Road surface flatness line scanning type monitoring method

Abstract

The invention belongs to the field of intelligent detection, and provides a road surface flatness line scanning type monitoring method. The objective of the invention is to solve the problems of high data processing complexity, high working intensity, low accuracy, low detection efficiency and the like in an existing pavement flatness monitoring method. According to the main scheme, a motor vehicle is used as a carrier, an identification reading device and a scanning device are fixedly installed at the tail of the carrier, a laser scanning face is formed, and linear array three-dimensional point cloud data representing road surface information are obtained in a line scanning mode. The detection method comprises the following steps: performing least square fitting processing according to linear array point cloud data to obtain a road surface fitting straight line; in combination with the pavement fitting straight line, obtaining key form and position parameters for evaluating the pavement flatness degree; reading the identifiers arranged on the road shoulder at equal intervals through an identifier reading device, and performing sectional detection on the road surface to obtain the distance from the abnormal position to the identifier; and in combination with the key form and position parameters of the pavement flatness, detecting the abnormity and variation of the pavement flatness through a threshold method and a reference template comparison method.

Description

Technical Field

[0001] The invention belongs to the field of intelligent detection, and in particular relates to a line scanning monitoring method for road surface flatness. Background Art

[0002] The flatness of the road surface is related to the smooth operation of vehicles, especially special roads such as airport runways have extremely strict requirements on flatness. Abnormal flatness monitoring can be used for the timely discovery of early road diseases, assessment of road conditions and aging, and project acceptance, etc., and is also of great significance for road repair and maintenance. Manual regular inspections or some existing road surface flatness monitoring methods and equipment often have shortcomings such as high work intensity, low accuracy, and low detection efficiency. The road surface flatness detection methods that have been proposed today, which are also based on three-dimensional point cloud processing technology, mostly require additional information to be obtained through the assistance of additional attitude sensors (such as IMU inertial navigation), and through the setting of targets and data splicing, the three-dimensional reconstruction of the entire road surface is achieved. The cost, layout complexity, and data processing difficulty are all relatively large. And it is difficult to compare the changes before and after the same position. Summary of the invention

[0003] The purpose of the present invention is to realize the monitoring of the road surface flatness with economy, accuracy and real-time, so as to effectively overcome the shortcomings of the traditional road surface monitoring method. The present invention designs a simple and efficient data acquisition and processing mode to solve the problem of high data processing complexity of the existing three-dimensional point cloud detection method; uses the least squares method to design a road surface benchmark determination method, so that the attitude sensor is no longer required to obtain additional posture information; different from the conventional road surface flatness detection method (such as determining the international flatness IRI value), a road surface flatness quantification method that best fits the linear point cloud data attributes is proposed to more comprehensively evaluate the road surface flatness; a unique identification reading method is proposed to realize segmented detection, so as to facilitate and accurately locate the detection position; combining the two monitoring modes can simultaneously accurately identify and locate the anomalies and changes in the road surface flatness, which greatly facilitates the acceptance, maintenance and repair of road surface engineering.

[0004] In order to achieve the above purpose, the present invention adopts the following technical means:

[0005] The present invention provides a line scanning monitoring method for road surface flatness, comprising:

[0006] Vehicle (1): a conventional four-wheeled motor vehicle, with a logo reading device fixed at its rear end for reading logos arranged at equal intervals on the road shoulder; a cleaning device (101) is installed at the front end of the vehicle (1) for removing road garbage, floating stones and foreign objects embedded in pits, so that the scanning device (2) can obtain real road surface topography information, wherein the logos include logo I, logo II, etc.;

[0007] Scanning device (2): installed at the rear of the vehicle (1), the laser source (201) thereon projects laser light vertically downward to form a laser scanning surface, the scanning device (2) acquires the y and z coordinates of dense points distributed on the "I" laser line along the width direction of the road surface, and the x coordinate of each point is obtained by combining the real-time speed of the vehicle (1) and the timestamp of each frame of linear point cloud data, thereby acquiring linear three-dimensional point cloud data representing road surface information in a line scanning manner;

[0008] The detection steps are:

[0009] Step (1), performing least squares fitting processing on the linear point cloud data to obtain a road surface fitting straight line;

[0010] Step (2), combining the road surface fitting straight line to obtain key shape and position parameters for evaluating the road surface flatness;

[0011] Step (3), reading the signs arranged at equal intervals on the road shoulder by a sign reading device (102), and performing segmented detection on the road surface to obtain the distance from the abnormal position to the sign;

[0012] Step (4), combining the key shape and position parameters of the road surface flatness, detecting the abnormality and abnormality of the road surface flatness through two modes: the threshold method and the reference template comparison method.

[0013] In the above scheme, a cleaning device (101) is installed at the front end of the vehicle (1) to remove road garbage, floating stones, and foreign objects embedded in pits, etc., so that the scanning device (2) can obtain real road surface topography information.

[0014] In the above scheme, the road surface fitting information is solved in two steps in step (1):

[0015] The first step is to perform zy straight line fitting based on the least squares method with all the coordinate points of each frame of the linear point cloud as a set to obtain the fitting line

[0016] The second step is to calculate the coordinates of each point to the fitting line If a point p i，j arrive If the distance is greater than the threshold T, the point and its neighboring points are filtered out, and the remaining points are refitted with the zy straight line to obtain the fitting straight line representing the current scanning section. That is, the representation of the entire road surface in the point cloud coordinate system after removing obvious bumpy accidental factors is determined.

