Line laser road flatness measurement method and device

Abstract

The application relates to the field of road surface detection, and provides a linear laser road flatness measurement method and device. The method comprises the following steps: converting three-dimensional cross-section data of a wheel track area on a to-be-detected road surface from an image side to an object side; performing measurement posture compensation on the converted data; processing abnormal measurement points in the compensated data; splicing the processed data in a longitudinal direction; correcting abnormal area data in the spliced data; filtering the corrected data; obtaining longitudinal profile data corresponding to a to-be-detected longitudinal profile area of the to-be-detected road surface from the filtered data; and calculating the flatness of the to-be-detected longitudinal profile area based on the longitudinal profile data. The application can effectively remove the influence of road surface cracks, speed bumps, shock lines and the like on the flatness, so as to improve the reliability of the flatness measurement result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of road surface detection, in particular to a line laser road flatness measurement method and device. BACKGROUND

[0002] The road flatness not only affects the driving comfort of drivers and passengers, but also is related to the vibration, running speed, tire friction and wear of the vehicle.

[0003] At present, the mainstream method for measuring the road flatness is to use a point laser range finder combined with an accelerometer, but this measurement method can only obtain the longitudinal profile elevation data of a measuring line along the driving direction of the wheel trace position, and the measurement result is easily affected by road cracks, speed bumps, shock lines and the like, thereby reducing the reliability of the flatness measurement result. SUMMARY

[0004] The embodiments of the present application provide a line laser road flatness measurement method and device to solve the technical problem that the current road flatness measurement method can only obtain the longitudinal profile elevation data of a measuring line along the driving direction of the wheel trace position, and the measurement result is easily affected by road cracks, speed bumps, shock lines and the like, thereby reducing the reliability of the flatness measurement result.

[0005] In a first aspect, the embodiments of the present application provide a line laser road flatness measurement method, comprising:

[0006] Converting three-dimensional cross-sectional data of a wheel trace area on a to-be-measured road surface from an image side to an object side to obtain initial object side cross-sectional data;

[0007] Performing measurement posture compensation on the initial object side cross-sectional data to obtain compensated object side cross-sectional data;

[0008] Processing abnormal measurement points in the compensated object side cross-sectional data to obtain target object side cross-sectional data;

[0009] Splicing the target object side cross-sectional data along the longitudinal direction to obtain three-dimensional road surface data of the wheel trace area;

[0010] Correcting abnormal area data in the three-dimensional road surface data to obtain three-dimensional corrected data of the wheel trace area;

[0011] Filtering the three-dimensional corrected data to obtain road surface trend surface data of the wheel trace area;

[0012] Obtaining longitudinal profile data corresponding to a to-be-measured longitudinal profile area of the to-be-measured road surface from the road surface trend surface data, and calculating the flatness of the to-be-measured longitudinal profile area based on the longitudinal profile data.

[0013] In one embodiment, the three-dimensional cross-section data is acquired based on the following manner:

[0014] acquiring three-dimensional cross-section data of the wheel track area by using a line scanning three-dimensional measurement sensor;

[0015] the line scanning three-dimensional measurement sensor comprises a laser and a three-dimensional camera;

[0016] the laser is configured to project a laser line along the width direction of the road surface to be measured;

[0017] the three-dimensional camera is configured to acquire three-dimensional cross-section data of the wheel track area corresponding to the laser line.

[0018] In one embodiment, the installation height of the line scanning three-dimensional measurement sensor is less than or equal to 500 mm, and the measurement width is less than or equal to 500 mm;

[0019] the lateral sampling interval of the three-dimensional cross-section data is greater than or equal to 0.1 mm and less than or equal to 0.5 mm, the longitudinal sampling interval is greater than or equal to 1 mm and less than or equal to 6 mm, and the sampling accuracy in the height direction is better than 0.2 mm;

[0020] the wheel track area comprises a left wheel track area and / or a right wheel track area, and any wheel track area corresponds to an independently installed line scanning three-dimensional measurement sensor.

[0021] In one embodiment, the abnormal area data in the three-dimensional road surface data is corrected to obtain three-dimensional corrected data of the wheel track area, comprising:

[0022] detecting the abnormal area data in the three-dimensional road surface data;

[0023] correcting the abnormal area data based on the data on both sides of the abnormal area data to obtain three-dimensional corrected data of the wheel track area.

[0024] In one embodiment, the detection of the abnormal area data in the three-dimensional road surface data comprises:

[0025] calculating the aspect ratio of the longitudinal sampling interval and the lateral sampling interval of the three-dimensional cross-section data to obtain an aspect ratio;

[0026] taking the aspect ratio upward to obtain a lateral compression ratio;

[0027] based on the lateral compression ratio, the three-dimensional road surface data is compressed laterally to obtain compressed road surface data;

[0028] based on a target detection method, a target abnormal feature in the compressed road surface data is detected to obtain compressed abnormal data;

[0029] Based on the lateral compression ratio, the region data corresponding to the compressed abnormal data in the three-dimensional road surface data is determined, and the abnormal region data is obtained.

