CN115049813B - Coarse registration method, device and system based on first-order spherical harmonics - Google Patents
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Abstract
The invention relates to the technical field of three-dimensional data processing, solves the technical problem of low registration speed caused by long running time of the conventional coarse registration method, and particularly relates to a coarse registration method based on first-order spherical harmonics, which comprises the following steps: respectively constructing a spherical model for the registered original point cloud and the target point cloud, and performing spherical harmonic expansion on the spherical model to obtain the spherical harmonic coefficient of the limited original point cloud and the spherical harmonic coefficient of the target point cloud; according to the first-order spherical harmonic function, the spherical harmonic coefficient of the original point cloud and the spherical harmonic coefficient of the target point cloud, the primary spherical harmonic ellipsoid of the original point cloud and the target point cloud and the main shaft direction of the corresponding primary spherical harmonic ellipsoid are calculated, the rotation matrix of the coarse registration of the original point cloud and the target point cloud is calculated, the gravity center is aligned, and the coarse registration can be realized by applying the rotation matrix. According to the invention, the rotation matrix for coarse registration can be rapidly and accurately obtained by adopting the first-order spherical harmonic function, so that the coarse registration efficiency is improved, and the method has an important significance for rapidly performing a coarse registration task.
Description
Technical Field
The invention relates to the technical field of three-dimensional data processing, in particular to a coarse registration method, a coarse registration device and a coarse registration system based on first-order spherical harmonics.
Background
Compared with two-dimensional image information, the three-dimensional point cloud can describe the geometric attributes of the object more accurately and intuitively, but when scanning is carried out, all information of the measured object cannot be obtained at one time, so that scanning needs to be carried out in batches, and therefore point cloud data of different position information are spliced together, and a coordinate system is a basic and key work. However, when the point cloud data of different position information are spliced and coordinates are unified, the coarse registration is usually performed first, and a better initial transformation matrix is provided for the fine registration later.
For a registration task, an ICP (inductively coupled plasma) algorithm and a derivative algorithm thereof are a standard method of the registration task for many years, but the algorithm is based on the nearest search of points and points, is easy to fall into local optimum in a state of poor coarse posture to cause registration error, is not stable enough, and has high calculation cost.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a coarse registration method, a device and a system based on first-order spherical harmonics, solves the technical problem of low registration speed caused by long running time of the conventional coarse registration method, and achieves the purposes of quickly performing coarse registration and improving the working efficiency of a registration task.
In order to solve the technical problems, the invention provides the following technical scheme: a coarse registration method based on first-order spherical harmonics comprises the following steps:
s1, acquiring original point cloud data and target point cloud data acquired by point cloud acquisition equipment;
s2, respectively constructing an original point cloud spherical model and a target point cloud spherical model according to the original point cloud data and the target point cloud data;
s3, calculating first spherical harmonic coefficients of the original point cloud spherical model and the target point cloud spherical model;
s4, respectively calculating primary spherical harmonic ellipsoids of the original point cloud and the target point cloud according to the first-order spherical harmonic function and the primary spherical harmonic coefficients of the original point cloud spherical model and the target point cloud spherical model;
s5, respectively calculating rotation matrixes of rough registration of the original point cloud and the target point cloud according to primary spherical harmonic ellipsoids of the original point cloud and the target point cloud;
and S6, carrying out coarse registration on the original point cloud and the target point cloud according to the rotation matrix of the coarse registration of the original point cloud and the target point cloud to obtain a registration result.
Further, the step S2 of respectively constructing the original point cloud spherical model and the target point cloud spherical model specifically includes the following steps:
s201, respectively calculating the gravity centers of the original point cloud and the target point cloud according to the original point cloud data and the target point cloud data, and respectively converting all points in the original point cloud and the target point cloud into a spherical coordinate system;
s202, constructing a unit ball according to the gravity centers of the original point cloud and the target point cloud, and dividing the spherical surface of the unit ball to create a grid;
and S203, calculating lattices to which each point in the original point cloud and the target point cloud belongs respectively, and assigning a value to each lattice.
