliminate the negative values ​​of the determined index; then encoding through bit operations, and limiting the index bit width through bit mask, the voxel identifier corresponding to each voxel unit is determined by shift concatenation.

[0045] Among them, the voxel identifier corresponds to a single unsigned integer encoded value.

[0046] Here, the mask limits the number of bits used to encode each axis coordinate (e.g., 21 bits) to prevent data from overflowing into the bit segments of adjacent axes during displacement splicing, and indirectly limits the range of space that can be expressed. For example, the index range for each axis is -1048576 to 1048575 to ensure encoding accuracy.

[0047] For example, in the above example, when the voxel unit size is 1×1×1 cubic meters, the index range per axis is -1048576 to 1048575; the world coordinate range per axis is -1048576 to 1048575; when the voxel unit size is 0.5×0.5×0.5 cubic meters, the index range per axis is -1048576 to 1048575; the world coordinate range per axis is -524288 to 524287; when the voxel unit size is 0.25×0.25×0.25 cubic meters, the index range per axis is -1048576 to 1048575; the world coordinate range per axis is -262144 to 262143.

[0048] Furthermore, after determining at least one voxel unit and the voxel identifier of each voxel unit, the point cloud data backhaul frequency of each voxel unit can be determined.

[0049] S103. Based on the voxel identifier of each voxel unit, determine the point cloud data backhaul frequency of each voxel unit; the point cloud data backhaul frequency changes dynamically with the change of scanning time.

[0050] Here, the point cloud data backhaul frequency refers to the frequency of point cloud data updates during data acquisition, transmission, and processing. It can be determined based on the number of times the point cloud data acquired during LiDAR scanning falls into each voxel unit.

[0051] Specifically, the step "determine the point cloud data backhaul frequency for each voxel unit based on its voxel identifier" includes: b1: For each voxel unit, based on the voxel identifier of the voxel unit and the number of times the point cloud data falls into the voxel unit during the radar scan, determine the point cloud data backhaul frequency corresponding to the voxel unit.

[0052] Specifically, the number of data updates in each voxel unit can be recorded in real time, and the data can be accumulated and updated each time point cloud data falls into the voxel unit. Then, based on the number of times point cloud data falls into the voxel unit during the radar scan, the corresponding point cloud data backhaul frequency can be determined.

[0053] In one possible implementation, voxel units containing a low amount of point cloud data may not be statistically significant in subsequent statistical processes. In order to reduce the amount of data processing and improve data processing efficiency, voxel units containing less than a preset threshold of point cloud data can be removed.

[0054] Specifically, the display evaluation method further includes: c1: In response to reaching a preset statistical time interval, determine the number of point cloud data contained in each voxel unit.

[0055] c2: Clear voxel units containing less than a preset threshold number of point cloud data.

[0056] In one possible implementation, in order to ensure the accuracy of point cloud data accumulation, a point cloud data acquisition time interval can be preset. After determining that the preset statistical time interval has been reached, the number of point cloud data contained in each voxel unit within the entire preset time interval is counted, and voxel units containing less than a preset number threshold of point cloud data are deleted.

[0057] Here, the preset quantity threshold can be set according to the voxel unit division accuracy and the radar equipment type's ability to collect point cloud data. The specific setting method will not be elaborated here.

[0058] In one possible implementation, after determining the point cloud return frequency of each voxel unit, the scanning confidence level of the target can be determined based on the point cloud return frequency of each voxel unit, thereby determining the display parameters of the corresponding spatial region and displaying the scanning effect of each spatial region.

[0059] S104. Based on the point cloud data backhaul frequency and scan confidence value of each voxel unit, determine the display parameters of the spatial region corresponding to each voxel unit, and render each spatial region according to the display parameters; wherein, the display parameters are used to characterize the dynamic change of the radar scan quality of the corresponding spatial region as the scan time changes.

[0060] In one possible implementation, the display parameters of the spatial region include at least the regional display color of the spatial region, that is, different scan confidence levels correspond to different regional display colors, so as to determine the radar scanning effect of the current spatial region by displaying the regional display color, and then evaluate the radar scanning quality.

[0061] In another possible implementation, the display parameters of the spatial region may also include the display area of ​​the spatial region, that is, different scanning confidence levels correspond to different display areas, so as to determine the radar scanning effect of the current spatial region by the size of the displayed area, and then evaluate the radar scanning quality; here, the larger the display area of ​​the spatial region, the higher the corresponding radar scanning quality.

