CN111127530B - Multi-period road three-dimensional laser point cloud accurate registration method - Google Patents

Multi-period road three-dimensional laser point cloud accurate registration method Download PDF

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CN111127530B
CN111127530B CN201911341604.1A CN201911341604A CN111127530B CN 111127530 B CN111127530 B CN 111127530B CN 201911341604 A CN201911341604 A CN 201911341604A CN 111127530 B CN111127530 B CN 111127530B
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marking
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CN111127530A (en
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刘如飞
卢秀山
邢恺强
马新江
杨雷
杨凯
杨继奔
王飞
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Shandong University of Science and Technology
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    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06T2207/10028—Range image; Depth image; 3D point clouds
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Abstract

The invention discloses a multi-period road three-dimensional laser point cloud accurate registration method, which comprises the steps of firstly segmenting point cloud data according to a certain distance to obtain multi-segment data, then registering the multi-segment data respectively, firstly correcting offset perpendicular to the direction of a driving lane, secondly correcting offset in the direction of the driving lane, and finally correcting elevation to obtain final registered data. According to the invention, large scene point cloud data are processed in a segmentation mode, and meanwhile, independent ground objects and road marks are adopted as registration primitives, so that the registration flow is optimized, and the problem of low speed of a point-to-point registration mode of massive point cloud data points is solved.

Description

Multi-period road three-dimensional laser point cloud accurate registration method
Technical Field
The invention relates to the technical field of mobile measurement system large-scene point cloud fine registration, in particular to a multi-period road three-dimensional laser point cloud fine registration method.
Background
Because of the development of social economy, the construction of roads and bridges in China is more and more extensive, and the road and bridge data contained in the cloud data of large scene points are more and more abundant, and along with the increase of service life, the change monitoring under the scene is very important for ensuring the driving safety.
The basic premise of the change detection is to realize high-precision registration of the multi-period point cloud data, and at present, the high-precision registration of the multi-period point cloud data at home and abroad mainly depends on manual interaction to select homonymous independent features for high-precision registration, so as to acquire all parameters required by the registration. The method can greatly reduce the time required by registration and the data quantity of the registration primitives, and improve the registration accuracy and efficiency.
In the process of realizing the invention, the inventor finds that the existing method has the following defects: in large scene point cloud data, because the data volume is large, it is difficult to directly utilize all point cloud data to perform point-to-point registration, and in scenes such as highways, the necessary independent features are extremely lacking as registration primitives, and the high-precision registration requirement cannot be met.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a multi-period road three-dimensional laser point cloud accurate registration method.
The technical scheme adopted by the invention is that the method for accurately registering the three-dimensional laser point cloud of the multi-period road specifically comprises the following steps:
a first step of; acquiring multi-period point cloud data of a monitored road, selecting any two-period original point cloud data, and carrying out equidistant segmentation processing on the original point cloud data to generate multi-period point cloud data;
and a second step of: carrying out offset correction perpendicular to the direction of a driving lane on each piece of segment point cloud data:
and a third step of: carrying out offset correction on the driving lane direction of each piece of sectional point cloud data;
fourth step: carrying out elevation correction on each piece of segmented point cloud data to finally obtain a complete registration result of the two-stage data;
fifth step: and selecting two-period original point cloud data, repeating the first step to the fourth step, and the like to obtain a complete registration result of the multi-period data.
Further: the second step specifically comprises the following steps:
2.1: selecting two-period identical road surface marking point clouds according to each single-section data, and solving the mutation points between the marking and the ground point on the single scanning line containing the marking point clouds by using a Sigmoid mathematical function model according to the mutation characteristics of the point cloud intensity value on the single scanning line between the ground point and the marking point, wherein the mutation points are used as marking boundary points;
and 2.2, calculating each central line point of the marked line according to the calculated marked line boundary points, using a least square fitting straight line as a marked line central line, calculating the marked line central lines of the marked lines with the same name in two periods, and calculating the offset of the marked lines in the two periods in the direction perpendicular to the road driving direction according to the marked line central lines.