[0030] In one embodiment, after obtaining the three-dimensional correction data of the wheel track region, the process includes:

[0031] Obtain the target cross-sectional data from the three-dimensional correction data;

[0032] Based on the target cross-section data, the target construction depth of the target cross-section is obtained using the target construction depth calculation method.

[0033] The target cross-sectional data is the data within a circular region centered on the center point of the target cross-section and with a radius equal to half the length of the data calculation unit required by the target construction depth calculation method.

[0034] Secondly, embodiments of this application provide a linear laser road surface evenness measuring device, comprising:

[0035] The data conversion module is used to convert the three-dimensional cross-sectional data of the wheel track area on the road surface to be measured from the image plane to the object plane, so as to obtain the initial cross-sectional data of the object plane.

[0036] The attitude compensation module is used to: measure and compensate the initial cross-sectional data of the object to obtain compensated cross-sectional data of the object.

[0037] The anomaly handling module is used to process the abnormal measurement points in the object compensation cross-section data to obtain the object target cross-section data.

[0038] The data stitching module is used to stitch the cross-sectional data of the object along the longitudinal direction to obtain the three-dimensional road surface data of the wheel track area;

[0039] The data correction module is used to: correct abnormal area data in the three-dimensional road surface data to obtain three-dimensional corrected data of the wheel track area;

[0040] The data filtering module is used to: filter the three-dimensional correction data to obtain the road surface trend data of the wheel track area;

[0041] The smoothness calculation module is used to: obtain the longitudinal contour data corresponding to the longitudinal contour area of ​​the road surface to be tested from the road trend surface data, and calculate the smoothness of the longitudinal contour area to be tested based on the longitudinal contour data.

[0042] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the program to implement the steps of the line laser pavement evenness measurement method described in the first aspect.

[0043] Fourthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the line laser pavement smoothness measurement method described in the first aspect.

[0044] Fifthly, embodiments of this application provide a non-transitory computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the line laser pavement evenness measurement method described in the first aspect.

[0045] The linear laser pavement smoothness measurement method and apparatus provided in this application converts the three-dimensional cross-sectional data of the wheel track area on the pavement to be measured from the image side to the object side to obtain the initial cross-sectional data of the object side. Measurement attitude compensation is performed on the initial cross-sectional data of the object side to obtain the compensated cross-sectional data of the object side. Abnormal measurement points in the compensated cross-sectional data of the object side are processed to obtain the target cross-sectional data of the object side. The target cross-sectional data of the object side are stitched together along the longitudinal direction to obtain the three-dimensional pavement data of the wheel track area. Abnormal area data in the three-dimensional pavement data are corrected to obtain the three-dimensional corrected data of the wheel track area. The three-dimensional corrected data is filtered to obtain the pavement trend surface data of the wheel track area. The longitudinal contour data corresponding to the longitudinal contour area of ​​the pavement to be measured is obtained from the pavement trend surface data. The smoothness of the longitudinal contour area of ​​the pavement to be measured is calculated based on the longitudinal contour data. This application performs image-object conversion, attitude compensation, anomaly processing, data evaluation, data correction, and data filtering on the three-dimensional cross-sectional data of the wheel track area of ​​the road surface to be tested before calculating the smoothness. By performing the above series of processing on the planar contour data of the wheel track, the influence of road surface cracks, speed bumps, rumble strips, etc. on the smoothness is effectively removed, thereby improving the reliability of the smoothness measurement results. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is one of the flowcharts illustrating the line laser pavement smoothness measurement method provided in the embodiments of this application;

[0048] Figure 2This is the second schematic flowchart of the line laser pavement smoothness measurement method provided in the embodiments of this application;

[0049] Figure 3 This is the third flowchart illustrating the linear laser pavement smoothness measurement method provided in this application embodiment;

[0050] Figure 4 This is the fourth flowchart illustrating the linear laser pavement smoothness measurement method provided in this application embodiment;

[0051] Figure 5 This is a schematic diagram of the structure of the line laser road surface smoothness measuring device provided in the embodiments of this application;

[0052] Figure 6 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0054] It should be noted that in the description of the embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The terms "upper," "lower," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly, for example, they can be fixed connections, detachable connections, or integral connections; they can be mechanical connections or electrical connections; they can be direct connections or indirect connections through an intermediate medium; and they can be internal connections between two elements. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0055] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, a first object can be one or more. Furthermore, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects have an "or" relationship.

