1. A road surface unevenness detection method is characterized by comprising the following steps:

acquiring road point cloud data acquired by a laser radar, wherein the laser radar is carried on a mobile platform and moves along with the mobile platform;

denoising the point cloud data;

carrying out registration processing on the cloud data subjected to denoising processing so as to increase the information quantity of the road surface point cloud;

performing point cloud data segmentation on the point cloud data subjected to registration processing to obtain point cloud data of a road pavement;

extracting pavement elevation information from point cloud data of a road pavement;

and calculating the power spectral density of the road surface according to the elevation information of the road surface, and determining the roughness grade of the road surface according to the power spectral density of the road surface.

2. The method for detecting the unevenness of a road surface according to claim 1, wherein the process of denoising the point cloud data is as follows:

the method adopts cloth simulation filtering to remove the interference of the vegetation on point cloud data, and filters the vegetation data on the point cloud acquired by each frame, and specifically comprises the following steps:

firstly, carrying out reverse processing on point cloud acquired by a laser radar;

then, covering the reverse surface with a piece of cloth, determining the positions of the cloth nodes by analyzing the interaction between the cloth nodes and the corresponding laser radar points, and generating an approximation of the ground;

finally, extracting ground points from the laser radar point cloud by comparing the original laser radar points with the generated surface;

and (3) performing noise reduction treatment on the point cloud data subjected to the cloth simulation filtering treatment by adopting statistical analysis filtering, and removing discrete points with the distance between the point cloud data and the center of the laser radar exceeding a threshold value.

3. The method for detecting the unevenness of the road surface according to claim 1, wherein the ICP algorithm is used to perform feature registration processing on the denoised point cloud data, and specifically comprises:

performing iterative optimization matrix, and calculating a set of nearest reference points of the target concentration points in the iterative process; meanwhile, calculating a translation matrix and a rotation matrix corresponding to the target concentration point to obtain a new target point set, starting the next iteration until the iteration is finished, obtaining a transformation matrix, and realizing the registration of the two point sets.

4. The road surface irregularity detecting method according to claim 1, wherein the point cloud data after the registration processing is subjected to point cloud data segmentation processing to obtain point cloud data of a road surface of a meter x b.

5. The method according to any one of claims 1 to 4, wherein the process of extracting road surface elevation information from the point cloud data of the road surface is as follows:

and extracting an intersection sequence q (l) of the moving direction of the left wheel of the mobile platform and the ground point cloud, an intersection sequence q (l) of the moving direction of the right wheel of the mobile platform and the ground point cloud, and an intersection sequence q (z) of the left-right symmetrical center line of the mobile platform and the ground point cloud from the point cloud data of the road surface to obtain a two-dimensional elevation sequence of the road surface in three directions.

6. The method for detecting the road surface unevenness according to claim 5, wherein the road surface power spectral density is calculated according to the road surface elevation information, and the specific process of determining the road surface unevenness grade according to the road surface power spectral density is as follows:

respectively averaging the acquired two-dimensional elevation sequences q (l), q (l) and q (z) of the road surface in three directions to obtainAnd

respectively solving based on power spectral density estimation function PyuearAndthe power spectral density of (d); obtaining the power spectral densities of q (l), q (r) and q (z) sequences respectively;

resolving the power spectral densities of the sequences q (l), q (r) and q (z) into each octave, and smoothing to obtain smoothed power spectral densities of the sequences q (l), q (r) and q (z);

and finding out the intersection points of the curves and the standard road surface grades from the smooth power spectral density curves of the sequences q (l), q (r) and q (z), respectively counting the curve points falling in each road surface grade interval by taking each intersection point as a boundary point, and calculating the proportion of the point number of each interval to the total number, thereby quantitatively describing the unevenness of the road surface.