Further, step 2.1 comprises the sub-steps of:
2.1.1: setting the distance, and carrying out data segmentation on point cloud data to be aligned and template point cloud data;
2.1.2: uniformly selecting a plurality of identical marks of the two-period point cloud data through manual interaction;
2.1.3: selecting a same-name marking, calculating marking boundary points on all scanning lines of the two-period marking by using a Sigmoid function model according to the mutation characteristics of the point cloud intensity values between the ground points and the marking points on the scanning lines containing the marking point clouds, wherein the function model is as follows:
Figure BDA0002332421030000021
s (x) is an intensity value corresponding to each marking boundary point;
x is the x, y coordinate value of each line boundary point, thus calculating each line boundary point as-b 2 ；
B in the formula 1 、b 3 、b 4 Each coefficient of the formula is represented respectively, wherein b 1 Representing the scale factor of the function curve in the x-axis, b 3 Representing the scale factor of the function curve in the y-axis, b 4 Representing the amount of translation of the function curve on the y-axis relative to the origin (0, 0); e is a natural coefficient of the academic vocabulary.
Further, step 2.2 comprises the sub-steps of:
2.2.1: calculating the center point of each marking according to the calculated boundary points of each marking;
2.2.2: fitting a marked line center line according to a least square fitting method by utilizing the marked line center points:
Figure BDA0002332421030000031
Figure BDA0002332421030000032
wherein x, y is the coordinate of each marking center point participating in the calculation, i is the coordinate base mark participating in the calculation, n is the number of coordinates participating in the calculation,
Figure BDA0002332421030000033
for the fitted midline slope, +.>
Figure BDA0002332421030000034
Is the fit midline intercept;
2.2.3: calculating the distance between lines in each same-name marking according to a point-to-line distance formula, and calculating the offset of the marking to be registered in each pair of the same-name marking in the x and y directions compared with the original marking;
Figure BDA0002332421030000035
Figure BDA0002332421030000036
wherein k and d respectively represent the slope and intercept of a linear equation of the central line of an original marking in a pair of same-name markings, and x and y are three-dimensional coordinates of points on the central line of the cloud marking to be registered;
2.2.4: and (3) performing the 2.2.1-2.2.3 steps on each selected homonymous marking, calculating the average value of the translation quantity of each homonymous marking, translating the point cloud to be registered to the x and y directions, and finishing the first correction.
Further: the third step specifically comprises the following steps:
3.1: and interactively selecting a plurality of independent object data with the same name on two sides of the two-period road as a registration primitive:
3.2: registering multiple homonymous independent features on two sides of two-term data road respectively by ICP algorithm, calculating offset OffsetX, offsetY in x and y directions, and calculating average value
Figure BDA0002332421030000041
Figure BDA0002332421030000042
3.3: respectively calculating the error Be according to the calculated offset in the x and y directions of a plurality of groups of independent features with the same name x ，б y And will be in range
Figure BDA0002332421030000043
Re-calculating the average value of the offset in the correction register offset for the second time;
3.4: and translating the point cloud to be registered to the x-direction and the y-direction to finish the second correction.
Further, the fourth step specifically includes the following steps:
4.1: the method comprises the steps of obtaining ground point cloud data from two-period data after data segmentation by using a pavement filtering algorithm, and establishing a unified grid index for the two-period identical-name road point cloud data, wherein the specific method comprises the following steps of:
4.1.1: obtaining a common minimum circumscribed rectangle of two-period road surface point cloud plane projection, namely: xmin, ymin, xmax, ymax;
4.1.2: setting a certain grid step length, and dividing a grid for two-period data:
Figure BDA0002332421030000044
Figure BDA0002332421030000045
ID＝(RID-1)×Col+CID
wherein: ID is the index number of the grid, row and Col are the number of rows and columns of the grid, RID and CID are the Row and column numbers, x i 、y i Coordinates of the point cloud, stepX and StepY are the length and width of the mesh, respectively.
