A method for detecting the integrity of lidar point clouds in service areas

Abstract

The present application discloses a method for detecting the integrity of a laser radar point cloud in a service area, comprising the following steps: obtaining multiple frames of continuous radar point cloud data and preprocessing them; constructing multiple cylindrical detection areas with the radar as the center and with increasing radii according to the maximum perception radius of the radar; projecting the point cloud data falling within each detection area onto the surface of the corresponding detection area along the emission direction to obtain a projection point set corresponding to each detection area; calculating the degree of discreteness of each projection point set on the surface of the corresponding detection area to perform point cloud integrity judgment. The beneficial effect of the present application: by projecting the point cloud onto the surface of the detection area with different radii, regular reconstruction of disordered and sparse three-dimensional point cloud data is achieved. Not only can the number of point clouds within different distance ranges be quantitatively counted, but it can also assist in determining the cause of the point cloud missing problem, thereby improving the accuracy and robustness of defect recognition.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation technology, and in particular to a method for detecting the integrity of a laser radar point cloud within a service area. Background Art

[0002] As key nodes in the modern highway transportation network, highway service areas play an important role in vehicle diversion, short-term parking, energy supply, and emergency management. With the continuous growth of traffic volume, service area scenes are becoming increasingly complex. The high density of vehicles, frequent entry and exit, and irregular parking have placed higher demands on real-time perception and precise management of service areas.

[0003] Fixed-mounted LiDAR (Light Detection and Ranging) sensors are becoming increasingly important for vehicle monitoring and environmental modeling in service area scenarios due to their all-weather capabilities, high precision, and interference resistance. Compared to traditional vision-based solutions, LiDAR provides dense and highly accurate 3D point cloud data, facilitating object detection and tracking regardless of lighting conditions.

[0004] However, in actual deployment and application, it is limited by the following factors: limitations of the radar's own technical parameters (such as vertical field of view angle and rotation frequency), occlusion during point cloud acquisition (such as large vehicles blocking small vehicles), possible delays or packet loss during data transmission, and incomplete point cloud fusion between multiple devices.

[0005] Due to these limitations, the point cloud data acquired from a single lidar at a given moment may exhibit spatial discontinuities, sparse regions, or even missing structures. This incomplete point cloud structure severely impacts the effectiveness of subsequent intelligent perception tasks, leading to issues such as vehicle identification errors, trajectory breakages, and misjudgment of behavior. This ultimately undermines the efficiency and safety of the service area's digital and intelligent operations. Summary of the Invention

[0006] One of the purposes of this application is to provide a method for detecting the integrity of lidar point clouds within a service area that can solve at least one of the defects in the above-mentioned background technology.

[0007] To achieve at least one of the above purposes, the technical solution adopted in this application is: a method for detecting the integrity of a lidar point cloud within a service area, comprising the following steps:

[0008] S100: Acquire multiple frames of radar point cloud data and perform preprocessing;

[0009] S200: Constructing a plurality of cylindrical detection areas with increasing radii, centered on the radar, based on the maximum perception radius of the radar.

[0010] S300: Projecting the point cloud data falling within each detection area onto the surface of the corresponding detection area along the emission direction to obtain a projection point set corresponding to each detection area;

[0011] S400: Calculating the degree of dispersion of each projection point set on the surface of the corresponding detection area to determine the integrity of the point cloud;

[0012] The degree of discreteness of the detection area surface is suitably represented by the continuous point density of the detection area in the circumferential direction, and the point cloud integrity is judged based on the ratio of the calculated actual point density to the corresponding theoretical point density.

[0013] Preferably, in step S200, the model expression of the detection area in the coordinate system is:

[0014] ;

[0015] Where x0 and y0 represent the horizontal and vertical coordinates of the radar center point, respectively; r represents the distance from the detection area surface to the radar center point; θ represents the angle between the radar installation posture and the horizontal direction; x, y, and z represent the three-dimensional coordinates of the point on the detection area surface, respectively.

[0016] Preferably, in step S200, the intervals between adjacent detection areas are equal.

[0017] Preferably, the number of detection areas is set to five, and the distances between each detection area and the center point of the radar are 20%, 40%, 60%, 80% and 100% of the maximum perception radius of the radar respectively.