[0017] In the above scheme, in step (2), key shape and position parameters are solved from two aspects: fitting line deviation and normal vector deviation to evaluate the road surface flatness, specifically including:

[0018] In terms of fitting line deviation, the average absolute deviation value is solved by calculating the average distance from the current frame linear array coordinate point to the scanning section fitting line; the coordinate points located farthest above and below the scanning section fitting line in each frame linear array coordinate point are taken as the maximum positive deviation point and the maximum negative deviation point, and the distances from the maximum positive deviation point and the maximum negative deviation point to the fitting line are taken as the maximum positive deviation value and the maximum negative deviation value, respectively, and the y coordinate values ​​of the maximum positive deviation point and the maximum negative deviation point are determined;

[0019] In terms of normal vector deviation, the three-point fitting line is determined with the current frame line array coordinate point and its left and right adjacent points as the point set, and the normal vector of the coordinate point is determined according to the vertical direction of the fitting line. The angle between the normal vector of each coordinate point of the current frame line array and the corresponding normal vector of the current scanning section fitting line is calculated, and [-90°, 90°] is divided into 9 value intervals with a step size of 20°. The proportion of the corresponding angle of each point in the current frame falling into the corresponding interval is counted, and the normal vector distribution nine-tuple is constructed to evaluate the normal distribution of the current scanning section; the angle with the largest absolute value is the maximum normal vector angle, and the y coordinate value of the coordinate point corresponding to the maximum angle is determined.

[0020] In the above scheme, the key parameters of the shape and position of the fitting line deviation are specifically solved as follows:

[0021] Mean absolute deviation

[0022] All coordinate points p of the current linear point cloud i，l (x i ,y i，1 , z i，1 )~p i，n (x i ,y i，n , z i，n ) to the aforementioned scanning section fitting straight line The average distance is determined by

[0023]

[0024] Where d i，j Refers to p i，1 ~p i，n Any point p i，j (x i ,y i，j , z i，j )arrive The distance represents the fitting straight line described in claim 3 The equation Ay+Bz+C=0 is always satisfied, where A, B, C represent the coefficients of the straight line equation, and z and y represent the vertical and horizontal coordinates in the road surface point cloud data;

[0025] Maximum positive deviation Maximum positive deviation point y coordinate

[0026] Current linear point cloud p i，1 ~p i，n The center is located in the scan section fitting straight line The point above and farthest from the straight line is the point of maximum positive deviation and is recorded as The point to The distance is the maximum positive deviation value The y coordinate of this point is recorded as

[0027] Maximum negative deviation Maximum negative deviation point y coordinate

[0028] Current linear point cloud p i，1 ~p i，n The center is located in the scan section fitting straight line The point below and farthest from the straight line is the point with the maximum negative deviation and is recorded as The point to The distance is the maximum negative deviation value The y coordinate of this point is recorded as

[0029] In the above scheme, the key parameters of the shape and position of the fitting line deviation are specifically solved as follows:

[0030] Normal vector distribution ennead

[0031] For all coordinate points p of the current linear point cloud i，1 ~p i，n Any point p in i，j , through its left and right adjacent points p i，j-1 and p i，j+1 Calculate the fitting line to get the normal vector n corresponding to the point i，j ;

[0032] Calculate n again i，j With n i The angle θ i，j , and stipulate that n i，j Located in i Clockwise, θ i，j When it is negative and located counterclockwise, θ i，j is positive;

[0033] Angle θ i，jThe value range of is [-90°, 90°], and [-90°, 90°] is divided into 9 intervals with a step size of 20°, namely, [-90°, -70°), [-70°, -50°), ..., [70°, 90°];

[0034] Calculate all coordinate points p of the current linear point cloud i，1 ~p i，n The corresponding normal vector n i，1 ~n i，n With n i The angle formed is θ i，1 ~θ i，n ;

[0035] Statistics θ i，1 ~θ i，n The number of points that fall into the above 9 intervals respectively, and the ratio of the number of points in each interval to the total number of points n of the current linear array is calculated Thus, we get the normal vector distribution nine-tuple

[0036] Maximum normal vector angle The y coordinate of the angle point with the maximum normal vector

[0037] The normal vector n corresponding to all coordinate points of the current linear point cloud i，l ~n i，n With n i The maximum angle formed is recorded as and The y coordinate of the corresponding coordinate point is

[0038] In the above scheme, in step (3), a marking segmented monitoring mechanism is used to determine the location where the abnormality occurs: by reading the corresponding serial number of the identifier through the identifier reading device (102) fixed on the carrier (1), it can be determined between which two identifiers the current monitoring position is located, and then combined with the abnormal frame acquisition time and the corresponding real-time speed information, the distance from the abnormal position to the identifier can be accurately determined.

