A device and method for measuring road evenness

Abstract

The application provides a device and method for measuring road flatness, and relates to the technical field of road detection, comprising: a mobile measuring body provided with a laser sensor, a distance detection device and a data processing device, the laser emission direction of the laser sensor is directly opposite to the road surface to be detected; the data processing device is used for, in the running process of the mobile measuring body, according to the point cloud data detected by the laser sensor and the running distance detected by the distance detection device, performing data optimization processing to obtain a road flatness measurement curve with the running distance as the horizontal axis and the road flatness represented by the point cloud data as the vertical axis and display. The beneficial effect is that the structure is simple, the labor operation cost is saved, the learning cost of the operator is reduced, the point cloud data can be automatically compensated and automatically corrected, the whole road flatness measurement curve can be kept on the same reference datum plane, the measurement result can keep good continuity, and the consistency of the measurement data is ensured.

Description

Technical Field

[0001] This invention relates to the field of road inspection technology, and in particular to an apparatus and method for measuring road smoothness. Background Technology

[0002] Road smoothness is an important indicator for road safety and comfort. Currently, the main devices for measuring road smoothness include a three-meter straightedge, a continuous smoothness meter (also known as a multi-wheel meter), a vehicle-mounted laser smoothness meter, and a vehicle-mounted bump accumulator.

[0003] The three-meter straightedge, mainly used in China, is the simplest instrument for measuring road surface smoothness. Made of hardwood or aluminum alloy, the straightedge is 3 meters long with a flat bottom. During measurement, the straightedge is placed horizontally on the selected road surface to be inspected, and a feeler gauge marked with height lines is inserted into the gap to measure the maximum gap height. While the three-meter straightedge method is simple and easy to operate, its disadvantages are also relatively obvious. These mainly include a lack of a unified benchmark for overall measurement, high workload for workers, significant subjective influence, and low work efficiency.

[0004] Continuous road surface leveling tester: Also known as a multi-wheel tester, its frame is supported by four support wheels at the front and four at the rear. The measuring wheel and recorder are mounted in the middle of the frame. During measurement, the measuring wheel moves up and down along the longitudinal profile curve of the road surface, and the longitudinal displacement of the leveling tester is recorded by a mileage recording device. This continuous road surface leveling tester can be towed manually or by a vehicle. Its working principle is similar to a 3m straightedge; the reference surface also changes, only mechanized operation replaces manual operation.

[0005] Vehicle-mounted laser road surface roughness meter: This non-contact method measures road surface roughness. The testing vehicle is equipped with laser sensors, accelerometers, and gyroscopes, along with an advanced data acquisition and processing system. The testing vehicle travels at a constant speed on the road being tested. A row of laser sensors is fixed to the front crossbeam of the vehicle. The laser beams are reflected to a reader to detect the road surface roughness. This value is subtracted from the value detected by the accelerometer to eliminate the influence of the vehicle's own vibration. The analog signals collected by the laser sensors are converted into digital signals and recorded. Data is collected at regular intervals, processed, and the roughness index is calculated. While vehicle-mounted laser road surface roughness meters offer high speed and accuracy, they also have several drawbacks: they require a clean road surface to minimize the impact on accuracy; the test road must be kept clear to ensure uniform vehicle speed; and the use of accelerometers to handle vehicle vibrations can lead to synchronization issues.

[0006] Vehicle-mounted bump accumulator: This is a reactive bump accumulator that efficiently and continuously collects and displays cross-sectional information of the test road surface, primarily reflecting the cumulative displacement of the rear axle and body of the vehicle during driving. This type of device requires frequent calibration because its results are related to vehicle parameters, load, and driving speed. The calibration process is time-consuming and labor-intensive, so developed countries have gradually stopped using this type of equipment in recent years. Summary of the Invention

[0007] To address the problems existing in the prior art, the present invention provides a device for measuring road smoothness, comprising a mobile measuring body, wherein the mobile measuring body is equipped with a laser sensor, a distance detection device and a data processing device, and the laser emission direction of the laser sensor is facing the road surface to be measured;

[0008] The data processing device is used to perform data optimization processing during the movement of the mobile measuring body to obtain a road smoothness measurement curve with the travel distance as the horizontal axis and the road smoothness represented by the point cloud data as the vertical axis, based on the point cloud data detected by the laser sensor and the travel distance detected by the distance detection device. The curve is then displayed.