[0018] Preferably, step S300 includes the following specific steps:

[0019] S310: Calculate the distance between each point in the entire point cloud data and the radar center point, and determine the detection area corresponding to each point in the point cloud data based on the calculated distance;

[0020] S320: Construct the direction vector from the radar center point to each point and parameterize it;

[0021] S330: Based on the perpendicular intersection of the direction vector and the corresponding detection area, the parameterized direction vector is substituted into the expression of the detection area for solution to obtain the coordinate position of each point in the entire point cloud data projected onto the surface of the corresponding detection area;

[0022] S340: Summarize the coordinates of the projection points on the surface of each detection area to obtain a corresponding projection point set.

[0023] Preferably, step S400 includes the following specific processes:

[0024] S410: Projecting each detection area along the axial direction to form a corresponding plurality of projection circles, and simultaneously mapping the projection points on the surface of the detection area to the corresponding projection circles;

[0025] S420: Divide each projection circle into a plurality of sectors of equal area along the circumferential direction, and obtain the total number of points corresponding to each detection area and the number of points corresponding to each sector;

[0026] S430: Calculating the theoretical point density of each detection area based on the total number of points, and calculating the actual point density corresponding to each sector based on the number of points corresponding to the sector;

[0027] S440: Determine the completeness of the point cloud based on the ratio of the actual point density of each sector to the corresponding theoretical point density.

[0028] Preferably, in step S400, the ratio of the actual point density to the corresponding theoretical point density is used as the first completeness of the sector; the multiple sectors corresponding to the detection area are divided into multiple groups, each group includes at least one sector; the average completeness of each group of sectors in multiple consecutive frames is calculated; if the calculated average completeness is less than the set first threshold, and the number of points corresponding to the sector in multiple consecutive frames is less than the minimum value of the theoretical number of points, it is determined that there is a high missing area in the group of sectors.

[0029] Preferably, when judging the integrity of the point cloud in step S400, it is also necessary to judge the distribution of the point cloud in each sector. The specific judgment process is: the multiple sectors corresponding to the detection area are divided into multiple test areas, each test area includes multiple continuous sectors, and there is sector overlap between adjacent test areas; the spacing between adjacent points in the test area and the corresponding standard deviation are calculated; if the calculated standard deviation is greater than the set second threshold, or the maximum point spacing is greater than the set third threshold, it is determined that there is a fault in the corresponding sector.

[0030] Preferably, step S400 further includes the following process:

[0031] S450: Calculate the theoretical number of points in each sector based on the radar's angular resolution and the number of horizontal beams, and use the ratio of the actual number of points in the sector to the theoretical number of points as the second integrity level;

[0032] S460: Calculating the second completeness of the sectors corresponding to the same phase angle in each detection area in order from the smallest area radius to the largest area radius;

[0033] S470: If the second completeness of a sector corresponding to a certain phase angle does not continuously increase, it indicates that an abnormality exists in the sector corresponding to the phase angle.

[0034] Preferably, the specific judgment process for the abnormal situation in step S470 is: calculate the difference between the distances between two consecutive points in the abnormal sector; if the calculated difference is less than the set fourth threshold, it means that the point cloud of the sector is continuous and complete; if the calculated difference is greater than the set fourth threshold, and the number of points in the sector is less than the minimum value of the theoretical number of points, the sector is judged to be a missing or abnormal area; perform difference judgment on the missing or abnormal area for multiple consecutive frames, and if the same missing or abnormal area always exists, it is judged that the data transmission or radar sensor in the area is abnormal.

[0035] Compared with the prior art, the present invention has the following advantages:

[0036] By projecting point clouds onto the surfaces of detection areas of varying radius, regularized reconstruction of disordered, sparse 3D point cloud data is achieved. This not only quantifies the number of point clouds within different distance ranges, but also helps determine whether missing point clouds are caused by obstructions in the field of view, abnormalities in the radar sensor itself, or data transmission, thereby improving the accuracy and robustness of defect identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a schematic diagram of the overall workflow of this application.

[0038] Figure 2 Schematic diagram of the structure of the detection area constructed for this application.