4.2: calculating the normal vector of each grid point cloud by using a Principal Component Analysis (PCA) algorithm for each grid in the two-period pavement point cloud data; calculating an included angle theta between a normal vector of each grid point cloud of the two-period road point cloud data and a normal vector (0, 1) of a point cloud z axis:
Figure BDA0002332421030000051
Figure BDA0002332421030000052
4.3: respectively carrying out statistical analysis on the two-stage road point cloud data and determining a constant region (namely a stable region), wherein the method comprises the following steps of:
4.3.1: setting the same included angle interval step length, and counting the corresponding grid numbers and corresponding ID numbers in each interval;
4.3.2: the upper limit and the lower limit of the interval with the maximum grid number are statistically analyzed, the interval is taken as a standard interval, the point cloud area in the standard interval is regarded as a stable area in the two-period data, namely an area without serious deformation, and the ID number corresponding to the grid in the interval is recorded;
4.4: and counting common grid ID numbers of the two-period same-name ground data, dividing grids under the same standard, and then, inevitably generating overlapped grid IDs, respectively calculating average elevation of point clouds in the two-period same-name ground data common grids, calculating elevation deviation of the two-period data according to the difference value of the average elevation, and finally correcting.
Compared with the prior art, the invention has the beneficial effects that:
(1) According to the method, large scene point cloud data are processed in a segmented mode, and meanwhile, independent ground objects and road marks are adopted as registration primitives, so that the registration flow is optimized, and the problem that a point-to-point registration mode of massive point cloud data points is slower is solved;
(2) the invention uses the road mark as a part of the registration primitive, can directly adopt the manual interaction type extraction road mark as the registration primitive, overcomes the defect of lower accuracy of the registration result of the region with extremely deficient independent features, and realizes simpler, more direct and efficient registration.
(3) The invention utilizes principal component analysis and statistical analysis during elevation correction, so that the algorithm has stronger robustness, the convenience and reliability of data processing are improved, and the defect that mapping auxiliary tools such as targets are needed during common elevation correction is overcome.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a multi-phase road three-dimensional laser point cloud accurate registration method of the invention;
FIG. 2 is a flow chart of progressive refinement registration data based on an on-board three-dimensional laser point cloud in the present invention;
fig. 3 is a schematic diagram before and after correction of offset of point cloud data to be registered perpendicular to a driving lane direction, wherein fig. 3a and 3b are a top view and a cross-sectional view before correction, and fig. 3c and 3d are a top view and a cross-sectional view after correction;
figure 4 is a schematic diagram of calculating reticle boundary points using Sigmoid functions,
fig. 5a is an original point cloud image of point cloud data to be registered, and fig. 5b is a first corrected point cloud image of point cloud data to be registered perpendicular to a driving lane direction;
fig. 6 is a schematic plan view of a marking before and after correcting offset in the driving lane direction of the point cloud data to be registered and a schematic plan view of an independent feature, wherein fig. 6a and 6b are a schematic plan view of a marking before correcting the point cloud data to be registered and a schematic plan view of an independent feature, and fig. 6c and 6d are a schematic plan view of a marking after correcting the point cloud data to be registered and a schematic plan view of an independent feature.
Fig. 7 is a graph of the result before and after correction of the offset of the driving lane direction of the point cloud data to be registered; wherein fig. 7a and 7b are an example of a result graph after a first correction and fig. 7c and 7d are result graphs after a second correction for the examples given for fig. 7a and 7 b.
FIG. 8 is a schematic diagram of elevation correction of point cloud data to be registered, wherein FIG. 8a is a cross-sectional view of point cloud data before correction, and FIG. 8b is a cross-sectional view of point cloud data after correction;
FIG. 9 is a diagram of a standard interval determined by respectively performing statistical analysis on two-phase road point cloud data;
fig. 10 is a graph of elevation correction results of point cloud data to be registered.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making clear and defining the scope of the present invention.
As shown in fig. 1-2, the precise registration method of the multi-period road three-dimensional laser point cloud mainly comprises the following steps:
the first step: the original point cloud data is preprocessed, a certain distance length is mainly set, and two-period same-name original point cloud data are respectively processed in a segmentation mode to generate multi-segment point cloud data, so that the next processing is facilitated;
and a second step of: taking single segment data as an example, offset correction perpendicular to the driving lane direction is performed first:
as shown in fig. 3, the rectangle in the figure represents a road marking, and the purpose of this step is to convert the two-phase point clouds (the point clouds to be registered and the template point clouds) from the states of 3a and 3b to the states of 3c and 3d, so as to realize offset correction perpendicular to the direction of the driving lane. The specific process is as follows:
2.1: and uniformly selecting a plurality of identical marks of the two-period point cloud data in an interactive way, and managing the grouping numbers of the marks.