[0039] In the above scheme, the segmented monitoring specifically includes the following steps:

[0040] Marks are arranged at equal intervals of Δx on the road shoulder, and when a reading device (102) faces the mark, a serial number corresponding to the mark is read, and a specific location where the abnormal road surface flatness occurs is found according to the serial number;

[0041] Abnormal road surface roughness is detected at time t m , and the time t m At the time t of reading the identification I and reading the identification II I and t IIIf the anomaly is between markers I and II, then it can be seen that the anomaly must appear between markers I and II;

[0042] From I The calculation starts when the mark I is read at the time. If the flatness parameter of the m-th frame linear array data is abnormal, the abnormal identification time t m Corresponding to the mth frame of linear point cloud data after the identification I is read, the abnormal position can be accurately located according to the real-time speed information:

[0043]

[0044] Where v m represents the real-time speed of the vehicle (1) when acquiring the mth frame data, v1 represents the speed of the vehicle in the first frame, t1 represents the time of the first frame from the time when tag I is read, and x m_I represents the x-distance from the outlier point to the marker I, v i It indicates the real-time speed of vehicle 1 when the first frame of linear point cloud data is obtained from reading identification 1.

[0045] In the above scheme, in step (4), the A and B modes are combined to monitor the abnormality of the road surface flatness.

[0046] Mode A: Threshold discrimination method,

[0047] When the key shape and position parameters of a road surface flatness of a certain frame exceed the normal threshold range, it means that the flatness of the corresponding position of the frame is abnormal. Combined with step (3), the abnormal position is determined and targeted repairs are performed;

[0048] Mode B: Benchmark template comparison, first complete the pre-scan of all sections in the entire monitoring domain, obtain the key parameter vector of the flatness corresponding to each frame in each section, and then build the benchmark template;

[0049] Then, in actual monitoring, the interpolation method is used to calculate the difference vector between the key parameter vector of the current monitoring frame and the reference template;

[0050] The difference vector is used to determine the magnitude of the change in each vector element compared to the initial benchmark, thereby judging the change in the road surface smoothness before and after on a time scale.

[0051] In the above scheme, in mode B, it is assumed that during the pre-scanning process, the 1st to qth frames of point cloud data are collected in the segments divided by markers I and II, and the key parameter vectors S1, S2, ... S corresponding to the q frames of data are obtained. i， ……S q , forming a reference template, where any vector:

[0052]

[0053] Among them, x i Indicates S i The x-distance from the corresponding linear point cloud data to the mark I, and the remaining elements are the key shape and position parameters of the road surface flatness mentioned above;

[0054] In actual monitoring, when the vehicle passes through the section divided by markers I and II, the key parameter vector of the flatness corresponding to all frame linear point cloud data in the section will also be obtained, where a frame vector S m It is expressed as:

[0055]

[0056] If x i <x m <x i+1 , that is, S m The corresponding scanning position is between the reference template vector S i and S i+1 The difference vector ΔS can be calculated by interpolation. m :

[0057]

[0058] It is expressed as:

[0059]

[0060] ΔS m It characterizes the difference between the actual monitoring and the initial benchmark, and judges the change in the key parameters of each road surface smoothness through its second to last elements:

[0061] For example, in actual monitoring, the difference vector ΔS corresponding to the mth frame of a certain scanning position m ,

[0062] The second element If the value is greater than the specified value, the x after the scan position marker I m The deviation value of the fitting line at the corresponding position has a large change, indicating that the overall smoothness of the road surface has deteriorated significantly;

[0063] Element 3 and / or the 5th element If it is greater than the specified value, it indicates that the maximum fluctuation at the corresponding scanning surface is aggravated, and there is a hidden danger of road disease that affects driving safety;

[0064] Element 4 and / or the 6th element If the absolute value is greater than the specified value, it indicates that the maximum fluctuation point of the corresponding scanning surface has changed significantly compared with the initial benchmark, and a new disease damage area has been generated;

[0065] Elements 7 to 15: If the absolute value of each element is greater than the specified value, the normal vector distribution changes significantly compared to the initial benchmark, that is, the lateral fluctuation of the road surface changes significantly;

[0066] Element 16 If it is larger than the specified value, the maximum deviation of the normal direction of the current scanning surface will further increase significantly;

[0067] The 17th element If the absolute value is greater than the specified value, the maximum normal deviation point has changed significantly compared to the initial benchmark, and a new diseased and damaged area has been generated.

[0068] The above road surface smoothness scanning monitoring method can selectively or simultaneously work in mode A and mode B: mode A is used to identify abnormal road surface smoothness, while mode B is used to monitor the smoothness before and after changes.

[0069] Beneficial effects:

[0070] The present invention is aimed at monitoring the smoothness of road surface and designs a unique line scanning monitoring method based on three-dimensional point cloud processing technology to identify the characteristics of road surface smoothness.

[0071] A method for determining the position of the road surface based on the linear point cloud was designed using the least squares method. The fitting line of the scanned section was solved in two steps to obtain the overall road surface equation after removing obvious bumps and concavities. A set of solution methods for key parameters of the road surface shape and position were proposed from the two aspects of fitting line deviation and normal vector deviation to more comprehensively and effectively evaluate the road surface flatness. A marked segmented monitoring mechanism was designed to facilitate the location of the abnormal flatness. Two monitoring modes, the threshold discrimination method and the benchmark template comparison method, were designed to respectively identify abnormal and distorted road surface flatness.

