CN112132875A - Multi-platform point cloud matching method based on surface features - Google Patents
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Abstract
The invention discloses a multi-platform point cloud matching method based on surface features, and belongs to the technical field of mobile measurement point cloud matching. The implementation steps of the invention are as follows: acquiring point cloud data of a target object, carrying out data denoising pretreatment, and converting a pretreated original point cloud coordinate system into a local coordinate system which takes the gravity as a coordinate origin and has unchanged coordinate axis direction; uniformly selecting three or more pairs of matching planes from the acquired point cloud data, performing plane fitting by adopting a steady RANSAC algorithm to extract common and coplanar plane parameters (A, B, C and D), and performing self-flatness evaluation on the plane point cloud; converting the rotation parameters into a nonlinear model and then carrying out linearization processing on the basis of three or more pairs of plane parameters of corresponding planes, keeping the translation parameters unchanged, and carrying out parameter solution by adopting an integral least square method; evaluating the correlation of the planes to be registered based on the correction number of the adjustment result, and reselecting the planes which do not meet the requirements; and finishing the point cloud matching based on the coordinate conversion model.
Description
Technical Field
The invention discloses a multi-platform point cloud matching method based on surface features, and belongs to the technical field of mobile measurement point cloud matching.
Background
At present, vehicle-mounted, airborne and single-station LiDAR scanners are influenced by attitude of a carrying platform, precision of control points, precision of a GPS (global positioning system), precision of inertial navigation and the like, and small deviation exists between collected multi-source data coordinate systems. The above-mentioned each platform acquisition mode all has certain application condition, in the actual production application, need many platforms point cloud data to match and fuse to obtain the multidimensional, many space-time point cloud data of target ground object. In order to fully utilize information in the multi-source data, a coordinate system of the multi-source point cloud data needs to be matched, and the point cloud is spliced according to homonymous features (points, lines and surfaces). The precision of point cloud splicing affects the precision of subsequent data processing, and therefore point cloud splicing is a key step of data processing.
In the volume measurement mode, there may be no common point for two measurements of the same surface, so a model-based or feature-based coordinate transformation method needs to be studied; the plane is a regular model widely existing in nature, and if a plurality of planes can be fitted from the point cloud data and coordinate conversion is performed by using a common plane, the defect that common points need to be searched from massive point cloud data in the prior art can be overcome.
Disclosure of Invention
The invention discloses a road marking line extraction method based on driving direction structural feature constraint, which aims to solve the problem that in the prior art, public points need to be searched from massive point cloud data.
A multi-platform point cloud matching method based on surface features comprises the following steps:
s1, acquiring ground objects of a target area through data acquisition equipment erected on different platforms, and acquiring and preprocessing original point cloud data under the different platforms;
s2, uniformly selecting three or more pairs of non-coplanar matching planes from the preprocessed point cloud data in a man-machine interaction mode, performing plane fitting by adopting a steady RANSAC algorithm, extracting parameters of common planes, and performing self-flatness evaluation on the plane point cloud;
s3, converting the rotation parameters into a nonlinear model based on three or more pairs of plane parameters of corresponding planes, performing linearization processing, keeping the translation parameters unchanged, and performing parameter solution by adopting an integral least square method; evaluating the correlation of the planes to be registered through the correction number based on the adjustment result, and reselecting the planes which do not meet the requirements;
and S4, completing point cloud matching based on a coordinate conversion model.
Step S1, in the data processing stage, filtering and denoising original point cloud data of different platforms, and converting a preprocessed point cloud coordinate system into a local coordinate system which takes the gravity center as a coordinate origin and is unchanged in coordinate axis direction; the local origin of coordinates calculation is as follows:
wherein, [ x ]0，y0，z0]Denotes the origin of a local coordinate system, [ x ]i，yi，zi]Representing the ith point cloud coordinate; the calculation formula of the transformed point cloud coordinates is as follows: [ x ', y ', z ']＝[xi，yi，zi]-[x0，y0，z0]Wherein, [ x ', y ', z ']The transformed point cloud coordinates are represented, and m represents the number of point clouds.
