A road detection system and method based on three-dimensional laser technology

Abstract

The present invention discloses a road detection system and method based on 3D laser technology, which relates to the field of road detection technology using 3D laser technology. The data acquisition module is used to scan the road and obtain 3D point cloud data. The data calculation and processing module is used to calculate and output the height of each laser point relative to the reference plane, that is, the height G of the i-th laser point relative to the reference plane i , calculate and output the slope angle and outliers between adjacent laser points, that is, the slope angle θ between detection points i and the outlier K of the i-th laser point i , calculate and output the standard deviation of flatness T according to the outlier K of the i-th laser point i , use the detection and evaluation module, and based on the international roughness index IRI, evaluate whether the standard deviation of flatness T meets the standard, store the data, and use the data calculation and processing module to make adjustments. The present invention achieves the purpose of improving the detection efficiency and accuracy, enhancing the abnormal detection ability, and the loop influence mechanism of feedback of the analysis results to the next scan

Description

Technical Field

[0001] The present invention relates to the technical field of road detection using 3D laser technology, and specifically to a road detection system and method based on 3D laser technology. Background Art

[0002] As an infrastructure of modern society, roads carry the busy operation of people and goods. Therefore, road detection technology is of great significance for ensuring road safety, improving driving comfort, and extending the service life of roads. With the progress of technology, people have begun to explore more efficient and accurate road detection technologies, among which 3D laser technology has gradually emerged.

[0003] Currently, when dealing with 3D point cloud data, existing road detection systems often face problems such as large data volume and high processing complexity, resulting in low detection efficiency. Moreover, due to environmental interference and the accuracy limitations of the scanner itself, there will be large errors when calculating the height, slope angle, and abnormality degree of laser points in existing systems, thereby affecting the accuracy of detection results. In addition, existing technologies often only focus on the analysis of single scan results and lack a loop influence mechanism to feedback the analysis results to the next scan, resulting in the detection results may not reflect the true condition of the road. In terms of anomaly detection, existing road detection systems can usually only identify relatively obvious anomaly points, and it is often difficult to accurately identify subtle road deformations and anomalies. Summary of the Invention

[0004] The purpose of the present invention is to provide a road detection system based on 3D laser technology, which solves the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides a road detection method based on 3D laser technology, and the specific implementation steps are as follows:

[0006] Step S1: Use the data acquisition module to scan the road and obtain 3D point cloud data, where the 3D point cloud data includes the X-axis coordinates, Y-axis coordinates, Z-axis coordinates, and slope of each detection laser point on the road.

[0007] Step S2: Use the data calculation and processing module to calculate and output the height of each laser point relative to the reference plane, that is, the height G of the i-th laser point relative to the reference plane i ;

[0008] Calculate and output the slope angle and anomaly value between adjacent laser points, that is, the slope angle θ between detection points i and the anomaly value K of the i-th laser point i ;

[0009] According to the anomaly value K of the i-th laser point i Calculate and output the flatness standard deviation T;

[0010] Step S3: Use the detection and evaluation module, and based on the International Roughness Index (IRI), evaluate whether the standard deviation T of flatness meets the standard;

[0011] Step S4: Based on the evaluation results, perform data storage and use the data calculation and processing module for adjustment;

[0012] Among them, the data calculation and processing module includes a point cloud data and height processing and calculation unit, a slope and abnormal point detection and processing unit, and a road flatness evaluation and feedback adjustment unit;

[0013] The devices used by the data acquisition module include a three-dimensional laser scanner;

[0014] The devices used by the data calculation and processing module include a computer processing system;

[0015] The devices used by the detection and evaluation module include a software platform.

[0016] Optionally, the calculation formula of the point cloud data and height processing and calculation unit is as follows:

[0017]

[0018] Where:

[0019] G i is the height of the i-th laser point relative to the reference plane;

[0020] X i is the X-axis coordinate of the i-th laser point;

[0021] Y i is the Y-axis coordinate of the i-th laser point;

[0022] Z i is the Z-axis coordinate of the i-th laser point;

[0023] X i 、Y i and Z i reflect the three-dimensional coordinates of the i-th laser point in three-dimensional space;

[0024] Z ref is the reference plane height, and Z ref reflects the average height of the reference design height during road design.

