Line laser pavement flatness measuring method and device

Abstract

The invention relates to the field of road surface detection, and provides a line laser road surface flatness measuring method and device. The method comprises the following steps: converting three-dimensional cross section data of a wheel track area on a road surface to be measured from an image space to an object space; performing measurement attitude compensation on the converted data; processing the abnormal measuring points in the compensated data; splicing the processed data along the longitudinal direction; correcting the abnormal region data in the spliced data; filtering the corrected data; and acquiring longitudinal contour data corresponding to the longitudinal contour region to be measured of the road surface to be measured from the filtered data, and calculating the flatness of the longitudinal contour region to be measured based on the longitudinal contour data. According to the method, the influence of pavement cracks, deceleration strips, oscillation marking lines and the like on the flatness can be effectively removed, so that the reliability of a flatness measurement result is improved.

Description

Technical Field

[0001] The present application relates to the technical field of road surface detection, and in particular to a method and device for measuring the flatness of a road surface using a linear laser. Background Art

[0002] The smoothness of the road surface not only affects the driving comfort of the driver and passengers, but is also related to the vehicle's vibration, running speed, tire friction and wear, etc.

[0003] At present, the mainstream method for measuring road surface flatness is to use a point laser rangefinder combined with an accelerometer. However, this measurement method can only obtain the longitudinal elevation data of a measuring line along the driving direction of the wheel track position. The measurement results are easily affected by road cracks, speed bumps, oscillating markings, etc., thereby reducing the reliability of the flatness measurement results. Summary of the invention

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

[0005] In a first aspect, an embodiment of the present application provides a line laser road surface flatness measurement method, comprising: The three-dimensional cross-sectional data of the wheel track area on the road surface to be tested are converted from the image space to the object space to obtain the initial cross-sectional data of the object space; Performing measurement posture compensation on the object space initial cross-sectional data to obtain object space compensated cross-sectional data; Processing abnormal measurement points in the object-space compensation cross-section data to obtain object-space target cross-section data; Splicing the object target cross-section data longitudinally to obtain three-dimensional road surface data of the wheel track area; Correcting abnormal area data in the three-dimensional road surface data to obtain three-dimensional corrected data of the wheel track area; Filtering the three-dimensional corrected data to obtain road surface trend surface data of the wheel track area; The longitudinal profile data corresponding to the longitudinal profile area to be measured of the road surface to be measured is obtained from the road surface trend surface data, and the flatness of the longitudinal profile area to be measured is calculated based on the longitudinal profile data.

[0006] In one embodiment, the three-dimensional cross-sectional data is obtained based on the following method: Acquiring three-dimensional cross-sectional data of the wheel track area using a line scanning three-dimensional measurement sensor; The line scanning three-dimensional measurement sensor includes a laser and a three-dimensional camera; The laser is used to project a laser line along the width direction of the road surface to be tested; The three-dimensional camera is used to obtain three-dimensional cross-sectional data of the wheel track area corresponding to the laser line.

[0007] 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; The transverse sampling spacing 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 longitudinal sampling spacing 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 area includes 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.

[0008] In one embodiment, the correcting the abnormal area data in the three-dimensional road surface data to obtain the three-dimensional corrected data of the wheel track area includes: Detecting abnormal area data in the three-dimensional road surface data; Based on the data on both sides of the abnormal area data, the abnormal area data is corrected to obtain three-dimensional corrected data of the wheel track area.

[0009] In one embodiment, detecting abnormal area data in the three-dimensional road surface data includes: Calculating the ratio of the longitudinal sampling interval to the transverse sampling interval of the three-dimensional cross-sectional data to obtain an aspect ratio; Rounding up the aspect ratio to obtain a 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, detecting target abnormal features in the compressed road surface data to obtain compressed abnormal 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 to obtain the abnormal region data.

[0010] In one embodiment, after obtaining the three-dimensional corrected data of the wheel track area, the method includes: Acquiring target cross-sectional data in the three-dimensional correction data; Based on the target cross-section data, a target structural depth of the target cross-section is obtained by using a target structural depth calculation method; The target cross-section data is data in a circular area in the target cross-section with the center point of the target cross-section as the center and half the length of the data calculation unit required by the target structure depth calculation method as the radius.