[0056] Figure 1 This is one of the flowcharts illustrating the line laser pavement smoothness measurement method provided in this application embodiment. (Refer to...) Figure 1 This application provides a method for measuring the smoothness of a road surface using a linear laser, which may include:

[0057] 101. Convert the three-dimensional cross-sectional data of the wheel track area on the road surface to be measured from the image side to the object side to obtain the initial cross-sectional data of the object side;

[0058] 102. Perform attitude compensation on the initial cross-sectional data of the object to obtain the compensated cross-sectional data of the object.

[0059] 103. Process the abnormal measuring points in the cross-sectional data of the object compensation section to obtain the target cross-sectional data of the object.

[0060] 104. The cross-sectional data of the target object are stitched together along the longitudinal direction to obtain the three-dimensional road surface data of the wheel track area;

[0061] 105. Correct the abnormal area data in the three-dimensional road surface data to obtain the three-dimensional corrected data of the wheel track area;

[0062] 106. Filter the three-dimensional correction data to obtain the road surface trend data of the wheel track area;

[0063] 107. Obtain the longitudinal contour data corresponding to the longitudinal contour area of ​​the road surface to be tested from the road trend surface data, and calculate the smoothness of the longitudinal contour area to be tested based on the longitudinal contour data.

[0064] In step 101, the mapping relationship between the three-dimensional cross-sectional data in image space and object space can be obtained through the calibration file, and the three-dimensional cross-sectional data can be converted from image space to object space through the mapping relationship.

[0065] In step 102, the measurement attitude noise of the initial cross-sectional data of the object can be compensated based on the measurement attitude information and installation parameters of the measuring device that acquires the three-dimensional cross-sectional data, thereby obtaining the compensated cross-sectional data of the object.

[0066] In step 103, abnormal measurement points in the cross-sectional data of the object compensation can be removed or smoothed to eliminate or reduce the impact of these points on the data.

[0067] In step 104, the cross-sectional data of the object target are stitched together along the driving direction to form three-dimensional road surface data of the wheel track area.

[0068] In step 105, the abnormal area data may include road surface crack area data, speed bump area data, rumble strip area data, etc. Correcting these areas can effectively remove the influence of these area data on the smoothness calculation.

[0069] In step 106, the 3D correction data is filtered to further remove noise from the data and ensure data quality.

[0070] In step 107, the flatness of the longitudinal contour area to be measured can be calculated using any flatness calculation method, and there is no limitation here. In this embodiment, the flatness of the longitudinal contour area to be measured can be calculated using the International Flatness Index Formula.

[0071] The linear laser pavement smoothness measurement method provided in this embodiment converts the three-dimensional cross-sectional data of the wheel track area on the pavement to be measured from the image side to the object side to obtain the initial cross-sectional data of the object side. Measurement attitude compensation is performed on the initial cross-sectional data of the object side to obtain the compensated cross-sectional data of the object side. Abnormal measurement points in the compensated cross-sectional data of the object side are processed to obtain the target cross-sectional data of the object side. The target cross-sectional data of the object side are stitched together along the longitudinal direction to obtain the three-dimensional pavement data of the wheel track area. Abnormal area data in the three-dimensional pavement data are corrected to obtain the three-dimensional corrected data of the wheel track area. The three-dimensional corrected data is filtered to obtain the pavement trend surface data of the wheel track area. The longitudinal contour data corresponding to the longitudinal contour area of ​​the pavement to be measured is obtained from the pavement trend surface data. The smoothness of the longitudinal contour area of ​​the pavement to be measured is calculated based on the longitudinal contour data. In this embodiment, the three-dimensional cross-sectional data of the wheel track area of ​​the road surface to be tested are processed by image-object conversion, attitude compensation, anomaly handling, data evaluation, data correction, and data filtering before the smoothness calculation is performed. By performing the above series of processing on the planar contour data of the wheel track, the influence of road surface cracks, speed bumps, rumble strips, etc. on smoothness is effectively removed, thereby improving the reliability of the smoothness measurement results.

[0072] In one embodiment, three-dimensional cross-sectional data can be obtained in the following manner:

[0073] Three-dimensional cross-sectional data of the wheel track region were acquired using a line-scan three-dimensional measurement sensor.

[0074] Line-scan 3D measurement sensors include lasers and 3D cameras;

[0075] The laser is used to project a laser line along the width of the road surface to be tested;

[0076] A 3D camera is used to acquire 3D cross-sectional data of the region corresponding to the laser line's wheel track.

[0077] The width direction is the cross-sectional direction. Any line-scanning 3D measurement sensor can obtain the elevation data of a cross-section of the wheel track area corresponding to the laser line in a single measurement. The line-scanning 3D measurement sensor can move along the driving direction and sample to obtain the elevation data of multiple cross-sections of the wheel track area along the wheel track extension direction.