[0072] Compared with the existing intelligent monitoring methods for road surface flatness, the present invention has a simple layout, low computational complexity, good real-time performance, and high detection efficiency. It can accurately and comprehensively evaluate the road surface flatness, and can not only identify abnormalities in flatness parameters through the threshold method, but also accurately obtain the change in flatness characterization parameters before and after a period of time. It can also accurately locate the location where the flatness abnormality occurs, which is convenient for repair and maintenance. In addition, compared with the conventional image processing monitoring method based on pixel RGB color values, the present invention can avoid the influence of lighting conditions and color differences, and is expected to achieve more reliable monitoring in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a schematic diagram of the method of the present invention;

[0074] Figure 2 It is a schematic diagram of the cleaning device of the present invention;

[0075] Figure 3 It is a schematic diagram of solving the two-step scanning cross-section fitting information of the present invention;

[0076] Figure 4 It is a schematic diagram of solving the relevant parameters of the fitted line deviation of the present invention;

[0077] Figure 5 A schematic diagram of solving the normal vector deviation related parameters of the present invention;

[0078] Figure 6 It is a schematic diagram of the marked segmented monitoring mechanism of the present invention.

[0079] Description of Figure Numbers:

[0080] 1-carrier, 2-scanning device, 101-cleaning device, 102-label reading device, 2-scanning device, 201-laser source, 301-label I, 302-label II, 303-label III. DETAILED DESCRIPTION

[0081] like Figure 1 As shown, the present invention proposes a line scanning monitoring method for road surface flatness, which mainly includes a vehicle 1 and a scanning device 2.

[0082] The vehicle 1 is a conventional four-wheeled vehicle, and a cleaning device 101 (such as Figure 2 As shown in the figure), it is used to remove road garbage, floating stones, and foreign objects embedded in pits, so that the scanning device 2 can obtain real road surface morphology information. A reading device 102 is fixed at the rear of the vehicle 1. When the vehicle passes by, the reading device 102 can read the information of the identification array (identification I (301), identification II (302), identification III (303)...) arranged equidistantly on the shoulder of the road, thereby realizing the segmentation of the monitored road surface, which is convenient for abnormal location and effective realization of monitoring functions.

[0083] The scanning device 2 is installed at the rear of the vehicle. The laser source 201 installed on the scanning device 2 projects fan-shaped laser vertically downward to form a laser scanning surface. The scanning device 2 obtains dense points distributed on the "I" laser line along the width direction of the road surface. y , z coordinates, and their x coordinates are obtained by combining the real-time speed of the vehicle and the timestamp of each frame of linear point cloud data (the scanning device coordinate system is defined as: the vehicle running direction is x, the road width direction is v, and the vehicle vertical direction is z). In this way, the linear array 3D point cloud data representing the road surface information can be obtained by line scanning {P1{(x1, y 1，1 , z 1，1 ), (x1, y 1，2 , z 1，2 )...,(x1,y1，n , z 1，n )}, P2{(x2, y 2，1 , z 2，1 ), (x2, y 2，2 , z 2，2 )...,(x2,y 2，n , z 2，n )}, ...P i {(x i ,y i，l , z i，l ), (x i ,y i，2 , z i，2 )...,(x i ,y i，n , z i，n The vehicle speed can be obtained by detecting the wheel linear speed or rotation speed through a dedicated speed sensor, or can be directly obtained in real time by communicating with the instrument provided by the vehicle 1.

[0084] Based on the line scanning data acquisition mode, a two-step scanning section fitting information acquisition method is designed.

[0085] Step 1: If Figure 3 As shown in (a), the least squares method zy straight line fitting is performed on all coordinate points in the linear array point cloud obtained by the current scan to obtain the fitting straight line

[0086] Step 2: Calculate the distance from each point to the fitting line If a point p i，j arrive If the distance is greater than the threshold value T, it means that the point is an obvious pothole or bulge, then the point and several points in its neighborhood (all the points in the rectangular box in the figure) are removed from the line array, such as Figure 3 As shown in (b), the remaining points are refitted with the zy straight line to obtain the fitting straight line that can represent the current scanning section. This straight line is the representation of the entire road surface in the point cloud coordinate system after removing the obvious bumps and bumps. Represents the fitted straight line The equation Ay+Bz+C=0 is always satisfied, where A, B, C represent the coefficients of the straight line equation, and z and y represent the coordinates of the vehicle in the vertical direction and the road width direction in the road point cloud data.

[0087] Furthermore, based on the line scanning linear array point cloud data acquisition mode, a set of calculation methods for solving key parameters that can characterize the road surface flatness is proposed. i {(x i ,y i，l , zi，l ), (x i ,y i，2 , z i，2 )...,(x i ,y i，n , z i，n )} as an example:

[0088] Mean absolute deviation All coordinate points p of the current linear point cloud i，1 (x i ,y i，1 , z i，1 )~p i，n (x i ,y i，n , z i，n ) to the aforementioned scanning section fitting straight line The average distance is determined by

[0089]

[0090] Where d i，j Refers to p i，1 ~p i，n Any point P i，j (x i ,y i，j , z i，j )arrive distance;

[0091] Maximum positive deviation Maximum positive deviation point y coordinate like Figure 4 As shown, the current linear point cloud p i，1 ~p i，n The center is located in the scan section fitting straight line The point above and farthest from the straight line is the point of maximum positive deviation (denoted as ), the point to The distance is the maximum positive deviation value The v coordinate of the point is recorded as