2.2: and calculating the boundary points of the marked lines, and calculating the coordinates of the central points of the marked lines according to the calculated boundary points of the marked lines.
Taking a single scan line as an example, the x, y coordinate values and the intensity values of each point on the scan line have a corresponding relationship as shown in fig. 4, wherein the abscissa axis is the x or y coordinate value, and the ordinate axis is the intensity value of the point. Fig. 4 reflects the trend of the change of the intensity value of the point cloud on the scan line from the ground point to the marking point, and then from the marking point back to the ground point, according to this, as shown in fig. 4, the Sigmoid function can be adopted to linearly fit the change curve, and the corresponding point with the maximum change rate is calculated as the boundary point of the two ends of the marking:
Figure BDA0002332421030000081
wherein S (x) is the intensity value corresponding to each point, x is the x or y coordinate value of the point, and the calculated x or y coordinate value of the marked line boundary point is-b 2 The boundary point coordinates of the reticle on the single scan line can thus be obtained.
And carrying out the calculation processing on each scanning line in the manually selected marking point cloud, thereby obtaining the boundary point of the whole marking.
According to the calculated boundary points of each marking, calculating the coordinates of the central point of each marking:
Figure BDA0002332421030000082
Figure BDA0002332421030000083
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0002332421030000084
and->
Figure BDA0002332421030000085
Is the abscissa of the left and right boundary points of the reticle, +.>
Figure BDA0002332421030000086
And->
Figure BDA0002332421030000087
Is the ordinate of the left and right boundary points of the marked line, +.>
Figure BDA0002332421030000088
And->
Figure BDA0002332421030000089
Is the reticle intermediate point coordinates.
2.3: fitting the line center line.
Fitting a marked line center line according to a least square fitting method by using the center points of the marked lines, wherein a center line equation is calculated as follows:
Figure BDA00023324210300000810
Figure BDA00023324210300000811
and the center line of the same-name marking of the two-period point cloud data is obtained by the method.
2.4: and calculating the offset of the two-stage marked lines in the direction perpendicular to the road driving direction.
Firstly, according to a point-to-straight line distance formula, calculating the distance between the central lines of the same-name marked lines of two-period point cloud data, wherein the point-to-straight line distance calculating method comprises the following steps:
Figure BDA0002332421030000091
wherein x and y are three-dimensional coordinates of points on a point cloud marking line to be registered, d is a distance from the point cloud marking line to be registered to a template point cloud marking line, and k and b represent the slope and intercept of a linear equation respectively.
Calculating the offset in the x and y directions according to the distance between lines in the same-name marked lines and a neutral line equation:
Figure BDA0002332421030000092
Figure BDA0002332421030000093
calculating the translation quantity among a plurality of groups of homonymous marks selected by manual interaction in the section of point cloud data, and obtaining an average value
Figure BDA0002332421030000094
And->
Figure BDA0002332421030000095
2.5: respectively translating x and y coordinates of the point cloud data to be registered
Figure BDA0002332421030000096
And->
Figure BDA0002332421030000097
The amount is corrected for the first time, i.e., the correction perpendicular to the road running direction, and the correction result is shown in fig. 5.
And a third step of: taking single segment data as an example, secondly, carrying out offset correction of the driving lane direction:
as shown in fig. 6a-6d and fig. 7a-7d, the rectangle in the figure represents a road marking, the circle represents a street lamp or other independent feature, and the purpose of this step is to implement offset correction of the driving lane direction by two-stage point clouds (point clouds to be registered and template point clouds) from 6a, 6b, 7a, 7b to 6c, 6d, 7c, 7 d.
3.1: and uniformly selecting a plurality of independent features with the same name of the two-stage point cloud data in an interactive way, and managing the grouping numbers of the independent features.
3.2: error-aware multi-primitive ICP point cloud registration.