[0078] The installation height of the line scan 3D measurement sensor is less than or equal to 500 mm, and the measurement width is less than or equal to 500 mm. In this embodiment, the measurement width can be 350 mm.

[0079] The lateral sampling interval of the three-dimensional cross-sectional data is greater than or equal to 0.1 mm and less than or equal to 0.5 mm. Further, the lateral sampling interval can be greater than or equal to 0.1 mm and less than or equal to 0.2 mm. More specifically, the lateral sampling interval can be 0.15 mm.

[0080] The longitudinal sampling interval of the three-dimensional cross-sectional data, that is, the sampling interval between adjacent cross-sections, is greater than or equal to 1 mm and less than or equal to 6 mm. Further, the longitudinal sampling interval can be greater than or equal to 1 mm and less than or equal to 5 mm. More specifically, the longitudinal sampling interval can be 5 mm.

[0081] The sampling accuracy of the three-dimensional cross-sectional data in the elevation direction is better than 0.2 mm, and further, this sampling accuracy can be better than 0.1 mm, or even better than 0.05 mm.

[0082] The wheel track region includes the left wheel track region and / or the right wheel track region, and each wheel track region corresponds to an independently installed line scan three-dimensional measurement sensor.

[0083] In traditional three-dimensional road surface measurement systems, in order to obtain full-width three-dimensional road surface data, the measurement system is usually installed at a high position, which poses a certain challenge to the accurate compensation of the measurement posture. In addition, the data acquisition accuracy is relatively low, with the lateral sampling interval usually only 1 mm and the elevation accuracy usually only 0.3 mm.

[0084] In this embodiment, the installation height of the line-scan 3D measurement sensor is less than or equal to 500 mm, which is significantly lower than the installation height of traditional measurement systems. This reduces the difficulty of compensating for the measurement posture, enables accurate compensation of the measurement posture, and improves data acquisition accuracy. The lateral sampling interval is increased from 1 mm to 0.15 mm, and the elevation accuracy is increased from 0.3 mm to 0.05 mm, providing a good data foundation for subsequent road surface smoothness calculation.

[0085] Figure 2 This is the second schematic flowchart of the line laser pavement smoothness measurement method provided in the embodiments of this application. (Refer to...) Figure 2 In one embodiment, correcting abnormal area data in three-dimensional road surface data to obtain three-dimensional corrected data of wheel track areas may include:

[0086] 201. Detect abnormal areas in 3D road surface data;

[0087] 202. Based on the data from both sides of the abnormal region, the abnormal region data is corrected to obtain the three-dimensional corrected data of the wheel track region.

[0088] Abnormal area data refers to data from road surface crack areas, speed bump areas, and rumble strip areas that affect the calculation of smoothness. The data on both sides of the abnormal area data can be data from the left and right sides adjacent to the abnormal area. Since this part of the data is located outside the abnormal area, it is relatively normal data compared to the abnormal area data, and it is closer to the abnormal area and has regional similarity. Therefore, this part of normal data can be used to correct the abnormal area data.

[0089] This embodiment detects and identifies abnormal region data, and uses the region data on both sides of the abnormal region as reference data to correct the abnormal region data, thereby obtaining three-dimensional corrected data with the maximum regional similarity to the original abnormal region data after the abnormality is eliminated.

[0090] Figure 3 This is the third schematic flowchart of the line laser pavement smoothness measurement method provided in the embodiments of this application. (Refer to...) Figure 3 In one embodiment, detecting abnormal area data in three-dimensional road surface data may include:

[0091] 301. Calculate the ratio of the longitudinal sampling interval to the transverse sampling interval of the three-dimensional cross-sectional data to obtain the aspect ratio;

[0092] 302. Round the aspect ratio up to obtain the lateral compression ratio;

[0093] 303. Based on the lateral compression ratio, the three-dimensional road surface data is laterally compressed to obtain compressed road surface data;

[0094] 304. Based on the target detection method, the abnormal features of targets in the compressed road surface data are detected to obtain compressed abnormal data;

[0095] 305. Based on the lateral compression ratio, determine the corresponding regional data of the abnormal data in the three-dimensional road surface data after compression, and obtain the abnormal region data.

[0096] In step 303, due to the huge difference in horizontal and vertical resolution in the original three-dimensional cross-sectional data, the aspect ratio of the three-dimensional road surface data will be severely imbalanced. Therefore, it is necessary to perform lateral compression on the three-dimensional road surface data to alleviate the problem of aspect ratio imbalance.

[0097] In step 304, the target detection method can be image segmentation, target matching, target extension and denoising, etc. The target anomaly features can be local elevation change features, continuous linear features or network features for road crack areas, or surface elevation change features, geometric size features, surface texture distribution features for speed bump areas, etc.

[0098] Target detection methods can identify the above-mentioned abnormal features of targets, thereby detecting compressed abnormal data, such as data on road surface crack areas and speed bump areas.