7. A road surface irregularity detecting device, comprising:

the point cloud data acquisition module is used for acquiring road point cloud data acquired by a laser radar, wherein the laser radar is carried on a mobile platform and moves along with the mobile platform;

the de-noising processing module is used for de-noising the point cloud data;

the registration processing module is used for carrying out registration processing on the cloud data subjected to denoising processing so as to increase the information quantity of the road surface point cloud;

the segmentation module is used for carrying out point cloud segmentation on the point cloud data after the registration processing to obtain point cloud data of a road pavement;

the elevation information extraction module is used for extracting road surface elevation information from the point cloud data of the road surface;

and the road surface unevenness determining module is used for calculating the power spectral density of the road surface according to the road surface elevation information and determining the grade of the road surface unevenness according to the power spectral density of the road surface.

8. A road surface unevenness detection system is characterized by comprising an upper computer, a laser radar and a mobile platform;

the laser radar is carried on the mobile platform and used for acquiring road point cloud data when moving along with the mobile platform;

the laser radar is connected with the upper computer and used for sending the collected road point cloud data to the upper computer;

the mobile platform is connected with an upper computer and used for feeding back mobile information to the upper computer;

the upper computer is used for executing the road surface unevenness detection method of any one of claims 1-6.

9. A storage medium storing a program which, when executed by a processor, implements the road surface irregularity detecting method according to any one of claims 1 to 6.

10. A computing device comprising a processor and a memory for storing a program executable by the processor, wherein the processor implements the method of detecting road surface unevenness according to any one of claims 1 to 6 when executing the program stored in the memory.

Background

When a vehicle travels on an uneven road surface, excitation caused by the uneven road surface greatly affects the structure of the vehicle itself, the vehicle occupants, the load carried by the vehicle, and the road surface structure. At present, some studies on the unevenness of the road surface have also appeared.

A3-meter ruler and a level gauge-mark post are common fixed reference pavement unevenness detection instruments. The 3 m ruler is placed on the road surface where the vehicle can pass through, the distance between the bottom surface of the 3 m ruler and the road surface can be measured by using the feeler gauge with the measuring range, and the change of the road surface unevenness can be measured by carrying out data acquisition at multiple points. The method has the advantages of low measurement cost and convenient operation, and has the defects of large human influence factor, low precision and low test efficiency during measurement. The measuring device has the characteristic of high measuring efficiency and has the defect that a vibration system is influenced by multiple factors and has poor stability.

From the point of view of the collection of the road surface unevenness, a great gap exists between the measurement of the road surface unevenness of the structured automobile road field and the research of the road surface unevenness of the unstructured orchard road field. At present, the structural road surface unevenness measuring device is developed relatively mature, but devices for directly measuring the road surface unevenness such as a 3-meter ruler, a level meter-mark post and a multi-wheel meter have low efficiency and are gradually eliminated; the BPR trailer type jolt accumulation instrument and the vehicle-mounted road surface flatness tester are not suitable for measuring the non-structured road surface unevenness with poor road surface conditions due to the limitation of applicable scenes.

Disclosure of Invention

The first purpose of the invention is to overcome the defects of the prior art and provide a road surface unevenness detection method, which is suitable for detecting the unstructured road surface unevenness with poor road surface condition and has the advantages of high detection efficiency and good stability.

A second object of the present invention is to provide a road surface irregularity detecting device.

A third object of the present invention is to provide a road surface irregularity detecting system.

A fourth object of the present invention is to provide a storage medium.

It is a fourth object of the invention to provide a computing device.

The first purpose of the invention is realized by the following technical scheme: a road surface unevenness detection method includes the steps:

acquiring road point cloud data acquired by a laser radar, wherein the laser radar is carried on a mobile platform and moves along with the mobile platform;

denoising the point cloud data;

carrying out registration processing on the cloud data subjected to denoising processing so as to increase the information quantity of the road surface point cloud;

performing point cloud data segmentation on the point cloud data subjected to registration processing to obtain point cloud data of a road pavement;

extracting pavement elevation information from point cloud data of a road pavement;

and calculating the power spectral density of the road surface according to the elevation information of the road surface, and determining the roughness grade of the road surface according to the power spectral density of the road surface.

