CN115718305A - Laser point cloud highway section processing method, device, equipment and storage medium - Google Patents
Laser point cloud highway section processing method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to a method, a device, equipment and a storage medium for processing a laser point cloud highway section, wherein the method comprises the following steps: acquiring laser point cloud data of a road, and extracting section point cloud data from the laser point cloud data according to section information; preprocessing the cross-section point cloud data, and projecting the preprocessed cross-section point cloud data to a two-dimensional plane to obtain two-dimensional cross-section point cloud data; calculating a right deviation value of the two-dimensional cross section point cloud data, and filtering the two-dimensional cross section point cloud data according to the right deviation value; dividing the filtered two-dimensional cross-section point cloud data into a plurality of point cloud intervals, distributing a rarefying strategy according to the point cloud intervals, and rarefying the point cloud intervals based on the rarefying strategy to obtain the road cross-section point cloud. According to the laser point cloud highway section processing method, device, equipment and storage medium, provided by the invention, the point cloud data of the section are extracted, the highway section is produced according to the point cloud data of the section, the calculation range is small, and the production efficiency is improved.
Description
Technical Field
The invention relates to the technical field of highway surveying, in particular to a method, a device, equipment and a storage medium for processing a laser point cloud highway section.
Background
Laser radar (LiDAR) is an active radar detection technology integrating laser, global Positioning System (GPS) and Inertial Navigation System (INS) technologies, can overcome the influence of vegetation and shadow, is less influenced by weather, and can be used for rapidly, accurately and massively acquiring three-dimensional space information of ground and ground targets. The data density is large and the geometric accuracy is high, so that the accuracy requirement of a large-scale topographic map can be met by using a high-accuracy Digital Surface Model (DSM) acquired by LiDAR.
Obtaining accurate cross-sectional ground lines is an important task in highway survey design. The traditional production method of the surface section line comprises the steps of firstly obtaining ground points by LiDAR point cloud filtering, generating a digital ground model through the ground points, then mapping the digital ground model (generally, a high-precision digital surface model (DEM)) to obtain section information data, and producing a section product.
However, in production practice, the cross-sectional area of the target tends to be small, and the amount of data involved is small. However, the traditional method needs to filter global data to produce the DEM, which causes a large amount of data redundancy, thereby wasting computing power and seriously affecting production efficiency. In addition, as the real earth surface is very complex in a large area, fluctuation of various forms exists, and the earth surface coverage types are more various, the integral filtering operation is carried out on the point cloud data, not only is the filtering parameter threshold difficult to select, but also the uniform threshold can cause the filtering quality of the area with large difference to be low, so that the produced DEM is distorted, and the section quality is finally influenced.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a device and a storage medium for processing a laser point cloud highway section, so as to solve the problems that the traditional method in the prior art needs to filter global data, the processing efficiency of the laser point cloud highway section is seriously affected, the complexity of a real ground surface is high, and the threshold of a filtering parameter is difficult to select, resulting in poor section quality.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides a laser point cloud highway section processing method, which comprises the following steps:
acquiring laser point cloud data of a road, and extracting section point cloud data from the laser point cloud data according to section information;
preprocessing the section point cloud data, and projecting the preprocessed section point cloud data to a two-dimensional plane to obtain two-dimensional section point cloud data;
calculating a right deviation value of the two-dimensional cross-section point cloud data, and filtering the two-dimensional cross-section point cloud data according to the right deviation value;
dividing the two-dimensional cross section point cloud data after filtering into a plurality of point cloud intervals, distributing a rarefying strategy according to the point cloud intervals, and carrying out rarefying on the point cloud intervals based on the rarefying strategy to obtain the road cross section point cloud.
Preferably, the method includes the steps of obtaining laser point cloud data of a road, and extracting section point cloud data from the laser point cloud data according to section information, wherein the steps include:
acquiring a middle pile point coordinate, and calculating an azimuth angle of the middle pile point according to the middle pile point coordinate;
calculating an equation expression of a section straight line according to the azimuth angle of the middle pile point and the middle pile point coordinate;
determining the position of a side pile of the section straight line according to a preset side pile distance threshold value on the basis of the middle pile point;
and determining a section distance threshold according to the laser point cloud data, and extracting the section point cloud data according to the section distance threshold, the side pile position and the distance from the laser point cloud data to a section straight line.
Preferably, the laser point cloud data of the highway is obtained, and the cross section point cloud data is extracted from the laser point cloud data according to the cross section information, and the method further comprises the following steps:
if a plurality of pieces of section point cloud data are extracted, rotating the section point cloud data to enable a section straight line to be vertical to the abscissa axis;
and sorting according to the abscissa of the intersection point of the rotated section straight line and the abscissa axis, and establishing an index number based on a sorting result.