[0011] In a second aspect, an embodiment of the present application provides a linear laser road surface flatness measurement device, comprising: 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 tested from the image space to the object space to obtain the initial cross-sectional data of the object space; The posture compensation module is used to: perform measurement posture compensation on the object space initial cross-sectional data to obtain object space compensated cross-sectional data; An abnormality processing module is used to: process abnormal measurement points in the object space compensation cross-section data to obtain object space target cross-section data; A data splicing module is used to: splice the cross-sectional data of the object space target in the longitudinal direction to obtain the three-dimensional road surface data of the wheel track area; A data correction module, used to correct the abnormal area data in the three-dimensional road surface data to obtain three-dimensional corrected data of the wheel track area; A data filtering module, used to filter the three-dimensional corrected data to obtain road surface trend surface data of the wheel track area; The flatness calculation module is used to obtain longitudinal profile data corresponding to the longitudinal profile area to be measured of the road surface to be measured from the road surface trend surface data, and calculate the flatness of the longitudinal profile area to be measured based on the longitudinal profile data.

[0012] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory storing a computer program, wherein when the processor executes the program, the steps of the linear laser road surface flatness measurement method described in the first aspect are implemented.

[0013] In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the line laser road surface flatness measurement method described in the first aspect.

[0014] In a fifth aspect, an embodiment of the present application provides a non-transitory computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the linear laser road surface flatness measurement method described in the first aspect.

[0015] The line laser pavement flatness measurement method and device provided in the present application convert the three-dimensional cross-sectional data of the wheel track area on the road surface to be measured from the image space to the object space to obtain the initial cross-sectional data of the object space, perform measurement posture compensation on the initial cross-sectional data of the object space to obtain the compensated cross-sectional data of the object space, process the abnormal measurement points in the compensated cross-sectional data of the object space to obtain the target cross-sectional data of the object space, splice the target cross-sectional data of the object space along the longitudinal direction to obtain the three-dimensional road surface data of the wheel track area, correct the abnormal area data in the three-dimensional road surface data to obtain the three-dimensional corrected data of the wheel track area, filter the three-dimensional corrected data to obtain the road surface trend surface data of the wheel track area, obtain the longitudinal profile data corresponding to the longitudinal profile area to be measured of the road surface to be measured from the road surface trend surface data, and calculate the flatness of the longitudinal profile area to be measured based on the longitudinal profile data. The present application performs image-object conversion, attitude compensation, exception processing, data evaluation, data correction, and data filtering on the three-dimensional cross-sectional data of the wheel track area of ​​the road to be measured before performing flatness calculation. By performing the above series of processing on the surface contour data of the wheel track belt, the influence of road cracks, speed bumps, oscillation markings, etc. on the flatness is effectively removed, thereby improving the reliability of the flatness measurement results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 This is one of the flow charts of the line laser road surface flatness measurement method provided in the embodiment of the present application; Figure 2 This is the second flow chart of the line laser road surface flatness measurement method provided in the embodiment of the present application; Figure 3 This is the third flow chart of the line laser road surface flatness measurement method provided in the embodiment of the present application; Figure 4 This is the fourth flow chart of the line laser road surface flatness measurement method provided in the embodiment of the present application; Figure 5 It is a structural schematic diagram of a linear laser road surface flatness measurement device provided in an embodiment of the present application; Figure 6 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0019] It should be noted that in the description of the embodiments of the present application, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements. The orientation or position relationship indicated by the terms "upper", "lower" and the like is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. Unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be a connection between the two elements. For those skilled in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0020] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0021] Figure 1 This is one of the flow charts of the line laser road surface flatness measurement method provided in the embodiment of the present application. Figure 1 , the present application embodiment provides a line laser road surface flatness measurement method, which may include: 101. Convert the three-dimensional cross-sectional data of the wheel track area on the road surface to be tested from the image space to the object space to obtain initial cross-sectional data of the object space; 102. Perform measurement posture compensation on the initial cross-sectional data of the object space to obtain object space compensated cross-sectional data; 103. Process the abnormal measurement points in the object space compensation cross-section data to obtain the object space target cross-section data; 104. Splice the cross-section data of the object space target in the longitudinal direction to obtain three-dimensional road surface data of the wheel track area; 105. Correcting abnormal area data in the three-dimensional road surface data to obtain three-dimensional corrected data of the wheel track area; 106. Filter the three-dimensional corrected data to obtain road surface trend surface data of the wheel track area; 107. Obtain longitudinal profile data corresponding to the longitudinal profile area to be measured of the road surface to be measured from the road surface trend surface data, and calculate the flatness of the longitudinal profile area to be measured based on the longitudinal profile data.