[0009] Preferably, the data processing device includes:

[0010] The caching module is used to cache the point cloud data acquired in real time and the travel distance;

[0011] The data optimization module, connected to the cache module, is used to trigger data optimization processing on the point cloud data associated with the preset distance when the travel distance represents the preset distance that the moving measurement body has traveled in the current time, so as to obtain optimized point cloud data.

[0012] The curve generation module, connected to the data optimization module, is used to draw a segmented curve with the current preset distance advanced as the horizontal axis and the optimized point cloud data as the vertical axis based on the optimized point cloud data. The segmented curve is then spliced ​​with the segmented curve generated in the previous step after advancing the preset distance. This process is repeated until the moving measurement body reaches the measurement endpoint and completes all measurements, thus obtaining the road smoothness measurement curve.

[0013] Preferably, the data optimization process includes at least one of eliminating installation errors, removing noise, and removing the effects of vibration.

[0014] Preferably, when the data optimization processing includes eliminating installation errors, the data optimization module includes:

[0015] The data calibration unit is used to acquire the point cloud data as a reference surface data for the road before the mobile measurement body moves forward.

[0016] An error elimination unit is installed and connected to the data calibration unit. This unit is used to calculate the difference between the point cloud data associated with the preset distance and the reference datum surface data during the movement of the mobile measurement body to obtain the optimized point cloud data.

[0017] Preferably, when the data optimization processing includes noise removal, the data optimization module consists only of the second denoising unit, or is composed of both the first denoising unit and the second denoising unit. Then:

[0018] The first denoising unit is used to calculate the absolute value of the difference between the average value of the Z-axis value of each data point in the point cloud data associated with the preset distance and the Z-axis value of each data point, extract the data points whose absolute value of the difference exceeds a first threshold, then count the number of consecutive data points extracted, and when the number of consecutive data points exceeds a second threshold, replace the Z-axis value of the corresponding consecutive data points with the average value to obtain the optimized point cloud data.

[0019] The second denoising unit is used to denoise the point cloud data associated with the preset distance using the envelope method to obtain the optimized point cloud data.

[0020] Preferably, when the data optimization processing includes removing vibration effects, the data optimization module includes a vibration effect removal unit, which includes:

[0021] The first calculation subunit is used to obtain the overlapping part between the current point cloud data measured when advancing the preset distance and the previous point cloud data measured when advancing the preset distance, and to obtain the first average value of each Z-axis value corresponding to the overlapping part in the current point cloud data and the second average value of each Z-axis value corresponding to the previous point cloud data.

[0022] The second calculation subunit, connected to the first calculation subunit, is used to calculate the difference between the first average value and the second average value as the vibration error;

[0023] The vibration elimination subunit, connected to the second calculation subunit, is used to add the Z-axis values ​​corresponding to the current point cloud data to the vibration error to obtain the optimized point cloud data.

[0024] Preferably, the curve generation module includes a curve stitching unit, which is used to retain the non-overlapping part of the previous point cloud data after each advance of the preset distance, generate the segmented curve based on the optimized point cloud data obtained by comparing the Z-axis values ​​corresponding to the current point cloud data with the vibration error, and stitch the segmented curve to the non-overlapping part of the previous point cloud data.

[0025] The present invention also provides a method for measuring road smoothness, using the above-described apparatus, the method comprising:

[0026] Step S1: During the process of traveling, the device for measuring road smoothness acquires the point cloud data detected by the laser sensor and the travel distance detected by the distance detection device in real time.

[0027] Step S2: The device for measuring road smoothness performs data optimization processing based on the point cloud data and the travel distance to obtain a road smoothness measurement curve with the travel distance as the horizontal axis and the road smoothness represented by the point cloud data as the vertical axis, and displays it.