[0062] Specifically, the step "determine the display parameters of the spatial region corresponding to each voxel unit based on the point cloud data backhaul frequency and scan confidence value of each voxel unit" includes: d1: Determine the scan confidence value of each voxel unit based on the point cloud data backhaul frequency of each voxel unit; wherein, the scan confidence value is determined based on the point cloud data backhaul frequency of each voxel unit and the region backhaul frequency corresponding to each voxel unit.

[0063] d2: For each voxel, based on the scan confidence value of that voxel, determine the display parameters of the corresponding spatial region for each voxel.

[0064] In one possible implementation, for each voxel unit, a scan confidence value is determined based on the frequency of point cloud data backhaul corresponding to that voxel unit. Specifically, the frequency of region backhaul corresponding to that voxel unit can be set, and the scan confidence value is determined based on the ratio between the frequency of point cloud data backhaul in that voxel unit and the frequency of region backhaul corresponding to that voxel unit.

[0065] For example, if the area backhaul frequency of the LiDAR model 1 in the corresponding spatial region is 125 times, and the point cloud data backhaul frequency of the current voxel unit is 100 times, then the scanning confidence value of the current voxel unit can be 0.8.

[0066] In one possible implementation, to ensure the uniformity of subsequent statistical calculations, an initial scan confidence value can be pre-calculated for each voxel unit, and the initial scan confidence value can be mapped to a preset interval using a preset normalization function.

[0067] Here, for the sake of calculation accuracy, the region backhaul frequency corresponding to each voxel unit is generally set to the optimal backhaul frequency. If the point cloud data backhaul frequency in the voxel unit exceeds the region backhaul frequency within a preset time interval, the corresponding point cloud data backhaul frequency will be set to the region backhaul frequency in the subsequent statistical process for boundary restriction processing.

[0068] In one possible implementation, after setting the region return frequency for each voxel unit, different return frequency intervals can be divided within the region return frequency, with each return frequency interval corresponding to a scan confidence value.

[0069] For example, in the above example, for the LiDAR model 1, the area return frequency in the corresponding spatial area is 125 times. The point cloud data return frequency of 0-50 can be set to correspond to the scan confidence value X; the point cloud data return frequency of 50-100 can correspond to the scan confidence value Y; the point cloud data return frequency of 100-125 can correspond to the scan confidence value Z; if the point cloud data return frequency corresponding to the current voxel unit is 120, then the current corresponding scan confidence value Z.

[0070] Specifically, the display evaluation method further includes: e1: For each voxel unit, if the scan confidence value corresponding to the voxel unit is greater than the preset scan confidence threshold, the scan confidence value corresponding to the voxel unit is determined as the preset scan confidence threshold.

[0071] For example, in the above example, if the area backhaul frequency of the LiDAR model 1 in the corresponding spatial area is 125 times, and the point cloud data backhaul frequency of the current voxel unit is 200 times, then the display parameters of the spatial area corresponding to the voxel unit are determined based on 125.

[0072] Furthermore, the display parameters of the spatial region corresponding to each voxel unit can be determined based on the scan confidence value corresponding to each voxel unit.

[0073] Specifically, the step "For each voxel unit, based on the scan confidence value of that voxel unit, determine the display parameters of the corresponding spatial region for each voxel unit" includes: f1: For each voxel, based on the scan confidence value of the voxel and the mapping relationship between the preset scan confidence value and the display parameters, determine the display parameters of the spatial region corresponding to the voxel.

[0074] In one possible implementation, different confidence intervals can be divided, with each confidence interval corresponding to a specific display parameter, thereby determining the mapping relationship between the preset scan confidence value and the display parameter.

[0075] Specifically, the scan confidence value corresponding to each voxel unit is mapped to normalized display parameters, and the corresponding spatial region is dynamically updated and displayed on the display terminal through the function method provided by the rendering engine.

[0076] For example, taking the display parameter as the display color parameter of the spatial region as an example, for the LiDAR model 1, the region feedback frequency in the corresponding spatial region is 125 times. If the feedback frequency of the point cloud data corresponding to the current voxel unit is also 125, the corresponding scan confidence level is determined to be M, and the corresponding display parameter is green. When the display color of the spatial region is green, the scan quality of the current spatial region is determined to be better. If the feedback frequency of the point cloud data corresponding to the current voxel unit is also 20, the corresponding scan confidence level is determined to be N, and the corresponding display parameter is red. When the display color of the spatial region is red, the scan quality of the current spatial region is determined to be poor.