[0092] Maximum negative deviation Maximum negative deviation point y coordinate like Figure 4 As shown, the current linear point cloud p i，1 ~p i，n The center is located in the scan section fitting straight line The point below and farthest from the straight line is the point with the maximum negative deviation (denoted as ), the point to The distance is the maximum negative deviation value The v coordinate of the point is recorded as

[0093] In order to fully characterize the smoothness of the road surface, the lateral fluctuation of the road surface is evaluated by additionally calculating the normal vector deviation related parameters:

[0094] like Figure 5 As shown, the fitting straight line obtained by the above solution The normal vector n of the current scan section fitting line can be determined i (The vector direction is perpendicular to the fitted straight line).

[0095] Normal vector distribution ennead For all coordinate points p of the current linear point cloud i，1 ~p i，n Any point p in i，j , through its left and right adjacent points p i，j-1 and p i，j+1 Calculate the fitting line to get the normal vector n corresponding to the point i，j ; Calculate n again i，j With n i The angle θ i，j , and stipulate that n i，j Located in i Clockwise, θ i，j When it is negative and located counterclockwise, θ i，j is positive. Therefore, the angle θ i，j The value range of is [-90°, 90°], and [-90°, 90°] is divided into 9 intervals with a step size of 20°, namely [-90°, -70°), [-70°, -50%, ..., [70°, 90°]; calculate all the coordinate points p of the current linear point cloud i，1 ~p i，n The corresponding normal vector n i，1 ~n i，n With n i The angle formed is θ i，1 ~θ i，n ; Statistics θ i，1 ~θ i，n The number of points that fall into the above 9 intervals respectively, and the ratio of the number of points in each interval to the total number of points n of the current linear array is calculated Thus, we get the normal vector distribution nine-tuple

[0096] Maximum normal vector angle The y coordinate of the angle point with the maximum normal vector The normal vector n corresponding to all coordinate points of the current linear point cloud i，l ~n i，n With n i The maximum angle (absolute value) formed is recorded as and The y coordinate of the corresponding coordinate point is

[0097] Furthermore, in order to facilitate accurate positioning when monitoring the flatness of the road surface, the present invention also proposes a segmented marking monitoring mechanism. Figure 6 As shown, the signs I, II, III, etc. are arranged at equal intervals of Δx on the shoulder (i.e. Figure 1 301, 302, 303, etc.). The identification can be an RFID electronic tag, a barcode, a QR code, a color code, etc. A identification reading device 102 is fixed on the carrier 1, and the signal receiving direction of the device is coplanar with the laser scanning surface. When 102 is facing the identification, the corresponding serial number of the identification can be read. This makes it easy to find the specific location where the road surface flatness abnormality occurs. For example, the road surface flatness abnormality is identified at time t m , and this moment is at the moment of reading identification I and reading identification II (t I and t II ), then we know that the anomaly must appear between markers I and II. Further, assuming that from t I At the moment when the mark I is read, the calculation starts. The flatness parameter of the m-th frame linear array data is abnormal, that is, the abnormality recognition time t m Corresponding to the mth frame of linear point cloud data after the identification I is read. The abnormal position can be accurately located according to the real-time speed information:

[0098]

[0099] Where v m Indicates the real-time speed of vehicle 1 when acquiring the mth frame data, x m_I represents the x-distance from the outlier point to the marker I, v i It indicates the real-time speed of vehicle 1 when acquiring the linear point cloud data of the i-th frame (calculated from reading to identification I).

[0100] In this way, by using the segmentation mechanism, the small segment Δx is used as the abnormality analysis unit, and the location where the abnormality occurs can be located more accurately.

[0101] Furthermore, according to the aforementioned key parameters of the road surface flatness corresponding to each frame of the linear point cloud that can be calculated and the marked segmented monitoring mechanism, the abnormal road surface flatness is monitored through mode A and mode B:

[0102] Mode A: Identify road surface flatness anomalies through simple threshold judgment. When a key parameter of the flatness of a frame exceeds the normal threshold range, it means that the flatness of the corresponding position of the frame is abnormal. Combined with the abnormal frame positioning method mentioned above, the abnormal position is determined and targeted repairs are performed.

[0103] Mode B: To accurately and effectively judge abnormal changes in the road surface flatness, the shape and position differences of the road surface are determined by comparing with the reference template. Specifically, when the road surface is completed or the road surface flatness meets the requirements, let the vehicle 1 run slowly and evenly to complete the pre-scan of the entire monitored road section, and obtain the series of key parameters of flatness corresponding to all linear point cloud data in the pre-scan process according to the above parameter solution calculation method. Figure 6 Taking the section divided by the marks I and II as an example, the 1st to qth frames of point cloud data are collected in this section, and the key parameter vectors S1, S2, ... S corresponding to each of the q frames of data are obtained. i , ... S q , forming a reference template, where any vector

[0104] Among them, x i Indicates S i The x-direction distance from the corresponding linear point cloud data to the marker I (can be obtained from the x in formula (2) m_I The remaining elements are the key parameters of road surface smoothness mentioned above (i.e. Figure 4 The fitted line shown deviates from the relevant parameters and Figure 5 Normal vector related parameters shown).