[0022] In step 101, a mapping relationship between image space and object space of three-dimensional cross-sectional data may be obtained through a calibration file, and the three-dimensional cross-sectional data may be converted from image space to object space through the mapping relationship.

[0023] In step 102, the measurement attitude noise of the object-side initial cross-sectional data may be compensated based on the measurement attitude information and installation parameters of the measurement device for acquiring the three-dimensional cross-sectional data, thereby obtaining object-side compensated cross-sectional data.

[0024] In step 103, the abnormal measurement points in the object-space compensated cross-sectional data may be eliminated, smoothed, etc., to eliminate or reduce the influence of these points on the data.

[0025] In step 104, the object-space target cross-sectional data is spliced ​​along the driving direction into three-dimensional road surface data of the wheel track area.

[0026] In step 105, the abnormal area data may include road crack area data, speed bump area data, oscillating marking area data, etc. Correcting these areas can effectively remove the influence of these area data on the flatness calculation.

[0027] In step 106, the three-dimensional corrected data is filtered to further remove noise in the data and ensure data quality.

[0028] In step 107, the flatness of the longitudinal contour area to be measured can be calculated using any flatness calculation method, which is not limited here. In this embodiment, the flatness of the longitudinal contour area to be measured can be calculated using the International Flatness Index formula.

[0029] The line laser road surface flatness measurement method provided in the present embodiment converts the three-dimensional cross-sectional data of the wheel track area on the road surface to be measured from the image space to the object space to obtain the initial cross-sectional data of the object space, performs measurement posture compensation on the initial cross-sectional data of the object space to obtain the compensated cross-sectional data of the object space, processes the abnormal measurement points in the compensated cross-sectional data of the object space to obtain the target cross-sectional data of the object space, splices the target cross-sectional data of the object space along the longitudinal direction to obtain the three-dimensional road surface data of the wheel track area, corrects the abnormal area data in the three-dimensional road surface data to obtain the three-dimensional corrected data of the wheel track area, filters the three-dimensional corrected data to obtain the road surface trend surface data of the wheel track area, obtains the longitudinal profile data corresponding to the longitudinal profile area to be measured of the road surface to be measured from the road surface trend surface data, and calculates the flatness of the longitudinal profile area to be measured based on the longitudinal profile data. This embodiment performs image-object conversion, posture compensation, exception processing, data evaluation, data correction, and data filtering on the three-dimensional cross-sectional data of the wheel track area of ​​the road to be measured before calculating the flatness. By performing the above series of processing on the surface contour data of the wheel track band, the influence of road cracks, speed bumps, oscillation markings, etc. on the flatness is effectively removed, thereby improving the reliability of the flatness measurement results.

[0030] In one embodiment, the three-dimensional cross-sectional data may be obtained based on the following method: Using a line scanning 3D measurement sensor to obtain 3D cross-sectional data of the wheel track area; Line scanning 3D measurement sensors include lasers and 3D cameras; The laser is used to project a laser line along the width direction of the road surface to be measured; The three-dimensional camera is used to obtain three-dimensional cross-sectional data of the wheel track area corresponding to the laser line.

[0031] 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 moves along the driving direction and performs sampling, so as to obtain the elevation data of multiple cross sections of the wheel track area along the extension direction of the wheel track.