[0077] Here, as the scanning time increases, the frequency of point cloud data transmission to the corresponding spatial region will also change, and consequently, the display parameters of the corresponding spatial region will also change. Based on the changes in the display parameters corresponding to the spatial region, the dynamic changes in the radar scanning quality of the spatial region following the scanning time can be determined.

[0078] For example, in the above example, the display color of the spatial region is red. As the scanning time increases, the frequency of point cloud data feedback corresponding to the current voxel unit is 125, the corresponding scanning confidence is determined to be M, and the corresponding display parameter is green. That is, as the scanning time increases, the radar scanning quality also improves accordingly.

[0079] Specifically, based on the display parameters corresponding to the spatial area, the process of the scanning area gradually improving over time can be presented intuitively. Operators can judge whether the scanning effect has reached the expected standard by the color change or the change of display parameters, evaluate the radar scanning effect, and promptly rescan areas with poor scanning quality or control the lidar to stop scanning the corresponding spatial area.

[0080] The evaluation process of radar scanning quality in the embodiments of this application will be illustrated below with specific examples: Specifically, please refer to Figure 3 , Figure 3 This is a schematic diagram illustrating the scanning effect presentation process provided in the embodiments of this application, such as... Figure 3 As shown, after the scan begins, the spatial coordinate information of the point cloud data is received; the space is divided based on the spatial coordinate information to determine the index value; the index value is offset to obtain the unsigned encoded value; the encoding boundary is checked and the mask overflow prevention clipping is performed; the displacement is stitched to determine the voxel identifier; the corresponding scan confidence value is written / updated; and the corresponding display parameters are determined according to the scan confidence, where the display parameters can be the region display color.

[0081] The radar scanning quality display evaluation method provided in this application embodiment receives a three-dimensional point cloud data stream transmitted back from a lidar scan in real time. The three-dimensional point cloud data stream includes spatial coordinate information of at least one point cloud data. Based on the spatial coordinate information of the at least one point cloud data, the data is divided according to a preset spatial resolution to determine at least one voxel unit and a voxel identifier for each voxel unit. Based on the voxel identifier of each voxel unit, the point cloud data transmission frequency of each voxel unit is determined. The point cloud data transmission frequency dynamically changes with the scanning time. Based on the point cloud data transmission frequency and scanning confidence value of each voxel unit, display parameters for the spatial region corresponding to each voxel unit are determined, and each spatial region is rendered according to the display parameters. The display parameters characterize the dynamic changes in the radar scanning quality of the corresponding spatial region as the scanning time changes. In this way, the real-time acquired point cloud data stream is spatially divided to determine multiple voxel units. Based on the frequency of point cloud data backhaul in each voxel unit, the display parameters for the spatial region corresponding to the voxel unit are determined, and the spatial region is rendered. Then, through the real-time rendered spatial region, the radar scanning quality of the region is intuitively displayed, which helps to promptly detect scanning blind spots and data anomaly areas, thereby improving the timeliness and accuracy of radar scanning quality assessment.

[0082] Based on the same inventive concept, this application also provides a radar scanning quality display evaluation device corresponding to the radar scanning quality display evaluation method. Since the principle of the device in this application is similar to the radar scanning quality display evaluation method described above in this application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0083] Please see Figure 4 , Figure 4 This is a schematic diagram of a radar scanning quality display and evaluation device provided in an embodiment of this application. Figure 4 As shown, the display evaluation device 400 includes: The point cloud data receiving module 410 is used to receive the three-dimensional point cloud data stream transmitted back by the LiDAR scan in real time; wherein the three-dimensional point cloud data stream includes the spatial coordinate information of at least one point cloud data. The voxel identifier determination module 420 is used to divide the spatial coordinate information of at least one point cloud data according to a preset spatial resolution, and determine at least one voxel unit after division and the voxel identifier of each voxel unit. The return frequency determination module 430 is used to determine the point cloud data return frequency of each voxel unit based on the voxel identifier of each voxel unit; the point cloud data return frequency changes dynamically with the change of scanning time. The quality assessment module 440 is used to determine the display parameters of the spatial region corresponding to each voxel unit based on the frequency of point cloud data backhaul and the scan confidence value of each voxel unit, and to render each spatial region according to the display parameters; wherein, the display parameters are used to characterize the dynamic change of the radar scan quality of the corresponding spatial region as the scan time changes.