S2 includes the following substeps:
s2.1, setting related parameters: iteration times n, an effective point proportion threshold p and an effective threshold t;
s2.2, randomly selecting 3 non-collinear points in the point cloud set, and calculating a corresponding plane equation, wherein the formula is as follows: a is1x+b1y+c1z＝d1Wherein (a)1，b1，c1) Normal vector expressing the face, d1Represents the distance from the origin to the plane;
s2.3, calculating the distances from all the observed values to the plane, wherein the formula is as follows: di＝(a1xj+b1yj+c1zj+d1) (ii) a If d isiWhen t is less than or equal to t, thenConsidering the point as a valid point or an interior point, otherwise, the point is an invalid point;
s2.4. calculating plane m1F is the proportion of the effective points in the total points, and if f is more than or equal to p, the M is regarded as plane1If the point cloud is an effective plane, otherwise, the point cloud is an invalid plane, and the self flatness evaluation of the point cloud of the surface is finished;
s2.5, sampling for multiple times according to the set iteration times n, and selecting a plane with the highest effective point ratio as a final fitting plane;
s2.6, performing parameter calculation on the plane obtained by fitting in the S2.5 by adopting a characteristic value method to obtain a plane parameter equation as follows: ax + By + Cz + D ═ 0, where (a, B, C, D) are planar parameters;
s2.7, uniformly selecting more than or equal to 3 pairs of unparallel opposite planes on point cloud data of different platforms, defining the positive direction of a normal vector, calculating plane parameters (A, B, C and D) of the plane parameters, and determining the plane parameters uniquely.
Step S3 includes the following sub-steps:
s3.1, constructing a coordinate conversion model:
let the expressions of the same plane in different coordinate systems be (a)1，b1，c1，d1)，(a2，b2，c2，d2) Then the affine transformation model is:
wherein the content of the first and second substances,
the parameters of the rotation matrix are represented by,
representing translation matrix parameters;
s3.2, carrying out linearization on the rotation matrix, converting the 9 parameters into 3 parameters:
and replacing the rotation matrix and the translation matrix by coordinate conversion parameters, wherein the formula is as follows:
wherein, alpha is the rotation angle around the x axis, beta is the rotation angle around the y axis, and gamma is the rotation angle around the z axis;
normal vector (a)2，b2，c2) The calculation formula is as follows:
a2＝cosγcosβa1+(cosγsinβsinα+sinγcosα)b1+(sinγsinα-cosγsinβcosα)c1；
b2＝-sinγcosβa1+(cosγcosα-sinγsinβsinα)b1+(sinγsinβcosα+cosγsinα)c1；
c2＝sinβa1-cosβsinαb1+cosβcosαc1；
s3.3, constructing an indirect adjustment model:
separately relating alpha, beta and gamma to a2、b2And c2Partial derivatives of (a):
d2the linear processing is not needed, and the calculation formula is as follows: d2＝txa1+tyb1+tzc1+d1；
The indirect adjustment model formula is as follows:
the above equation is rewritten to the form of the least squares error equation as follows:
V＝AX-L；
wherein the content of the first and second substances,
X＝(dα dβ dγ dtx dty dtz)′，
s3.4, solving parameters by using an integral least square method:
assuming that the observation error in the observed quantity L is E, and the error in the coefficient matrix A is EAThen the least squares error equation can be written as:
(A+EA)X＝L+e，
namely existence of
Wherein E ═ EA e]；
The optimization constraint conditions at this time are:
solving by using a singular value decomposition method, and performing singular value decomposition on the augmentation matrix [ A L ]:
decomposing to obtain
An orthogonal matrix of m +1 eigenvectors,
the estimate of the parameter X can be expressed as:
and S3.5, evaluating the correlation of the plane to be registered according to the correction number of the adjustment result, wherein the correlation is weaker when the correction number is larger, and the plane pair is worse, the plane pair which does not meet the requirement needs to be reselected.
Step S4 includes the following sub-steps:
s4.1, converting corresponding point cloud coordinate systems of different platforms according to the calculated 3 rotation parameters, wherein a conversion model is as follows:
s4.2, converting the converted point cloud from the local coordinate system to the original coordinate system to complete multi-platform point cloud matching based on the surface characteristics, wherein the formula is as follows: [ x, y, z ]]＝[x2，y2，z2]+[x0，y0，z0]Wherein [ x, y, z)]Representing the final coordinates after the matching transformation.
Compared with the prior art, the invention has the beneficial effects that:
(1) by establishing a local coordinate system, point cloud coordinates of different platforms are converted into respective local coordinate systems, so that matching errors caused by overlarge numerical difference of the point cloud coordinates of the different platforms are avoided, and matching precision is improved;
(2) the 9-parameter rotation model of the plane conversion model is converted into the 3-parameter model, and the parameter solution is carried out by adopting an integral least square method in consideration of the possibility of errors in an observation vector and a coefficient matrix, so that the parameter solution precision is effectively improved, the robustness of the algorithm is increased, and powerful support is provided for point cloud matching;
(3) because the point cloud matching method is based on the surface characteristics, the matching precision is determined by the selection and fitting precision of the surfaces, so that the evaluation indexes of the two surfaces are set to select a better plane and reduce the influence of plane errors on the matching precision.