[0032] 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. In this embodiment, the measurement width may be 350 mm. The transverse sampling spacing 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 transverse sampling spacing may be greater than or equal to 0.1 mm and less than or equal to 0.2 mm. More specifically, the transverse sampling spacing may be 0.15 mm. The longitudinal sampling spacing of the three-dimensional cross-sectional data, that is, the sampling spacing of adjacent cross-sections is greater than or equal to 1 mm and less than or equal to 6 mm. Further, the longitudinal sampling spacing may be greater than or equal to 1 mm and less than or equal to 5 mm. More specifically, the longitudinal sampling spacing may be 5 mm. The sampling accuracy of the three-dimensional cross-sectional data in the elevation direction is better than 0.2 mm. Furthermore, the sampling accuracy can be better than 0.1 mm, or even better than 0.05 mm.

[0033] The wheel track area includes 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.

[0034] In traditional 3D road measurement systems, in order to obtain full-width 3D road data, the measurement system is usually installed at a high position. Accurate compensation of the measurement posture poses certain challenges, and the data acquisition accuracy is relatively low. The lateral sampling spacing is usually only 1 mm, and the elevation accuracy is usually only 0.3 mm.

[0035] In this embodiment, the installation height of the line scanning three-dimensional measurement sensor is less than or equal to 500 mm, which is greatly lower than the installation height of the traditional measurement system. It can reduce the difficulty of compensating the measurement posture, achieve accurate compensation of the measurement posture, and improve 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 basis for subsequent road surface flatness calculations.

[0036] Figure 2 This is the second flow chart of the line laser road surface flatness measurement method provided in the embodiment of the present application. Figure 2 In one embodiment, correcting the abnormal area data in the three-dimensional road surface data to obtain the three-dimensional corrected data of the wheel track area may include: 201. Detect abnormal area data in three-dimensional road surface data; 202. Based on the data on both sides of the abnormal area data, the abnormal area data is corrected to obtain three-dimensional corrected data of the wheel track area.

[0037] The abnormal area data refers to the road crack area data, speed bump area data, oscillation marking area data, etc. that affect the flatness calculation. The data on both sides of the abnormal area data can be the data of the left and right areas 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 is closer to the abnormal area and has regional similarity. Therefore, this part of the normal data can be used to correct the abnormal area data.

[0038] 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 having the maximum region similarity with the original abnormal region data after the abnormality is eliminated.

[0039] Figure 3 This is the third flow chart of the line laser road surface flatness measurement method provided in the embodiment of the present application. Figure 3 In one embodiment, detecting abnormal area data in three-dimensional road surface data may include: 301. Calculate the ratio of the longitudinal sampling interval to the transverse sampling interval of the three-dimensional cross-sectional data to obtain an aspect ratio; 302. Round up the aspect ratio to obtain a lateral compression ratio; 303. Based on the lateral compression ratio, the three-dimensional road surface data is laterally compressed to obtain compressed road surface data; 304. Based on the target detection method, detect target abnormal features in the compressed road surface data to obtain compressed abnormal data; 305. Based on the lateral compression ratio, determine the area data corresponding to the compressed abnormal data in the three-dimensional road surface data to obtain the abnormal area data.

[0040] In step 303, since the original collected 3D cross-sectional data has a huge difference in horizontal and vertical resolutions, the aspect ratio of the 3D road surface data will be seriously imbalanced. Therefore, the 3D road surface data needs to be compressed laterally to alleviate the aspect ratio imbalance problem.

[0041] In step 304, the target detection method may be image segmentation, target matching, target extension and denoising, etc., and the target abnormal feature may be a local elevation mutation feature, a continuous linear feature or a mesh feature for a road crack area, or a surface elevation mutation feature, a geometric size feature, an apparent texture distribution feature, etc. for a speed bump area.

[0042] The target detection method can identify the above target abnormal features, thereby detecting compressed abnormal data, such as road crack area data, speed bump area data, etc.

[0043] It should be noted that since the measurement width of the three-dimensional cross-sectional data is small, in order to prevent false detection, after detecting the surface elevation mutation characteristics and geometric size characteristics, the apparent texture distribution characteristics can be further detected to improve the detection rate of the speed bump area data.

[0044] In step 305, the compressed abnormal data is converted to the original ratio to obtain uncompressed abnormal area data.