Further, the assigning of each lattice in step S203 includes:
if the lattice contains point clouds, the value of the lattice is the average value of the distances from the middle point to the origin point of the lattice;
if there is no point cloud in the grid, the value of the grid is 0.
Further, step S4 specifically includes the following steps:
s401, respectively decomposing the grid density functions of the original point cloud spherical model and the target point cloud spherical model in the form of radial functions into function forms under a Cartesian coordinate system;
s402, respectively applying inverse spherical harmonic transformation according to the three components of the function form under the Cartesian coordinate system to obtain primary spherical harmonic ellipsoids of the original point cloud and the target point cloud.
Further, step S5 specifically includes the following steps:
s501, calculating corresponding characteristic values and characteristic vectors according to three vectors of primary spherical harmonic ellipsoids of the original point cloud and the target point cloud which are aligned with the main shaft respectively;
s502, respectively normalizing the first-order spherical harmonic ellipsoids of the original point cloud and the target point cloud to form a rotation matrix from a parameter space to an object space;
and S503, respectively calculating a rotation matrix of the original point cloud transformed to the intermediate state and a rotation matrix of the target point cloud transformed to the intermediate state according to the rotation matrix of the object space, wherein the intermediate state refers to the intermediate state of the original point cloud and the target point cloud.
The invention also provides a technical scheme that: a first-order spherical harmonic based coarse registration apparatus, comprising:
a data acquisition module for acquiring original point cloud data and target point cloud data acquired by a point cloud acquisition device;
the spherical model building module is used for respectively building an original point cloud spherical model and a target point cloud spherical model according to the original point cloud data and the target point cloud data;
the first computing module is used for computing the first-order spherical harmonic coefficients of the original point cloud spherical model and the target point cloud spherical model;
the second calculation module is used for calculating primary spherical harmonic ellipsoids of the original point cloud and the target point cloud according to the first-order spherical harmonic function and the primary spherical harmonic coefficients of the original point cloud spherical model and the target point cloud spherical model;
the third calculation module is used for calculating a rotation matrix of rough registration of the original point cloud and the target point cloud according to the primary spherical harmonic ellipsoid of the original point cloud and the target point cloud;
a registration module for performing coarse registration on the original point cloud and the target point cloud according to the rotation matrix of the coarse registration of the original point cloud and the target point cloud to obtain a registration result
Further, the spherical model building module comprises:
the coordinate conversion unit is used for respectively calculating the gravity centers of the original point cloud and the target point cloud according to the original point cloud data and the target point cloud data and respectively converting all points in the original point cloud and the target point cloud into a spherical coordinate system;
the grid creating unit is used for constructing a unit ball according to the gravity centers of the original point cloud and the target point cloud and dividing the spherical surface of the unit ball to create a grid;
and the lattice value calculating unit is used for calculating lattices to which each point in the original point cloud and the target point cloud belongs respectively and assigning values to each lattice.
Further, the second calculation module includes:
the function decomposition unit is used for decomposing the mesh density functions of the original point cloud spherical model and the target point cloud spherical model in the radial function form into function forms under a Cartesian coordinate system;
and the primary spherical harmonic ellipsoid calculation unit is used for respectively applying the inverse spherical harmonic transformation according to the three components of the function form under the Cartesian coordinate system to obtain the primary spherical harmonic ellipsoid corresponding to the original point cloud and the target point cloud.
Further, the third computing module comprises:
the characteristic value and characteristic vector calculating unit is used for calculating corresponding characteristic values and characteristic vectors according to three vectors of the primary spherical harmonic ellipsoids of the original point cloud and the target point cloud which are aligned with the main shaft respectively;
the rotation matrix calculation unit is used for forming a rotation matrix from a parameter space to an object space by respectively normalizing the feature vectors of the primary harmonic ellipsoids of the original point cloud and the target point cloud;
and the intermediate state conversion unit is used for respectively calculating a rotation matrix of the original point cloud transformed to the intermediate state and a rotation matrix of the target point cloud transformed to the intermediate state according to the rotation matrix of the object space.