[0028] Preferably, step S2 includes:

[0029] Step S21, the device for measuring road smoothness determines in real time whether the acquired travel distance has reached a preset distance during its journey:

[0030] If not, then cache the point cloud data and the travel distance obtained in real time, and then return to step S21;

[0031] If so, proceed to step S22;

[0032] Step S22, the device for measuring road smoothness triggers data optimization processing on the cached point cloud data associated with the preset distance to obtain optimized point cloud data;

[0033] Step S23: The device for measuring road smoothness draws a segmented curve with the current preset distance traveled as the horizontal axis and the optimized point cloud data as the vertical axis based on the optimized point cloud data. The segmented curve is then spliced ​​with the segmented curve generated when the preset distance was traveled in the previous step. This process is repeated until the measurement endpoint is reached and all measurements are completed, thus obtaining the road smoothness measurement curve.

[0034] Preferably, the data optimization process includes at least one of eliminating installation errors, removing noise, and removing the effects of vibration.

[0035] The above technical solution has the following advantages or beneficial effects:

[0036] 1) The device has a simple structure, and its core computing part is basically all handled by the data processing device. For operators, the device is easy to use and operate. Only a few simple steps are needed to complete the measurement, which not only saves labor costs but also greatly reduces the learning cost for operators.

[0037] 2) Throughout the measurement process, the measured point cloud data can be optimized, including but not limited to eliminating installation errors, removing noise and vibration effects, and achieving automatic compensation and correction of point cloud data. This ensures that the entire road smoothness measurement curve remains on a relatively stable reference surface, thereby not only maintaining good continuity of measurement results but also ensuring the consistency of measurement data. Attached Figure Description

[0038] Figure 1 A schematic diagram of the structure of a device for measuring road smoothness is shown in a preferred embodiment of the present invention.

[0039] Figure 2 This is a preferred embodiment of the present invention, showing the overall connection of the various components;

[0040] Figure 3 A schematic diagram of a data processing device in a preferred embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of the data coordinate system of point cloud data in a preferred embodiment of the present invention;

[0042] Figure 5 A noise diagram of point cloud data in a preferred embodiment of the present invention;

[0043] Figure 6 This is a schematic diagram of point cloud data collected three times consecutively in a preferred embodiment of the present invention.

[0044] Figure 7 A flowchart illustrating a method for measuring road smoothness is shown in a preferred embodiment of the present invention.

[0045] Figure 8 This is a schematic diagram of the sub-process of step S2 in a preferred embodiment of the present invention. Detailed Implementation

[0046] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The present invention is not limited to this embodiment; other embodiments that conform to the spirit of the present invention may also fall within the scope of the present invention.

[0047] In a preferred embodiment of the present invention, based on the aforementioned problems existing in the prior art, an apparatus for measuring road smoothness is provided, such as... Figure 1 and Figure 2 As shown, it includes a mobile measuring body 1, which is equipped with a laser sensor 2, a distance detection device 3 and a data processing device 4. The laser emission direction of the laser sensor 2 is facing the road surface to be detected.

[0048] The data processing device 4 is used to perform data optimization processing on the moving measurement body 1 during its movement, based on the point cloud data detected by the laser sensor 2 and the travel distance detected by the distance detection device 3, to obtain and display a road smoothness measurement curve with the travel distance as the horizontal axis and the road smoothness represented by the point cloud data as the vertical axis.

[0049] Specifically, in this embodiment, the mobile measuring body 1 is preferably a detection trolley with four wheels, and the laser sensor 2 is preferably a line laser sensor (line lidar). During installation, the line laser emission direction of the laser sensor 2 must be directly facing the road surface to be detected, so that the collected point cloud data can be considered as a line along the direction of travel of the mobile measuring body 1. The line laser sensor can collect a large amount of point cloud data at high frequency; in this embodiment, 1920 data points can be collected, but this is not a limitation. The distance detection device 3 is preferably a meter-counting wheel, installed at the rear of the detection trolley, and operates by moving forward with the trolley while remaining in contact with the ground. Specifically, it can be implemented using a magnetic encoder combined with a rolling wheel.