[0039] Figure 3 This is a schematic diagram of the structure of multiple detection areas projected along the axial direction in this application.

[0040] Figure 4 This is a schematic diagram of the structure of sector division of the detection area in this application.

[0041] Figure 5 This is a schematic diagram of the structure of point cloud projection for sectors in this application. DETAILED DESCRIPTION

[0042] Below, the present application is further described in conjunction with specific implementation methods. It should be noted that, in the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like are intended to mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms should not be understood as necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0043] In the description of this application, it should be noted that for directional words, such as the terms "center", "horizontal", "longitudinal", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and so on, indicating the orientation and position relationship are based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing this application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and cannot be understood as limiting the specific scope of protection of this application.

[0044] It should be noted that the terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0045] In this application, unless otherwise expressly specified or limited, terms such as "mounted," "connected," "connect," and "fixed" should be understood in a broad sense. For example, they may refer to connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; and internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on specific circumstances.

[0046] In this application, unless otherwise expressly specified or limited, a first feature being "above" or "below" a second feature may include the first and second features being in direct contact, or may include the first and second features being in contact not directly but through another feature between them. Moreover, a first feature being "above," "above," and "above" a second feature may include the first feature being directly above or obliquely above the second feature, or may simply mean that the first feature is higher in level than the second feature. A first feature being "below," "below," and "below" a second feature may include the first feature being directly below or obliquely below the second feature, or may simply mean that the first feature is lower in level than the second feature.

[0047] The terms "comprises" and "having" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to those steps or units expressly listed, but may include other steps or units not expressly listed or inherent to such process, method, product or apparatus.

[0048] One of the preferred embodiments of this application is as follows: Figure 1 and Figure 2As shown, a method for detecting the integrity of a lidar point cloud within a service area includes the following steps:

[0049] S100: Acquire multiple frames of radar point cloud data and perform preprocessing.

[0050] S200: Constructing a plurality of cylindrical detection areas with the radar as the center and with successively increasing radii according to the maximum perception radius of the radar.

[0051] S300: Projecting the point cloud data falling within each detection area onto the surface of the corresponding detection area along the emission direction to obtain a projection point set corresponding to each detection area.

[0052] S400: Calculating the degree of dispersion of each projection point set on the surface of the corresponding detection area to determine the integrity of the point cloud.

[0053] The degree of discreteness of the detection area surface is suitably represented by the continuous point density of the detection area in the circumferential direction, and the point cloud integrity is judged based on the ratio of the calculated actual point density to the corresponding theoretical point density.

[0054] It is understandable that the point cloud data collected by the radar is spatially disordered, and it is difficult or impossible to directly judge its integrity. The technical solution of the present application can achieve regular reconstruction of disordered and sparse three-dimensional point cloud data by projecting the point cloud onto the surface of a cylindrical detection area with different radii. Not only can the number of point clouds within different distance ranges be quantitatively counted, but it can also assist in determining the specific principles of the point cloud missing problem, thereby improving the accuracy and robustness of defect identification. In order to facilitate the understanding of the technical solution of the present application, each step will be described in detail below.

[0055] In this embodiment, for step S100, radar point cloud data of consecutive T frames can be acquired, and the acquired point cloud data It can be expressed as: , t=1, 2, ..., T; where, represents the i-th point in the point cloud data, Represents the spatial coordinates corresponding to the i-th point.

[0056] It's understandable that after obtaining the required point cloud data, preprocessing is necessary to ensure the accuracy of subsequent integrity assessments. This includes necessary denoising, time synchronization, and registration to eliminate the effects of occasional interference and data fluctuations. Furthermore, the multi-frame data collected provides a more comprehensive picture of the radar's stable output capability and coverage, providing a foundation for subsequent integrity assessments.

[0057] In this embodiment, according to the working principle of the laser radar, the point cloud scanned by the radar presents a form in which the point cloud is denser when the distance from the radar center point is small and the point cloud is sparser when the distance is large. Figure 2 and Figure 3 As shown in the figure, in order to reduce the point distortion caused by the radar sensing range, according to the theoretical maximum sensing radius R of the radar, several sensing ratios are selected to construct corresponding cylindrical areas, namely detection areas, for hierarchical evaluation of the spatial coverage of the point cloud.