The invention also provides another technical scheme: a system for realizing a coarse registration method based on first-order spherical harmonics comprises a point cloud acquisition device and a computer device;
the point cloud acquisition equipment is in data communication with computer equipment and is used for acquiring an original point cloud and a target point cloud;
the computer equipment comprises at least one processor and a memory, and is used for receiving the original point cloud and the target point cloud acquired by the point cloud acquisition equipment, and calling and operating a registration algorithm in the memory through the processor to perform rough registration processing on the input point cloud data.
By means of the technical scheme, the invention provides a coarse registration method, a coarse registration device and a coarse registration system based on first-order spherical harmonics, which at least have the following beneficial effects:
1. according to the method, the spherical models of the original point cloud and the target point cloud are constructed, the first-order spherical harmonic coefficients of the spherical models are respectively calculated, then the first-order spherical harmonic functions are combined, the rotation matrix of rough registration between the original point cloud and the target point cloud can be calculated, then the centers of gravity of the original point cloud and the target point cloud are aligned, and the rotation matrix of the rough registration is applied, so that the rough registration of the point cloud is conveniently realized, and the point cloud registration speed is improved.
2. According to the invention, the first-order spherical harmonic function is adopted to calculate the rotation matrix for coarse registration of the original point cloud and the target point cloud, so that the coarse registration error is reduced, the point cloud registration precision is improved, and the method has an important significance for quickly and accurately performing a coarse registration task.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of a coarse registration method of the present invention;
FIG. 2 is a three-dimensional point cloud, a spherical model and a primary spherical harmonic ellipsoid of the present invention;
FIG. 3 is a flowchart of the method for constructing a spherical model according to the present invention;
FIG. 4 is a flow chart of the present invention for calculating primary spherical harmonic ellipsoids;
FIG. 5 is a flow chart of the present invention for computing a rotation matrix for coarse registration;
FIG. 6 is a schematic diagram of the coarse registration process based on primary ellipsoid according to the present invention;
FIG. 7 is a schematic block diagram of the coarse registration apparatus of the present invention;
FIG. 8 is a schematic block diagram of a spherical model building block in the coarse registration apparatus of the present invention;
FIG. 9 is a schematic block diagram of a second computing module in the coarse registration apparatus of the present invention;
FIG. 10 is a functional block diagram of a third computing module of the coarse registration apparatus of the present invention;
fig. 11 is a functional block diagram of the coarse registration system of the present invention.
In the figure: 10. a data acquisition module; 20. a spherical model construction module; 21. a coordinate conversion module; 22. a mesh creating unit; 23. a grid value calculation unit; 30. a first calculation module; 40. a second calculation module; 41. a function decomposition unit; 42. a first harmonic ellipsoid calculation unit; 50. a third calculation module; 51. a feature vector calculation unit; 52. a rotation matrix calculation unit; 53. an intermediate state transition unit; 60. and a registration module.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof. Therefore, the realization process of how to apply technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
Referring to fig. 1 to fig. 6, a first-order spherical harmonic-based coarse registration method according to the present embodiment is shown, where the coarse registration is generally used for registering two point clouds whose initial relative positions are completely unknown, and as shown in fig. 1, the coarse registration method specifically includes the following steps:
s1, acquiring original point cloud data and target point cloud data acquired by point cloud acquisition equipment.
Specifically, by performing data acquisition using a common point cloud acquisition device such as a laser radar, a three-dimensional point cloud can be acquired as shown in fig. 2 (a).
In this embodiment, two original point cloud data and target point cloud data, of which any relative position is completely unknown, are acquired by a laser radar acquisition device, and the original point cloud data is set as a point set P having n points, that is, the point set P is
The target point cloud data is of
Set of points Q, i.e. of points
。
S2, respectively constructing an original point cloud spherical model and a target point cloud spherical model according to the original point cloud data and the target point cloud data. As shown in fig. 3, the specific steps of constructing the original point cloud spherical model and the target point cloud spherical model are as follows:
(1) The method comprises the following steps of constructing an original point cloud spherical model according to original point cloud data, and specifically comprises the following steps:
s201, calculating the gravity center of the original point cloud according to the original point cloud data, and converting all points in the original point cloud into a spherical coordinate system.