[0050] The aforementioned data processing device 4 is preferably a computer with a human-computer interaction interface. This computer is preferably equipped with road smoothness detection software. When road smoothness measurement is required, the operator can start the road smoothness detection software. During the movement of the detection trolley, the road smoothness detection software can acquire point cloud data collected by the laser sensor at high frequency and monitor the rotation of the measuring wheel. It reads the rotation data of the measuring wheel and calculates the travel distance of the detection trolley. The travel distance and point cloud data are then processed, and the processed data is plotted into a curve diagram of the road surface, i.e., the road smoothness measurement curve, which is then displayed on the human-computer interaction interface for the operator to view in real time.

[0051] More preferably, the device for measuring road smoothness of the present invention further includes a power supply 5, which can be an uninterruptible power supply (UPS) to power the entire device. Considering that the various components of the device require different power supply voltages, the device also includes a power conversion device 6, which can be a regulated power supply, to convert the power supply into the DC power required by each component. The overall connection diagram of the various components of the device is shown below. Figure 2As shown, the data processing device 4 requires 220V AC voltage, while the laser sensor 2 and the distance detection device 3 require 24V DC voltage. Voltage conversion is achieved through the power conversion device 6. The laser sensor 2 and the data processing device 4 are preferably connected via LAN communication, and the data processing device 4 and the distance detection device 3 are preferably connected via RS485 bus.

[0052] In practical use, the operator first needs to turn on the power supply 5 and power conversion device 6 to power on the data processing device 4, laser sensor 2, and distance detection device 3. Then, the operator opens the road smoothness detection software mounted on the data processing device 4 and performs relevant operations within the software to establish communication connections between the software, laser sensor 2, and distance detection device 3. Before starting road smoothness measurement, it is preferable to perform initial position calibration first. This involves acquiring point cloud data at the initial position as the initial position data information. Subsequent data processing will use this initial position as a reference to construct a reference datum. The road smoothness detection software can then begin high-frequency acquisition of point cloud data from the laser sensor 2 and the travel distance of the distance detection device 3, processing the data to obtain the road smoothness measurement curve.

[0053] In a preferred embodiment of the present invention, such as Figure 3 As shown, the data processing device 4 includes:

[0054] Cache module 41 is used to cache the real-time acquired point cloud data and travel distance;

[0055] The data optimization module 42 is connected to the cache module 41. It is used to trigger the data optimization processing of the point cloud data associated with the preset distance when the travel distance represents the current travel distance of the moving measurement body 1 to obtain the optimized point cloud data.

[0056] The curve generation module 43 is connected to the data optimization module 42. It is used to draw a segmented curve with the current preset distance forward as the horizontal axis and the optimized point cloud data as the vertical axis based on the optimized point cloud data. The segmented curve is then spliced ​​with the segmented curve generated in the previous preset distance forward. This process is repeated until the moving measurement body reaches the measurement endpoint and completes all measurements, thus obtaining the road smoothness measurement curve.

[0057] Specifically, in this embodiment, during the road smoothness measurement process, a data optimization and curve generation process is performed once after each preset distance is traveled. Taking the laser sensor 2, which can collect 1920 data points, as an example, in actual measurement, the distance corresponding to 20-50 data points can be set as the preset distance (i.e., the measurement step size per time), but it is not limited to this and can be customized according to the actual measurement requirements. The system can determine whether the preset travel distance has been reached based on the travel distance detected by the distance detection device 3. If not, data collection continues. If the preset travel distance has been reached, the cached point cloud data at the current preset distance is optimized, and then a segmented curve corresponding to the current preset distance is drawn. Finally, the curves are stitched together to obtain the curve from the start of the measurement to the current preset distance. That is, every time the moving measurement body 1 advances a preset distance, it triggers a data processing, and the road smoothness detection software updates and draws a new curve. If the moving measurement body 1 continues to move after the current curve drawing is completed, it means that the measurement should continue. The corresponding segmented curves are processed and stitched together until all measurements are completed, thus obtaining the complete road smoothness measurement curve. After the measurement ends, the operator can first close the road smoothness detection software, then disconnect the power to the laser sensor 2, distance detection device 3, and data processing device 4, and finally turn off the power conversion device 6 and power supply 5.