[0105] In actual monitoring, when the vehicle passes through the section divided by markers I and II, the key parameter vector of the flatness corresponding to all frame linear point cloud data in the section is obtained. m For example, it is expressed as

[0106]

[0107] If x i <x m <x i+1 , that is, S m The corresponding scanning position is between the reference template vector S i and S i+1 The difference vector ΔS can be calculated by interpolation. m :

[0108]

[0109] It is expressed as

[0110]

[0111] ΔS mIt represents the difference between the actual monitoring and the initial benchmark. The change in the key parameters of the road surface flatness can be determined by its second to last elements. For example, in actual monitoring, the difference vector ΔS corresponding to a certain scanning position (taking the mth frame as an example) m The second element is larger, it means that the corresponding scanning position (x after the mark I) m The deviation value of the fitting line at ) has a large change, which indicates that the overall smoothness of the road surface may have deteriorated significantly due to aging and other reasons; the third element and / or the fifth element Δ If the value is larger, it means that the maximum fluctuation of the corresponding scanning surface is aggravated, which may cause hidden dangers of road diseases that affect driving safety. and / or the 6th element If the absolute value is large, it means that the maximum fluctuation point of the corresponding scanning surface has changed significantly compared with the initial reference, and there may be new disease damage areas; 7th to 15th elements: If the absolute value of each element is large, it means that the normal vector distribution has changed significantly compared with the initial benchmark, that is, the lateral fluctuation of the road surface has changed significantly; the 16th element If the value is larger, it means that the maximum deviation of the normal direction of the current scanning surface is further increased significantly, and the 17th element A larger absolute value indicates that the maximum normal deviation point has changed significantly compared to the initial benchmark, and a new diseased and damaged area may have occurred.

[0112] The above road surface smoothness scanning monitoring method can selectively or simultaneously work in mode A and mode B: mode A is used to identify abnormal road surface smoothness, while mode B is used to monitor the smoothness before and after changes.

[0113] In summary, the present invention has the following characteristics:

[0114] By installing a scanning device on the vehicle to obtain linear point cloud data that can represent the road surface shape and position information, a two-step scanning section fitting information acquisition method is designed based on this. This technical means solves the problem of more efficient acquisition and processing of data in existing road surface flatness monitoring technology, and achieves the effect of improving the accuracy, real-time and economic performance of road surface flatness monitoring. Compared with traditional manual detection or image processing-based methods, this line scanning monitoring method is more accurate and efficient, especially when processing large-scale data and complex road morphology.

[0115] A road benchmark determination method is designed using the least squares method, which eliminates the need for attitude sensors to obtain additional posture information, thereby effectively reducing system hardware costs and data processing complexity.

[0116] A set of key parameter solution methods for road surface roughness evaluation was designed from the two perspectives of fitting line deviation and normal vector deviation. This design method solves the problem of key parameter extraction and analysis in road surface roughness evaluation, achieving a more comprehensive and accurate evaluation of road surface roughness.

[0117] A marking segmented monitoring mechanism is designed to facilitate the determination of the location of abnormalities. This mechanism solves the problem of inaccurate location of abnormalities in road surface flatness monitoring, and achieves the effect of improving monitoring efficiency and accuracy. Through marking segments and real-time speed information, the location of abnormal road surface flatness can be quickly and accurately located, facilitating timely repair and maintenance.

[0118] Combine Mode A (threshold discrimination method) and Mode B (reference template comparison method) to monitor abnormalities and changes in road surface flatness. This multi-mode monitoring method solves the problem that a single monitoring mode is difficult to fully evaluate the road surface flatness, and achieves the effect of improving monitoring accuracy and comprehensiveness. By combining the threshold discrimination method with the reference template comparison method, abnormalities and changes in road surface flatness can be more accurately identified, providing more comprehensive and accurate data support for road maintenance and repair.

Claims

1. A line scanning monitoring method for road surface flatness, characterized in that: include: Vehicle (1): A conventional four-wheeled motor vehicle with a sign reader installed at its rear end to read signs arranged evenly on the shoulder of the road. A cleaning device (101) is installed at the front end of the vehicle (1) to remove road garbage, floating stones and foreign objects embedded in pits, so that the scanning device (2) can obtain real road surface topography information; Scanning device (2): installed at the rear of the vehicle (1), the laser source (201) on it projects laser vertically downward to form a laser scanning surface. The scanning device (2) acquires the y and z coordinates of the dense points distributed on the "I" laser line along the width direction of the road surface, and the x coordinate of each point is obtained by combining the real-time speed of the vehicle (1) and the timestamp of each frame of linear point cloud data, thereby acquiring linear three-dimensional point cloud data representing road surface information in a line scanning manner; The detection steps are: Step (1), performing least squares fitting processing on the linear point cloud data to obtain a road surface fitting straight line; Step (2), combining the road surface fitting straight line to obtain key shape and position parameters for evaluating the road surface flatness; Step (3), reading the signs arranged at equal intervals on the road shoulder by a sign reading device (102), and performing segmented detection on the road surface to obtain the distance from the abnormal position to the sign; Step (4), combining the key shape and position parameters of the road surface flatness, detecting the abnormality and abnormality of the road surface flatness through two modes: the threshold method and the reference template comparison method.