In this embodiment, the center of gravity of the original point cloud is defined as
Then, then
Then, firstly, the origin of the coordinate system of the original point cloud is translated to the gravity center of the original point cloud
To obtain
Then, then
；
Then will be
Obtained by converting a Cartesian coordinate system into a spherical coordinate system
And then:
wherein,
is a zenith angle, and is a vertex angle,
is the azimuth angle.
S202, constructing a unit ball according to the gravity center of the original point cloud, and dividing the spherical surface of the unit ball to create a grid.
In this embodiment, assume that the band limit of the spherical function model is B, and the center of gravity of the original point cloud is used
Constructing a unit ball at the center of the ball, and establishing meshes for the spherical surface of the unit ball by adopting an equiangular mesh division method, namely respectively aiming at zenith angles
And azimuth angle
Dividing 2B parts at equal angles to create grids, dividing 2 Bx 2B grids in total, and the center of each grid is as follows:
where j and k are indices of the lattice.
It should be noted that, assuming that the band limit of the spherical function model is B, this means that the spherical harmonic coefficients are non-zero only in a limited frequency range, and the other coefficients outside the frequency are zero.
S203, calculating the grids to which each point in the original point cloud belongs, and assigning a value to each grid.
In this embodiment, each point in the original point cloud is first mapped to the center of gravity of the original point cloud in step S202
And assigning the grids according to points in each grid in the grids divided on the spherical surface of the unit ball constructed for the spherical center.
If the point cloud exists in the grid, taking the average value of the distances from the middle point of the grid to the origin of the coordinate system as the value of the grid, namely:
wherein
is a function of the r coordinate value under the returned spherical coordinate system; if there is no point cloud in the grid, 0 is taken as the value of the grid.
After the series of operations, the spherical model of the original point cloud can be obtained as follows:
wherein,
。
(2) The method for constructing the spherical model of the target point cloud is the same as the method for constructing the spherical model of the original point cloud according to the data of the target point cloud, therefore, the spherical model of the target point cloud can be obtained by adopting the same operation steps, and detailed description is not expanded.
In the present embodiment, an exemplary structure of the spherical model is shown in fig. 2 (b).
S3, calculating first spherical harmonic coefficients of the original point cloud spherical model and the target point cloud spherical model;
(1) Using spherical harmonic expansionTo calculate the original point cloud spherical model
First order spherical harmonic coefficient of
In particular, first order spherical harmonic coefficient
Is the attitude characteristic of the original point cloud in the frequency domain and the first-order spherical harmonic coefficient
The specific expression of (a) is as follows:
wherein,
is the order of spherical harmonic function, m is the order of spherical harmonic function, and satisfies
And with
，
Is associated with a Legendre function, and has an interval of
，
Are weights.
Weight of
The calculation formula of (a) is as follows:
(2) The method for calculating the first-order spherical harmonic coefficient of the target point cloud spherical surface model is the same as the method for calculating the first-order spherical harmonic coefficient of the original point cloud spherical surface model, therefore, the first-order spherical harmonic coefficient of the target point cloud spherical surface model can be obtained by adopting the same operation steps, and detailed description is not repeated.
And S4, respectively calculating primary spherical harmonic ellipsoids of the original point cloud and the target point cloud according to the first-order spherical harmonic function and the primary spherical harmonic coefficients of the original point cloud spherical model and the target point cloud spherical model. As shown in fig. 4, the specific steps of calculating the primary spherical harmonic ellipsoid of the original point cloud and the target point cloud are as follows:
(1) Calculating a first harmonic ellipsoid of the original point cloud according to the first harmonic function and the first harmonic coefficient of the spherical model of the original point cloud, and the specific steps are as follows:
s401, decomposing the grid density function of the original point cloud spherical model in the radial function form into a function form under a Cartesian coordinate system.