[0058] Specifically, each detection area is centered on the radar projection point O, with a radius r=kR and a height equal to the radar installation height H; where k represents the perception ratio of each detection area. The expression for the constructed detection area projected on the ground is: (x-x0) 2 +(y-y0) 2 =r 2 ; Among them, x0 and y0 represent the horizontal and vertical coordinates of the radar center point on the ground, and x and y represent the horizontal and vertical coordinates of the point where the detection area is projected onto the ground.

[0059] It is understandable that the above expression for the detection area projection is for a scenario where the axis of the detection area is aligned along the z-axis. However, in actual use, the radar installation scenario is not necessarily horizontal and may be uneven, or the radar may be installed at an angle. In order to accurately represent the detection area formed in any radar posture, the model expression of the detection area in earth coordinates can be constructed in step S200 as follows:

[0060] ;

[0061] Where r represents the distance from the surface of the detection area to the center point of the radar, that is, the radius of the detection area; θ represents the angle between the radar installation posture and the horizontal direction, and the value range of θ is [0, 2π); x, y, and z represent the three-dimensional coordinates of the point on the surface of the detection area, respectively.

[0062] It should be noted that, when constructing the detection area, in order to ensure the integrity of the point cloud analysis of each detection area, the spacing between adjacent detection areas can be set to be equal in step S200. For example, the spacing between the surfaces of adjacent detection areas is 0.1R or 0.2R, etc., which can be set according to the actual needs of those skilled in the art; in this embodiment, 0.2R is taken as an example, that is, Figure 3 As shown in the figure, there are five detection areas, and the distances between each detection area and the radar center point are 20%, 40%, 60%, 80% and 100% of the radar's maximum perception radius R, respectively. To facilitate the description of subsequent content, these five detection areas can be defined as detection area a, detection area b, detection area c, detection area d and detection area e, respectively.

[0063] In this embodiment, step S300 includes the following specific steps:

[0064] S310: Calculate the distance from each point in the entire point cloud data to the radar center point, and determine the detection area corresponding to each point in the point cloud data based on the calculated distance.

[0065] S320: Construct the direction vector from the radar center point to each point and parameterize it.

[0066] S330: Based on the perpendicular intersection of the direction vector and the corresponding detection area, the parameterized direction vector is substituted into the expression of the detection area for solution to obtain the coordinate position of each point in the entire point cloud data projected on the surface of the corresponding detection area.

[0067] S340: Summarize the coordinates of the projection points on the surface of each detection area to obtain a corresponding projection point set.

[0068] It is understandable that the main purpose of step S300 is to project all point cloud data within different detection areas onto the surface of the corresponding detection area. That is, to calculate the point cloud data for each point p(x p ,y p , z p ) to the radar center O(x0, y0, z0). The detection area in which the point is located can be determined based on the calculated distance D. For example, if the distance D from a point to the radar center is 0.5R, it can be determined that the point is located in detection area a and detection area b. More specifically, points in detection area a also fall within detection areas b, c, d, and e; points in detection area b also fall within detection areas c, d, and e; points in detection area c also fall within detection areas d and e; and points in detection area d also fall within detection area e.

[0069] After completing the area division of the point cloud data, in order to realize the projection of the spatial point to the surface of the detection area, the ray method can be used for projection; specifically, the direction vector pointing from the center point of the radar to point p can be constructed. After that, the perpendicular intersection of the ray and the detection area can be calculated. To facilitate the calculation, the direction vector of the ray can be used to reflect the direction of the ray. Parameterized as:

[0070] .

[0071] in, represents the parametric form of the ray, and λ represents the parameter.

[0072] Will Substituting this into the model expression of the detection area, we can obtain a quadratic equation about the parameter λ:

[0073] .

[0074] Solving the above equation yields the nearest positive real root of λ. Based on this positive real root, the intersection of the ray and the detection area can be calculated, thereby determining the coordinates of the intersection point in the detection area model. This leads to the projection point set of all points within each radius onto the corresponding detection area surface, which is then used for subsequent density and continuity analysis.