2. A road surface flatness line scanning monitoring method according to claim 1, characterized in that: The front end of the vehicle (1) is equipped with a cleaning device (101) for removing road surface garbage, floating stones, and foreign objects embedded in pits, etc., so that the scanning device (2) can obtain real road surface topography information.

3. The line scanning monitoring method for road surface flatness according to claim 1 is characterized in that: In step (1), the road surface fitting information is solved in two steps: The first step is to perform zy straight line fitting based on the least squares method with all the coordinate points of each frame of the linear point cloud as a set to obtain the fitting line The second step is to calculate the coordinate points to the fitting line If a point p i，j arrive If the distance is greater than the threshold T, the point and its neighboring points are filtered out, and the remaining points are refitted with the zy straight line to obtain the fitting straight line representing the current scanning section. That is, the representation of the entire road surface in the point cloud coordinate system after removing obvious bumpy accidental factors is determined.

4. A road surface flatness line scanning monitoring method according to claim 1, characterized in that: In step (2), key geometric parameters are solved from two aspects: fitting line deviation and normal vector deviation to evaluate the road surface smoothness, including: In terms of fitting line deviation, the average absolute deviation value is solved by calculating the average distance from the current frame linear array coordinate point to the scanning section fitting line; the coordinate points located farthest above and below the scanning section fitting line in each frame linear array coordinate point are taken as the maximum positive deviation point and the maximum negative deviation point, and the distances from the maximum positive deviation point and the maximum negative deviation point to the fitting line are taken as the maximum positive deviation value and the maximum negative deviation value, respectively, and the y coordinate values ​​of the maximum positive deviation point and the maximum negative deviation point are determined; In terms of normal vector deviation, the three-point fitting line is determined by taking the current frame line array coordinate point and its left and right adjacent points as the point set, and the normal vector of the coordinate point is determined according to the vertical direction of the fitting line, and the normal vector of each coordinate point of the current frame line array and the current scanning section fitting line are calculated. Corresponding to the angle between normal vectors, [-90°, 90°] is divided into 9 value intervals with a step size of 20°, and the proportion of the corresponding angle of each point in the current frame falling into the corresponding interval is counted, thereby constructing a nine-tuple of normal vector distribution to evaluate the normal distribution of the current scanning section; the angle with the largest absolute value is the maximum angle of the normal vector, and the y coordinate value of the coordinate point corresponding to the maximum angle is determined.

5. A road surface flatness line scanning monitoring method according to claim 4, characterized in that: The key parameters of the shape and position of the fitted line deviation are specifically solved as follows: Mean absolute deviation All coordinate points p of the current linear point cloud i，1 (x i ,y i，1 , z i，1 )~p i，n (x i ,y i，n , z i，n ) to the aforementioned scanning section fitting straight line The average distance of Ay+Bz+C=0 is determined, that is Where d i，j Refers to p i，1 ~p i，n Any point p i，j (x i ,y i，j , z i，j )arrive The distance Ay+Bz+C=0 represents the fitting straight line described in claim 3 The equation Ay+Bz+C=0 is always satisfied, where A, B, C represent the coefficients of the straight line equation, and z and v represent the vertical and horizontal coordinates in the road point cloud data; Maximum positive deviation Maximum positive deviation point y coordinate Current linear point cloud p i，1 ~p i，n The center is located in the scan section fitting straight line The point above and farthest from the straight line is the point of maximum positive deviation and is recorded as The point to The distance is the maximum positive deviation value The y coordinate of this point is recorded as Maximum negative deviation Maximum negative deviation point y coordinate Current linear point cloud p i，1 ~p i，n The center is located in the scan section fitting straight line The point below and farthest from the straight line is the point with the maximum negative deviation and is recorded as The point to The distance is the maximum negative deviation value The y coordinate of this point is recorded as 6. A road surface flatness line scanning monitoring method according to claim 4, characterized in that: The key parameters of the shape and position of the fitted line deviation are specifically solved as follows: Normal vector distribution ennead For all coordinate points p of the current linear point cloud i，1 ~p i，n Any point p in i，j , through its left and right adjacent points p i，j-1 and p i，j+1 Calculate the fitting line to get the normal vector n corresponding to the point i，j ; Calculate n again i，j With n i The angle θ i，j , and stipulate that n i，j Located in i Clockwise, θ i，j When it is negative and located counterclockwise, θ i，j is positive; Angle θ i，j The value range of is [-90°, 90°], and [-90°, 90°] is divided into 9 intervals with a step size of 20°, namely, [-90°, -70°), [-70°, -50°), ..., [70°, 90°]; Calculate all coordinate points p of the current linear point cloud i，1 ~p i，n The corresponding normal vector n i，1 ~n i，n With n i The angle formed is θ i，1 ~θ i，n ; Statistics θ i，1 ~θ i，n The number of points that fall into the above 9 intervals respectively, and calculate the ratio of the number of points in each interval to the total number of points n of the current linear array Thus, we get the normal vector distribution nine-tuple Maximum normal vector angle The y coordinate of the angle point with the maximum normal vector The normal vector n corresponding to all coordinate points of the current linear point cloud i，1 ~n i，n With n i The maximum angle formed is recorded as and The y coordinate of the corresponding coordinate point is 7. The line scanning monitoring method for road surface flatness according to claim 1 is characterized in that: In step (3), a marking segmented monitoring mechanism is used to determine the location where the abnormality occurs: by reading the corresponding serial number of the identifier through the identifier reading device (102) fixed on the carrier (1), it can be determined between which two identifiers the current monitoring position is located, and then combined with the abnormal frame acquisition time and the corresponding real-time speed information, the distance from the abnormal position to the identifier can be accurately determined.