Preferably, the cross-section point cloud data is preprocessed, and the preprocessed cross-section point cloud data is projected to a two-dimensional plane to obtain two-dimensional cross-section point cloud data, which comprises the following steps:
preprocessing the section point cloud data through a Gaussian filtering algorithm to obtain denoised section point cloud data;
and projecting the denoised section point cloud data to a two-dimensional plane to obtain two-dimensional section point cloud data.
Preferably, a right bias value of the two-dimensional cross-section point cloud data is calculated, and the two-dimensional cross-section point cloud data is subjected to filtering processing according to the right bias value, wherein the filtering processing comprises the following steps:
dividing the two-dimensional section point cloud data into a plurality of point cloud data sections according to a preset interval;
connecting point cloud straight lines with the minimum vertical coordinate in all the point cloud data segments to obtain an initial terrain fold line;
determining the ground clearance from the two-dimensional section point cloud data to the initial terrain fold line, and calculating the right deviation value of the ground clearance of the two-dimensional section point cloud data according to the ground clearance;
and when the ground clearance right deviation value is larger than zero, arranging the point cloud data of the two-dimensional section according to the ground clearance from large to small, sequentially deleting the maximum value points of the ground clearance, and recalculating the ground clearance right deviation value until the ground clearance right deviation value is not larger than zero.
Preferably, the filtered two-dimensional section point cloud data is divided into a plurality of point cloud intervals, and the rarefying strategy is distributed according to the point cloud intervals, and the method comprises the following steps:
dividing the filtered two-dimensional section point cloud data into a plurality of point cloud intervals according to a preset interval and calculating the fluctuation degree of each point cloud interval;
dividing the point cloud interval into a flat area and a non-flat area according to the waviness and a first preset threshold;
dividing the non-flat area into a rough area and a non-rough area according to a second preset threshold;
distributing a thinning strategy for each point cloud interval according to the division type of the point cloud intervals; the thinning strategy reserves the elevation extreme points in all point cloud intervals and the high curvature points in all non-flat areas.
Preferably, dividing the non-flat area into a rough area and a non-rough area according to a second preset threshold includes:
performing curve fitting on all point clouds in the non-flat area to determine a fitting elevation value;
determining the surface roughness of the non-flat area according to the fitting elevation values and the actual elevation values of all point clouds in the non-flat area;
and dividing the non-flat area into a rough area and a non-rough area according to the surface roughness and a second preset threshold value.
In a second aspect, the present invention further provides a laser point cloud highway section processing apparatus, including:
the extraction module is used for acquiring laser point cloud data of the road and extracting the cross section point cloud data from the laser point cloud data according to the cross section information;
the preprocessing module is used for preprocessing the cross-section point cloud data and projecting the preprocessed cross-section point cloud data to a two-dimensional plane to obtain two-dimensional cross-section point cloud data;
the filtering module is used for calculating a right deviation value of the two-dimensional cross-section point cloud data and filtering the two-dimensional cross-section point cloud data according to the right deviation value;
and the rarefying module is used for dividing the filtered two-dimensional section point cloud data into a plurality of point cloud intervals, distributing a rarefying strategy according to the point cloud intervals, and carrying out rarefying on the point cloud intervals based on the rarefying strategy to obtain the road section point cloud.
In a third aspect, the present invention also provides an electronic device comprising a memory and a processor, wherein,
a memory for storing a program;
and the processor is coupled with the memory and is used for executing the program stored in the memory so as to realize the steps of the laser point cloud highway section processing method in any implementation mode.
In a fourth aspect, the present invention further provides a computer-readable storage medium for storing a computer-readable program or instruction, where the program or instruction, when executed by a processor, can implement the steps in the laser point cloud highway section processing method in any one of the above-mentioned implementation manners.