[0075] In this embodiment, Figure 4 As shown, step S400 includes the following specific processes:

[0076] S410: Projecting each detection area along the axial direction to form a corresponding plurality of projection circles, and synchronously mapping the projection points on the surface of the detection area to the corresponding projection circles.

[0077] S420: Divide each projection circle into a plurality of sectors of equal area along the circumferential direction, and obtain the total number of points corresponding to each detection area and the number of points corresponding to each sector.

[0078] S430: Calculate the theoretical point density of each detection area according to the total number of points, and calculate the actual point density corresponding to each sector according to the number of points corresponding to the sector.

[0079] S440: Determine the completeness of the point cloud based on the ratio of the actual point density of each sector to the corresponding theoretical point density.

[0080] It is understandable that the integrity of radar point cloud data can be mainly regarded as the uniformity of the distribution of point clouds in the radar radiation direction, or the degree of discreteness of the distribution; then the integrity of radar point cloud data can be obtained by partitioning the detection area in the circumferential direction and calculating the point cloud density of each area separately. Since the detection area is cylindrical as a whole, the calculation of spatial point density can be simplified to represent the plane point density of spatial points mapped to the axial projection circle of the detection area; of course, it can also be simplified to represent the line point density of spatial points mapped to the axial projection circle of the detection area; the specific selection can be made according to the actual needs of those skilled in the art. In this embodiment, it is preferred to use plane point density to judge the integrity of the point cloud. By dividing the radar's perception space into multiple sector-shaped areas, the overall point cloud data is structured. Combined with the subsequent continuity analysis method, the point cloud integrity of each sector can be quickly and effectively evaluated, and the precise positioning of the missing point cloud area can be achieved. This method has the advantages of high computational efficiency and clear spatial positioning, providing a reliable basis for subsequent point cloud completion, anomaly detection and other tasks.

[0081] Specifically, the axial projection circle of the detection area can be divided into M sectors, and the area of ​​each sector is A i =πr2 / M, for the number of points N in each sector i It can be obtained by projecting the point set, then the actual point density corresponding to each sector is For the theoretical point density , the total number of points N corresponding to the entire detection area can be obtained through the projection point set, then the total number of points N and the projection circle area πr corresponding to the detection area 2 The required theoretical point density can be obtained by Finally, the ratio of actual point density to theoretical point density can be used as the evaluation index of integrity, which can be evaluated by the first integrity C i To express, that is The value of M can be selected according to the actual needs of those skilled in the art, for example Figure 4 As shown, the value of M is 36, that is, the central angle corresponding to a single sector is 10°.

[0082] It should be noted that in order to ensure the accuracy of the judgment of the integrity of the sector point cloud, the average integrity of a single sector in multiple consecutive frames can be calculated. After obtaining the average integrity of each sector of the detection area, the calculated average integrity can be used to calculate the average integrity of each sector. With the first threshold set For comparison, the first threshold The specific value of can be selected according to actual needs, for example, it can be 0.8~0.9. Then when the average completeness value is less than the first threshold And the number of points corresponding to sectors in multiple consecutive frames are all less than the minimum theoretical number of points N min When , the sector can be identified as a high missing area, otherwise it can be preliminarily judged that the point cloud integrity of the sector is normal.

[0083] It's important to note that during actual radar point cloud data measurement, refraction and reflection errors may cause slight point jitter. This means that even if the scene remains unchanged, two consecutive frames of radar data may differ. Therefore, simply calculating the average completeness of multiple consecutive frames for a single sector may lead to inconsistent calculations due to point jitter, thus affecting the determination of point cloud integrity.

[0084] In this embodiment, the influence of point jitter is suppressed by jointly calculating the average completeness of multiple sectors. Specifically, the multiple sectors corresponding to the detection area are divided into multiple groups, each group including at least two sectors. The average completeness of each group of sectors in multiple consecutive frames is calculated. If the calculated average completeness is less than the set first threshold, and the number of points corresponding to the sectors in multiple consecutive frames is less than the minimum value of the theoretical number of points, it is determined that there is a high missing area in this group of sectors.