Specifically, the mesh density function of the original point cloud spherical model in the form of radial function
Decomposed into the functional form G in the cartesian coordinate system, then:
s402, respectively applying inverse spherical harmonic transformation according to the three components of the function form under the Cartesian coordinate system to obtain a primary spherical harmonic ellipsoid of the original point.
Specifically, a spherical harmonic inverse transformation is applied to three components of the functional form G in the cartesian coordinate system, and a three-dimensional ellipsoid corresponding to the object space is obtained
And then:
wherein the expression of M is as follows:
(2) The method for calculating the primary spherical harmonic ellipsoid of the target point cloud is the same as the method for calculating the primary spherical harmonic ellipsoid of the original point cloud, and therefore the primary spherical harmonic ellipsoid of the target point cloud can be obtained by adopting the same operation. In this embodiment, an exemplary structure of the first order spherical harmonic ellipsoid is shown in fig. 2 (c).
And S5, respectively calculating rotation matrixes of the rough registration of the original point cloud and the target point cloud according to the primary spherical harmonic ellipsoids of the original point cloud and the target point cloud. As shown in fig. 5, the specific steps of calculating the rotation matrices for the coarse registration of the original point cloud and the target point cloud respectively are as follows:
(1) Calculating a rotation matrix of the original point cloud transformed to the intermediate state according to the primary spherical harmonic ellipsoid of the original point cloud, and specifically comprising the following steps of:
s501, corresponding characteristic values and characteristic vectors are calculated according to three vectors of the primary spherical harmonic ellipsoid of the original point cloud, which are aligned with the main shaft.
In this embodiment, the primary spherical harmonic ellipsoid of the original point cloud
The three vectors aligned with the principal axis have the longest vectors
Shortest vector
And a centered saddle point vector
By calculating
The eigenvalue and eigenvector of (2) can be obtained
、
、
Respectively corresponding to the non-negative characteristic value
。
S502, forming a rotation matrix from a parameter space to an object space by respectively normalizing the feature vectors of the first harmonic ellipsoids of the original point cloud.
In this embodiment, the primary spherical harmonic ellipsoid of the original point cloud
Normalized eigenvectors form a rotation matrix of the parameter space
A rotation matrix R to the object space, wherein the specific expression of R is as follows:
it should be noted that, the left-hand multiplication is half of the length of three main axes of the first harmonic ellipsoid of the diagonal elements of the diagonal matrix, and the main function is to normalize the main axis vector, so as to ensure that the rotation matrix R of the object space does not have the coordinate axis stretching effect, and the original point cloud can be rotated to a uniform intermediate state through the rotation matrix R of the object space.
S503, calculating a rotation matrix of the original point cloud transformed to the intermediate state according to the rotation matrix of the object space, wherein the intermediate state refers to the intermediate state of the original point cloud and the target point cloud.
In this embodiment, the intermediate state between the original point cloud and the target point cloud is defined asOThe method of step S502 is adopted to obtain the original point cloudPChange toOIs a rotation matrix of
。
(2) According to the first spherical harmonic ellipsoid of the target point cloud, a method for calculating a rotation matrix of the target point cloud transformed to the intermediate state and a method for calculating a rotation matrix of the original point cloud transformed to the intermediate state are adopted, therefore, the same operation is adopted to obtain the target point cloudQChange toOIs a rotation matrix of
。
Thus, from the target point cloudQAdjusted to the original point cloudPIs a rotation matrix of
。
And S6, carrying out coarse registration on the original point cloud and the target point cloud according to the rotation matrixes of the coarse registration of the original point cloud and the target point cloud to obtain a registration result.
Specifically, as shown in fig. 6, the original point cloud is first obtainedPTranslation of
To obtain
Then to
Using a rotation matrix
Then is translated again
Obtaining the original point cloudPResult of the registration of
I.e. by
To further realize the original point cloudPTo target point cloudQCoarse registration of (3).