And registering the point cloud to be registered and the template point cloud by utilizing an ICP algorithm for each group of independent features. The core idea of the ICP algorithm is as follows: in the point cloud P and the target point cloud Q to be registered, respectively, according to a certain constraint condition, the nearest point (P i ,q i ) Then, optimal matching parameters R and t are calculated so that the error function is minimized. The error function is E (R, t) is:
Figure BDA0002332421030000101
where n is the number of nearest neighbor point pairs, p i For a point in the point cloud P to be registered, q i And p in target point cloud Q i The corresponding nearest point, R is the rotation matrix and t is the translation vector.
ICP algorithm step:
(1) taking a point set P in a point cloud P to be registered i ∈P；
(2) Finding a corresponding point set Q in the target point cloud Q i E Q, such that p i -q i ||＝min；
(3) The rotation matrix R and the translation matrix t are calculated, so that an error function is minimum, wherein the rotation matrix R is an identity matrix because registration between two-stage point clouds in the registration method is regarded as rigid transformation, namely:
Figure BDA0002332421030000102
(4) p pair of i Performing rotation and translation transformation by using the rotation matrix R and the translation matrix t obtained in the previous step to obtain a new corresponding point set p i ′＝{p i ′＝Rp i +t,p i ∈P}；
(5) Calculation of p i ' and corresponding Point set q i Average distance d of (c):
Figure BDA0002332421030000103
(6) if d is less than a given threshold or greater than a preset maximum number of iterations, the iterative calculation is stopped. Otherwise, returning to the step 2 until the convergence condition is met. After iterative calculation, the obtained translation matrix t is t= [ t ] x ，t y ，t z ] T 。
3.3: calculating translation quantities of a plurality of homonymous independent features selected by manual interaction in the section of point cloud data
Figure BDA0002332421030000106
Averaging merit->
Figure BDA0002332421030000104
And->
Figure BDA0002332421030000105
According to the calculated offset in the x and y directions of multiple groups of homonymous independent features
Figure BDA0002332421030000111
Respectively calculating the error sigma x ，б y ：
Figure BDA0002332421030000112
Figure BDA0002332421030000113
Will be in range
Figure BDA0002332421030000114
The offset in is recalculated to mean +.>
Figure BDA0002332421030000115
And->
Figure BDA0002332421030000116
As a second correction registration offset;
3.4: respectively translating x and y coordinates of the point cloud data to be registered
Figure BDA0002332421030000117
And->
Figure BDA0002332421030000118
The second correction, i.e. correction of the direction of the driving lane, is done, the result of which is shown in fig. 7c-7 d.
Taking single segment data as an example, and finally carrying out elevation offset correction:
4.1: as shown in fig. 8a-8b, fig. 8a is a cross-sectional view of two-phase identical-name road point cloud data, and the purpose of this step is to convert two-phase point clouds (point clouds to be registered and template point clouds) from the state of fig. 8a to the state of fig. 8b, so as to implement elevation offset correction.
4.2: and establishing a unified grid index for the two-period identical-name road point cloud data.
Obtaining a common minimum circumscribed rectangle of two-period road surface point cloud plane projection, namely: xmin, ymin, xmax, ymax.
Setting a certain grid step length, dividing grids for two-period data, numbering the grids, and establishing indexes:
Figure BDA0002332421030000119
Figure BDA00023324210300001110
ID＝(RID-1)×Col+CID
wherein, ID is the index number of the grid, row and Col are the number of rows and columns of the grid, and RID and CID are the number of rows and columns.
4.3: and calculating the normal vector of each grid point cloud by using a PCA algorithm for each grid point in the two-period pavement point cloud data.
PCA algorithm step:
(1) forming an n-row 3-column matrix by x, y and z coordinates of the point cloud in each grid;
(2) zero-equalizing each row of the matrix, namely subtracting the average value of the row;
(3) constructing a covariance matrix:
Figure BDA0002332421030000121
(4) the minimum eigenvalue and the corresponding eigenvector of the covariance matrix are obtained as normal vectors: normal, normal.