[0099] It should be noted that, due to the small measurement width of three-dimensional cross-sectional data, in order to prevent false detections, after detecting the abrupt changes in surface elevation and geometric dimensions, the apparent texture distribution characteristics can be further detected to improve the detection rate of speed bump area data.

[0100] In step 305, the compressed abnormal data is converted back to its original scale to obtain uncompressed abnormal region data.

[0101] This embodiment alleviates the problem of aspect ratio imbalance by laterally compressing the three-dimensional road surface data. Then, based on the characteristics of abnormal areas, a targeted detection method is adopted to identify the abnormal data after compression from the compressed road surface data. Finally, the compressed abnormal data is converted into abnormal area data with the original ratio, so as to achieve rapid and accurate detection of abnormal area data.

[0102] Figure 4 This is the fourth flowchart illustrating the line laser pavement smoothness measurement method provided in this application. (Refer to...) Figure 4 In one embodiment, after obtaining the three-dimensional correction data of the wheel track region, the following may be included:

[0103] 401. Obtain the target cross-sectional data from the three-dimensional correction data;

[0104] 402. Based on the target cross-section data, the target construction depth of the target cross-section is obtained using the target construction depth calculation method.

[0105] The target cross-section data is the data within a circular area centered on the center point of the target cross-section and with a radius equal to half the length of the data calculation unit required by the target construction depth calculation method.

[0106] In step 401, data corresponding to any cross section in the three-dimensional correction data can be obtained.

[0107] In step 402, the target construction depth can be calculated using MPD (Mean profile depth), MTD (Mean Texture Depth), or SMTD (Sensor Measured Texture Depth), resulting in three methods for calculating the cross-sectional construction depth:

[0108] 1. For any cross section, obtain the three-dimensional correction data within a circular area with its center point as the center point and half the length of the data calculation unit required by the MPD calculation method, for example, a radius of 50 mm, to obtain the target cross section data. Then, use the target cross section data in combination with the MPD calculation method to obtain the construction depth of the cross section.

[0109] 2. For any cross section, obtain the three-dimensional correction data within a circular area with its center point as the center point and half the length of the data calculation unit required by the MTD calculation method, for example, a radius of 50 mm, to obtain the target cross section data. Using this target cross section data in combination with the MTD calculation method, obtain the construction depth of the cross section.

[0110] 3. For any cross section, obtain the three-dimensional correction data within a circular area with its center point as the center point and half the length of the data calculation unit required by the SMTD calculation method, for example, a radius of 150 mm, to obtain the target cross section data. Then, use the target cross section data in combination with the SMTD calculation method to obtain the construction depth of the cross section.

[0111] The structural depth of the wheel track region can be obtained by combining the structural depths of all cross-sections of the wheel track region.

[0112] This embodiment obtains the data required for different structural depth calculation methods from the three-dimensional correction data of multiple cross sections. This data can be combined with the corresponding structural depth calculation methods to obtain the structural depth of multiple cross sections under different calculation methods. Furthermore, these structural depths can be integrated to obtain the structural depth of the wheel track region under different calculation methods.

[0113] In one embodiment, the line laser pavement smoothness measurement system used in the line laser pavement smoothness measurement method is described below:

[0114] The system uses a measuring vehicle as a measuring platform, which is equipped with a power supply module, a positioning module, and a control module.

[0115] The measurement vehicle can be a small or medium-sized passenger car, a small or medium-sized truck, or a sports utility vehicle as the platform.

[0116] The power supply module includes an integrated power supply unit that accepts at least one power source from mains power, generator power, and battery power, and outputs this power to provide reliable and stable power to the entire measurement system. The output of the integrated power supply unit is divided into DC and AC components, which are respectively output to corresponding junction boxes to power different devices.

[0117] Meanwhile, the integrated power supply unit also monitors the power operation status of the system and sends relevant operating parameters to the control module so that the control module can cut off the power system and issue an alarm when the operating parameters are abnormal, and then control the integrated power supply unit to complete the selection, conversion and distribution of electrical energy again.

[0118] The positioning module includes an optoelectronic encoder and a GNSS receiver. The optoelectronic encoder is installed on the wheels of the measurement vehicle to obtain the detection mileage information of the measurement vehicle. The GNSS receiver is installed on the top of the measurement vehicle to obtain the GNSS signal of the measurement vehicle. The detection mileage information and GNSS information are fused and input into the synchronization control module to generate time and space references, providing data fusion conditions for the measurement data.

[0119] The line-scan 3D measurement sensor is installed in front of the front wheel, behind the rear wheel, or between the front and rear wheels, along the direction of the wheel tracks of the measurement vehicle.