[0025] Optionally, the calculation formula of the slope and abnormal point detection and processing unit is as follows:

[0026] θ i =arctan[(G i -Gi-1 ) / P i，i-1 ;

[0027] K i =|θ i -θ avg |×(G i / G max );

[0028] Where:

[0029] θ i is the slope angle between detection points, θ i reflects the slope angle between the i-th laser point and the (i - 1)-th laser point;

[0030] K i is the outlier of the i-th laser point, K i reflects the degree of abnormality of the i-th laser point;

[0031] G i-1 is the height of the (i - 1)-th laser point relative to the reference plane;

[0032] P i，i-1 is the horizontal distance between detection points, P i，i-1 reflects the horizontal distance between the i-th laser point and the (i - 1)-th laser point;

[0033] θ avg is the average slope, θ avg reflects the average degree of the slopes of all laser detection points in the current detection section;

[0034] G max is the maximum relative height, G max reflects the maximum value of the heights of all laser detection points relative to the reference plane in the current detection section.

[0035] Optionally, the calculation formula for the horizontal distance P between detection points is as follows: i，i-1 The following:

[0036]

[0037] ΔX = X i - X i-1 ;

[0038] ΔY = Y i - Y i-1 ;

[0039] ΔX is the X - coordinate difference, reflecting the difference degree of the X - axis coordinates between the i-th laser point and the (i - 1)-th laser point;

[0040] X i-1 is the X - axis coordinate of the (i - 1)-th laser point;

[0041] ΔY is the Y - coordinate difference, reflecting the difference degree of the Y - axis coordinates between the i - th laser point and the (i - 1) - th laser point;

[0042] Y i-1 is the Y - axis coordinate of the (i - 1) - th laser point.

[0043] Optionally, the calculation formula of the road flatness evaluation and feedback adjustment unit is as follows:

[0044]

[0045] Where:

[0046] T is the standard deviation of flatness, and T reflects the overall flatness of road detection;

[0047] I is the total number of laser points;

[0048] K avg is the abnormal average value, and K avg reflects the average abnormal degree of the total number I of laser points in road detection.

[0049] Optionally, the calculation formula of the average slope angle θ avg is as follows:

[0050] θ avg =(θ1 + θ2 + θ3+......+θ I ) / I;

[0051] θ1 is the slope of the first laser point, θ2 is the slope of the second laser point, θ3 is the slope of the third laser point, θ I is the slope of the I - th laser point;

[0052] The abnormal average value K avg has the following calculation formula:

[0053] K avg =(K1 + K2 + K3+......+K I ) / I;

[0054] K1 is the abnormal value of the first laser point, K2 is the abnormal value of the second laser point, K3 is the abnormal value of the third laser point, K I is the abnormal value of the I - th laser point;

[0055] Where;

[0056] All the laser detection points in the slope and abnormal point detection and processing unit are the total number I of laser points.

[0057] Optionally, the detection and analysis based on the standard deviation of flatness T and the international roughness index IRI are as follows:

[0058] If T < IRI / T = IRI, it indicates that the road evenness meets the standard and no adjustment is required;

[0059] If T > IRI, it indicates that the road evenness does not meet the standard. The specific calculation and adjustment formula are as follows:

[0060]

[0061] G new，1 = G adg，1 + (G adg，I - G adg，1 ) × [X1 / (X I - X1)];

[0062] Z new，ref = Z ref + (G new，1 - G adg，avg );

[0063] G adg，i is the height of the i-th laser point after adjustment relative to the reference plane, G adg，1 is the height of the first laser point after adjustment relative to the reference plane, G adg，I is the height of the I-th laser point after adjustment relative to the reference plane;

[0064] G adg，avg is the average value of the heights of the first to I-th laser points after adjustment relative to the reference plane;

[0065] G new，1 is the new height value of the first laser point after adjustment;

[0066] X1 is the X-axis coordinate of the first laser point, X I is the X-axis coordinate of the I-th laser point;

[0067] Z new，ref is the height of the reference plane after adjustment, and the height of the reference plane Z new，ref after adjustment is used as the input value of the reference plane height Z ref for the next iterative calculation.

[0068] The present invention also provides a road detection system based on three-dimensional laser technology, including a data acquisition module, a data calculation and processing module, and a detection and evaluation module;

[0069] The data acquisition module is used to obtain three-dimensional point cloud data, and the three-dimensional point cloud data includes the X-axis coordinate, Y-axis coordinate, Z-axis coordinate, and slope of each detection laser point on the road;

[0070] The data calculation and processing module is used to sequentially calculate and output the height G of the i-th laser point relative to the reference planei , the slope angle θ between detection points i and the outlier K of the i-th laser point i , as well as the standard deviation T of flatness;

[0071] The said detection and evaluation module is used to evaluate the detection result of the standard deviation T of flatness;

[0072] The said data calculation and processing module is used to adjust the unqualified standard deviation T of flatness for the next iteration, and calculate and output the adjusted reference plane height Z new，ref .