Preferably, the process of denoising the point cloud data is as follows:

the method adopts cloth simulation filtering to remove the interference of the vegetation on point cloud data, and filters the vegetation data on the point cloud acquired by each frame, and specifically comprises the following steps:

firstly, carrying out reverse processing on point cloud acquired by a laser radar;

then, covering the reverse surface with a piece of cloth, determining the positions of the cloth nodes by analyzing the interaction between the cloth nodes and the corresponding laser radar points, and generating an approximation of the ground;

finally, extracting ground points from the laser radar point cloud by comparing the original laser radar points with the generated surface;

and (3) performing noise reduction treatment on the point cloud data subjected to the cloth simulation filtering treatment by adopting statistical analysis filtering, and removing discrete points with the distance between the point cloud data and the center of the laser radar exceeding a threshold value.

Preferably, the ICP algorithm is used to perform feature registration processing on the denoised point cloud data, specifically:

performing iterative optimization matrix, and calculating a set of nearest reference points of the target concentration points in the iterative process; meanwhile, calculating a translation matrix and a rotation matrix corresponding to the target concentration point to obtain a new target point set, starting the next iteration until the iteration is finished, obtaining a transformation matrix, and realizing the registration of the two point sets.

Preferably, the point cloud data after the registration processing is subjected to point cloud data segmentation processing to obtain point cloud data of a road pavement of a meter a x b.

Preferably, the process of extracting the road surface elevation information from the point cloud data of the road surface is as follows:

and extracting an intersection sequence q (l) of the moving direction of the left wheel of the mobile platform and the ground point cloud, an intersection sequence q (l) of the moving direction of the right wheel of the mobile platform and the ground point cloud, and an intersection sequence q (z) of the left-right symmetrical center line of the mobile platform and the ground point cloud from the point cloud data of the road surface to obtain a two-dimensional elevation sequence of the road surface in three directions.

Furthermore, the road surface power spectrum density is calculated through the road surface elevation information, and the concrete process of determining the road surface roughness grade according to the road surface power spectrum density is as follows:

respectively averaging the acquired two-dimensional elevation sequences q (l), q (l) and q (z) of the road surface in three directions to obtainAnd

respectively solving based on power spectral density estimation function PyuearAndthe power spectral density of (d); obtaining the power spectral densities of q (l), q (r) and q (z) sequences respectively;

resolving the power spectral densities of the sequences q (l), q (r) and q (z) into each octave, and smoothing to obtain smoothed power spectral densities of the sequences q (l), q (r) and q (z);

and finding out the intersection points of the curves and the standard road surface grades from the smooth power spectral density curves of the sequences q (l), q (r) and q (z), respectively counting the curve points falling in each road surface grade interval by taking each intersection point as a boundary point, and calculating the proportion of the point number of each interval to the total number, thereby quantitatively describing the unevenness of the road surface.

The second purpose of the invention is realized by the following technical scheme: a road surface irregularity detecting device comprising:

the point cloud data acquisition module is used for acquiring road point cloud data acquired by a laser radar, wherein the laser radar is carried on a mobile platform and moves along with the mobile platform;

the de-noising processing module is used for de-noising the point cloud data;

the registration processing module is used for carrying out registration processing on the cloud data subjected to denoising processing so as to increase the information quantity of the road surface point cloud;

the segmentation module is used for carrying out point cloud segmentation on the point cloud data after the registration processing to obtain point cloud data of a road pavement;

the elevation information extraction module is used for extracting road surface elevation information from the point cloud data of the road surface;

and the road surface unevenness determining module is used for calculating the power spectral density of the road surface according to the road surface elevation information and determining the grade of the road surface unevenness according to the power spectral density of the road surface.