The present embodiment also provides a system for implementing the above coarse registration method based on first-order spherical harmonics, as shown in fig. 7 to 10, specifically including:
the data acquisition module 10, the data acquisition module 10 is used for acquiring the original point cloud data and the target point cloud data acquired by the point cloud acquisition equipment.
The spherical model building module 20, the spherical model building module 20 is used for respectively building an original point cloud spherical model and a target point cloud spherical model according to the original point cloud data and the target point cloud data; the spherical model building module 20 includes:
the coordinate conversion unit 21 is used for respectively calculating the gravity centers of the original point cloud and the target point cloud according to the original point cloud data and the target point cloud data and respectively converting all points in the original point cloud and the target point cloud into a spherical coordinate system;
the grid creating unit 22 is used for creating a unit ball according to the gravity centers of the original point cloud and the target point cloud, and dividing the spherical surface of the unit ball to create a grid;
and the lattice value calculating unit 23 is used for calculating lattices to which each point in the original point cloud and the target point cloud belongs respectively and assigning a value to each lattice.
The first calculating module 30, the first calculating module 30 is used for calculating the first order spherical harmonic coefficients of the original point cloud spherical model and the target point cloud spherical model.
The second calculation module 40 is used for calculating primary spherical harmonic ellipsoids of the original point cloud and the target point cloud respectively according to the first-order spherical harmonic function and the primary spherical harmonic coefficients of the original point cloud spherical model and the target point cloud spherical model; the second calculation module 40 includes:
the function decomposition unit 41 is used for decomposing the mesh density functions of the original point cloud spherical model and the target point cloud spherical model in the radial function form into function forms in a Cartesian coordinate system respectively;
the primary spherical harmonic ellipsoid calculation unit 42 is configured to apply inverse spherical harmonic transformation to the three components of the function form in the cartesian coordinate system, respectively, to obtain a primary spherical harmonic ellipsoid corresponding to the original point cloud and the target point cloud.
The third calculation module 50 is used for calculating rotation matrixes of rough registration of the original point cloud and the target point cloud respectively according to the primary spherical harmonic ellipsoids of the original point cloud and the target point cloud; the third calculation module 50 includes:
the characteristic vector calculation unit 51, the characteristic value and characteristic vector calculation unit 51 is used for calculating corresponding characteristic values and characteristic vectors according to three vectors of primary spherical harmonic ellipsoids of the original point cloud and the target point cloud which are respectively aligned with the main shaft;
the rotation matrix calculating unit 52, the rotation matrix calculating unit 52 is configured to form a rotation matrix from the parameter space to the object space by using the feature vectors obtained by respectively normalizing the first-order spheroids of the original point cloud and the target point cloud;
and the intermediate state conversion unit 53, wherein the intermediate state conversion unit 53 is used for respectively calculating a rotation matrix of the original point cloud transformed to the intermediate state and a rotation matrix of the target point cloud transformed to the intermediate state according to the rotation matrix of the object space.
And the registration module 60, wherein the registration module 60 is configured to perform coarse registration on the original point cloud and the target point cloud according to the rotation matrix of the coarse registration of the original point cloud and the target point cloud to obtain a registration result.
As shown in fig. 11, the present embodiment further provides a system for implementing the above-mentioned coarse registration method based on first-order spherical harmonics, which includes a point cloud acquisition apparatus 100 and a computer apparatus 200;
the point cloud collection device 100 establishes data communication with the computer device 200, and the point cloud collection device 100 is used for collecting an original point cloud and a target point cloud;
the computer device 200 includes at least one processor and a memory, and is configured to receive the original point cloud and the target point cloud acquired by the point cloud acquiring device 100, and perform a coarse registration process on the input point cloud data by using the processor to invoke and run a registration algorithm in the memory.
According to the embodiment, the spherical models of the original point cloud and the target point cloud are established and the first-order spherical harmonic coefficients of the spherical models are calculated respectively, then the first-order spherical harmonic function is combined, the rotation matrix of coarse registration between the original point cloud and the target point cloud can be calculated, then the centers of gravity of the original point cloud and the target point cloud are aligned and the rotation matrix of the coarse registration is applied, so that the coarse registration can be realized quickly and accurately, and the method has important significance for quickly performing a coarse registration task.