The beneficial effects of adopting the embodiment are as follows: the invention relates to a method, a device, equipment and a storage medium for processing a laser point cloud highway section, wherein the method comprises the following steps: acquiring laser point cloud data of a road, and extracting section point cloud data from the laser point cloud data according to section information; preprocessing the section point cloud data, and projecting the preprocessed section point cloud data to a two-dimensional plane to obtain two-dimensional section point cloud data; calculating a right deviation value of the two-dimensional section point cloud data, and filtering the two-dimensional section point cloud data according to the right deviation value; dividing the two-dimensional cross-section point cloud data after filtering into a plurality of point cloud intervals, distributing a rarefying strategy according to the point cloud intervals, and rarefying the point cloud intervals based on the rarefying strategy to obtain the road cross-section point cloud. According to the laser point cloud highway section processing method, the laser point cloud road section processing device, the laser point cloud road section processing equipment and the storage medium, section point cloud data are extracted from the laser point cloud data according to section information, global data are prevented from being processed, the number of processed data is reduced, the calculation range is narrowed, the section processing efficiency is improved, filtering processing is carried out according to the right deviation value of the two-dimensional section point cloud data, the condition that the filtering quality of regions with large differences is low due to the fact that uniform threshold values are used is avoided, the quality of final section production is guaranteed, different thinning strategies are distributed according to different regions, the precision of section production is improved, and the quality of sections is also improved.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of a method for processing a laser point cloud highway section according to the present invention;
FIG. 2 is a flowchart illustrating an embodiment of step S101 in FIG. 1;
FIG. 3 is a three-dimensional schematic diagram of one embodiment of creating a section index according to the present invention;
FIG. 4 is a flowchart illustrating an embodiment of step S103 in FIG. 1;
FIG. 5 is a schematic diagram of filtering two-dimensional cross-section point cloud data according to an embodiment of the present invention;
FIG. 6 is a flowchart illustrating an embodiment of step S104 in FIG. 1;
FIG. 7 is a flowchart illustrating an embodiment of step S603 in FIG. 6;
FIG. 8 is a schematic structural diagram of an embodiment of a laser point cloud highway section processing apparatus according to the present invention;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;
FIG. 10 is a schematic point cloud diagram of an exemplary experimental area provided herein;
FIG. 11 is a point cloud side view of an embodiment of a cross-sectional point cloud provided by the present invention;
FIG. 12 is a diagram illustrating the filtering result of an embodiment of the cross-sectional point cloud provided by the present invention;
FIG. 13 is a graph of thinning results of a cross-sectional point cloud according to an embodiment of the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
Before the embodiments of the present invention are explained, the related words are explained:
pile point centering: the piles or marks with pile numbers are arranged along the center line of the route to show the position, line shape and the like of the center line. The azimuth angle of the center pile point is defined as the included angle between the connecting line of the current center pile point and the next center pile point and the true north direction. And the azimuth angle of the last pile center point is specified to be the same as that of the previous pile center point.
Elevation: the distance from a certain point to an absolute base plane along the direction of the plumb line is called absolute elevation, and is called elevation for short.
In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The invention provides a method, a device, equipment and a storage medium for processing a laser point cloud highway section, which are respectively explained below.
Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of a laser point cloud highway section processing method provided by the present invention, and an embodiment of the present invention discloses a laser point cloud highway section processing method, including:
s101, acquiring laser point cloud data of a road, and extracting cross-section point cloud data from the laser point cloud data according to cross-section information;
s102, preprocessing the cross-section point cloud data, and projecting the preprocessed cross-section point cloud data to a two-dimensional plane to obtain two-dimensional cross-section point cloud data;
s103, calculating a right deviation value of the two-dimensional section point cloud data, and filtering the two-dimensional section point cloud data according to the right deviation value;
and S104, dividing the filtered two-dimensional section point cloud data into a plurality of point cloud intervals, distributing a rarefying strategy according to the point cloud intervals, and rarefying the point cloud intervals based on the rarefying strategy to obtain the road section point cloud.
In the above embodiment, the laser radar is used to acquire data of the target road to obtain laser point cloud data of the target road, and when the section information is used to determine the target road, relevant information of the road design can be obtained, including section information and relevant information of the middle pile point, for example, a section is obtained every 20 meters, and the section point cloud data can be quickly found from a large amount of laser point cloud data through the information, so that the calculation range is greatly reduced.
The noise of the laser point cloud data is usually caused by the fact that a laser beam irradiates an air obstacle such as a bird or the like, or the laser beam irradiates the surface of an object and is reflected for multiple times, and a spatially isolated feature, also called an outlier, is presented in the data. The outliers affect the filtering quality, which causes inaccurate section information, and the outliers need to be preprocessed, i.e., denoised.
The ground clearance from the ground point to an irregular triangular network (TIN) model is subject to normal distribution, the ground object point is a noise point influencing the distribution, namely the ground clearance from the ground object point to the TIN is a gross deviation influencing the distribution, the distribution curve is subjected to right deviation, the center limit theorem is not met, and the point cloud data of the two-dimensional section are filtered through the right deviation of the ground clearance.
In actual production, in order to meet the requirements of hardware and efficiency, the redundancy of laser point cloud data is often required to be reduced, so that certain requirements are imposed on the number of cross-section point clouds. However, the laser point cloud data with the maximum density is usually obtained as much as possible during the laser point cloud data acquisition, and even if the laser point cloud data is filtered, the number of the cross section point clouds is still large, so that the cross section processing is influenced. If the number of point clouds on a section is reduced by reducing the width of the section, the section cannot accurately describe the surface relief change. In summary, the cross-section point cloud is thinned to reduce the number of point clouds, but at the same time, it is ensured that the topographic features are preserved. And (4) obtaining cross section point cloud data after thinning, namely the point cloud data of the road cross section.