[0084] In one possible implementation, the voxel identifier determination module 420 is used to determine the voxel identifier of each voxel unit through the following steps: For each voxel unit, determine the index value corresponding to the voxel unit when performing spatial partitioning; After offsetting the index value, it is encoded using bitwise operations. The encoded values ​​are then shifted and concatenated to determine the voxel identifier of the voxel unit.

[0085] In one possible implementation, when determining the point cloud data return frequency for each voxel unit based on its voxel identifier, the return frequency determination module 430 is configured to: For each voxel unit, the point cloud data backhaul frequency corresponding to that voxel unit is determined based on the voxel identifier of that voxel unit and the number of times the point cloud data falls into that voxel unit during the radar scan.

[0086] In one possible implementation, the display evaluation device 400 further includes a voxel unit clearing module (not shown in the figure), the voxel unit clearing module being used for: In response to reaching a preset statistical time interval, the number of point cloud data contained in each voxel unit is determined; Remove voxel units containing less than a preset threshold number of point cloud data.

[0087] In one possible implementation, when determining the display parameters of the spatial region corresponding to each voxel unit based on the point cloud data backhaul frequency and scan confidence value of each voxel unit, the quality assessment module 440 is used to: The scan confidence value of each voxel is determined based on the point cloud data backhaul frequency of each voxel; wherein the scan confidence value is determined based on the point cloud data backhaul frequency of each voxel and the region backhaul frequency corresponding to each voxel. For each voxel, the display parameters of the corresponding spatial region are determined based on the scan confidence value of that voxel.

[0088] In one possible implementation, when the quality assessment module 440 determines the display parameters of the spatial region corresponding to each voxel unit based on the scan confidence value of that voxel unit, the quality assessment module 440 is used to: For each voxel unit, the display parameters of the spatial region corresponding to that voxel unit are determined based on the scan confidence value of that voxel unit and the mapping relationship between the preset scan confidence value and the display parameters. The display parameters include at least the display color parameters of the spatial area.

[0089] In one possible implementation, the display evaluation device 400 further includes a confidence value adjustment module (not shown in the figure), the confidence value adjustment module being used for: For each voxel unit, if the scan confidence value corresponding to the voxel unit is greater than the preset scan confidence threshold, the scan confidence value corresponding to the voxel unit is determined as the preset scan confidence threshold.

[0090] The radar scanning quality display evaluation device provided in this application embodiment receives a three-dimensional point cloud data stream transmitted back from a lidar scan in real time. The three-dimensional point cloud data stream includes spatial coordinate information of at least one point cloud data. Based on the spatial coordinate information of at least one point cloud data, the data is divided according to a preset spatial resolution to determine at least one voxel unit and a voxel identifier for each voxel unit. Based on the voxel identifier of each voxel unit, the point cloud data transmission frequency of each voxel unit is determined. The point cloud data transmission frequency dynamically changes with the scanning time. Based on the point cloud data transmission frequency and scanning confidence value of each voxel unit, display parameters for the spatial region corresponding to each voxel unit are determined, and each spatial region is rendered according to the display parameters. The display parameters characterize the dynamic changes in the radar scanning quality of the corresponding spatial region as the scanning time changes. In this way, the real-time acquired point cloud data stream is spatially divided to determine multiple voxel units. Based on the frequency of point cloud data backhaul in each voxel unit, the display parameters for the spatial region corresponding to the voxel unit are determined, and the spatial region is rendered. Then, through the real-time rendered spatial region, the radar scanning quality of the region is intuitively displayed, which helps to promptly detect scanning blind spots and data anomaly areas, thereby improving the timeliness and accuracy of radar scanning quality assessment. Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 500 includes a processor 510, a memory 520, and a bus 530.

[0091] The memory 520 stores machine-readable instructions executable by the processor 510. When the electronic device 500 is running, the processor 510 and the memory 520 communicate via the bus 530. When the machine-readable instructions are executed by the processor 510, they can perform the operations described above. Figure 1 The specific implementation of the radar scanning quality display evaluation method in the illustrated method embodiment can be found in the method embodiment, and will not be repeated here.