[0058] In a preferred embodiment of the present invention, for the cached point cloud data, in order to obtain a set of usable, correct data that reflects the true condition of the road surface smoothness, before generating the road smoothness measurement curve, data optimization processing is required to obtain optimized point cloud data that reflects the true condition of the road surface. The data optimization processing includes at least one of eliminating installation errors, removing noise, and removing vibration effects. Preferably, the data optimization processing simultaneously includes eliminating installation errors, removing noise, and removing vibration effects. The preferred approach is to first perform the data optimization operation to eliminate installation errors, then perform the data optimization operation to remove noise, and finally perform the data optimization operation to remove vibration effects.

[0059] Specifically, the aforementioned point cloud data is a dataset containing three-dimensional position information of multiple measurement points, acquired by laser sensor 2. This application uses a line laser sensor, so the Y-axis data in the point cloud data is all zero, and the data in this direction is irrelevant to the measurement. The specific data coordinate system definition is as follows: Figure 4 As shown, the X-axis is the forward direction of the moving measurement body 1, and the Z-axis is the longitudinal section direction of the road. The corresponding Z-axis value can reflect the road smoothness.

[0060] When the laser sensor 2 is mounted on the mounting bracket of the mobile measuring body, although the laser emission direction is kept as straight as possible towards the road surface to be detected, some installation errors will always occur. Specifically, when the data optimization processing includes eliminating installation errors, the data optimization module 42 includes:

[0061] The data calibration unit 421 is used to acquire point cloud data as reference reference surface data for the road before the moving measurement body moves.

[0062] The installation error elimination unit 422 is connected to the data calibration unit 421, which is used to calculate the difference between the point cloud data associated with the preset distance and the reference datum surface data during the movement of the mobile measurement body to obtain optimized point cloud data.

[0063] Furthermore, regarding the point cloud data collected by laser sensor 2, the noise can be broadly categorized into two types:

[0064] One type of noise is large, randomly occurring spikes in the data during the measurement process, such as... Figure 5 As shown in a′, this type of randomly occurring large spike noise is processed by the data optimization module 42, which includes:

[0065] The first denoising unit 423 is used to calculate the absolute value of the difference between the average value of the Z-axis value of each data point in the point cloud data associated with the preset distance and the Z-axis value of each data point, extract the data points whose absolute value of the difference exceeds the first threshold, then count the number of consecutive data points extracted, and when the number of consecutive data points exceeds the second threshold, replace the Z-axis value of the corresponding consecutive data points with the average value to obtain the optimized point cloud data.

[0066] in, Z A Used to represent the average value of Z-axis data, Z i The value of the i-th data point in the point cloud data is used to represent the Z-axis value, and n is used to represent the total number of all data points in the point cloud data associated with a preset distance.

[0067] If the absolute value of the difference between the associated data points exceeds the first threshold and the number of consecutive data points exceeds the second threshold, then the consecutive data segment is considered to be large noise spikes. This part of the data is then removed and filled with the average value of the Z-axis data to ensure that the shape of the envelope is not affected.

[0068] Another type of noise is dense, fine burrs that are closely related to the actual physical conditions of the measured road surface, such as... Figure 5As shown in curve b, the small wavy lines represent noise that is always present during actual operation; however, the height and width of the waves vary depending on road conditions. This small burr noise is processed by the data optimization module 42, which includes:

[0069] The second denoising unit 424 is used to denoise the point cloud data associated with a preset distance using the envelope method, i.e., the piecewise maximum value method, to obtain optimized point cloud data. The envelope method is existing technology, and its specific algorithm principle will not be elaborated here. The denoised envelope is as follows: Figure 5 Curve a is shown in the figure.

[0070] In practical applications, small noise is always present, so a second noise reduction unit 424 is needed to remove it. Large noise, on the other hand, occurs randomly and may not even occur under ideal conditions. Based on this, the first noise reduction unit 423 can be flexibly configured according to the actual measurement scenario requirements. In other words, the data optimization module 42 can be composed of only the second noise reduction unit 424, or it can be composed of both the first noise reduction unit 423 and the second noise reduction unit 424.