Compared with the prior art, the method for processing the laser point cloud road section provided by the embodiment comprises the following steps: acquiring laser point cloud data of a road, and extracting section point cloud data from the laser point cloud data according to section information; preprocessing the section point cloud data, and projecting the preprocessed section point cloud data to a two-dimensional plane to obtain two-dimensional section point cloud data; calculating a right deviation value of the two-dimensional section point cloud data, and filtering the two-dimensional section point cloud data according to the right deviation value; dividing the two-dimensional cross-section point cloud data after filtering into a plurality of point cloud intervals, distributing a rarefying strategy according to the point cloud intervals, and rarefying the point cloud intervals based on the rarefying strategy to obtain the road cross-section point cloud. According to the laser point cloud highway section processing method, the laser point cloud road section processing device, the laser point cloud road section processing equipment and the storage medium, section point cloud data are extracted from the laser point cloud data according to section information, global data are prevented from being processed, the number of processed data is reduced, the calculation range is narrowed, the section processing efficiency is improved, filtering processing is carried out according to the right deviation value of the two-dimensional section point cloud data, the condition that the filtering quality of regions with large differences is low due to the fact that uniform threshold values are used is avoided, the quality of final section production is guaranteed, different thinning strategies are distributed according to different regions, the precision of section production is improved, and the quality of sections is also improved.
Referring to fig. 2, fig. 2 is a schematic flowchart illustrating an embodiment of step S101 in fig. 1, in some embodiments of the present invention, acquiring laser point cloud data of a road, and extracting cross-section point cloud data from the laser point cloud data according to cross-section information includes:
s201, acquiring a middle pile point coordinate, and calculating an azimuth angle of a middle pile point according to the middle pile point coordinate;
s202, calculating an equation expression of a section straight line according to the azimuth angle of the middle pile point and the middle pile point coordinate;
s203, determining the position of a side pile of the section straight line according to a preset side pile distance threshold value based on the middle pile point;
s204, determining a section distance threshold according to the laser point cloud data, and extracting the section point cloud data according to the section distance threshold, the side pile position and the distance from the laser point cloud data to a section straight line.
In the above embodiment, the point cloud of the middle pile point in the laser point cloud data is determined, the laser point cloud data includes coordinates of the point cloud, and the coordinates of the middle pile point can be determined when the coordinates are established, so that the coordinates of the middle pile point can be directly obtained. By two adjacent points P 1 (x 1 ,y 1 ) And P 2 (x 2 ,y 2 ) For example, P 1 The calculation formula of the azimuth angle of the middle pile point is as follows:
in the formula: alpha is the azimuth angle of the middle pile point, and delta x = x 2 -x 1 ，Δy＝y 2 -y 1 Depending on the quadrant in which (Δ x, Δ y) is located, the value of α can be converted between 0 and 2 π.
Because the section is perpendicular to the direction of the middle pile point, the linear equation of the section line obtained according to the azimuth angle alpha of the middle pile point is as follows:
y＝kx+b；
in the formula: k = tan (α + π/2). And substituting the coordinates (x, y) of the middle pile point to obtain a linear equation where the cross section line is located.
And (4) with the middle pile point as a boundary, presetting side pile distance thresholds along two sides of the direction of the transverse section line respectively to determine the side pile position of the section line. The preset side pile distance threshold value can be set according to the actual situation, and the scheme does not further describe the threshold value. The initial range of the cross section is determined by determining the position of the side pile, and the subsequent operation only needs to process the point cloud in the position of the side pile.
Determining a section distance threshold according to the point density of the laser point cloud data, calculating the distance from the laser point cloud data to a section straight line, extracting the point cloud with the distance from the laser point cloud data to the section straight line within the section distance threshold to obtain the section point cloud data, and discarding the rest laser point cloud data, so that the range of data volume to be calculated is reduced, and the calculation efficiency is improved.
Referring to fig. 3, fig. 3 is a three-dimensional schematic diagram of an embodiment of creating a cross-section index according to the present invention, in some embodiments of the present invention, the method includes obtaining laser point cloud data of a road, and extracting the cross-section point cloud data from the laser point cloud data according to cross-section information, and further includes:
if a plurality of pieces of cross-section point cloud data are extracted, rotating the cross-section point cloud data to enable the cross-section straight line to be perpendicular to the abscissa axis;
and sorting according to the abscissa of the intersection point of the rotated section straight line and the abscissa axis, and establishing an index number based on a sorting result.
In the above embodiment, the section point cloud data can be quickly retrieved and calculated by establishing the section point cloud index.
In some embodiments of the present invention, the pre-processing the cross-section point cloud data, and projecting the pre-processed cross-section point cloud data to a two-dimensional plane to obtain two-dimensional cross-section point cloud data includes:
preprocessing the section point cloud data through a Gaussian filtering algorithm to obtain denoised section point cloud data;
and projecting the denoised point cloud data of the section to a two-dimensional plane to obtain the point cloud data of the two-dimensional section.
In the above embodiment, the gaussian filter algorithm considers that the distances from the point cloud to the neighboring points are normally distributed in the space, and the noise points are distributed in the area beyond 2 σ to 3 σ far from the mean value of the gaussian distribution, so that the noise points can be determined and removed. The method comprises the following specific steps:
step 1: setting k adjacent search parameters;
and 2, step: traversing the point cloud data of the cross section, and counting the average distance from each point cloud to the k point clouds nearest to the point cloud;
and step 3: calculating the average distance mean and variance of all the point clouds k;
and 4, step 4: and eliminating the point clouds with the average distance exceeding 3 sigma.