The third purpose of the invention is realized by the following technical scheme: a road surface unevenness detection system comprises an upper computer, a laser radar and a mobile platform;

the laser radar is carried on the mobile platform and used for acquiring road point cloud data when moving along with the mobile platform;

the laser radar is connected with the upper computer and used for sending the collected road point cloud data to the upper computer;

the mobile platform is connected with an upper computer and used for feeding back mobile information to the upper computer;

the upper computer is used for executing the road surface unevenness detecting method of the first purpose of the invention.

The fourth purpose of the invention is realized by the following technical scheme: a storage medium stores a program that, when executed by a processor, implements the road surface irregularity detecting method according to the first object of the invention.

The fifth purpose of the invention is realized by the following technical scheme: the road surface unevenness detecting method comprises a processor and a memory for storing an executable program of the processor, and when the processor executes the program stored in the memory, the road surface unevenness detecting method achieves the first aim of the invention.

Compared with the prior art, the invention has the following advantages and effects:

(1) the invention discloses a road surface unevenness detection method, which comprises the steps of firstly, acquiring road point cloud data collected by a laser radar which is carried on a mobile platform and moves along with the mobile platform; then, denoising, registering and segmenting the point cloud data to obtain point cloud data of the road pavement and point cloud data of the road pavement; extracting pavement elevation information from point cloud data of a road pavement; and calculating the power spectral density of the road surface according to the elevation information of the road surface, and determining the roughness grade of the road surface according to the power spectral density of the road surface. According to the method, the point cloud data of the road surface are acquired through the laser radar, so that the method has lower measurement error when a large-distance target is measured, can acquire the point cloud data with more abundant position information, and improves the accuracy of road surface unevenness detection. The road surface point cloud data for driving can be determined after denoising, registering and segmenting are conducted on the road surface point cloud data, and further road surface elevation information can be accurately determined based on the road surface point cloud data, so that the road surface unevenness is detected, the method is suitable for detecting the unstructured road surface unevenness with poor road surface conditions, and the method has the advantages of being high in detection efficiency and good in stability; the problem of detection vacancy of non-structural road surface unevenness such as mountain area fruit tea garden can be solved, the design and research and development level of modern agricultural machinery is improved, the research and development cycle is shortened, the research and development cost is reduced, and the accuracy of the power spectrum indoor simulation test of the agricultural machinery is improved.

(2) In the method for detecting the road surface unevenness, during denoising, the noise of vegetation is removed from point cloud data by using distributed analog filtering, the point cloud data far away from the center of a radar in the point cloud data is removed by using statistical analysis filtering, in addition, the road surface point cloud information amount is increased by using point cloud registration, the information density is improved, and the road surface elevation information which is most likely to be driven by a vehicle for a meter (for example, 4 multiplied by 20 meters) is directly left for subsequent extraction by using point cloud segmentation. Based on the method, the influence of other interference factors of unstructured roads such as mountain fruit tea gardens can be eliminated, and more accurate road surface unevenness information can be obtained.

(3) In the method for detecting the road surface unevenness, the corresponding power spectral densities are respectively obtained based on the power spectral density estimation function Pyular aiming at the obtained two-dimensional road surface elevation sequence, and finally the road surface unevenness grade is determined through the power spectral density curve.

Drawings

FIG. 1 is a flow chart of the method of the present invention.

Fig. 2a is a soil section of a laser radar collected point cloud data.

FIG. 2b is the point cloud data corresponding to the soil section of FIG. 2 a.

FIG. 2c is a dirt road from which point cloud data is collected by the lidar.

FIG. 2d is a single frame of point cloud data corresponding to the dirt pavement of FIG. 2 c.

Fig. 3a is an actual scene of the laser radar collecting point cloud data.

FIG. 3b is the point cloud data of the actual scene of FIG. 3 a.

Fig. 3c and 3d are the results of cloth-simulated filtering of ground points and non-ground points in the actual scene shown in fig. 3 a.

FIG. 3e shows the result of the statistical analysis after filtering.

Fig. 4 is point cloud data after ICP registration in the method of the invention.

Fig. 5a is a registration map of multi-frame point cloud data.