[0085] It should be noted that the specific number of sectors in each group can be selected according to actual needs. For example, three or four sectors can be selected for joint calculation. It should be noted that the more sectors in each group, the better the suppression effect of point jitter in the multi-frame measurement process, but the worse the judgment effect for high-missing areas; therefore, in this embodiment, the number of sectors in each group should not be too many, and it is preferred to use a three-sector joint calculation. In layman's terms, for three consecutive sectors ABC, if the average completeness of a single sector is calculated for sector B, then during the measurement of multiple consecutive frames, some points may jump to sector A or sector C, that is, the points at both ends of sector B will jump to adjacent sectors; if the three sectors ABC are used for joint calculation, then the points in sector B that jump must be located in sector A or sector C, and only the points in sectors A and C at the ends of the joint area will jump to other sectors. That is, the number of points that jump out of the range of multiple sectors remains unchanged compared to a single sector, but the total number of points used for average integrity calculation increases several times, which effectively suppresses the impact of jumping points on the average integrity calculation results.

[0086] It's important to note that the first completeness measure only indicates the overall point cloud distribution or dispersion within the inspection area, and its analysis unit is the sector. Within a sector, adjacent points may differ significantly, which the first completeness measure cannot capture. Therefore, after initially assessing the completeness of the point cloud using the first completeness measure, it's necessary to determine the distance between points within each sector.

[0087] In this embodiment, Figure 5 As shown, the adjacent point distance d and the corresponding standard deviation of the point cloud in each sector can be calculated. For the calculation of the distance d between adjacent points, the phase angle between adjacent points can be calculated by the position coordinates of the corresponding points in the sector, and then the distance d between adjacent points can be calculated according to the arc length calculation formula. It can be calculated by the following formula:

[0088] .

[0089] Among them, n represents the number of adjacent points in the sector, d j represents the distance between the jth adjacent points, Represents the average spacing of all points within a sector.

[0090] After completing the adjacent point spacing d j and the corresponding standard deviation After the calculation of the adjacent point distance d, the obtained j and the corresponding standard deviation and the set second threshold d max and the third threshold If there is a distance between one or more adjacent points in the sector that is greater than the second threshold d max , or the entire standard deviation is greater than the third threshold , then combined with the preliminary judgment result of the first completeness, it can be determined that there is a fault in this sector.

[0091] It should be known that for the second threshold d max and the third threshold The specific value of depends on parameters such as the radar sensor's field of view and resolution. By incorporating the distance change between adjacent points as the core criterion for completeness, this method is more suitable for addressing spatial sparsity issues caused by occlusion and reflections, compared to traditional point density metrics. On a unified projection surface, the geometric relationship between points is more clearly defined, facilitating quantitative analysis of the continuity of the point cloud structure, thereby improving the stability and spatial adaptability of completeness assessment.

[0092] It is understandable that the calculation of the above-mentioned adjacent point spacing and standard deviation is for a single sector, and abnormal endpoint spacing may exist in adjacent sectors. For example, if there are consecutive points a1, a2, a3, a4, and a5 in one sector, and consecutive points b1, b2, b3, b4, and b5 in another adjacent sector. In the former sector, the adjacent point spacings are a1-a2, a2-a3, a3-a4, and a4-a5, and in the latter sector, the adjacent point spacings are b1-b2, b2-b3, b3-b4, and b4-b5. There is also an endpoint spacing a5-b1 between the two sectors. For the above-mentioned point spacings, it is possible that the point spacings a1-a2, a2-a3, a3-a4, a4-a5, b1-b2, b2-b3, b3-b4, and b4-b5 are normal, while the point spacing a5-b1 is abnormal. In this case, if only the distance between adjacent points in a single sector is analyzed, it will not be discovered, which will lead to inaccurate judgment of the integrity of the radar point cloud.

[0093] Therefore, in the technical solution of the present application, the multiple sectors corresponding to the detection area can be divided into multiple areas to be tested, each area to be tested includes multiple continuous sectors, and there is sector overlap between adjacent areas to be tested. That is, the technical solution of the present application adopts a multi-sector sliding window strategy for the calculation of the distance between adjacent points. In layman's terms, a sliding window can be set for the multiple sectors of a single detection area, and the sliding window contains at least two sectors. Then, the distance between adjacent points and the standard deviation can be calculated for the points corresponding to all sectors in the sliding window. After each sliding window calculation is completed, the sliding window can be moved one sector until the calculation of the distance between adjacent points and the standard deviation of all sectors in the detection area is completed.