And projecting the denoised point cloud to a ZOY plane (as shown in figure 3) for filtering.
Referring to fig. 4, fig. 4 is a schematic flowchart illustrating an embodiment of step S103 in fig. 1, in some embodiments of the present invention, calculating a right bias value of the two-dimensional cross-section point cloud data, and performing a filtering process on the two-dimensional cross-section point cloud data according to the right bias value, including:
s401, dividing the two-dimensional section point cloud data into a plurality of point cloud data sections according to a preset interval;
s402, connecting point cloud straight lines with the minimum vertical coordinate in all point cloud data segments to obtain an initial terrain fold line;
s403, determining the ground clearance from the two-dimensional section point cloud data to the initial terrain fold line, and calculating the right deviation value of the ground clearance of the two-dimensional section point cloud data according to the ground clearance;
s404, when the ground clearance right deviation value is larger than zero, arranging the point cloud data of the two-dimensional cross section according to the ground clearance from large to small, sequentially deleting the maximum value points of the ground clearance, and recalculating the ground clearance right deviation value until the ground clearance right deviation value is not larger than zero.
In the above embodiment, the preset distance may be set according to actual situations, which is not further limited in the present invention. Dividing the two-dimensional section point cloud data into a plurality of point cloud data sections, wherein the lengths of all the point cloud data sections are the same.
And searching the lowest point cloud in each point cloud data segment, namely the point cloud with the minimum vertical coordinate in the point cloud data segment, connecting the point clouds with the minimum vertical coordinate in all the point cloud data segments by using a straight line, and obtaining a broken line as an initial terrain broken line.
The distance between the point clouds and the initial terrain fold line along the plumb line direction is the ground clearance, the ground clearance of each point cloud in the two-dimensional section point cloud data is determined and recorded as X i The height difference from the ground of the ith point cloud is represented, and the mean value of the height difference from the ground is calculated according to the height difference from the ground:
calculating the standard deviation of the ground clearance according to the mean value of the ground clearance:
calculating a ground clearance right deviation value according to the ground clearance standard deviation:
in the formula: n is the total point number, mu is the ground clearance mean value of the residual point cloud, sigma is the ground clearance standard deviation of the residual point cloud, S K And the right deviation value of the height difference between the residual point cloud and the initial broken line is obtained.
Referring to fig. 5, fig. 5 is a schematic diagram of filtering of two-dimensional cross-section point cloud data according to an embodiment of the present invention, wherein in an ideal state, S K And the height difference between the residual point cloud and the initial broken line segment satisfies the normal distribution at the moment, which is supposed to be 0. Therefore, only the maximum value points of the height difference are deleted in sequence from large to small, and the S is recalculated K Up to S K Stopping when the temperature is less than or equal to 0. The filtering method has the advantages that parameters do not need to be set, and ground points and ground object points can be dynamically classified according to a high-difference skewness curve of the target point cloud.
Referring to fig. 6, fig. 6 is a schematic flowchart illustrating an embodiment of step S104 in fig. 1, in some embodiments of the present invention, the dividing the filtered two-dimensional cross-section point cloud data into a plurality of point cloud intervals, and allocating a rarefying strategy according to the point cloud intervals includes:
s601, dividing the filtered two-dimensional section point cloud data into a plurality of point cloud intervals according to a preset interval and calculating the fluctuation degree of each point cloud interval;
s602, dividing the point cloud interval into a flat area and a non-flat area according to the waviness and a first preset threshold;
s603, dividing the non-flat area into a rough area and a non-rough area according to a second preset threshold;
s604, distributing a rarefaction strategy for each point cloud interval according to the division type of the point cloud intervals; the thinning strategy reserves the elevation extreme points in all point cloud intervals and the high curvature points in all non-flat areas.
In the above embodiment, the preset distance may be the same as the preset distance when the two-dimensional cross-section point cloud data is divided into a plurality of point cloud data segments, or may be set to other values. The elevation maximum value and the elevation minimum value in each point cloud interval are calculated, and the fluctuation degree of the point cloud intervals is obtained by the difference between the elevation maximum value and the elevation minimum value in the intervals.
The first preset threshold is related to the setting of the size of the interval, generally does not exceed 1/2 of the side length of the interval, the interval of which the undulation degree is smaller than the threshold is marked as a flat area, the interval of which the undulation degree is larger than the threshold is marked as a non-flat area, the excessive interval is divided into the flat areas when the first preset threshold is set too large, and the key points of the terrain are removed; if the setting is too small, the number of point clouds in a non-flat area is increased, and the calculation efficiency is influenced.
The rough area and the non-rough area are further divided for the non-flat area to realize accurate classification of different areas, so that the thinning strategy of the areas is better determined, and accurate section point cloud is obtained by thinning the areas.