Fig. 5b is a point cloud data segmentation result diagram.

FIG. 6 is a plot of the power spectral density of the q (l), q (r), and q (z) sequences of the method of the present invention.

FIG. 7 is a plot of the smoothed power spectral density for the q (l), q (r), and q (z) sequences in the method of the present invention.

Fig. 8 is a block diagram of the apparatus of the present invention.

Fig. 9 is a schematic diagram of the system architecture of the present invention.

Detailed Description

The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.

Example 1

The existing road surface unevenness measurement mainly aims at the structured road surface, but the problems of large artificial influence factor, low precision, poor stability and unsuitability for the unstructured road surface exist based on the existing structured road surface unevenness measurement. Based on this, the embodiment discloses a road surface irregularity detection method suitable for non-structural drawing road surfaces such as mountain fruit tea gardens, and the detection efficiency and the detection stability of the road surface irregularity are improved.

To facilitate understanding of the present embodiment, first, a xx method disclosed by an embodiment of the present application is described in detail, referring to a flowchart of a road surface unevenness detecting method shown in fig. 1, where the method includes the following steps:

and S1, acquiring road point cloud data collected by the laser radar carried on the mobile platform and moving along with the mobile platform. Fig. 2a and 2c show a soil section and a dirt road surface, respectively, fig. 2b shows fitting point cloud data of the soil section shown in fig. 2a collected by a laser radar moving along with a mobile platform, and fig. 2c shows single-frame point cloud data of the dirt road surface shown in fig. 2c collected by the laser radar.

In this embodiment, the laser radar may be a VLP-16 laser radar, and the measurement accuracy of the VLP-16 laser radar is much higher than that of a millimeter wave radar and a single line laser radar, so that the spatial information of the object to be measured can be more accurately reflected. The VLP-16 laser radar has the weight of only 830g, is small in size and convenient to carry and externally use; the rotating speed of a rotating part in the laser radar can be adjusted in an upper computer, and the adjusting range is 300r/min to 1200 r/min; the radar can measure point cloud data of about 30 ten thousand positions per second, and the position information is rich.

S2, denoising the point cloud data, specifically as follows:

s21, removing interference of vegetation on point cloud data by adopting cloth simulation filtering, and filtering the vegetation data on the point cloud acquired by each frame, wherein the method specifically comprises the following steps:

firstly, the point cloud collected by the laser radar is processed in a reverse way.

Then, a piece of cloth is covered on the reverse surface, and the positions of the cloth nodes are determined by analyzing the interaction between the cloth nodes and the corresponding laser radar points, so that the approximation of the ground is generated.

And finally, extracting ground points from the laser radar point cloud by comparing the original laser radar points with the generated surface.

For the point cloud data as shown in fig. 3b collected from the actual scene of fig. 3a, after the point cloud data is subjected to the simulation filtering process of the present embodiment, the results as shown in fig. 3c and 3d are obtained, where fig. 3c is the result of the simulation filtering for the ground points, and fig. 3d is the result of the simulation filtering for the non-ground points.

And S22, performing noise reduction processing on the point cloud data subjected to the cloth simulation filtering processing by adopting statistical analysis filtering, and removing discrete points with the distance between the point cloud data and the center of the laser radar exceeding a threshold value. The point cloud collected by the laser radar has important characterization significance on the road surface, the local features are clearer when the point cloud number is larger, the local features are sparse when the point cloud number is smaller, the outlier can be understood as useless features, and the filtering removal can be performed on the outlier due to the fact that the outlier is too sparse and the characterization significance is not strong.

The point cloud data shown in fig. 3b is subjected to the simulation filtering and the statistical analysis filtering of the embodiment, and the result is shown in fig. 3 e.