[0120] Any line-scan 3D measurement sensor includes a laser, a 3D camera, an attitude measurement sensor, and a controller. The laser is used to project a laser line along the width of the road surface to be measured. The 3D camera is used to acquire the elevation data of the road cross section of the wheel track area corresponding to the laser line using the principle of triangulation. The attitude measurement sensor is used to acquire the measurement attitude information of the line-scan 3D measurement sensor. The controller is used to control the coordinated operation of the laser, the 3D camera, and the attitude measurement sensor.

[0121] The following describes the line laser pavement smoothness measuring device provided in the embodiments of this application. The line laser pavement smoothness measuring device described below can be referred to in correspondence with the line laser pavement smoothness measuring method described above.

[0122] Figure 5 This is a schematic diagram of the structure of the line laser road surface evenness measuring device provided in an embodiment of this application. (Refer to...) Figure 5 This application provides a linear laser road surface evenness measuring device, which may include:

[0123] Data conversion module 501 is used to convert the three-dimensional cross-sectional data of the wheel track area on the road surface to be measured from the image to the object, so as to obtain the initial cross-sectional data of the object.

[0124] The attitude compensation module 502 is used to: measure and compensate the initial cross-sectional data of the object to obtain compensated cross-sectional data of the object.

[0125] Anomaly processing module 503 is used to: process the abnormal measurement points in the object compensation cross section data to obtain object target cross section data;

[0126] Data stitching module 504 is used to: stitch the cross-sectional data of the object along the longitudinal direction to obtain the three-dimensional road surface data of the wheel track area;

[0127] Data correction module 505 is used to: correct abnormal area data in the three-dimensional road surface data to obtain three-dimensional corrected data of the wheel track area;

[0128] Data filtering module 506 is used to: filter the three-dimensional correction data to obtain the road surface trend data of the wheel track area;

[0129] The smoothness calculation module 507 is used to: obtain the longitudinal contour data corresponding to the longitudinal contour area of ​​the road surface to be tested from the road trend surface data, and calculate the smoothness of the longitudinal contour area to be tested based on the longitudinal contour data.

[0130] The linear laser pavement smoothness measuring device provided in this embodiment converts the three-dimensional cross-sectional data of the wheel track area on the pavement to be measured from the image side to the object side to obtain the initial cross-sectional data of the object side. It then performs measurement attitude compensation on the initial cross-sectional data of the object side to obtain the compensated cross-sectional data of the object side. It processes the abnormal measurement points in the compensated cross-sectional data of the object side to obtain the target cross-sectional data of the object side. It then stitches the target cross-sectional data of the object side along the longitudinal direction to obtain the three-dimensional pavement data of the wheel track area. It then corrects the abnormal area data in the three-dimensional pavement data to obtain the three-dimensional corrected data of the wheel track area. Finally, it filters the three-dimensional corrected data to obtain the pavement trend surface data of the wheel track area. It then obtains the longitudinal contour data corresponding to the longitudinal contour area of ​​the pavement to be measured from the pavement trend surface data and calculates the smoothness of the longitudinal contour area of ​​the pavement to be measured based on the longitudinal contour data. In this embodiment, the three-dimensional cross-sectional data of the wheel track area of ​​the road surface to be tested are processed by image-object conversion, attitude compensation, anomaly handling, data evaluation, data correction, and data filtering before the smoothness calculation is performed. By performing the above series of processing on the planar contour data of the wheel track, the influence of road surface cracks, speed bumps, rumble strips, etc. on smoothness is effectively removed, thereby improving the reliability of the smoothness measurement results.

[0131] In one embodiment, the three-dimensional cross-sectional data is obtained based on the following method:

[0132] Three-dimensional cross-sectional data of the wheel track region were acquired using a line-scan three-dimensional measurement sensor.

[0133] The line-scan three-dimensional measurement sensor includes a laser and a three-dimensional camera;

[0134] The laser is used to project laser lines along the width of the road surface to be tested;

[0135] The 3D camera is used to acquire 3D cross-sectional data of the region corresponding to the laser line's wheel track.

[0136] In one embodiment, the installation height of the line-scan three-dimensional measurement sensor is less than or equal to 500 mm, and the measurement width is less than or equal to 500 mm.

[0137] The horizontal sampling interval of the three-dimensional cross-sectional data is greater than or equal to 0.1 mm and less than or equal to 0.5 mm, the vertical sampling interval is greater than or equal to 1 mm and less than or equal to 6 mm, and the sampling accuracy in the elevation direction is better than 0.2 mm.

[0138] The wheel track region includes a left wheel track region and / or a right wheel track region, and each wheel track region corresponds to an independently installed line scan three-dimensional measurement sensor.

[0139] In one embodiment, the data correction module 505 is specifically used for:

[0140] Detect abnormal areas in the three-dimensional road surface data;

[0141] Based on the data from both sides of the abnormal region data, the abnormal region data is corrected to obtain the three-dimensional corrected data of the wheel track region.