And calculating the point cloud curvatures of all non-flat areas according to a curvature formula, thinning the rest points in a random sampling mode on the basis of reserving the elevation extreme points in all the intervals, wherein the flat areas are thinned at a high thinning rate, the non-rough areas are thinned at a high thinning rate after reserving the high curvature points, and the rough areas are thinned at a low thinning rate after reserving the high curvature points. It should be noted that the evacuation rate may be set according to actual conditions, or a section of area may be selected to simulate in an actual process to determine a suitable evacuation rate, and the present invention does not specifically limit the evacuation rate.
The point cloud data left after the thinning of the cross-section point cloud data through the thinning strategy is the data of the cross-section point cloud, namely the road cross section is produced.
Referring to fig. 7, fig. 7 is a flowchart illustrating an embodiment of step S603 in fig. 6, in some embodiments of the present invention, dividing the non-flat area into the rough area and the non-rough area according to a second predetermined threshold includes:
s701, performing curve fitting on all point clouds in the non-flat area to determine a fitting elevation value;
s702, determining the surface roughness of the non-flat area according to the fitting elevation values and the actual elevation values of all point clouds in the non-flat area;
and S703, dividing the non-flat area into a rough area and a non-rough area according to the surface roughness and a second preset threshold value.
In the above embodiment, curve fitting is performed separately for each non-flat area, the fitting elevation values of all point clouds in the interval are calculated after curve fitting, and the actual elevation values of the point clouds are known from the point cloud data, so that the absolute values of the differences between the fitting elevation values and the actual elevation values of all points in the interval are averaged to obtain the surface roughness.
The second preset threshold is related to the number of point clouds in the interval, is usually 0.1, the mark of the point cloud interval with the surface roughness larger than the second preset threshold is a rough area, the mark of the point cloud interval with the surface roughness smaller than the second preset threshold is a non-rough area, the rough area is increased due to the fact that the second preset threshold is set too small, and finally the number of reserved points is increased; if the setting is too large, the rough area is reduced, and the key points may be deleted by mistake.
In order to better implement the laser point cloud highway section processing method in the embodiment of the present invention, on the basis of the laser point cloud highway section processing method, correspondingly, please refer to fig. 8, fig. 8 is a schematic structural diagram of an embodiment of the laser point cloud highway section processing apparatus provided in the present invention, and an embodiment of the present invention provides a laser point cloud highway section processing apparatus 800, including:
the extraction module 810 is used for acquiring laser point cloud data of a road and extracting cross-section point cloud data from the laser point cloud data according to cross-section information;
the preprocessing module 820 is used for preprocessing the cross-section point cloud data and projecting the preprocessed cross-section point cloud data to a two-dimensional plane to obtain two-dimensional cross-section point cloud data;
the filtering module 830 is configured to calculate a right bias value of the two-dimensional cross-section point cloud data, and perform filtering processing on the two-dimensional cross-section point cloud data according to the right bias value;
and the rarefying module 840 is used for dividing the filtered two-dimensional section point cloud data into a plurality of point cloud intervals, distributing a rarefying strategy according to the point cloud intervals, and rarefying the point cloud intervals based on the rarefying strategy to obtain the road section point cloud.
Here, it should be noted that: the apparatus 800 provided in the foregoing embodiment may implement the technical solutions described in the foregoing method embodiments, and the specific implementation principles of the modules or units may refer to the corresponding contents in the foregoing method embodiments, which are not described herein again.
Referring to fig. 9, fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the invention. Based on the laser point cloud highway section processing method, the invention also correspondingly provides laser point cloud highway section processing equipment which can be computing equipment such as a mobile terminal, a desktop computer, a notebook computer, a palm computer, a server and the like. The laser point cloud highway section processing device comprises a processor 910, a memory 920 and a display 930. Fig. 9 shows only some of the components of the electronic device, but it should be understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead.
The storage 920 may be an internal storage unit of the laser point cloud road section processing apparatus in some embodiments, for example, a hard disk or a memory of the laser point cloud road section processing apparatus. The memory 920 may also be an external storage device of the laser point cloud highway section processing device in other embodiments, such as a plug-in hard disk provided on the laser point cloud highway section processing device, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 920 may also include both an internal storage unit and an external storage device of the laser point cloud road section processing device. The memory 920 is used for storing application software installed in the laser point cloud highway section processing equipment and various data, such as program codes for installing the laser point cloud highway section processing equipment. The memory 920 may also be used to temporarily store data that has been output or is to be output. In an embodiment, the memory 920 stores a laser point cloud highway section processing program 940, and the laser point cloud highway section processing program 940 can be executed by the processor 910, so as to implement the laser point cloud highway section processing method according to the embodiments of the present application.
The processor 910 may be a Central Processing Unit (CPU), a microprocessor or other data Processing chip in some embodiments, and is used to execute program codes stored in the memory 920 or process data, such as performing a laser point cloud highway section Processing method.