[0073] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0074] First, the road detection system of the present invention based on three-dimensional laser technology significantly improves the detection efficiency and accuracy by optimizing the data processing algorithm and introducing a loop influence mechanism. Specifically, the point cloud data and height processing calculation unit simplifies the processing process of point cloud data by calculating the height of each laser point relative to the reference plane, thereby reducing the computational complexity. The slope and outlier detection and processing unit utilizes the slope angle and height difference between adjacent laser points to achieve rapid detection of road outliers and improve the detection accuracy, thus providing more reliable data support for road maintenance and repair.

[0075] Second, the road flatness evaluation and feedback adjustment unit in the present invention calculates the standard deviation T of flatness, and based on the outlier K of the i-th laser point i and the standard deviation T of flatness, and then adjusts the height G of the i-th laser point of each laser point relative to the reference plane after adjustment adg，1 , and finally calculates the new height value G of the first laser point after adjustment using the adjusted height value new，1 , as the adjustment basis for the reference plane height Z in the next iteration. Specifically, it is to use the calculated and output adjusted reference plane height Z ref to replace the reference plane height Z during the next iteration new，ref , and this mechanism realizes the loop influence of the detection result on subsequent scans, making the detection result closer to the true condition of the road. ref

[0076] Third, the calculation of the outlier K of the i-th laser point in the slope and outlier detection and processing unit of the present invention not only considers the difference in slope angle, but also combines the maximum relative height G i , making the outlier detection more comprehensive and accurate. In addition, the road flatness evaluation and feedback adjustment unit further enhances the system's ability to identify subtle road deformations and outliers by adjusting the height value of the laser point. max Description of the Drawings

[0077] Figure 1 is the method flow chart of the road detection system based on three-dimensional laser technology;

[0078] Figure 2 is the three-dimensional coordinate schematic diagram of the road detection system based on three-dimensional laser technology;

[0079] Figure 3 is the structural schematic diagram of the data calculation and processing module of the present invention;

[0080] Figure 4 is the iterative schematic diagram of the feedback loop of the present invention. Specific embodiments

[0081] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0082] Regarding this road detection system based on three-dimensional laser technology, different from the existing road detection systems, when the existing road detection systems face the problems of large data volume and high processing complexity, it will lead to low detection efficiency and large errors, thus affecting the accuracy of the detection results. In addition, the existing technologies often only focus on the analysis of single-scan results and lack a loop influence mechanism that feeds the analysis results back to the next scan. For subtle road deformations and anomalies, it is often difficult to accurately identify, while this algorithm unit achieves the purpose of improving detection efficiency and accuracy, enhancing the anomaly detection ability, and the loop influence mechanism that feeds the analysis results back to the next scan.

[0083] Example 1, please refer to Figures 1 to 4 , this embodiment provides a road detection method based on three-dimensional laser technology, and the specific implementation process is as follows:

[0084] Using the data acquisition module, perform road scanning and obtain three-dimensional point cloud data. The three-dimensional point cloud data includes the X-axis coordinates, Y-axis coordinates, Z-axis coordinates, and slope of each detection laser point on the road;

[0085] Using the data calculation and processing module, calculate and output the height of each laser point relative to the reference plane, that is, the height G of the i-th laser point relative to the reference plane i ;

[0086] Calculate and output the slope angle and anomaly value between adjacent laser points, that is, the slope angle θ between detection points i and the anomaly value K of the i-th laser point i ;

[0087] According to the abnormal value K of the i-th laser point i Calculate and output the flatness standard deviation T;

[0088] Use the detection and evaluation module, and based on the international roughness index IRI, evaluate whether the flatness standard deviation T meets the standard;

[0089] Based on the evaluation results, store the data and perform adjustment using the data calculation and processing module;

[0090] Among them, the data calculation and processing module includes a point cloud data and height processing and calculation unit, a slope and abnormal point detection and processing unit, and a road flatness evaluation and feedback adjustment unit;

[0091] The equipment used by the data acquisition module includes a three-dimensional laser scanner;

[0092] The equipment used by the data calculation and processing module includes a computer processing system;

[0093] The equipment used by the detection and evaluation module includes a software platform;

[0094] The present invention also provides a road detection system based on three-dimensional laser technology, including a data acquisition module, a data calculation and processing module, and a detection and evaluation module;

[0095] The data acquisition module is used to obtain three-dimensional point cloud data, and the three-dimensional point cloud data includes the X-axis coordinates, Y-axis coordinates, Z-axis coordinates and slope of each detection laser point on the road;

[0096] The data calculation and processing module is used to calculate and output the height G of the i-th laser point relative to the reference plane in sequence i , the slope angle θ between detection points i and the abnormal value K of the i-th laser point i , as well as the flatness standard deviation T;

[0097] The detection and evaluation module is used to evaluate the detection results of the flatness standard deviation T;

[0098] The data calculation and processing module is used to adjust the unqualified flatness standard deviation T for the next iteration, and calculate and output the adjusted reference plane height Z new，ref .