[0045] This embodiment compresses the three-dimensional road surface data laterally to alleviate the problem of data aspect ratio imbalance, and then adopts a targeted detection method based on the characteristics of the abnormal area to identify the compressed abnormal data from the compressed road surface data, and finally converts the compressed abnormal data into abnormal area data of the original ratio, thereby realizing fast and accurate detection of abnormal area data.

[0046] Figure 4 This is the fourth flow chart of the line laser road surface flatness measurement method provided in the embodiment of the present application. Figure 4 In one embodiment, after obtaining the three-dimensional correction data of the wheel track area, the following steps may be included: 401. Acquire target cross-sectional data in the three-dimensional correction data; 402. Based on the target cross-section data, a target structural depth of the target cross-section is obtained by using a target structural depth calculation method.

[0047] The target cross-section data is data within a circular area in the target cross-section with the center point of the target cross-section as the center and half the length of the data calculation unit required by the target structure depth calculation method as the radius.

[0048] In step 401, data corresponding to any cross section in the three-dimensional corrected data may be obtained.

[0049] In step 402, the target structure depth calculation method may be an MPD (Mean profile depth) calculation method, an MTD (Mean Texture Depth) calculation method, or an SMTD (Sensor Measured Texture Depth) calculation method. There may be three methods for calculating the cross-sectional structure depth: 1. For any cross section, obtain the three-dimensional corrected data within a circular area with a radius of half the length of the data calculation unit required by the MPD calculation method, such as 50 mm, with its center point as a circle, and obtain the target cross section data. Use the target cross section data in combination with the MPD calculation method to obtain the structural depth of the cross section.

[0050] 2. For any cross section, obtain the three-dimensional corrected data within a circular area with a radius of half the length of the data calculation unit required by the MTD calculation method, such as 50 mm, with its center point as a circle, and obtain the target cross-sectional data. Use the target cross-sectional data in combination with the MTD calculation method to obtain the structural depth of the cross section.

[0051] 3. For any cross section, obtain the three-dimensional corrected data within a circular area with a radius of half the length of the data calculation unit required by the SMTD calculation method, such as 150 mm, with its center point as a circle, and obtain the target cross-section data. Use the target cross-section data in combination with the SMTD calculation method to obtain the structural depth of the cross section.

[0052] The structural depth of the wheel track area can be obtained by combining the structural depths of all cross sections in the wheel track area.

[0053] This embodiment obtains the data required for different structural depth calculation methods from the three-dimensional corrected data of multiple cross sections, so that these data can be used in combination with the corresponding structural depth calculation methods to obtain the structural depths of multiple cross sections under different calculation methods, and then these structural depths can be combined to obtain the structural depths of the wheel track area under different calculation methods.

[0054] In one embodiment, a line laser road surface roughness measurement system used in the line laser road surface roughness measurement method is described as follows: The system uses a measuring vehicle as a measuring platform, on which a power supply module, a positioning module and a control module are arranged; 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 vehicle platform.

[0055] The power supply module includes an integrated power supply unit, which receives at least one of the power supply from the mains, the generator and the battery, and outputs the power supply to provide reliable and stable power for the entire measurement system. The output of the integrated power supply unit is divided into a DC part and an AC part, which are respectively output to the corresponding junction boxes to power different devices.

[0056] At the same time, the integrated power supply unit also monitors the power operation status of the system and sends relevant operation parameters to the control module so that the control module can cut off the power system and alarm when the operation parameters are abnormal, and then control the integrated power supply unit to complete the selection, conversion and distribution of electric energy again; The positioning module includes a photoelectric encoder and a global navigation satellite system GNSS receiver. The photoelectric encoder is installed on the wheel of the measuring vehicle to obtain the detection mileage information of the measuring vehicle. The global navigation satellite system GNSS receiver is installed on the top of the measuring vehicle to obtain the GNSS signal of the measuring vehicle. The detection mileage information and the GNSS information are fused and input into the synchronization control module to generate a time reference and a space reference, providing data fusion conditions for the measurement data; The line scanning 3D measurement sensor is installed in front of the front wheel or behind the rear wheel, or between the front and rear wheels, along the extension direction of the wheel track of the measuring vehicle.