And S3, carrying out registration processing on the cloud data subjected to denoising processing so as to increase the information quantity of the road surface point cloud. In this embodiment, the ICP algorithm is used to perform feature registration processing on the denoised point cloud data, specifically: performing iterative optimization matrix, and calculating a set of nearest reference points of the target concentration points in the iterative process; meanwhile, a translation matrix and a rotation matrix corresponding to the target concentration point are calculated to obtain a new target point set, next iteration is started until the iteration is finished, a more accurate transformation matrix is obtained, and registration of the two point sets is achieved, wherein the point cloud data after ICP registration is shown in FIG. 4.

S4, carrying out point cloud data segmentation on the point cloud data after registration processing to obtain point cloud data of a road pavement a and b; in this embodiment, after the point cloud data segmentation process, point cloud data of a road pavement of 4 × 20 meters can be obtained, and after the multi-frame point cloud registration chart shown in fig. 5a is subjected to the point cloud data segmentation in this step, a segmentation result shown in fig. 5b is obtained.

And S5, extracting road surface elevation information from the point cloud data of the road surface. In this embodiment, from the point cloud data of the road surface, an intersection sequence q (l) of the moving direction of the left wheel of the mobile platform and the ground point cloud, an intersection sequence q (r) of the moving direction of the right wheel of the mobile platform and the ground point cloud, and an intersection sequence q (z) of the left-right symmetric center line of the mobile platform and the ground point cloud are extracted, so as to obtain two-dimensional elevation sequences of the road surface in three directions, which correspond to three longitudinal sequences of the road surface. Wherein:

q(l)＝{q₁(l),q₂(l),q₃(l),...,q_N(l)}；

q(r)＝{q₁(r),q₂(r),q₃(r),...,q_N(r)}；

q(z)＝{q₁(z),q₂(z),q₃(z),...,q_N(z)}；

wherein q is₁(l) To q_N(l) The cloud data of the 1 st to N points in the q (l) sequence correspond to the test values of the 1 st to N times of the laser radar; q. q.s₁(r) to q_N(r) is the 1 st to N point cloud data in the q (r) sequence, corresponding to the 1 st to N times of test values of the laser radar; q. q.s₁(z) to q_NAnd (z) is 1 st to N point cloud data in a q (z) sequence, and corresponds to the 1 st to N times of test values of the laser radar.

S6, calculating the power spectral density of the road surface according to the elevation information of the road surface, and determining the grade of the road surface unevenness according to the power spectral density of the road surface, wherein the concrete engineering is as follows:

s61, averaging the acquired two-dimensional elevation sequences q (l), q (r) and q (z) of the road surface in three directions respectively to obtainAnd

s62, respectively calculating by adopting an AR model power spectral density estimation function PyularAndcorresponding to the power spectral densities of the q (l), q (r) and q (z) sequences.

As shown in fig. 6, the power spectral densities of three longitudinal sequences q (l), q (r), and q (z) of the cement pavement for orchard obtained by this step in this embodiment are shown, and it can be seen from the figure that the cement pavement has abundant frequency components in the high frequency region, because the power spectral densities are calculated by using the fixed bandwidth analysis, the so-called true power spectral distribution or the fluctuation of the power spectral densities caused by noise is highlighted.

S63, smoothing the power spectral density decomposition of the sequences q (l), q (r) and q (z) in each octave to obtain the smoothed power spectral density of the sequences q (l), q (r) and q (z).

The relationship between the smoothed power spectral density and the spatial frequency in logarithmic coordinates obtained after the power spectral density of the three longitudinal sequences q (l), q (r) and q (z) shown in fig. 6 is smoothed is shown in fig. 7.

S64, finding out the intersection points of the curve and the standard road surface grading from the smooth power spectral density curves of the sequences q (l), q (r) and q (z), respectively counting the curve points falling in each road surface grade interval by taking each intersection point as a boundary point, and calculating the proportion of each interval point to the total, thereby quantitatively describing the unevenness of the road surface.