[0094] It should be noted that the above point cloud integrity judgment is only for a single detection area, that is, it is judged on multiple parts of the radar point cloud radiation range. In order to further improve the accuracy of point cloud integrity judgment, the point cloud integrity can be further judged in all detection areas. The following will be described in detail.

[0095] In this embodiment, step S400 further includes the following process:

[0096] S450: According to the angular resolution of the radar Calculate the theoretical number of points N in each sector using the number of horizontal beams L theory , and the actual number of points in the sector N i and theoretical points N theory The ratio of the second degree of integrity Q i .

[0097] Specifically, , Q i =N i / N theory ;in, Indicates the central angle of the circle corresponding to the sector.

[0098] S460: Calculate the second completeness of the sectors corresponding to the same phase angle in each detection area in order from small to large area radius.

[0099] S470: If the second completeness of a sector corresponding to a certain phase angle does not continuously increase, it indicates that an abnormality exists in the sector corresponding to the phase angle.

[0100] As you can see, the second completeness metric provides the ratio of the actual number of radar points earned within a given sector to the total number of points expected. Theoretically, this ratio should increase with increasing sector radius r. By combining this second completeness metric with the density metric described above, the accuracy of the completeness calculation model can be effectively enhanced.

[0101] It should be noted that if the second integrity of a phase angle sector within a small radius, such as 0.2R or 0.4R, does not reach a set threshold, such as 80%, and the value does not significantly improve subsequently, there are two possibilities for this area. Possibility one: the area is relatively open, so distant points are not reflected back to the radar and data is received; possibility two: there is a problem with the radar or data transmission in this area. For these two possibilities, specific judgments can be made using the aforementioned method for determining the distance between adjacent points.

[0102] Specifically, the specific judgment process for the abnormal situation in step S470 is: the difference d between the distances between two consecutive points in the abnormal sector i+1 -d i Perform calculation. If the calculated difference d i+1 -d i If the calculated difference d is less than the set fourth threshold ε, it means that the point cloud of the sector is continuous and complete, which corresponds to possibility 1. i+1 -d i is greater than the set fourth threshold ε, and the number of points in the sector N i Less than the theoretical minimum number of points N min , the sector is determined to be a missing or abnormal area; for the detected missing or abnormal area, it is necessary to check multiple frames of data over multiple periods of time; if the same missing or abnormal area exists in multiple frames of data over multiple periods of time, it is determined that there is a problem with the data transmission or radar sensor in this area, which corresponds to possibility two.

[0103] The above describes the basic principles, main features, and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-described embodiments. The above-described embodiments and the specification merely illustrate the principles of the present application. Various changes and improvements may be made to the present application without departing from the spirit and scope of the present application. These changes and improvements fall within the scope of the present application for which protection is sought. The scope of protection claimed by the present application is defined by the appended claims and their equivalents.

Claims

1. A method for detecting the integrity of a laser radar point cloud within a service area, characterized in that: The steps include: S100: Acquire multiple frames of radar point cloud data and perform preprocessing; S200: Constructing a plurality of cylindrical detection areas with increasing radii centered on the radar according to the maximum perception radius of the radar; S300: Projecting the point cloud data falling within each detection area onto the surface of the corresponding detection area along the emission direction to obtain a projection point set corresponding to each detection area; S400: Calculating the degree of dispersion of each projection point set on the surface of the corresponding detection area to determine the integrity of the point cloud; The degree of discreteness of the detection area surface is appropriately represented by the density of continuous points in the circumferential direction of the detection area, and the point cloud integrity is judged based on the ratio of the calculated actual point density to the corresponding theoretical point density. Step S400 includes the following specific processes: S410: Projecting each detection area along the axial direction to form a corresponding plurality of projection circles, and simultaneously mapping the projection points on the surface of the detection area to the corresponding projection circles; S420: Divide each projection circle into a plurality of sectors of equal area along the circumferential direction, and obtain the total number of points corresponding to each detection area and the number of points corresponding to each sector; S430: Calculating the theoretical point density of each detection area based on the total number of points, and calculating the actual point density corresponding to each sector based on the number of points corresponding to the sector; S440: Determine the completeness of the point cloud based on the ratio of the actual point density of each sector to the corresponding theoretical point density; In step S400, the ratio of the actual point density to the corresponding theoretical point density is used as the first completeness of the sector; Dividing the multiple sectors corresponding to the detection area into multiple groups, each group including at least one sector; Calculate the average completeness of each group of sectors within multiple consecutive frames; If the calculated average completeness is less than the set first threshold, and the number of points corresponding to sectors in multiple consecutive frames is less than the minimum theoretical number of points, it is determined that a high-missing area exists in the group of sectors.