(5) Calculating an included angle theta between a normal vector of each grid point cloud of the two-period road point cloud data and a normal vector (0, 1) of a point cloud z axis:
Figure BDA0002332421030000122
Figure BDA0002332421030000123
4.4: respectively carrying out statistical analysis on the two-stage road point cloud data:
as shown in fig. 9, the same included angle interval step length is set, and the corresponding grid numbers and the corresponding ID numbers in each interval are counted;
meanwhile, the upper limit and the lower limit of the interval with the maximum grid number are statistically analyzed, the interval is taken as a standard interval, the point cloud area in the standard interval is regarded as a stable area in the two-period data, namely an area without serious deformation, and the ID number corresponding to the grid in the interval is recorded;
if the common grid ID numbers of the two-period same-name ground data are counted, and the grids are divided under the same standard, overlapping grid IDs can be necessarily generated, for example, the standard interval ID numbers of the point cloud to be registered are 1,2,3,4 and 5, and the standard interval ID numbers of the template point cloud are 3,4,5,6 and 7, and then the common grid ID numbers are 3,4 and 5.
4.5: respectively calculating the average elevation of point clouds in the two-period same-name ground data common grid, and calculating the elevation deviation of the two-period data according to the difference value of the average elevation of the two-period data;
4.6: and carrying out final correction according to the elevation deviation of the obtained two-stage data, wherein the correction result of the step is shown in fig. 10.
Fifth step: and carrying out the second to fifth steps of registration schemes on each piece of segmented data to finally obtain a complete registration result of the two-stage data.
The above is merely a specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that do not undergo the inventive work should be covered in the scope of the present invention. Accordingly, the scope of the invention should be defined by the following claims.

Claims (4)

1. The method for accurately registering the three-dimensional laser point clouds of the multi-period road is characterized by comprising the following steps of:
a first step of; acquiring multi-period point cloud data of a monitored road, selecting any two-period original point cloud data, and carrying out equidistant segmentation processing on the original point cloud data to generate multi-period point cloud data;
and a second step of: carrying out offset correction perpendicular to the direction of a driving lane on each piece of segmented point cloud data, and specifically comprising the following steps of:
2.1: selecting two-period identical road surface marking point clouds according to each single-section data, and solving the mutation points between the marking and the ground point on the single scanning line containing the marking point clouds by using a Sigmoid mathematical function model according to the mutation characteristics of the point cloud intensity value on the single scanning line between the ground point and the marking point, wherein the mutation points are used as marking boundary points;
2.2: calculating each central line point of the marking according to the calculated marking boundary points, using a least square fitting straight line as a marking central line, calculating marking central lines of two periods of same-name marking, and calculating the offset of the two periods of marking in the direction vertical to the road driving direction according to the marking central lines;
and a third step of: carrying out offset correction on the driving lane direction of each piece of sectional point cloud data; the method specifically comprises the following steps:
3.1: and interactively selecting a plurality of independent object data with the same name on two sides of the two-period road as a registration primitive:
3.2: registering multiple homonymous independent features on two sides of two-term data road respectively by utilizing ICP algorithm, and calculating offset t in x and y directions xi 、t yi And calculate the average value
Figure FDA0004158130980000011
3.3: respectively calculating the errors according to the calculated offsets of multiple groups of identical-name independent features in the x and y directions
Figure FDA0004158130980000013
And will be within the range->
Figure FDA0004158130980000012
Re-calculating the average value of the offset in the correction register offset for the second time;
3.4: translating the point cloud to be registered to the x-direction and the y-direction to finish the second correction;
fourth step: carrying out elevation correction on each piece of segmented point cloud data to finally obtain a complete registration result of the two-stage data;
fifth step: and selecting two-period original point cloud data, repeating the first step to the fourth step, and the like to obtain a complete registration result of the multi-period data.