[0092] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 The steps of the radar scanning quality display evaluation method in the illustrated method embodiment can be found in the method embodiment for specific implementation, and will not be repeated here.

[0093] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0094] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0095] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0096] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0097] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0098] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for evaluating the display quality of radar scanning, characterized in that, The display evaluation method includes: The system receives a 3D point cloud data stream from a LiDAR scanner in real time; wherein the 3D point cloud data stream includes spatial coordinate information of at least one point cloud data. Based on the spatial coordinate information of at least one point cloud data, the data is divided according to a preset spatial resolution to determine at least one voxel unit after the division and the voxel identifier of each voxel unit. Based on the voxel identifier of each voxel unit, the point cloud data backhaul frequency of each voxel unit is determined; the point cloud data backhaul frequency changes dynamically with the change of scanning time. Based on the point cloud data backhaul frequency and scan confidence value of each voxel unit, the display parameters of the spatial region corresponding to each voxel unit are determined, and each spatial region is rendered according to the display parameters; wherein, the display parameters are used to characterize the dynamic changes in the radar scan quality of the corresponding spatial region as the scan time changes.

2. The display evaluation method according to claim 1, characterized in that, The voxel identifier for each voxel unit is determined using the following steps: For each voxel unit, determine the index value corresponding to the voxel unit when performing spatial partitioning; After offsetting the index value, it is encoded using bitwise operations. The encoded values ​​are then shifted and concatenated to determine the voxel identifier of the voxel unit.

3. The display evaluation method according to claim 1, characterized in that, The determination of the point cloud data backhaul frequency for each voxel unit based on its voxel identifier includes: For each voxel unit, the point cloud data backhaul frequency corresponding to that voxel unit is determined based on the voxel identifier of that voxel unit and the number of times the point cloud data falls into that voxel unit during the radar scan.

4. The display evaluation method according to claim 1, characterized in that, The display evaluation method further includes: In response to reaching a preset statistical time interval, the number of point cloud data contained in each voxel unit is determined; Remove voxel units containing less than a preset threshold number of point cloud data.

5. The display evaluation method according to claim 1, characterized in that, The display parameters for the spatial region corresponding to each voxel unit are determined based on the point cloud data return frequency and scan confidence value of each voxel unit, including: The scan confidence value of each voxel is determined based on the point cloud data backhaul frequency of each voxel; wherein the scan confidence value is determined based on the point cloud data backhaul frequency of each voxel and the region backhaul frequency corresponding to each voxel. For each voxel, the display parameters of the corresponding spatial region are determined based on the scan confidence value of that voxel.

6. The display evaluation method according to claim 5, characterized in that, For each voxel unit, based on the scan confidence value of that voxel unit, the display parameters of the corresponding spatial region are determined, including: For each voxel unit, the display parameters of the spatial region corresponding to that voxel unit are determined based on the scan confidence value of that voxel unit and the mapping relationship between the preset scan confidence value and the display parameters. The display parameters include at least the display color parameters of the spatial area.

7. The display evaluation method according to claim 5, characterized in that, The display evaluation method further includes: For each voxel unit, if the scan confidence value corresponding to the voxel unit is greater than the preset scan confidence threshold, the scan confidence value corresponding to the voxel unit is determined as the preset scan confidence threshold.

8. A display and evaluation device for radar scanning quality, characterized in that, The display evaluation device includes: A point cloud data receiving module is used to receive the three-dimensional point cloud data stream transmitted back by LiDAR scanning in real time; wherein the three-dimensional point cloud data stream includes the spatial coordinate information of at least one point cloud data. The voxel identifier determination module is used to divide the spatial coordinate information of at least one point cloud data according to a preset spatial resolution, and determine at least one voxel unit after division and the voxel identifier of each voxel unit. The return frequency determination module is used to determine the point cloud data return frequency of each voxel unit based on the voxel identifier of each voxel unit; the point cloud data return frequency changes dynamically with the change of scanning time. The quality assessment module is used to determine the display parameters of the spatial region corresponding to each voxel unit based on the frequency of point cloud data backhaul and the scan confidence value of each voxel unit, and to render each spatial region according to the display parameters; wherein, the display parameters are used to characterize the dynamic changes of the radar scan quality of the corresponding spatial region as the scan time changes.

9. An electronic device, characterized in that, include: The device includes a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the display evaluation method for radar scan quality as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the display evaluation method for radar scan quality as described in any one of claims 1 to 7.

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