[0142] In one embodiment, the data correction module 505 is specifically used for:

[0143] The aspect ratio is obtained by calculating the ratio of the longitudinal sampling interval to the transverse sampling interval of the three-dimensional cross-sectional data.

[0144] The aspect ratio is rounded up to obtain the lateral compression ratio;

[0145] Based on the lateral compression ratio, the three-dimensional road surface data is laterally compressed to obtain compressed road surface data;

[0146] Based on the target detection method, the target anomaly features in the compressed road surface data are detected to obtain compressed anomaly data;

[0147] Based on the lateral compression ratio, the region data corresponding to the compressed abnormal data in the three-dimensional road surface data is determined, and the abnormal region data is obtained.

[0148] In one embodiment, a depth calculation module (not shown) is further included for:

[0149] Obtain the target cross-sectional data from the three-dimensional correction data;

[0150] Based on the target cross-section data, the target construction depth of the target cross-section is obtained using the target construction depth calculation method.

[0151] The target cross-sectional data is the data within a circular region centered on the center point of the target cross-section and with a radius equal to half the length of the data calculation unit required by the target construction depth calculation method.

[0152] Figure 6 is a schematic diagram of the structure of the electronic device provided in an embodiment of this application, as shown below. Figure 6 As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other via the communication bus 640. The processor 610 can call the computer program in the memory 630 to execute the steps of the linear laser pavement evenness measurement method, such as including:

[0153] The three-dimensional cross-sectional data of the wheel track area on the road surface to be tested is converted from the image to the object to obtain the initial cross-sectional data of the object.

[0154] The initial cross-sectional data of the object are measured and attitude compensation is performed to obtain the compensated cross-sectional data of the object.

[0155] The abnormal measurement points in the object compensation cross-sectional data are processed to obtain the object target cross-sectional data.

[0156] The cross-sectional data of the object are stitched together along the longitudinal direction to obtain the three-dimensional road surface data of the wheel track area;

[0157] The abnormal area data in the three-dimensional road surface data is corrected to obtain the three-dimensional corrected data of the wheel track area;

[0158] The three-dimensional correction data is filtered to obtain the road surface trend data of the wheel track area;

[0159] The longitudinal contour data corresponding to the longitudinal contour region of the road surface to be tested is obtained from the road surface trend surface data, and the smoothness of the longitudinal contour region to be tested is calculated based on the longitudinal contour data.

[0160] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a 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 part 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.

[0161] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the steps of the linear laser pavement evenness measurement method provided in the above embodiments, such as including:

[0162] The three-dimensional cross-sectional data of the wheel track area on the road surface to be tested is converted from the image to the object to obtain the initial cross-sectional data of the object.

[0163] The initial cross-sectional data of the object are measured and attitude compensation is performed to obtain the compensated cross-sectional data of the object.

[0164] The abnormal measurement points in the object compensation cross-sectional data are processed to obtain the object target cross-sectional data.

[0165] The cross-sectional data of the object are stitched together along the longitudinal direction to obtain the three-dimensional road surface data of the wheel track area;

[0166] The abnormal area data in the three-dimensional road surface data is corrected to obtain the three-dimensional corrected data of the wheel track area;

[0167] The three-dimensional correction data is filtered to obtain the road surface trend data of the wheel track area;

[0168] The longitudinal contour data corresponding to the longitudinal contour region of the road surface to be tested is obtained from the road surface trend surface data, and the smoothness of the longitudinal contour region to be tested is calculated based on the longitudinal contour data.

[0169] On the other hand, embodiments of this application also provide a non-transitory computer-readable storage medium storing a computer program thereon, the computer program being used to cause a processor to execute the steps of the line laser pavement evenness measurement method provided in the above embodiments, for example including:

[0170] The three-dimensional cross-sectional data of the wheel track area on the road surface to be tested is converted from the image to the object to obtain the initial cross-sectional data of the object.

[0171] The initial cross-sectional data of the object are measured and attitude compensation is performed to obtain the compensated cross-sectional data of the object.

[0172] The abnormal measurement points in the object compensation cross-sectional data are processed to obtain the object target cross-sectional data.

[0173] The cross-sectional data of the object are stitched together along the longitudinal direction to obtain the three-dimensional road surface data of the wheel track area;

[0174] The abnormal area data in the three-dimensional road surface data is corrected to obtain the three-dimensional corrected data of the wheel track area;

[0175] The three-dimensional correction data is filtered to obtain the road surface trend data of the wheel track area;

[0176] The longitudinal contour data corresponding to the longitudinal contour region of the road surface to be tested is obtained from the road surface trend surface data, and the smoothness of the longitudinal contour region to be tested is calculated based on the longitudinal contour data.