Drawings
Fig. 1 is a technical flowchart of a multi-platform point cloud matching method based on surface features.
Detailed Description
The present invention will be described in further detail with reference to specific embodiments below:
a multi-platform point cloud matching method based on surface features is disclosed, a technical flow chart is shown in figure 1, and the method comprises the following steps:
s1, acquiring ground objects of a target area through data acquisition equipment erected on different platforms, and acquiring and preprocessing original point cloud data under the different platforms;
s2, uniformly selecting three or more pairs of non-coplanar matching planes from the preprocessed point cloud data in a man-machine interaction mode, performing plane fitting by adopting a steady RANSAC algorithm, extracting parameters of common planes, and performing self-flatness evaluation on the plane point cloud;
s3, converting the rotation parameters into a nonlinear model based on three or more pairs of plane parameters of corresponding planes, performing linearization processing, keeping the translation parameters unchanged, and performing parameter solution by adopting an integral least square method; evaluating the correlation of the planes to be registered through the correction number based on the adjustment result, and reselecting the planes which do not meet the requirements;
and S4, completing point cloud matching based on a coordinate conversion model.
Step S1, in the data processing stage, filtering and denoising original point cloud data of different platforms, and converting a preprocessed point cloud coordinate system into a local coordinate system which takes the gravity center as a coordinate origin and is unchanged in coordinate axis direction; the local origin of coordinates calculation is as follows:
wherein, [ x ]0，y0，z0]Denotes the origin of a local coordinate system, [ x ]i，yi，zi]Representing the ith point cloud coordinate; the calculation formula of the transformed point cloud coordinates is as follows: [ x ', y ', z ']＝[xi，yi，zi]-[x0，y0，z0]Wherein, [ x ', y ', z ']Representing transformed point cloud coordinates, m representingNumber of point clouds.
S2 includes the following substeps:
s2.1, setting related parameters: iteration times n, an effective point proportion threshold p and an effective threshold t;
s2.2, randomly selecting 3 non-collinear points in the point cloud set, and calculating a corresponding plane equation, wherein the formula is as follows: a is1x+b1y+c1z＝d1Wherein (a)1，b1，c1) Normal vector expressing the face, d1Represents the distance from the origin to the plane;
s2.3, calculating the distances from all the observed values to the plane, wherein the formula is as follows: di＝(a1xj+b1yj+c1zj+d1) (ii) a If d isiIf the t is less than or equal to t, the point is regarded as a valid point or an inner point, otherwise, the point is regarded as an invalid point;
s2.4. calculating plane m1F is the proportion of the effective points in the total points, and if f is more than or equal to p, the M is regarded as plane1If the point cloud is an effective plane, otherwise, the point cloud is an invalid plane, and the self flatness evaluation of the point cloud of the surface is finished;
s2.5, sampling for multiple times according to the set iteration times n, and selecting a plane with the highest effective point ratio as a final fitting plane;
s2.6, performing parameter calculation on the plane obtained by fitting in the S2.5 by adopting a characteristic value method to obtain a plane parameter equation as follows: ax + By + Cz + D ═ 0, where (a, B, C, D) are planar parameters;
s2.7, uniformly selecting more than or equal to 3 pairs of unparallel opposite planes on point cloud data of different platforms, defining the positive direction of a normal vector, calculating plane parameters (A, B, C and D) of the plane parameters, and determining the plane parameters uniquely.