2. The method for detecting the integrity of a laser radar point cloud within a service area according to claim 1, wherein: In step S200, the model expression of the detection area in the coordinate system is: ； Where x0 and y0 represent the horizontal and vertical coordinates of the radar center point, respectively; r represents the distance from the detection area surface to the radar center point; θ represents the angle between the radar installation posture and the horizontal direction; x, y, and z represent the three-dimensional coordinates of the point on the detection area surface, respectively.

3. The method for detecting the integrity of a laser radar point cloud within a service area according to claim 1, wherein: In step S200 , the intervals between adjacent detection areas are equal.

4. The method for detecting the integrity of a laser radar point cloud within a service area according to claim 3, wherein: There are five detection areas, and the distances between each detection area and the radar center point are 20%, 40%, 60%, 80% and 100% of the radar's maximum perception radius respectively.

5. The method for detecting the integrity of a laser radar point cloud within a service area according to any one of claims 2 to 4, wherein: Step S300 includes the following specific steps: S310: Calculate the distance between each point in the entire point cloud data and the radar center point, and determine the detection area corresponding to each point in the point cloud data based on the calculated distance; S320: Construct the direction vector from the radar center point to each point and parameterize it; S330: Based on the perpendicular intersection of the direction vector and the corresponding detection area, the parameterized direction vector is substituted into the expression of the detection area for solution to obtain the coordinate position of each point in the entire point cloud data projected onto the surface of the corresponding detection area; S340: Summarize the coordinates of the projection points on the surface of each detection area to obtain a corresponding projection point set.

6. The method for detecting the integrity of a laser radar point cloud within a service area according to claim 5, wherein: When determining the integrity of the point cloud in step S400, it is also necessary to determine the distribution of the point cloud within each sector. The specific determination process is as follows: Dividing the multiple sectors corresponding to the detection area into multiple areas to be tested, each area to be tested includes multiple continuous sectors, and there is sector overlap between adjacent areas to be tested; Calculate the distance between adjacent points in the measured area and the corresponding standard deviation; If there is at least one or more adjacent points with a distance greater than the set second threshold, or the calculated standard deviation is greater than the set third threshold, it is determined that a fault exists in the corresponding sector.

7. The method for detecting the integrity of a laser radar point cloud within a service area according to claim 6, wherein: Step S400 also includes the following process: S450: Calculate the theoretical number of points in each sector based on the radar's angular resolution and the number of horizontal beams, and use the ratio of the actual number of points in the sector to the theoretical number of points as the second integrity level; S460: Calculating the second completeness of the sectors corresponding to the same phase angle in each detection area in order from the smallest area radius to the largest area radius; S470: If the second completeness of a sector corresponding to a certain phase angle does not continuously increase, it indicates that an abnormality exists in the sector corresponding to the phase angle.

8. The method for detecting the integrity of a laser radar point cloud within a service area according to claim 7, wherein: The specific process of determining the abnormality in step S470 is as follows: Calculate the difference between the distances between two consecutive points in the abnormal sector; If the calculated difference is less than the set fourth threshold, it means that the point cloud of the sector is continuous and complete; If the calculated difference is greater than the set fourth threshold, and the number of points in the sector is less than the minimum theoretical number of points, the sector is determined to be a missing or abnormal area; Perform difference judgment on multiple consecutive frames of missing or abnormal areas. If the same missing or abnormal area always exists, it is determined that the data transmission or radar sensor in this area is abnormal.

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