[0071] Furthermore, since the laser sensor 2 is mounted on the mobile measuring body 1, as the mobile measuring body moves along the road, the body of the mobile measuring body 1 will vibrate up and down with the undulations of the road, causing the reference surface of the acquired point cloud data to change. Therefore, it is necessary to eliminate the vibration effect in each measurement to ensure that the point cloud data reflects the true condition of the road surface. Based on this, the data optimization module 42 includes a vibration effect removal unit 425, which includes:

[0072] The first calculation subunit 4251 is used to obtain the overlapping part between the current point cloud data measured when advancing the current preset distance and the previous point cloud data measured when advancing the previous preset distance, and to obtain the first average value of each Z-axis value corresponding to the overlapping part in the current point cloud data and the second average value of each Z-axis value corresponding to the previous point cloud data.

[0073] The second calculation subunit 4252 is connected to the first calculation subunit 4251 and is used to calculate the difference between the first average value and the first average value as the vibration error.

[0074] The vibration elimination subunit 4253 is connected to the second calculation subunit 4252 and is used to add the Z-axis values ​​corresponding to the current point cloud data with the vibration error to obtain the optimized point cloud data.

[0075] Specifically, in this embodiment, as Figure 6As shown, taking point cloud data collected three times consecutively as an example, it is preferred that the operations of clearing installation errors and removing noise have been performed. The meanings of each variable are as follows:

[0076] x(1): Point cloud data collected in the first acquisition;

[0077] x(2): Point cloud data collected in the second acquisition;

[0078] x(3): Point cloud data collected in the third acquisition;

[0079] d: The forward distance set on the X-axis for each measurement, i.e., the preset distance. In actual measurement, it can be set to the distance corresponding to 20-50 points (relative to 1920 points);

[0080] L: The length on the X-axis corresponding to the total number of 1920 measuring points in each measurement.

[0081] A1: The overlapping part of x(1) and x(2);

[0082] A2: The overlapping part of x(2) and x(1).

[0083] This device uses high-frequency data acquisition. Theoretically, two consecutive measurements will inevitably have overlapping data, as follows: Figure 6 In the overlapping data segments A1 and A2 of x(1) and x(2), theoretically, the Z-axis data measured in these two data segments should be completely consistent. However, due to the bumps and vibrations of the vehicle body during travel, the measurement reference plane undergoes slight changes, resulting in the Z-axis data measured in these two overlapping areas not being completely identical, leading to inaccurate data measurement. To address this, vibration influence removal is required. The vibration influence removal process includes:

[0084] If the current point cloud data obtained by the current forward preset distance measurement is x(2), then the previous point cloud data obtained by the previous forward preset distance measurement is x(1). The overlapping part between the two is A1 and A2. Calculate the first average value Z of the Z-axis values ​​of each data point in A2 and A1 respectively. A2 Second average Z A1 And calculate the difference D between the two. Z2 =Z A2 -Z A1 This D Z2 This refers to the vibration error from these two measurements. Let's consider this D... Z2 The values ​​are added sequentially to the Z-axis values ​​of all data points in x(2) to obtain the new point cloud data x′(2) after eliminating the influence of vibration.

[0085] Similarly, the point cloud data x′(2) after eliminating the vibration effect is used as the new measurement point cloud data, and the third measurement point cloud data x(3) is obtained by the next measurement. At this time, the difference D between the average values ​​of the Z-axis data of the overlapping part of x′(2) and x(3) is calculated. Z3 This data is superimposed onto the Z-axis data of all points in x(3) to obtain new point cloud data x′(3) after eliminating the vibration effect. The point cloud data x′(3) after eliminating the vibration effect is used as the new measurement point cloud data. Therefore, for the subsequent measurement data x(i), the new point cloud data x′(i) after eliminating the vibration effect is calculated according to the above processing logic, then: x′(i)=x(i)+D Zi =x(i)+Z Ai -Z′ Ai-1 .

[0086] Understandably, if the point cloud data is the first point cloud data collected, since it is measured based on the calibrated reference standard surface and there is no previous point cloud data, there is no need to perform vibration elimination operation.