[0057] Any line scanning 3D measurement sensor includes a laser, a 3D camera, a posture measurement sensor and a controller. The laser is used to project a laser line along the width direction of the road surface to be measured. The 3D camera is used to obtain the road surface cross-sectional elevation data of the wheel track area corresponding to the laser line by using the triangulation principle. The posture measurement sensor is used to obtain the measurement posture information of the line scanning 3D measurement sensor. The controller is used to control the coordinated work of the laser, the 3D camera and the posture measurement sensor.

[0058] The following is a description of a linear laser road surface smoothness measuring device provided in an embodiment of the present application. The linear laser road surface smoothness measuring device described below and the linear laser road surface smoothness measuring method described above can be referenced to each other.

[0059] Figure 5 Schematic diagram of the structure of the linear laser road surface flatness measurement device provided in the embodiment of the present application. Figure 5 , the embodiment of the present application provides a linear laser road surface flatness measurement device, which may include: The 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 tested from the image space to the object space to obtain the initial cross-sectional data of the object space; The posture compensation module 502 is used to: perform measurement posture compensation on the object space initial cross-sectional data to obtain object space compensated cross-sectional data; The abnormality processing module 503 is used to: process the abnormal measurement points in the object-space compensation cross-section data to obtain the object-space target cross-section data; The data splicing module 504 is used to: splice the object-side target cross-section data in the longitudinal direction to obtain the three-dimensional road surface data of the wheel track area; The data correction module 505 is used to correct the abnormal area data in the three-dimensional road surface data to obtain the three-dimensional corrected data of the wheel track area; A data filtering module 506 is used to filter the three-dimensional corrected data to obtain road surface trend surface data of the wheel track area; The flatness calculation module 507 is used to obtain longitudinal profile data corresponding to the longitudinal profile area to be measured of the road surface to be measured from the road surface trend surface data, and calculate the flatness of the longitudinal profile area to be measured based on the longitudinal profile data.

[0060] The linear laser road surface smoothness measuring device provided in the present embodiment converts the three-dimensional cross-sectional data of the wheel track area on the road surface to be measured from the image space to the object space to obtain the initial cross-sectional data of the object space, performs measurement posture compensation on the initial cross-sectional data of the object space to obtain the compensated cross-sectional data of the object space, processes the abnormal measuring points in the compensated cross-sectional data of the object space to obtain the target cross-sectional data of the object space, splices the target cross-sectional data of the object space along the longitudinal direction to obtain the three-dimensional road surface data of the wheel track area, corrects the abnormal area data in the three-dimensional road surface data to obtain the three-dimensional corrected data of the wheel track area, filters the three-dimensional corrected data to obtain the road surface trend surface data of the wheel track area, obtains the longitudinal profile data corresponding to the longitudinal profile area to be measured of the road surface to be measured from the road surface trend surface data, and calculates the smoothness of the longitudinal profile area to be measured based on the longitudinal profile data. This embodiment performs image-object conversion, posture compensation, exception processing, data evaluation, data correction, and data filtering on the three-dimensional cross-sectional data of the wheel track area of ​​the road to be measured before calculating the flatness. By performing the above series of processing on the surface contour data of the wheel track band, the influence of road cracks, speed bumps, oscillation markings, etc. on the flatness is effectively removed, thereby improving the reliability of the flatness measurement results.

[0061] In one embodiment, the three-dimensional cross-sectional data is obtained based on the following method: Acquiring three-dimensional cross-sectional data of the wheel track area using a line scanning three-dimensional measurement sensor; The line scanning three-dimensional measurement sensor includes a laser and a three-dimensional camera; The laser is used to project a laser line along the width direction of the road surface to be tested; The three-dimensional camera is used to obtain three-dimensional cross-sectional data of the wheel track area corresponding to the laser line.

[0062] 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; The transverse sampling spacing 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 longitudinal sampling spacing 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 area includes 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.

[0063] In one embodiment, the data correction module 505 is specifically used to: Detecting abnormal area data in the three-dimensional road surface data; Based on the data on both sides of the abnormal area data, the abnormal area data is corrected to obtain three-dimensional corrected data of the wheel track area.