8. A road surface flatness line scanning monitoring method according to claim 7, characterized in that: The segmented monitoring includes the following steps: Marks are arranged at equal intervals of Δx on the road shoulder, and when a reading device (102) faces the mark, a serial number corresponding to the mark is read, and a specific location where the abnormal road surface flatness occurs is found according to the serial number; Abnormal road surface roughness is detected at time t m , and the time t m At the time t of reading the identification I and reading the identification II I and t II If t m At the time t of reading the mark II (302) and reading the mark III (303) II and t III If the anomaly is between marker II (302) and marker III (303), then it can be seen that the anomaly must occur between marker II (302) and marker III (303), and so on; The abnormal frame appears between the mark s and the mark s+1, where s = I, II, III, ..., from t s The calculation starts when the mark s is read at time t. If the flatness parameter of the m-th frame linear array data is abnormal, the abnormality recognition time t m Corresponding to the mth frame of linear point cloud data after the identification s is read, the abnormal position can be accurately located according to the real-time speed information: Where v m represents the real-time speed of vehicle (1) when acquiring the mth frame data, t1 and v1 represent the time of the first frame from reading the tag s and the real-time speed of vehicle l, respectively, m_s represents the x-distance from the outlier point to the marker s, v t It indicates the real-time speed of vehicle l when the i-th frame of linear point cloud data is obtained starting from the reading of identification s.

9. A road surface flatness line scanning monitoring method according to any one of claims 1, 4 or 8, characterized in that: In step (4), the A and B modes are combined to monitor the abnormality of the road surface flatness. Mode A: Threshold discrimination method, When the key shape and position parameters of a road surface flatness of a certain frame exceed the normal threshold range, it means that the flatness of the corresponding position of the frame is abnormal. Combined with step (3), the abnormal position is determined and targeted repairs are performed; Mode B: Benchmark template comparison, first complete the pre-scan of all sections in the entire monitoring domain, obtain the key parameter vector of the flatness corresponding to each frame in each section, and then build the benchmark template; Then, in actual monitoring, the interpolation method is used to calculate the difference vector between the key parameter vector of the current monitoring frame and the reference template; The difference vector is used to determine the magnitude of the change in each vector element compared to the initial benchmark, thereby judging the change in the road surface smoothness before and after on a time scale.

10. A road surface flatness line scanning monitoring method according to claim 9, characterized in that: In mode B, it is assumed that during the pre-scanning process, the 1st to qth frames of point cloud data are collected in the segment divided by the marker s and the marker s+1, and the key parameter vector S of the flatness corresponding to each of the q frames of data is obtained. l , S2, ... S i , ... S q , forming a reference template, where any vector: Among them, x i It represents the x-direction distance from the linear point cloud data corresponding to Si to the mark s, and the remaining elements are the key shape and position parameters of the road surface flatness mentioned above; In actual monitoring, when the vehicle passes through the segment divided by the identifiers s and s+l, the key parameter vector of the flatness corresponding to all frame linear point cloud data in the segment will also be obtained, where a frame vector S m It is expressed as: If x i <x m <x i+1 , that is, the scanning position corresponding to Sm is between the reference template vector S i and S i+1 The difference vector ΔS can be calculated by interpolation. m : It is expressed as: ΔS m It characterizes the difference between the actual monitoring and the initial benchmark, and judges the change in the key parameters of each road surface smoothness through its second to last elements: For example, in actual monitoring, the difference vector ΔS corresponding to the mth frame of a certain scanning position m , The second element If the value is greater than the specified value, the x after the scan position marker s m The deviation value of the fitting line at the corresponding position has a large change, indicating that the overall smoothness of the road surface has deteriorated significantly; Element 3 and / or the 5th element If it is greater than the specified value, it indicates that the maximum fluctuation at the corresponding scanning surface is aggravated, and there is a hidden danger of road disease that affects driving safety; Element 4 and / or the 6th element If the absolute value is greater than the specified value, it indicates that the maximum fluctuation point of the corresponding scanning surface has changed significantly compared with the initial benchmark, and a new disease damage area has been generated; Elements 7 to 15: If the absolute value of each element is greater than the specified value, the normal vector distribution changes significantly compared to the initial benchmark, that is, the lateral fluctuation of the road surface changes significantly; Element 16 If it is larger than the specified value, the maximum deviation of the normal direction of the current scanning surface will further increase significantly; The 17th element If the absolute value is greater than the specified value, the maximum normal deviation point has changed significantly compared to the initial benchmark, and a new diseased and damaged area has been generated. The above road surface smoothness scanning monitoring method can selectively or simultaneously work in mode A and mode B: mode A is used to identify abnormal road surface smoothness, while mode B is used to monitor the smoothness before and after changes.

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