[0099] In this embodiment, the system realizes the accurate evaluation and feedback adjustment of road flatness through the mutual cooperation of three algorithm units, combined with G i , θ i and K i and the four operation results of T. Specifically, G iis the height of the i-th laser point relative to the reference plane. This is the first step of the road detection system and the basis for subsequent analysis. By calculating the height of each laser point, a dataset on the height distribution of the road surface is obtained. This dataset contains detailed information about the road surface, including undulations, potholes, and bumps, and this information is crucial for subsequent slope calculation, outlier detection, and road roughness assessment, θ i is the slope angle between detection points, K i is the outlier value of the i-th laser point. The calculation of the slope angle helps to understand the inclination of the detected road surface, while the calculation of the outlier degree is to identify possible road outliers. By comparing the difference between the slope angle and the average slope angle and combining the height value of this point, we can calculate the outlier degree of each laser point, thus quickly locating the abnormal areas on the road, which is of great significance for the formulation of road maintenance and repair plans. T is the standard deviation of roughness. By calculating the standard deviation of road roughness, the flatness of the road surface can be objectively measured. Then, according to K i and the standard deviation T, the height value of each laser point is further adjusted to reflect the adjustment of road roughness, and the calculation result of T can also affect the feedback to G i 、θ i and K i The calculation makes the three algorithms of this system play important roles in the road detection system and method based on 3D laser technology, and the cyclic influence of T on G i further improves the accuracy and stability of the detection system.

[0100] Please refer to Figures 1 to 4 , the calculation formula of the point cloud data and height processing calculation unit is as follows:

[0101]

[0102] Among them:

[0103] G i is the height of the i-th laser point relative to the reference plane;

[0104] X i is the X-axis coordinate of the i-th laser point;

[0105] Y i is the Y-axis coordinate of the i-th laser point;

[0106] Z i is the Z-axis coordinate of the i-th laser point;

[0107] X i 、Y i and Z i reflect the three-dimensional coordinates of the i-th laser point in three-dimensional space;

[0108] Z ref is the reference plane height, Z ref reflects the average height of the reference design height during road design.

[0109] In this embodiment: First, in this algorithm unit, the calculation part "Z i -Z ref " is used to determine the vertical distance of each laser point relative to the reference plane, which is a key step in calculating the height of the laser point relative to the reference plane. In the point cloud data and height processing calculation unit, this difference is used to calculate the height Z of the laser point relative to the reference plane ref , and by comparing with the square root of the horizontal distance , the offset of the laser point in the vertical direction can be obtained, thereby evaluating the vertical deformation of the road;

[0110] This calculation part represents the square of the projection distance of the laser point on the horizontal plane and is used to compare with the vertical distance "Z i -Z ref " to obtain the height of the laser point relative to the reference plane. In fact, the square root of this sum of squares represents the position information of the laser point on the horizontal plane, which together with the vertical distance constitutes the three-dimensional coordinate information of the laser point. In the point cloud data and height processing calculation unit, it is mainly used to compare with the vertical distance to calculate the height of the laser point;

[0111] This algorithm unit directly uses the three-dimensional coordinates (Xi, Yi, Zi) of the laser point and the reference plane height Z of the road design reference ref for calculation, avoiding cumbersome spatial geometric transformations and complex mathematical model applications, thereby significantly reducing the time cost and calculation complexity of data processing. And the simplified processing flow enables the system to respond more quickly to data input, improving the overall detection efficiency;

[0112] By directly calculating the height G of the i-th laser point relative to the reference plane i , the errors introduced by coordinate conversion, interpolation calculation, and model fitting processes are reduced, thereby improving the accuracy and reliability of the height data. The high-precision data provides a solid foundation for subsequent analysis and helps to more accurately identify the tiny changes on the road surface;

[0113] The height G of the i-th laser point relative to the reference plane calculated by this algorithm unit i is the basis for subsequent slope calculation, outlier detection, and flatness evaluation analysis steps. These basic data not only provide the necessary input for road detection, but also enable the system to more comprehensively evaluate the road condition and provide a basis for maintenance and repair decisions.