The display 930 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch panel, or the like in some embodiments. The display 930 is used to display information at the laser point cloud road section processing device and to display a visual user interface. The components 910-930 of the laser point cloud highway section processing apparatus communicate with each other via a system bus.
In an embodiment, the steps in the laser point cloud road section processing method described above are implemented when the processor 910 executes the laser point cloud road section processing program 940 in the memory 920.
The present embodiment also provides a computer-readable storage medium, on which a laser point cloud highway section processing program is stored, which when executed by a processor implements the following steps:
acquiring laser point cloud data of a road, and extracting section point cloud data from the laser point cloud data according to section information;
preprocessing the section point cloud data, and projecting the preprocessed section point cloud data to a two-dimensional plane to obtain two-dimensional section point cloud data;
calculating a right deviation value of the two-dimensional cross-section point cloud data, and filtering the two-dimensional cross-section point cloud data according to the right deviation value;
dividing the two-dimensional cross section point cloud data after filtering into a plurality of point cloud intervals, distributing a rarefying strategy according to the point cloud intervals, and carrying out rarefying on the point cloud intervals based on the rarefying strategy to obtain the road cross section point cloud.
The invention also provides a mountain area laser point cloud highway section processing example collected by a certain highway project, which comprises the following concrete steps:
the total length of the project line is about 54.91km, the topography of the measuring area has large fluctuation, and the project line belongs to the topography of a heavy dune mountain ridge. The point cloud data is obtained by unmanned airborne LiDAR aviation flight, and the density (exposed surface) of the point cloud is better than 40/m 2 Referring to fig. 10, fig. 10 is a point cloud schematic diagram of an experimental area according to an embodiment of the present invention.
Extracting a cross-section point cloud according to the method, setting the distance between the middle piles to be 20m, adding piles in local special places, setting the width of the cross section to be 3m, and establishing an index for the extracted cross section, please refer to fig. 11, which is a point cloud side view of an embodiment of the cross-section point cloud provided by the invention.
The extracted cross-section point cloud data is denoised by the above method, and then filtered, please refer to fig. 12, where fig. 12 is a filtering result diagram of an embodiment of the cross-section point cloud provided by the present invention.
The comparison statistics of the cross-section point cloud filtering results and the real ground points are shown in table 1:
TABLE 1 Point cloud precision evaluation of fracture surface
Through statistics, the precision of the filtering method is 89.90%, the recall rate is 90.47%, the OA reaches 94.19%, and the result precision is high.
The filtered ground point cloud is subjected to point cloud thinning by the method to obtain simplified ground points, please refer to fig. 13, and fig. 13 is a thinning result diagram of an embodiment of the cross-section point cloud provided by the invention. From the two marked positions, it can be seen that the algorithm, after preserving the high curvature points, rarefs at a small rate at the trench bottom (rough area) and rarefs at a large rate at the gentle slope (non-rough area). The redundancy of the point cloud is effectively reduced while the local topographic features are reserved, and the key topographic features are prevented from being removed due to rarefaction.
In summary, the present embodiment provides a method, an apparatus, a device and a storage medium for processing a laser point cloud road section, where the method includes: acquiring laser point cloud data of a road, and extracting section point cloud data from the laser point cloud data according to section information; preprocessing the section point cloud data, and projecting the preprocessed section point cloud data to a two-dimensional plane to obtain two-dimensional section point cloud data; calculating a right deviation value of the two-dimensional section point cloud data, and filtering the two-dimensional section point cloud data according to the right deviation value; dividing the filtered two-dimensional cross-section point cloud data into a plurality of point cloud intervals, distributing a rarefying strategy according to the point cloud intervals, and rarefying the point cloud intervals based on the rarefying strategy to obtain the road cross-section point cloud. According to the laser point cloud highway section processing method, the laser point cloud highway section processing device, the laser point cloud highway section processing equipment and the storage medium, section point cloud data are extracted from the laser point cloud data according to section information, global data are prevented from being processed, the number of processed data is reduced, the calculation range is narrowed, therefore, the section processing efficiency is improved, filtering processing is carried out according to the right bias value of two-dimensional section point cloud data, the condition that the filtering quality of areas with large differences is low due to the fact that uniform threshold values are used is avoided, the quality of final section production is guaranteed, different thinning strategies are distributed according to different areas, the precision of section production is improved, and the quality of sections is also improved.
While the invention has been described with reference to specific preferred embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the following claims.
Claims (10)
1. A laser point cloud highway section processing method is characterized by comprising the following steps:
acquiring laser point cloud data of a road, and extracting section point cloud data from the laser point cloud data according to section information;
preprocessing the section point cloud data, and projecting the preprocessed section point cloud data to a two-dimensional plane to obtain two-dimensional section point cloud data;
calculating a right deviation value of the two-dimensional section point cloud data, and filtering the two-dimensional section point cloud data according to the right deviation value;
dividing the two-dimensional cross-section point cloud data after filtering into a plurality of point cloud intervals, distributing a rarefying strategy according to the point cloud intervals, and rarefying the point cloud intervals based on the rarefying strategy to obtain the road cross-section point cloud.