Step S3 includes the following sub-steps:
s3.1, constructing a coordinate conversion model:
let the expressions of the same plane in different coordinate systems be (a)1，b1，c1，d1)，(a2，b2，c2，d2) Then the affine transformation model is:
wherein the content of the first and second substances,
the parameters of the rotation matrix are represented by,
representing translation matrix parameters;
s3.2, carrying out linearization on the rotation matrix, converting the 9 parameters into 3 parameters:
and replacing the rotation matrix and the translation matrix by coordinate conversion parameters, wherein the formula is as follows:
wherein, alpha is the rotation angle around the x axis, beta is the rotation angle around the y axis, and gamma is the rotation angle around the z axis;
normal vector (a)2，b2，c2) The calculation formula is as follows:
a2＝cosγcosβa1+(cosγsinβsinα+sinγcosα)b1+(sinγsinα-cosγsinβcosα)c1；
b2＝-sinγcosβa1+(cosγcosα-sinγsinβsinα)b1+(sinγsinβcosα+cosγsinα)c1；
c2＝sinβa1-cosβsinαb1+cosβcosαc1；
s3.3, constructing an indirect adjustment model:
separately relating alpha, beta and gamma to a2、b2And c2Partial derivatives of (a):
d2the linear processing is not needed, and the calculation formula is as follows: d2＝txa1+tyb1+tzc1+d1；
The indirect adjustment model formula is as follows:
the above equation is rewritten to the form of the least squares error equation as follows:
V＝AX-L；
wherein V is (V)a2 Vb2 Vc2 Vd2)′，X＝(dα dβ dγ dtx dty dtz)′，
s3.4, solving parameters by using an integral least square method:
assuming that the observation error in the observed quantity L is E, and the error in the coefficient matrix A is EAThen the least squares error equation can be written as: (A + E)A)X＝L+e，
Namely existence of
Wherein E ═ EA e]；
The optimization constraint conditions at this time are:
solving by using a singular value decomposition method, and performing singular value decomposition on the augmentation matrix [ A L ]:
decomposing to obtain
An orthogonal matrix of m +1 eigenvectors,
the estimate of the parameter X can be expressed as:
and S3.5, evaluating the correlation of the plane to be registered according to the correction number of the adjustment result, wherein the correlation is weaker when the correction number is larger, and the plane pair is worse, the plane pair which does not meet the requirement needs to be reselected.
Step S4 includes the following sub-steps:
s4.1, converting corresponding point cloud coordinate systems of different platforms according to the calculated 3 rotation parameters, wherein a conversion model is as follows:
s4.2, converting the converted point cloud from the local coordinate system to the original coordinate system to complete multi-platform point cloud matching based on the surface characteristics, wherein the formula is as follows: [ x, y, z ]]＝[x2，y2，z2]+[x0，y0，z0]Wherein [ x, y, z)]Representing the final coordinates after the matching transformation.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.
Claims (5)
1. A multi-platform point cloud matching method based on surface features is characterized by comprising the following steps:
s1, acquiring ground objects of a target area through data acquisition equipment erected on different platforms, and acquiring and preprocessing original point cloud data under the different platforms;
s2, uniformly selecting three or more pairs of non-coplanar matching planes from the preprocessed point cloud data in a man-machine interaction mode, performing plane fitting by adopting a steady RANSAC algorithm, extracting parameters of common planes, and performing self-flatness evaluation on the plane point cloud;
s3, converting the rotation parameters into a nonlinear model based on three or more pairs of plane parameters of corresponding planes, performing linearization processing, keeping the translation parameters unchanged, and performing parameter solution by adopting an integral least square method; evaluating the correlation of the planes to be registered through the correction number based on the adjustment result, and reselecting the planes which do not meet the requirements;
and S4, completing point cloud matching based on a coordinate conversion model.
2. The method of claim 1, wherein in the step S1, in the data processing stage, the original data of different platform point clouds are filtered and denoised, and the coordinate system of the preprocessed point clouds is converted into a local coordinate system with the center of gravity as the origin of coordinates and the coordinate axis direction unchanged; the local origin of coordinates calculation is as follows:
wherein, [ x ]0，y0，z0]Denotes the origin of a local coordinate system, [ x ]i，yi，zi]Representing the ith point cloud coordinate; the calculation formula of the transformed point cloud coordinates is as follows: [ x ', y ', z ']＝[xi，yi，zi]-[x0，y0，z0]Wherein, [ x ', y ', z ']The transformed point cloud coordinates are represented, and m represents the number of point clouds.