[0114] Please refer to Figures 1 to 4 , and the calculation formulas of the slope and anomaly point detection and processing unit are as follows:

[0115] θ i = arctan[(G i - G i-1 ) / P i，i-1 ;

[0116] K i = |θ i - θ avg | × (G i / G max );

[0117] Where:

[0118] θ i is the slope angle between detection points, and θ i reflects the slope angle between the i-th laser point and the (i - 1)-th laser point;

[0119] K i is the anomaly value of the i-th laser point, and K i reflects the degree of anomaly of the i-th laser point;

[0120] G i-1 is the height of the (i - 1)-th laser point relative to the reference plane;

[0121] P i，i-1 is the horizontal distance between detection points, and P i，i-1 reflects the horizontal distance between the i-th laser point and the (i - 1)-th laser point;

[0122] θ avg is the average slope, and θ avg reflects the average degree of the slopes of all laser detection points in the current detection section;

[0123] G max is the maximum relative height, and G max reflects the maximum value of the heights of all laser detection points relative to the reference plane in the current detection section;

[0124] The calculation formula of the horizontal distance P between detection points i，i-1 is as follows:

[0125]

[0126] ΔX = X i - X i-1 ;

[0127] ΔY = Y i - Y i-1 ;

[0128] ΔX is the X - coordinate difference, reflecting the degree of difference in the X - axis coordinates between the i - th laser point and the (i - 1) - th laser point;

[0129] X i-1 is the X - axis coordinate of the (i - 1) - th laser point;

[0130] ΔY is the Y - coordinate difference, reflecting the degree of difference in the Y - axis coordinates between the i - th laser point and the (i - 1) - th laser point;

[0131] Y i-1 is the Y - axis coordinate of the (i - 1) - th laser point.

[0132] In this embodiment, first This calculation part is used to determine the horizontal distance between adjacent laser points, which is the basis for calculating the slope angle θ i between detection points. In the slope and outlier detection processing unit, the calculated horizontal distance P i，i-1 between detection points is used to calculate the slope angle θ i between detection points, that is, the arctangent of the ratio of the height difference between adjacent laser points to the horizontal distance. This slope angle reflects the rate of change of the road in the horizontal direction;

[0133] “G i -G i-1 ” This calculation part is used to determine the height difference between adjacent laser points and is a key step in calculating the slope angle θ i between detection points and the outlier value K i of the i - th laser point. In the slope and outlier detection processing unit, “G i -G i-1 ” is used to calculate the slope angle θ i，i-1 between detection points together with the horizontal distance P i between detection points. At the same time, it is also an important factor when calculating the outlier value K i of the i - th laser point because the degree of abnormality is calculated based on the difference between the slope angle and the average slope angle;

[0134] “|θ i -θ avg |” This calculation part is used to determine the difference between the slope angle of each laser point and the average slope angle, which is a key indicator for detecting road abnormalities. In the slope and outlier detection processing unit, “|θ i -θ avg |” is used to calculate the outlier value K i of the i - th laser point. When the difference between the slope angle of a certain laser point and the average slope angle is large, it indicates that there is an abnormality at this point. By comparing this difference with the maximum relative height G maxThe product can yield the degree of abnormality at that point;

[0135] This algorithm unit can quickly identify abnormal points on the road surface, including potholes and bumps, by comparing the slope angle with the average slope angle θ avg and combining the height value at that point. This rapid identification ability helps the system respond promptly to changes in road conditions and provides early warnings for emergency repairs and maintenance;

[0136] This slope and abnormal point detection and processing unit takes into account both the slope angle difference and the height value, making the abnormal detection more comprehensive and accurate. The change in the slope angle can reflect the minor undulations on the road surface, while the height value provides more intuitive information. This comprehensive detection method reduces the possibility of false alarms and missed detections, improving the accuracy and reliability of the detection;

[0137] The detection of abnormal points provides crucial information for subsequent road evenness assessment and repair decision-making. This information helps the system more accurately evaluate the overall condition of the road and provides a basis for formulating maintenance and repair plans.

[0138] Please refer to Figures 1 to 4 , the calculation formula of the road evenness assessment and feedback adjustment unit is as follows:

[0139]

[0140] Where:

[0141] T is the standard deviation of evenness, and T reflects the overall evenness of the road detection;

[0142] I is the total number of laser points;

[0143] K avg is the average abnormality, and K avg reflects the average degree of abnormality of the total number of laser points I in the road detection.