[0087] In a preferred embodiment of the present invention, the curve generation module 43 includes a curve splicing unit 431, which is used to retain the non-overlapping part of the previous point cloud data after each advance of a preset distance, generate a segmented curve based on the optimized point cloud data obtained by the Z-axis values ​​and vibration error corresponding to the current point cloud data, and splice the segmented curve to the non-overlapping part of the previous point cloud data.

[0088] Specifically, in this embodiment, as Figure 6 As shown, the previous point cloud data is x(1), and the current point cloud data is x(2). After obtaining the new point cloud data x′(2) after eliminating the influence of vibration, the non-overlapping data segment (the part before A1, with a length of d) in x(1) is retained. The new point cloud data x′(2) is then stitched to the non-overlapping data segment in x(1) to complete the first stitching, resulting in the curve SA(1) after the first stitching. In subsequent measurements, for the new point cloud data x′(i) after eliminating the influence of vibration, the non-overlapping data segment (with a length of d) in the curve SA(i-1) after the previous stitching is retained. The new point cloud data x′(i) after eliminating the influence of vibration is then stitched to the non-overlapping data segment in the curve SA(i-1) to complete the i-th stitching, resulting in the curve SA(i) after the i-th stitching. This process is repeated until all measurements are completed.

[0089] The present invention also provides a method for measuring road smoothness, using the above-described apparatus, such as... Figure 7 As shown, the method includes:

[0090] Step S1: During the process of traveling, the device used to measure road smoothness acquires point cloud data detected by the laser sensor and the travel distance detected by the distance detection device in real time.

[0091] Step S2: The device for measuring road smoothness performs data optimization processing based on point cloud data and travel distance to obtain and display a road smoothness measurement curve with travel distance as the horizontal axis and road smoothness represented by point cloud data as the vertical axis.

[0092] In a preferred embodiment of the present invention, such as Figure 8 As shown, step S2 includes:

[0093] Step S21: The device used to measure road smoothness determines in real time whether the acquired travel distance has reached the preset distance during the journey.

[0094] If not, cache the real-time acquired point cloud data and travel distance, and then return to step S21;

[0095] If so, proceed to step S22;

[0096] Step S22: The device for measuring road smoothness triggers data optimization processing on the cached point cloud data associated with a preset distance to obtain optimized point cloud data.

[0097] Step S23: The device for measuring road smoothness draws a segmented curve with the current preset distance advanced as the horizontal axis and the optimized point cloud data as the vertical axis based on the optimized point cloud data. The segmented curve is then spliced ​​with the segmented curve generated in the previous preset distance advanced. This process is repeated until the measurement endpoint is reached and all measurements are completed, resulting in the road smoothness measurement curve.

[0098] In a preferred embodiment of the present invention, the data optimization process includes at least one of eliminating installation errors, removing noise, and removing the effects of vibration.

[0099] The above description is merely a preferred embodiment of the present invention and does not limit the implementation and protection scope of the present invention. Those skilled in the art should realize that any equivalent substitutions and obvious changes made using the content of this specification and illustrations should be included within the protection scope of the present invention.