The present invention has been described in detail with reference to the foregoing embodiments, and the principles and embodiments of the present invention have been described herein with reference to specific examples, which are provided only to assist understanding of the methods and core concepts of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (8)
1. A coarse registration method based on first-order spherical harmonics is characterized by comprising the following steps:
s1, acquiring original point cloud data and target point cloud data acquired by point cloud acquisition equipment;
s2, respectively constructing an original point cloud spherical model and a target point cloud spherical model according to the original point cloud data and the target point cloud data;
s3, calculating the original point cloud spherical model and the target point cloud spherical modelFirst order spherical harmonic coefficient
The specific expression is as follows:
wherein,
is the order of spherical harmonic function, m is the order of spherical harmonic function, and satisfies
And
，
is associated with a Legendre function, and is associated with a Legendre function interval of
，
In order to be the weight of the weight,Bis the band-limit of the spherical function model,
the model is a spherical model and is characterized in that,
is a top corner angle, and the top corner angle,
is the azimuth;
s4, respectively calculating primary spherical harmonic ellipsoids of the original point cloud and the target point cloud according to the first-order spherical harmonic function and the primary spherical harmonic coefficients of the original point cloud spherical model and the target point cloud spherical model;
s5, respectively calculating rotation matrixes of rough registration of the original point cloud and the target point cloud according to primary spherical harmonic ellipsoids of the original point cloud and the target point cloud;
s6, carrying out coarse registration on the original point cloud and the target point cloud according to the rotation matrix of the coarse registration of the original point cloud and the target point cloud to obtain a registration result;
wherein, step S5 specifically includes the following steps:
s501, calculating corresponding characteristic values and characteristic vectors according to three vectors of primary spherical harmonic ellipsoids of the original point cloud and the target point cloud respectively aligned with a main shaft;
s502, respectively normalizing the feature vectors of the primary spherical harmonic ellipsoids of the original point cloud and the target point cloud to form a rotation matrix from a parameter space to an object space;
and S503, respectively calculating a rotation matrix of the original point cloud transformed to the intermediate state and a rotation matrix of the target point cloud transformed to the intermediate state according to the rotation matrix of the object space, wherein the intermediate state refers to the intermediate state of the original point cloud and the target point cloud.
2. The coarse registration method according to claim 1, wherein the step of separately constructing the original point cloud spherical model and the target point cloud spherical model in step S2 specifically comprises the steps of:
s201, respectively calculating the gravity centers of the original point cloud and the target point cloud according to the original point cloud data and the target point cloud data, and respectively converting all points in the original point cloud and the target point cloud into a spherical coordinate system;
s202, constructing a unit ball according to the gravity centers of the original point cloud and the target point cloud, and dividing the spherical surface of the unit ball to create a grid;
and S203, calculating lattices to which each point in the original point cloud and the target point cloud belongs respectively, and assigning a value to each lattice.
3. The coarse registration method according to claim 2, wherein the assigning each grid in step S203 comprises:
if the lattice contains point clouds, the value of the lattice is the average value of the distances from the middle point to the origin point of the lattice;
if there is no point cloud in the grid, the value of the grid is 0.
4. Coarse registration method according to claim 1, characterized in that step S4 comprises in particular the steps of:
s401, respectively decomposing the grid density functions of the original point cloud spherical model and the target point cloud spherical model in the form of radial functions into function forms in a Cartesian coordinate system;
s402, respectively applying inverse spherical harmonic transformation according to the three components of the function form in the Cartesian coordinate system to obtain a primary spherical harmonic ellipsoid corresponding to the original point cloud and the target point cloud.