[0144] In this embodiment, this algorithm unit first uses the calculation part of "K i - K avg " to determine the difference between the degree of abnormality of each laser point and the average degree of abnormality. It is the basis for calculating the standard deviation of evenness T. In the road evenness assessment and feedback adjustment unit, "K i - K avg " is used to calculate the standard deviation of evenness T. The standard deviation of evenness T reflects the fluctuation of the overall evenness of the road. When the difference between the degree of abnormality of a certain laser point and the average degree of abnormality is large, it indicates that this point makes a greater contribution to the road evenness. This calculation part is used to calculate the sum of squares of the differences between the abnormal degrees of all laser points and the average abnormal degree. It is the direct basis for calculating the standard deviation T of flatness. In the road flatness evaluation and feedback adjustment unit, this sum of squares is used to divide the total number of laser points, thereby reflecting the fluctuation degree of the overall road flatness. By comparing the standard deviation T of flatness of different roads and the same road at different times, the flatness and driving comfort of the road can be evaluated.

[0145] In this algorithm unit, by calculating the standard deviation T of flatness, the system can objectively and accurately evaluate the overall flatness of the road. The size of the standard deviation T of flatness reflects the degree of dispersion of the road surface height, that is, the quality of flatness. This precise evaluation method helps the system to more comprehensively understand the road conditions and provides a scientific basis for the formulation of maintenance and repair plans.

[0146] Please refer to Figures 1 to 4 , and the detection and analysis based on the standard deviation T of flatness and the international roughness index IRI are as follows:

[0147] If T < IRI / T = IRI, it reflects that the road flatness meets the standard and no adjustment is required;

[0148] If T > IRI, it reflects that the road flatness does not meet the standard. The specific calculation and adjustment formula are as follows:

[0149]

[0150] G new，1 = G adg，1 +(G adg，I - G adg，1 ) × [X1 / (X I - X1)];

[0151] Z new，ref = Z ref +(G new，1 - G adg，avg );

[0152] G adg，i is the height of the i-th laser point relative to the reference plane after adjustment, G adg，1 is the height of the 1st laser point relative to the reference plane after adjustment, G adg，I is the height of the I-th laser point relative to the reference plane after adjustment;

[0153] G adg，avg is the average height of the 1st to the I-th laser points relative to the reference plane after adjustment;

[0154] G new，1 is the new height value of the first laser point after adjustment;

[0155] X1 is the X-axis coordinate of the first laser point, and X I is the X-axis coordinate of the I-th laser point;

[0156] Z new，ref is the height of the adjusted reference plane, and the height of the adjusted reference plane Z new，ref serves as the input value of the reference plane height Z for the next iterative calculation. ref of the next iterative calculation.

[0157] In this embodiment, this algorithm unit This calculation part is used to adjust the height value according to the influence degree of the abnormality degree of each laser point on the road flatness. It is a key step to realize the feedback adjustment. In the road flatness evaluation and feedback adjustment unit, this calculation part is used to calculate the adjusted height value of each laser point, and by comparing the abnormality degree of different laser points and the influence degree on the flatness, the height of each laser point can be adjusted targeted to optimize the road flatness;

[0158] (G adg，I -G adg，1 ) × [X1 / (X I -X1)] This calculation part is used to calculate the adjustment amount of the new height value of the first laser point according to the height difference between the last laser point and the first laser point after adjustment and their position difference on the X-axis. It is a key step to realize the cyclic influence. In the road flatness evaluation and feedback adjustment unit, this calculation part is used to calculate the new height value of the first laser point, and by using this new height value as the adjustment basis of the reference plane height Z ref for the next iteration, the cyclic influence is realized, that is, the reference plane height for the next iteration is optimized according to the result of the previous iteration, so as to improve the measurement accuracy and evaluation effect of the road flatness;

[0159] According to the abnormal value K i of the i-th laser point and the flatness standard deviation T, the system adjusts the height value of each laser point. This adjustment makes the adjusted i-th laser point more able to reflect the true condition of the road relative to the reference plane height G adg，i , reducing the deviation caused by errors and interference factors. The adjusted i-th laser point is more able to reflect the true condition of the road relative to the reference plane height G adg，i Especially the new height value G new，1 after adjustment of the first laser point can be used as the adjustment basis of the reference plane height Z ref for the next iteration. This cyclic influence mechanism enables the system to continuously adjust and optimize according to the new detection results;

[0160] Through continuous iteration and optimization, the system can achieve continuous monitoring and improvement of road conditions, providing more timely and accurate data support for road maintenance and repair. At the same time, this cyclic influence mechanism also helps to improve the stability and reliability of the system, enabling the system to maintain high precision and high efficiency for a longer time, improving the stability and reliability of the detection results. This improvement in stability helps the system to maintain high precision and high efficiency for a longer time, providing more reliable data support for road maintenance and repair;