The ratio of the number of curve points to the total in each road surface grade section in the sequences of q (l), q (r), and q (z) according to the intersection points with the standard road surface grades, in the sequences of q (l), q (r), and q (z), based on the smoothed power spectral densities of the sequences of q (l), q (r), and q (z) shown in fig. 7, is shown in table 1 below:

TABLE 1

	q(l)	q(r)	q(z)	Mean value of
					A	3％	2％	3％	2.67％
B	75％	84％	88％	82.33％
					C	22％	14％	9％	15％
D	0	0	0	0
					E	0	0	0	0
F	0	0	0	0
					G	0	0	0	0
H	0	0	0	0

Wherein, A to H are standard road surface unevenness grades, A grade represents the best road surface, H grade represents the worst road surface, A to H, and road conditions are decreased gradually. As can be seen from table 1, in the corresponding road section, the class a road surface accounts for 2.67%, the class B road surface accounts for 82.33%, and the class C road surface accounts for 15%, so that the road section is mainly the class B road surface.

Those skilled in the art will appreciate that all or part of the steps in the method according to the present embodiment may be implemented by a program to instruct the relevant hardware, and the corresponding program may be stored in a computer-readable storage medium. It should be noted that although the method operations of embodiment 1 are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Rather, the depicted steps may change the order of execution, some steps may be performed concurrently, some steps may additionally or alternatively be omitted, multiple steps may be combined into one step execution, and/or one step may be broken down into multiple step executions.

Example 2

The embodiment discloses a road surface unevenness detection device, which comprises a point cloud data acquisition module, a denoising processing module, a registration processing module, a segmentation module, an elevation information extraction module and a road surface unevenness determination module, wherein the functions of the modules are as follows:

the system comprises a point cloud data acquisition module, a point cloud data acquisition module and a data processing module, wherein the point cloud data acquisition module is used for acquiring road point cloud data acquired by a laser radar which is carried on a mobile platform and moves along with the mobile platform;

the de-noising processing module is used for de-noising the point cloud data;

the registration processing module is used for carrying out registration processing on the cloud data subjected to denoising processing so as to increase the information quantity of the road surface point cloud;

the segmentation module is used for carrying out point cloud segmentation on the point cloud data after the registration processing to obtain point cloud data of a road pavement;

the elevation information extraction module is used for extracting road surface elevation information from the point cloud data of the road surface;

and the road surface unevenness determining module is used for calculating the power spectral density of the road surface according to the road surface elevation information and determining the grade of the road surface unevenness according to the power spectral density of the road surface.

Further, in this embodiment, the denoising processing module includes a cloth simulation filtering module and a statistical analysis filtering module, where:

the cloth analog filtering module is used for removing the interference of the vegetation on the point cloud data by adopting cloth analog filtering, filtering the vegetation data on the point cloud acquired by each frame,

and the statistical analysis filtering module is used for performing noise reduction processing on the point cloud data subjected to the cloth simulation filtering processing by adopting statistical analysis filtering, and removing discrete points with the distance between the point cloud data and the center of the laser radar exceeding a threshold value.

For specific implementation of each module in this embodiment, reference may be made to embodiment 1, and details are not described here. It should be noted that, the apparatus provided in this embodiment is only illustrated by dividing the functional modules, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure is divided into different functional modules to complete all or part of the functions described above.

Example 3

The embodiment discloses a road surface unevenness detection system, as shown in fig. 9, which comprises an upper computer, a laser radar, a mobile platform and a power supply.

The laser radar is carried on the mobile platform and used for collecting road point cloud data when moving along with the mobile platform. In this embodiment, the mobile platform may be a tracked vehicle, the lidar may be fixed to the tracked vehicle at an angle using a lidar mounting block, and the lidar may be a VLP-16 lidar.

The laser radar is connected with the upper computer and used for sending the collected road point cloud data to the upper computer.

The mobile platform is connected with the upper computer and used for feeding back mobile information to the upper computer; in this embodiment, mobile platform feeds back the mobile information to the host computer, and when mobile platform began to remove, the host computer controlled laser radar began to gather work, and when mobile platform stopped to remove, the host computer controlled laser radar stopped to gather work, controlled laser radar promptly and carried out the collection of a cloud information to the environment of every side at mobile platform driving in-process, and the back point cloud information that finishes of traveling finishes promptly gathers promptly. In this embodiment, the upper computer is also directly mounted on the mobile platform.