2. The multi-phase road three-dimensional laser point cloud accurate registration method of claim 1, wherein step 2.1 comprises the sub-steps of:
2.1.1: setting the distance, and carrying out data segmentation on point cloud data to be aligned and template point cloud data;
2.1.2: uniformly selecting a plurality of identical marks of the two-period point cloud data through manual interaction;
2.1.3: selecting a same-name marking, calculating marking boundary points on all scanning lines of the two-period marking by using a Sigmoid function model according to the mutation characteristics of the point cloud intensity values between the ground points and the marking points on the scanning lines containing the marking point clouds, wherein the function model is as follows:
Figure FDA0004158130980000021
s (x) is an intensity value corresponding to each marking boundary point;
x is the x, y coordinate value of each line boundary point, thus calculating each line boundary point as-b 2 ；
B in the formula 1 、b 3 、b 4 Each coefficient of the formula is represented respectively, wherein b 1 Representing the scale factor of the function curve in the x-axis, b 3 Representing the scale factor of the function curve in the y-axis, b 4 Representing the amount of translation of the function curve on the y-axis relative to the origin (0, 0); e is a natural coefficient of the academic vocabulary.
3. The multi-phase road three-dimensional laser point cloud accurate registration method of claim 1, wherein step 2.2 comprises the sub-steps of:
2.2.1: calculating the center point of each marking according to the calculated boundary points of each marking;
2.2.2: fitting a marked line center line according to a least square fitting method by utilizing the marked line center points:
Figure FDA0004158130980000022
Figure FDA0004158130980000031
wherein x, y is the coordinate of each marking center point participating in the calculation, i is the coordinate base mark participating in the calculation, n is the number of coordinates participating in the calculation,
Figure FDA0004158130980000032
for the fitted midline slope, +.>
Figure FDA0004158130980000033
Is the fit midline intercept;
2.2.3: calculating the distance between lines in each same-name marking according to a point-to-line distance formula, and calculating the offset of the marking to be registered in each pair of the same-name marking in the x and y directions compared with the original marking;
Figure FDA0004158130980000034
Figure FDA0004158130980000035
wherein k and d respectively represent the slope and intercept of a linear equation of the central line of an original marking in a pair of same-name markings, and x and y are three-dimensional coordinates of points on the central line of the cloud marking to be registered;
2.2.4: and (3) performing the 2.2.1-2.2.3 steps on each selected homonymous marking, calculating the average value of the translation quantity of each homonymous marking, translating the point cloud to be registered to the x and y directions, and finishing the first correction.
4. The method for accurately registering the multi-phase road three-dimensional laser point cloud as set forth in claim 1, wherein the fourth step specifically comprises the steps of:
4.1: the method comprises the steps of obtaining ground point cloud data from two-period data after data segmentation by using a pavement filtering algorithm, and establishing a unified grid index for the two-period identical-name road point cloud data, wherein the specific method comprises the following steps of:
4.1.1: obtaining a common minimum circumscribed rectangle of two-period road surface point cloud plane projection, namely: xmin, ymin, xmax, ymax;
4.1.2: setting a certain grid step length, and dividing a grid for two-period data:
Figure FDA0004158130980000041
Figure FDA0004158130980000042
ID＝(RID-1)×Col+CID
wherein: ID is the index number of the grid, row and Col are the number of rows and columns of the grid, RID and CID are the Row and column numbers, x i 、y i Coordinates of the point cloud are respectively, and StepX and StepY are respectively the length and the width of the grid;
4.2: calculating the normal vector of each grid point cloud by using a Principal Component Analysis (PCA) algorithm for each grid in the two-period pavement point cloud data; calculating an included angle theta between a normal vector of each grid point cloud of the two-period road point cloud data and a normal vector (0, 1) of a point cloud z axis:
Figure FDA0004158130980000043
Figure FDA0004158130980000044
4.3: respectively carrying out statistical analysis on the two-stage road point cloud data and determining an unchanged area, wherein the method comprises the following steps of:
4.3.1: setting the same included angle interval step length, and counting the corresponding grid numbers and corresponding ID numbers in each interval;
4.3.2: the upper limit and the lower limit of the interval with the maximum grid number are statistically analyzed, the interval is taken as a standard interval, the point cloud area in the standard interval is regarded as a stable area in the two-period data, namely an area without serious deformation, and the ID number corresponding to the grid in the interval is recorded;
4.4: and counting common grid ID numbers of the two-period same-name ground data, dividing grids under the same standard, and then, inevitably generating overlapped grid IDs, respectively calculating average elevation of point clouds in the two-period same-name ground data common grids, calculating elevation deviation of the two-period data according to the difference value of the average elevation, and finally correcting.
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