[0161] It should be noted that the cyclic influence mechanism between the road roughness evaluation and feedback adjustment unit and the point cloud data and height processing and calculation unit enables the detection system to continuously adjust and optimize according to new detection results. This dynamic adjustment process not only helps the system to adapt to changes in different road conditions and detection environments, but also gradually eliminates the influence of errors and interference factors through continuous iteration and optimization. This improvement in stability enables the system to maintain high precision and high efficiency for a longer time, providing more reliable data support for road maintenance and repair. At the same time, this cyclic influence mechanism also enhances the system's adaptive ability and robustness, enabling the system to maintain excellent performance when facing complex and variable road detection tasks;

[0162] In summary, the cyclic influence of the road roughness evaluation and feedback adjustment unit on the point cloud data and height processing and calculation unit significantly improves the accuracy and reliability of the road detection system by optimizing the reference plane height and enhancing the stability of the detection system. This cyclic influence mechanism not only enhances the system's adaptive ability and robustness, but also provides more accurate data support for road maintenance and repair, having important practical application value.

[0163] Example two, please refer to Figures 1 to 4 , the average value of the slope angle θ avg The calculation formula is as follows:

[0164] θ avg =(θ1 + θ2 + θ3 +......+ θ I ) / I;

[0165] θ1 is the slope of the first laser point, θ2 is the slope of the second laser point, θ3 is the slope of the third laser point, θ I is the slope of the I-th laser point;

[0166] The average value of anomalies K avg The calculation formula is as follows:

[0167] K avg =(K1 + K2 + K3 +......+ K I ) / I;

[0168] K1 is the first laser point outlier, K2 is the second laser point outlier, K3 is the third laser point outlier, and K I is the I-th laser point outlier;

[0169] wherein;

[0170] All the laser detection points in the slope and outlier detection and processing unit are the total number of laser points I.

[0171] In this embodiment, the total number of laser points I directly affects the calculation accuracy of the flatness standard deviation T. When the total number of laser points I is larger, that is, the more laser points are collected, the more accurately the calculated flatness standard deviation T can reflect the overall flatness of the road surface. This is because more data points can more comprehensively cover various micro-undulations and abnormal areas on the road surface, thereby reducing the errors caused by insufficient data. Therefore, introducing the total number of laser points as a calculation parameter helps to improve the accuracy of road flatness evaluation, making this feedback adjustment mechanism more accurate and effective. In addition, when the total number of laser points I is larger, the calculated flatness standard deviation T and the adjusted reference plane height Z new，ref can more accurately reflect the true condition of the road surface. In this way, in the subsequent iterative process, the reference plane height Z ref can be adjusted more accurately following the changes of the road surface, thereby further improving the accuracy and stability of the road detection system.

[0172] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it is understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A road detection method based on three-dimensional laser technology, characterized in that: The specific implementation steps are as follows: Step S1: Use the data acquisition module to perform road scanning and obtain 3D point cloud data. The 3D point cloud data includes the X-axis coordinates, Y-axis coordinates, Z-axis coordinates, and slope of each detection laser point on the road; Step S2: Using the data calculation processing module, calculate and output the height of each laser point relative to the reference plane, that is, the height G of the i-th laser point relative to the reference plane i ; Calculate and output the slope angle between adjacent laser points and the outlier value, that is, the slope angle θ between the detection points i And the outlier value K of the i-th laser point i ; According to the outlier value K of the i-th laser point i Calculate the output flatness standard deviation T; Step S3: Use the detection and evaluation module to evaluate whether the flatness standard deviation T meets the standard based on the international roughness index IRI; Step S4: Based on the evaluation results, perform data storage and use the data calculation and processing module for adjustment; Among them, the data calculation and processing module includes a point cloud data and height processing and calculation unit, a slope and abnormal point detection and processing unit, and a road flatness evaluation and feedback adjustment unit.

2. A road detection method based on three-dimensional laser technology according to claim 1, characterized in that: The calculation formula of the point cloud data and height processing and calculation unit is as follows: Where: G i is the height of the i-th laser point relative to the reference plane; X i is the X-axis coordinate of the i-th laser point; Y i is the Y-axis coordinate of the i-th laser point; Z i is the Z-axis coordinate of the i-th laser point; X i , Y i and Z i Reflects the three-dimensional coordinates of the i-th laser point in three-dimensional space; Z ref is the reference plane height, Z ref Reflects the average height used as a reference for road design.