Claims

1. A device for measuring road smoothness, characterized in that, It includes a mobile measuring body, which is equipped with a laser sensor, a distance detection device and a data processing device. The laser emission direction of the laser sensor is facing the road surface to be detected. The data processing device is used to perform data optimization processing on the point cloud data detected by the laser sensor and the travel distance detected by the distance detection device during the movement of the mobile measuring body, to obtain and display a road smoothness measurement curve with the travel distance as the horizontal axis and the road smoothness represented by the point cloud data as the vertical axis. The data processing device includes: The caching module is used to cache the point cloud data acquired in real time and the travel distance; The data optimization module, connected to the cache module, is used to trigger data optimization processing on the point cloud data associated with the preset distance when the travel distance represents the preset distance that the moving measurement body has traveled in the current time, so as to obtain optimized point cloud data. The curve generation module, connected to the data optimization module, is used to draw a segmented curve based on the optimized point cloud data, with the current preset distance advanced as the horizontal axis and the optimized point cloud data as the vertical axis. The segmented curve is then spliced ​​with the segmented curve generated in the previous advance of the preset distance. This process is repeated until the moving measurement body reaches the measurement endpoint and completes all measurements, thus obtaining the road smoothness measurement curve. The data optimization process includes at least one of eliminating installation errors, removing noise, and removing vibration effects. When the data optimization process includes removing vibration effects, the data optimization module includes a vibration effect removal unit, which includes: The first calculation subunit is used to obtain the overlapping part between the current point cloud data measured when advancing the preset distance and the previous point cloud data measured when advancing the preset distance, and to obtain the first average value of each Z-axis value corresponding to the overlapping part in the current point cloud data and the second average value of each Z-axis value corresponding to the previous point cloud data. The second calculation subunit, connected to the first calculation subunit, is used to calculate the difference between the first average value and the second average value as the vibration error; The vibration elimination subunit, connected to the second calculation subunit, is used to add the Z-axis values ​​corresponding to the current point cloud data to the vibration error to obtain the optimized point cloud data.

2. The apparatus according to claim 1, characterized in that, The data optimization processing includes eliminating installation errors, and the data optimization module includes: The data calibration unit is used to acquire the point cloud data as a reference surface data for the road before the mobile measurement body moves forward. An error elimination unit is installed and connected to the data calibration unit. This unit is used to calculate the difference between the point cloud data associated with the preset distance and the reference datum surface data during the movement of the mobile measurement body to obtain the optimized point cloud data.

3. The apparatus according to claim 1, characterized in that, When the data optimization processing includes noise removal, if the data optimization module consists only of the second denoising unit, or is composed of both the first denoising unit and the second denoising unit, then: The first denoising unit is used to calculate the absolute value of the difference between the average value of the Z-axis value of each data point in the point cloud data associated with the preset distance and the Z-axis value of each data point, extract the data points whose absolute value of the difference exceeds a first threshold, then count the number of consecutive data points extracted, and when the number of consecutive data points exceeds a second threshold, replace the Z-axis value of the corresponding consecutive data points with the average value to obtain the optimized point cloud data. The second denoising unit is used to denoise the point cloud data associated with the preset distance using the envelope method to obtain the optimized point cloud data.

4. The apparatus according to claim 1, characterized in that, The curve generation module includes a curve stitching unit, which is used to retain the non-overlapping part of the previous point cloud data after each advance of the preset distance, generate the segmented curve based on the optimized point cloud data obtained by comparing the Z-axis values ​​corresponding to the current point cloud data with the vibration error, and stitch the segmented curve to the non-overlapping part of the previous point cloud data.

5. A method for measuring road smoothness, characterized in that, The method, using the apparatus as described in any one of claims 1-4, comprises: Step S1: During the process of traveling, the device for measuring road smoothness acquires the point cloud data detected by the laser sensor and the travel distance detected by the distance detection device in real time. Step S2: The device for measuring road smoothness performs data optimization processing based on the point cloud data and the travel distance to obtain a road smoothness measurement curve with the travel distance as the horizontal axis and the road smoothness represented by the point cloud data as the vertical axis, and displays it.

6. The method according to claim 5, characterized in that, Step S2 includes: Step S21, the device for measuring road smoothness determines in real time whether the acquired travel distance has reached a preset distance during its journey: If not, then cache the point cloud data and the travel distance obtained in real time, and then return to step S21; If so, proceed to step S22; Step S22, the device for measuring road smoothness triggers data optimization processing on the cached point cloud data associated with the preset distance to obtain optimized point cloud data; Step S23: The device for measuring road smoothness draws a segmented curve with the current preset distance traveled as the horizontal axis and the optimized point cloud data as the vertical axis based on the optimized point cloud data. The segmented curve is then spliced ​​with the segmented curve generated when the preset distance was traveled in the previous step. This process is repeated until the measurement endpoint is reached and all measurements are completed, thus obtaining the road smoothness measurement curve.

7. The method according to claim 6, characterized in that, The data optimization process includes at least one of eliminating installation errors, removing noise, and removing the effects of vibration.

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