[0177] The non-transitory computer-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0178] The device embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0179] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0180] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications 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.

Claims

1. A method for measuring road surface smoothness using linear laser, characterized in that, include: The three-dimensional cross-sectional data of the wheel track area on the road surface to be tested is converted from the image to the object to obtain the initial cross-sectional data of the object. The initial cross-sectional data of the object are measured and attitude compensation is performed to obtain the compensated cross-sectional data of the object. The abnormal measurement points in the object compensation cross-sectional data are processed to obtain the object target cross-sectional data. The cross-sectional data of the object are stitched together along the longitudinal direction to obtain the three-dimensional road surface data of the wheel track area; The abnormal area data in the three-dimensional road surface data is corrected to obtain the three-dimensional corrected data of the wheel track area, including: Detecting abnormal areas in the three-dimensional road surface data includes: The aspect ratio is obtained by calculating the ratio of the longitudinal sampling interval to the transverse sampling interval of the three-dimensional cross-sectional data. The aspect ratio is rounded up to obtain the lateral compression ratio; Based on the lateral compression ratio, the three-dimensional road surface data is laterally compressed to obtain compressed road surface data; Based on the target detection method, the target anomaly features in the compressed road surface data are detected to obtain compressed anomaly data; Based on the lateral compression ratio, the region data corresponding to the compressed abnormal data in the three-dimensional road surface data is determined, and the abnormal region data is obtained. Based on the data from both sides of the abnormal region data, the abnormal region data is corrected to obtain the three-dimensional corrected data of the wheel track region. The three-dimensional correction data is filtered to obtain the road surface trend data of the wheel track area; The longitudinal contour data corresponding to the longitudinal contour region of the road surface to be tested is obtained from the road surface trend surface data, and the smoothness of the longitudinal contour region to be tested is calculated based on the longitudinal contour data.

2. The method for measuring pavement smoothness using linear laser as described in claim 1, characterized in that, The three-dimensional cross-sectional data was obtained in the following way: Three-dimensional cross-sectional data of the wheel track region were acquired using a line-scan three-dimensional measurement sensor. The line-scan three-dimensional measurement sensor includes a laser and a three-dimensional camera; The laser is used to project laser lines along the width of the road surface to be tested; The 3D camera is used to acquire 3D cross-sectional data of the region corresponding to the laser line's wheel track.

3. The method for measuring pavement smoothness using linear laser as described in claim 2, characterized in that, The installation height of the line-scan three-dimensional measurement sensor is less than or equal to 500 mm, and the measurement width is less than or equal to 500 mm. The horizontal sampling interval of the three-dimensional cross-sectional data is greater than or equal to 0.1 mm and less than or equal to 0.5 mm, the vertical sampling interval is greater than or equal to 1 mm and less than or equal to 6 mm, and the sampling accuracy in the elevation direction is better than 0.2 mm. The wheel track region includes a left wheel track region and / or a right wheel track region, and each wheel track region corresponds to an independently installed line scan three-dimensional measurement sensor.

4. The method for measuring pavement smoothness using linear laser as described in claim 1, characterized in that, After obtaining the three-dimensional correction data of the wheel track region, the process includes: Obtain the target cross-sectional data from the three-dimensional correction data; Based on the target cross-section data, the target construction depth of the target cross-section is obtained using the target construction depth calculation method. The target cross-sectional data is the data within a circular region centered on the center point of the target cross-section and with a radius equal to half the length of the data calculation unit required by the target construction depth calculation method.

5. A linear laser road surface evenness measuring device, characterized in that, The method for performing the line laser pavement smoothness measurement as described in claim 1 includes: The data conversion module is used to convert the three-dimensional cross-sectional data of the wheel track area on the road surface to be measured from the image plane to the object plane, so as to obtain the initial cross-sectional data of the object plane. The attitude compensation module is used to: measure and compensate the initial cross-sectional data of the object to obtain compensated cross-sectional data of the object. The anomaly handling module is used to process the abnormal measurement points in the object compensation cross-section data to obtain the object target cross-section data. The data stitching module is used to stitch the cross-sectional data of the object along the longitudinal direction to obtain the three-dimensional road surface data of the wheel track area; The data correction module is used to: correct abnormal area data in the three-dimensional road surface data to obtain three-dimensional corrected data of the wheel track area; The data filtering module is used to: filter the three-dimensional correction data to obtain the road surface trend data of the wheel track area; The smoothness calculation module is used to: obtain the longitudinal contour data corresponding to the longitudinal contour area of ​​the road surface to be tested from the road trend surface data, and calculate the smoothness of the longitudinal contour area to be tested based on the longitudinal contour data.

6. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the line laser pavement smoothness measurement method according to any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the line laser pavement smoothness measurement method according to any one of claims 1 to 4.

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