3. The method for matching a multi-platform point cloud based on surface features as claimed in claim 1, wherein the step S2 comprises the following sub-steps:
s2.1, setting related parameters: iteration times n, an effective point proportion threshold p and an effective threshold t;
s2.2, randomly selecting 3 non-collinear points in the point cloud set, and calculating a corresponding plane equation, wherein the formula is as follows: a is1x+b1y+c1z＝d1Wherein (a)1，b1，c1) Normal vector expressing the face, d1Represents the distance from the origin to the plane;
s2.3, calculating the distances from all the observed values to the plane, wherein the formula is as follows: di＝(a1xj+b1yj+c1zj+d1) (ii) a If d isiIf the t is less than or equal to t, the point is regarded as a valid point or an inner point, otherwise, the point is regarded as an invalid point;
s2.4. calculating plane m1F is the proportion of the effective points in the total points, and if f is more than or equal to p, the M is regarded as plane1If the point cloud is an effective plane, otherwise, the point cloud is an invalid plane, and the self flatness evaluation of the point cloud of the surface is finished;
s2.5, sampling for multiple times according to the set iteration times n, and selecting a plane with the highest effective point ratio as a final fitting plane;
s2.6, performing parameter calculation on the plane obtained by fitting in the S2.5 by adopting a characteristic value method to obtain a plane parameter equation as follows: ax + By + Cz + D ═ 0, where (a, B, C, D) are planar parameters;
s2.7, uniformly selecting more than or equal to 3 pairs of unparallel opposite planes on point cloud data of different platforms, defining the positive direction of a normal vector, calculating plane parameters (A, B, C and D) of the plane parameters, and determining the plane parameters uniquely.
4. The method for matching a multi-platform point cloud based on surface features as claimed in claim 1, wherein the step S3 comprises the following sub-steps:
s3.1, constructing a coordinate conversion model:
let and holdThe representation of a plane in different coordinate systems is respectively (a)1，b1，c1，d1)，(a2，b2，c2，d2) Then the affine transformation model is:
wherein the content of the first and second substances,
the parameters of the rotation matrix are represented by,
representing translation matrix parameters;
s3.2, carrying out linearization on the rotation matrix, converting the 9 parameters into 3 parameters:
and replacing the rotation matrix and the translation matrix by coordinate conversion parameters, wherein the formula is as follows:
wherein, alpha is the rotation angle around the x axis, beta is the rotation angle around the y axis, and gamma is the rotation angle around the z axis;
normal vector (a)2，b2，c2) The calculation formula is as follows:
a2＝cosγcosβa1+(cosγsinβsinα+sinγcosα)b1+(sinγsinα-cosγsinβcosα)c1；
b2＝-sinγcosβa1+(cosγcosα-sinγsinβsinα)b1+(sinγsinβcosα+cosγsinα)c1；
c2＝sinβa1-cosβsinαb1+cosβcosαc1；
s3.3, constructing an indirect adjustment model:
separately relating alpha, beta and gamma to a2、b2And c2Partial derivatives of (a):
d2the linear processing is not needed, and the calculation formula is as follows: d2＝txa1+tyb1+tzc1+d1；
The indirect adjustment model formula is as follows:
the above equation is rewritten to the form of the least squares error equation as follows:
V＝AX-L；
wherein the content of the first and second substances,
X＝(dα dβ dγ dtx dty dtz)′，
s3.4, solving parameters by using an integral least square method:
assuming that the observation error in the observed quantity L is E, and the error in the coefficient matrix A is EAThen the least squares error equation can be written as: (A + E)A)X＝L+e，
Namely existence of
Wherein E ═ EA e]；
The optimization constraint conditions at this time are:
solving by using a singular value decomposition method, and performing singular value decomposition on the augmentation matrix [ A L ]:
decomposing to obtain
An orthogonal matrix of m +1 eigenvectors,
the estimate of the parameter X can be expressed as:
and S3.5, evaluating the correlation of the plane to be registered according to the correction number of the adjustment result, wherein the correlation is weaker when the correction number is larger, and the plane pair is worse, the plane pair which does not meet the requirement needs to be reselected.
5. The method for matching a multi-platform point cloud based on surface features as claimed in claim 1, wherein the step S4 comprises the following sub-steps:
s4.1, converting corresponding point cloud coordinate systems of different platforms according to the calculated 3 rotation parameters, wherein a conversion model is as follows:
s4.2, converting the converted point cloud from the local coordinate system to the original coordinate system to complete multi-platform point cloud matching based on the surface characteristics, wherein the formula is as follows: [ x, y, z ]]＝[x2，y2，z2]+[x0，y0，z0]Wherein [ x, y, z)]Representing the final coordinates after the matching transformation.
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| CN114755666A (en) * | 2022-06-01 | 2022-07-15 | 苏州一径科技有限公司 | Point cloud expansion evaluation method, device and equipment |
| CN115830080A (en) * | 2022-10-27 | 2023-03-21 | 上海神玑医疗科技有限公司 | Point cloud registration method and device, electronic equipment and storage medium |
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