The power supply is connected with the laser radar and the upper computer and used for supplying power to the laser radar and the upper computer.

The upper computer is used for executing the road surface unevenness detection method in embodiment 1, and comprises the following steps:

acquiring road point cloud data acquired by a laser radar which is carried on a mobile platform and moves along with the mobile platform;

denoising the point cloud data;

carrying out registration processing on the cloud data subjected to denoising processing so as to increase the information quantity of the road surface point cloud;

performing point cloud data segmentation on the point cloud data subjected to registration processing to obtain point cloud data of a road pavement;

extracting pavement elevation information from point cloud data of a road pavement;

and calculating the power spectral density of the road surface according to the elevation information of the road surface, and determining the roughness grade of the road surface according to the power spectral density of the road surface.

For specific implementation of each process, reference may be made to embodiment 1, which is not described in detail herein. In this embodiment, the upper computer may be a computer, a server, an industrial personal computer, or other intelligent terminals.

Example 4

The present embodiment discloses a storage medium storing a program which, when executed by a processor, implements the road surface irregularity detecting method described in embodiment 1, as follows:

acquiring road point cloud data acquired by a laser radar which is carried on a mobile platform and moves along with the mobile platform;

denoising the point cloud data;

carrying out registration processing on the cloud data subjected to denoising processing so as to increase the information quantity of the road surface point cloud;

performing point cloud data segmentation on the point cloud data subjected to registration processing to obtain point cloud data of a road pavement;

extracting pavement elevation information from point cloud data of a road pavement;

and calculating the power spectral density of the road surface according to the elevation information of the road surface, and determining the roughness grade of the road surface according to the power spectral density of the road surface.

In this embodiment, specific implementation of each process may be referred to in embodiment 1, which is not described herein again.

In this embodiment, the storage medium may be a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a Random Access Memory (RAM), a usb disk, a removable hard disk, or other media.

Example 5

The embodiment discloses a computing device, which comprises a processor and a memory for storing an executable program of the processor, and is characterized in that when the processor executes the program stored in the memory, the road unevenness detection method of the embodiment 1 is implemented as follows:

acquiring road point cloud data acquired by a laser radar which is carried on a mobile platform and moves along with the mobile platform;

denoising the point cloud data;

carrying out registration processing on the cloud data subjected to denoising processing so as to increase the information quantity of the road surface point cloud;

performing point cloud data segmentation on the point cloud data subjected to registration processing to obtain point cloud data of a road pavement;

extracting pavement elevation information from point cloud data of a road pavement;

and calculating the power spectral density of the road surface according to the elevation information of the road surface, and determining the roughness grade of the road surface according to the power spectral density of the road surface.

In this embodiment, specific implementation of each process may be referred to in embodiment 1, which is not described herein again. In this embodiment, the computing device may be a desktop computer, a notebook computer, a PDA handheld terminal, a tablet computer, or other terminal devices.

In this embodiment, the computing device includes: the system comprises a processor, a memory, a bus and a communication interface, wherein the processor, the communication interface and the memory are connected through the bus; the processor is configured to execute an executable module, such as a computer program, stored in the memory.

The Memory may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network and the like can be used.

The bus may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, and the like.

The storage is configured to store a program, and the processor executes the program after receiving an execution instruction, and the method performed by the apparatus defined by the flow program disclosed in the foregoing embodiments of the present application may be applied to or implemented by the processor.

The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSP), Application Specific Integrated Circuits (ASIC), Field-Programmable Gate arrays (FPGA) or other Programmable logic devices, discrete Gate or transistor logic devices, and discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium that is mature in the art, such as a random access memory, a flash memory and/or a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory and/or a register, and the storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware thereof.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.