[0064] In one embodiment, the data correction module 505 is specifically used to: Calculating the ratio of the longitudinal sampling interval to the transverse sampling interval of the three-dimensional cross-sectional data to obtain an aspect ratio; Rounding up the aspect ratio to obtain a 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, detecting target abnormal features in the compressed road surface data to obtain compressed abnormal 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 to obtain the abnormal region data.

[0065] In one embodiment, it further includes constructing a depth calculation module (not shown in the figure) for: Acquiring target cross-sectional data in the three-dimensional correction data; Based on the target cross-section data, a target structural depth of the target cross-section is obtained by using a target structural depth calculation method; The target cross-section data is data in a circular area in the target cross-section with the center point of the target cross-section as the center and half the length of the data calculation unit required by the target structure depth calculation method as the radius.

[0066] FIG6 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. 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 through the communication bus 640. The processor 610 may call a computer program in the memory 630 to execute the steps of the line laser road surface flatness measurement method, for example including: The three-dimensional cross-sectional data of the wheel track area on the road surface to be tested are converted from the image space to the object space to obtain the initial cross-sectional data of the object space; Performing measurement posture compensation on the object space initial cross-sectional data to obtain object space compensated cross-sectional data; Processing abnormal measurement points in the object-space compensation cross-section data to obtain object-space target cross-section data; Splicing the object target cross-section data longitudinally to obtain three-dimensional road surface data of the wheel track area; Correcting abnormal area data in the three-dimensional road surface data to obtain three-dimensional corrected data of the wheel track area; Filtering the three-dimensional corrected data to obtain road surface trend surface data of the wheel track area; The longitudinal profile data corresponding to the longitudinal profile area to be measured of the road surface to be measured is obtained from the road surface trend surface data, and the flatness of the longitudinal profile area to be measured is calculated based on the longitudinal profile data.

[0067] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0068] On the other hand, an embodiment of the present application further provides a computer program product, the computer program product comprising a computer program, the computer program can be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer can execute the steps of the line laser road surface flatness measurement method provided in the above embodiments, for example, including: The three-dimensional cross-sectional data of the wheel track area on the road surface to be tested are converted from the image space to the object space to obtain the initial cross-sectional data of the object space; Performing measurement posture compensation on the object space initial cross-sectional data to obtain object space compensated cross-sectional data; Processing abnormal measurement points in the object-space compensation cross-section data to obtain object-space target cross-section data; Splicing the object target cross-section data longitudinally to obtain three-dimensional road surface data of the wheel track area; Correcting abnormal area data in the three-dimensional road surface data to obtain three-dimensional corrected data of the wheel track area; Filtering the three-dimensional corrected data to obtain road surface trend surface data of the wheel track area; The longitudinal profile data corresponding to the longitudinal profile area to be measured of the road surface to be measured is obtained from the road surface trend surface data, and the flatness of the longitudinal profile area to be measured is calculated based on the longitudinal profile data.

[0069] On the other hand, an embodiment of the present application further provides a non-transitory computer-readable storage medium on which a computer program is stored, and the computer program is used to enable a processor to execute the steps of the line laser road surface flatness measurement method provided in the above embodiments, for example, including: The three-dimensional cross-sectional data of the wheel track area on the road surface to be tested are converted from the image space to the object space to obtain the initial cross-sectional data of the object space; Performing measurement posture compensation on the object space initial cross-sectional data to obtain object space compensated cross-sectional data; Processing abnormal measurement points in the object-space compensation cross-section data to obtain object-space target cross-section data; Splicing the object target cross-section data longitudinally to obtain three-dimensional road surface data of the wheel track area; Correcting abnormal area data in the three-dimensional road surface data to obtain three-dimensional corrected data of the wheel track area; Filtering the three-dimensional corrected data to obtain road surface trend surface data of the wheel track area; The longitudinal profile data corresponding to the longitudinal profile area to be measured of the road surface to be measured is obtained from the road surface trend surface data, and the flatness of the longitudinal profile area to be measured is calculated based on the longitudinal profile data.

[0070] The non-transitory computer-readable storage medium can be any available medium or data storage device that can be accessed by the processor, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)), etc.