2. The laser point cloud highway section processing method according to claim 1, wherein the step of acquiring laser point cloud data of a highway and extracting section point cloud data from the laser point cloud data according to section information comprises the following steps:
acquiring a middle pile point coordinate, and calculating an azimuth angle of the middle pile point according to the middle pile point coordinate;
calculating an equation expression of a section straight line according to the azimuth angle of the middle pile point and the coordinates of the middle pile point;
based on the middle pile point, determining the position of the side pile of the section straight line according to a preset side pile distance threshold value;
and determining a section distance threshold according to the laser point cloud data, and extracting the section point cloud data according to the section distance threshold, the side pile position and the distance from the laser point cloud data to the section straight line.
3. The laser point cloud highway section processing method of claim 2, wherein the obtaining laser point cloud data of a highway and extracting section point cloud data from the laser point cloud data according to section information further comprises:
if a plurality of pieces of cross-section point cloud data are extracted, rotating the cross-section point cloud data to enable the cross-section straight line to be perpendicular to the abscissa axis;
and sorting according to the abscissa of the intersection point of the rotated section straight line and the abscissa axis, and establishing an index number based on a sorting result.
4. The laser point cloud highway section processing method of claim 1, wherein the step of preprocessing the section point cloud data and projecting the preprocessed section point cloud data onto a two-dimensional plane to obtain two-dimensional section point cloud data comprises the steps of:
preprocessing the section point cloud data through a Gaussian filtering algorithm to obtain denoised section point cloud data;
and projecting the denoised point cloud data of the section to a two-dimensional plane to obtain the point cloud data of the two-dimensional section.
5. The laser point cloud highway section processing method according to claim 4, wherein the calculating a right bias value of the two-dimensional section point cloud data and performing filtering processing on the two-dimensional section point cloud data according to the right bias value comprises:
dividing the two-dimensional section point cloud data into a plurality of point cloud data sections according to a preset interval;
connecting point cloud straight lines with the smallest vertical coordinates in all the point cloud data segments to obtain an initial terrain fold line;
determining the ground clearance from the two-dimensional section point cloud data to the initial terrain fold line, and calculating the right deviation value of the ground clearance of the two-dimensional section point cloud data according to the ground clearance;
and when the ground clearance right deviation value is larger than zero, arranging the point cloud data of the two-dimensional cross section according to the ground clearance from large to small, sequentially deleting the maximum value points of the ground clearance, and recalculating the ground clearance right deviation value until the ground clearance right deviation value is not larger than zero.
6. The laser point cloud highway section processing method of claim 5, wherein the dividing the filtered two-dimensional section point cloud data into a plurality of point cloud intervals, and assigning a thinning strategy according to the point cloud intervals comprises:
dividing the filtered two-dimensional section point cloud data into a plurality of point cloud intervals according to the preset spacing and calculating the fluctuation degree of each point cloud interval;
dividing the point cloud interval into a flat area and a non-flat area according to the undulation degree and a first preset threshold value;
dividing the non-flat area into a rough area and a non-rough area according to a second preset threshold value;
distributing a thinning strategy for each point cloud interval according to the division type of the point cloud intervals; and the thinning strategy reserves the elevation extreme points in all point cloud intervals and all high curvature points in the non-flat area.
7. The laser point cloud highway section processing method of claim 6, wherein the dividing the non-flat area into a rough area and a non-rough area according to a second preset threshold comprises:
performing curve fitting on all point clouds in the non-flat area to determine a fitting elevation value;
determining the surface roughness of the non-flat area according to the fitting elevation values and the actual elevation values of all point clouds in the non-flat area;
and dividing the non-flat area into a rough area and a non-rough area according to the surface roughness and a second preset threshold value.
8. The utility model provides a laser point cloud highway section processing apparatus which characterized in that includes:
the extraction module is used for acquiring laser point cloud data of a road and extracting cross-section point cloud data from the laser point cloud data according to cross-section information;
the preprocessing module is used for preprocessing the section point cloud data and projecting the preprocessed section point cloud data to a two-dimensional plane to obtain two-dimensional section point cloud data;
the filtering module is used for calculating a right deviation value of the two-dimensional section point cloud data and filtering the two-dimensional section point cloud data according to the right deviation value;
and the thinning module is used for dividing the filtered two-dimensional section point cloud data into a plurality of point cloud intervals, distributing a thinning strategy according to the point cloud intervals, and thinning the point cloud intervals based on the thinning strategy to obtain the road section point cloud.
9. An electronic device comprising a memory and a processor, wherein,
the memory is used for storing programs;
the processor is coupled with the memory and is used for executing the program stored in the memory so as to realize the steps in the laser point cloud road section processing method of any one of the claims 1 to 7.
10. A computer-readable storage medium for storing a computer-readable program or instructions, which when executed by a processor, can implement the steps of the laser point cloud highway section processing method of any one of claims 1 to 7.
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