5. A first-order spherical harmonic-based coarse registration apparatus, comprising:
a data acquisition module (10), the data acquisition module (10) being used for acquiring original point cloud data and target point cloud data acquired by a point cloud acquisition device;
a spherical model construction module (20), wherein the spherical model construction module (20) is used for respectively constructing an original point cloud spherical model and a target point cloud spherical model according to the original point cloud data and the target point cloud data;
a first computing module (30), the first computing module (30) being used for computing the first order spherical harmonic coefficient of the original point cloud spherical model and the target point cloud spherical model
The specific expression is as follows:
wherein,
is the order of spherical harmonic function, and m is the order of spherical harmonic function
And
，
is associated with a Legendre function, and is associated with a Legendre function interval of
，
In order to be the weight, the weight is,Bis the band limit of the spherical function model,
the model is a spherical model and is characterized in that,
is a zenith angle, and is a vertex angle,
is the azimuth;
the second computing module (40), the second computing module (40) is used for respectively computing primary spherical harmonic ellipsoids of the original point cloud and the target point cloud according to the first order spherical harmonic function and the primary spherical harmonic coefficients of the original point cloud spherical model and the target point cloud spherical model;
a third calculation module (50), wherein the third calculation module (50) is used for calculating rotation matrixes of rough registration of the original point cloud and the target point cloud respectively according to the first order sphero-harmonious ellipsoid of the original point cloud and the target point cloud;
a registration module (60), wherein the registration module (60) is used for carrying out coarse registration on the original point cloud and the target point cloud according to the rotation matrix of the coarse registration of the original point cloud and the target point cloud to obtain a registration result;
wherein the third calculation module (50) comprises:
the characteristic vector calculation unit (51), the characteristic vector calculation unit (51) is used for calculating corresponding characteristic values and characteristic vectors according to three vectors of the primary spherical harmonic ellipsoid of the original point cloud and the target point cloud which are respectively aligned with the main shaft;
the rotation matrix calculation unit (52), the rotation matrix calculation unit (52) is used for forming a rotation matrix from a parameter space to an object space for the feature vectors respectively normalized by the primary spherical harmonic ellipsoids of the original point cloud and the target point cloud;
and the intermediate state conversion unit (53) is used for respectively calculating a rotation matrix of the original point cloud transformed to the intermediate state and a rotation matrix of the target point cloud transformed to the intermediate state according to the rotation matrix of the object space.
6. Coarse registration apparatus according to claim 5, characterized in that the spherical model building module (20) comprises:
the coordinate conversion unit (21) is used for respectively calculating the gravity centers of the original point cloud and the target point cloud according to the original point cloud data and the target point cloud data and respectively converting all points in the original point cloud and the target point cloud into a spherical coordinate system;
the grid creating unit (22) is used for constructing a unit ball according to the gravity centers of the original point cloud and the target point cloud and dividing the spherical surface of the unit ball to create a grid;
and the grid value calculating unit (23) is used for calculating grids to which each point in the original point cloud and the target point cloud belongs respectively and assigning values to each grid.
7. Coarse registration device according to claim 5, characterized in that said second calculation module (40) comprises:
a function decomposition unit (41), wherein the function decomposition unit (41) is used for decomposing the mesh density functions of the original point cloud spherical model and the target point cloud spherical model in the form of radial functions into function forms under a Cartesian coordinate system;
the primary spherical harmonic ellipsoid calculation unit (42) is used for respectively applying spherical harmonic inverse transformation according to the three components of the function form under the Cartesian coordinate system to obtain a primary spherical harmonic ellipsoid corresponding to the original point cloud and the target point cloud.
8. A system for implementing the coarse registration method of any of the above claims 1-4, characterized by comprising a point cloud acquisition device (100) and a computer device (200);
the point cloud acquisition equipment (100) is in data communication with the computer equipment (200), and the point cloud acquisition equipment (100) is used for acquiring an original point cloud and a target point cloud;
the computer device (200) comprises at least one processor and a memory, and is used for receiving the original point cloud and the target point cloud acquired by the point cloud acquisition device (100), and performing coarse registration processing on the input point cloud data by calling and operating a registration algorithm in the memory through the processor.
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