3. The road detection method based on three-dimensional laser technology according to claim 2, characterized in that: The calculation formula of the slope and abnormal point detection and processing unit is as follows: θ i =arctan[(G i -G i-1 ) / P i，i-1 ]; K i =|θ i -θ avg |×(G i / G max ); Where: θ i is the slope angle between the detection points, θ i Reflects the slope angle between the i-th laser point and the i-1-th laser point; K i is the outlier value of the i-th laser point, K i Reflects the abnormality degree of the i-th laser point; G i-1 is the height of the i-1th laser point relative to the reference plane; P i，i-1 is the horizontal distance between detection points, P i，i-1 Reflects the horizontal distance between the i-th laser point and the i-1-th laser point; θ avg is the average slope angle, θ avg Reflects the average slope angle of all laser detection points in the current detection section; G max is the maximum relative height, G max Reflects the maximum value of the height of all laser detection points in the current detection section relative to the reference plane; The horizontal distance P between the detection points i，i-1 The calculation formula is as follows: ΔX=X i -X i-1 ; ΔY=Y i -Y i-1 ; ΔX is the X coordinate difference, reflecting the degree of difference in the X-axis coordinates between the i-th laser point and the i-1-th laser point; X i-1 is the X-axis coordinate of the i-1th laser point; ΔY is the Y coordinate difference, reflecting the degree of difference in the Y-axis coordinates between the i-th laser point and the i-1-th laser point; Y i-1 is the Y-axis coordinate of the i-1th laser point.

4. The road detection method based on three-dimensional laser technology according to claim 3 is characterized in that: The calculation formula of the road flatness evaluation and feedback adjustment unit is as follows: Where: T is the flatness standard deviation, and T reflects the overall flatness of the road detection; I is the total number of laser points; K avg is the abnormal mean, K avg Reflects the average abnormality of the total number of laser points I detected on the road.

5. The road detection method based on three-dimensional laser technology according to claim 4, characterized in that: The slope angle average value θ avg The calculation formula is as follows: i avg =(θ1+θ2+θ3+......+θ I ) / I; θ1 is the slope of the first laser point, θ2 is the slope of the second laser point, θ3 is the slope of the third laser point, θ I is the slope of the Ith laser point; The abnormal mean value K avg The calculation formula is as follows: K avg =(K1+K2+K3+......+K I ) / I; K1 is the first laser point outlier value, K2 is the second laser point outlier value, K3 is the third laser point outlier value, K I is the outlier value of the I-th laser point; Where; All the laser detection points in the slope and abnormal point detection and processing unit are the total number of laser points I.

6. The road detection method based on three-dimensional laser technology according to claim 5, characterized in that: The detection and analysis based on the flatness standard deviation T and the international roughness index IRI are as follows: If T < IRI / T = IRI, it reflects that the road flatness meets the standard and no adjustment is required; If T > IRI, it reflects that the road flatness does not meet the standard. The specific calculation and adjustment formula is as follows: G new，1 =G adg，1 +(G adg，I -G adg，1 )×[X1 / (X I -X1)]; Z new，ref =Z ref +(G new，1 -G adg，avg ); G adg，i is the height of the ith laser point relative to the reference plane after adjustment, G adg，1 G is the height of the first laser point after adjustment relative to the reference plane. adg，I The height of the Ith laser point relative to the reference plane after adjustment; G adg，avg is the average value of the heights of the 1st to 1st laser points relative to the reference plane after adjustment; G new，1 The new height value after adjustment for the first laser point; X1 is the X-axis coordinate of the first laser point, I is the X-axis coordinate of the I-th laser point; Z new，ref is the height of the adjusted reference plane, and the height of the adjusted reference plane Z new，ref As the reference plane height Z for the next iteration calculation ref The input value of .

7. The road detection system according to claim 2, characterized in that: It includes a data acquisition module, a data calculation and processing module, and a detection and evaluation module; The data acquisition module is used to obtain 3D point cloud data. The 3D point cloud data includes the X-axis coordinates, Y-axis coordinates, Z-axis coordinates, and slope of each detection laser point on the road; The data calculation and processing module is used to sequentially calculate and output the height G of the i-th laser point relative to the reference plane. i , the slope angle θ between the detection points i and the outlier value K of the i-th laser point i , and the flatness standard deviation T.

8. The road detection system according to claim 7, characterized in that: The detection and evaluation module is used to evaluate the detection results of the flatness standard deviation T.

9. The road detection system according to claim 8, characterized in that: The detection and evaluation module is used to evaluate the detection results of the flatness standard deviation T; The data calculation and processing module is used to adjust the flatness standard deviation T that does not meet the standard in the next iteration, and calculate and output the adjusted reference plane height Z new，ref .

10. The road detection system according to claim 7, characterized in that: The equipment used by the data acquisition module includes a 3D laser scanner; The equipment used by the data calculation and processing module includes a computer processing system; The equipment used by the detection and evaluation module includes a software platform.

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