[0071] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0072] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A line laser road surface flatness measurement method, characterized in that: include: The three-dimensional cross-sectional data of the wheel track area on the road surface to be tested are converted from the image space to the object space to obtain the initial cross-sectional data of the object space; Performing measurement posture compensation on the object space initial cross-sectional data to obtain object space compensated cross-sectional data; Processing abnormal measurement points in the object-space compensation cross-section data to obtain object-space target cross-section data; Splicing the object target cross-section data longitudinally to obtain three-dimensional road surface data of the wheel track area; Correcting abnormal area data in the three-dimensional road surface data to obtain three-dimensional corrected data of the wheel track area; Filtering the three-dimensional corrected data to obtain road surface trend surface data of the wheel track area; The longitudinal profile data corresponding to the longitudinal profile area to be measured of the road surface to be measured is obtained from the road surface trend surface data, and the flatness of the longitudinal profile area to be measured is calculated based on the longitudinal profile data.

2. The line laser road surface flatness measurement method according to claim 1, characterized in that: The three-dimensional cross-sectional data is obtained based on the following method: Acquiring three-dimensional cross-sectional data of the wheel track area using a line scanning three-dimensional measurement sensor; The line scanning three-dimensional measurement sensor includes a laser and a three-dimensional camera; The laser is used to project a laser line along the width direction of the road surface to be tested; The three-dimensional camera is used to obtain three-dimensional cross-sectional data of the wheel track area corresponding to the laser line.

3. The line laser road surface flatness measurement method according to claim 2, characterized in that: 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; The transverse sampling spacing 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 longitudinal sampling spacing 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 area includes 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.

4. The line laser road surface flatness measurement method according to claim 1, characterized in that: The step of correcting the abnormal area data in the three-dimensional road surface data to obtain the three-dimensional corrected data of the wheel track area includes: Detecting abnormal area data in the three-dimensional road surface data; Based on the data on both sides of the abnormal area data, the abnormal area data is corrected to obtain three-dimensional corrected data of the wheel track area.

5. The line laser road surface flatness measurement method according to claim 4, characterized in that: The detecting of abnormal area data in the three-dimensional road surface data comprises: Calculating the ratio of the longitudinal sampling interval to the transverse sampling interval of the three-dimensional cross-sectional data to obtain an aspect ratio; Rounding up the aspect ratio to obtain a 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, detecting target abnormal features in the compressed road surface data to obtain compressed abnormal 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 to obtain the abnormal region data.

6. The line laser road surface flatness measurement method according to claim 1, characterized in that: After obtaining the three-dimensional correction data of the wheel track area, the method includes: Acquiring target cross-sectional data in the three-dimensional correction data; Based on the target cross-section data, a target structural depth of the target cross-section is obtained by using a target structural depth calculation method; The target cross-section data is data in a circular area in the target cross-section with the center point of the target cross-section as the center and half the length of the data calculation unit required by the target structure depth calculation method as the radius.

7. A linear laser road surface roughness measuring device, characterized in that: include: 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 tested from the image space to the object space to obtain the initial cross-sectional data of the object space; The posture compensation module is used to: perform measurement posture compensation on the object space initial cross-sectional data to obtain object space compensated cross-sectional data; An abnormality processing module is used to: process abnormal measurement points in the object space compensation cross-section data to obtain object space target cross-section data; A data splicing module is used to: splice the cross-sectional data of the object space target in the longitudinal direction to obtain the three-dimensional road surface data of the wheel track area; A data correction module, used to correct the abnormal area data in the three-dimensional road surface data to obtain three-dimensional corrected data of the wheel track area; A data filtering module, used to filter the three-dimensional corrected data to obtain road surface trend surface data of the wheel track area; The flatness calculation module is used to obtain longitudinal profile data corresponding to the longitudinal profile area to be measured of the road surface to be measured from the road surface trend surface data, and calculate the flatness of the longitudinal profile area to be measured based on the longitudinal profile data.

8. An electronic device comprising a processor and a memory storing a computer program, characterized in that: When the processor executes the computer program, the steps of the line laser road surface roughness measurement method according to any one of claims 1 to 6 are implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the line laser road surface roughness measurement method according to any one of claims 1 to 6 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the line laser road surface roughness measurement method according to any one of claims 1 to 6 are implemented.

Images