CN113409367B - Stripe projection measurement point cloud point-by-point weighting registration method, equipment and medium - Google Patents
Stripe projection measurement point cloud point-by-point weighting registration method, equipment and medium Download PDFInfo
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Abstract
The invention discloses a point-by-point weighted registration method, equipment and medium for fringe projection measurement point clouds, wherein the method combines three-dimensional measurement of fringe projection profilometry with a subsequent point cloud registration technology; the phase precision in the fringe projection profilometry can be represented by indexes such as fringe background items, fringe contrast and the like, and the phase precision is positively correlated with the three-dimensional measurement precision, so that the indexes such as the fringe background items, the fringe contrast and the like can represent the three-dimensional point cloud data quality; and building a splicing weighting factor according to indexes such as a stripe background item, stripe contrast and the like, performing point-by-point weighted ICP registration, increasing the weight of high-precision points in the splicing process through the constraint function of point-by-point weighting, and improving the overall registration precision of the point cloud.
Description
Technical Field
The invention belongs to the technical field of vision measurement, and particularly relates to a point-by-point weighting registration method for fringe projection measurement point clouds.
Background
The stripe projection profile operation is a three-dimensional measurement method with increasingly wide requirements, has the advantages of non-contact, information processing automation, full-field measurement, good environmental adaptability, high precision and the like, is a preferred means for full-field non-contact three-dimensional measurement in the modern manufacturing industry, can provide three-dimensional data support for product digital design, online processing, geometric quantity detection, dynamic performance test, mechanical performance analysis, reverse engineering and even product pattern recognition in the mechanical manufacturing, and finally helps the mechanical manufacturing and improves the scene perception level.
Because the field of view of the measuring equipment is limited, and the appearance of the workpiece is complex, the to-be-measured area of the object cannot be covered completely by single measurement, the workpiece needs to be measured from different view angles respectively, and then the full view angle three-dimensional appearance of the measured object is restored in a point cloud registration mode. The point cloud registration can be divided into coarse registration and fine registration. Coarse registration refers to coarser registration under the condition that the transformation between two point clouds is completely unknown, and aims to mainly provide a better transformation initial value for fine registration; the refinement criterion is given by an initial transformation, which is further optimized to get a more accurate transformation. The most widely used point cloud accurate registration algorithm at present is the iterative closest point algorithm (Iterative Closest Point, ICP) and its optimization algorithm.
In the fringe projection three-dimensional contour measurement, the three-dimensional measurement precision of different points is inconsistent due to the different phase precision of each point. In the subsequent registration process, if the point cloud registration is directly performed by adopting an ICP algorithm, the points to be matched with different precision are treated equally when the space transformation matrix is solved by adopting a least square method, and the data precision is not matched, so that the improvement of the final registration precision is not facilitated. This is a drawback of the ICP algorithm, which is to be complemented by the targeted study.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a stripe projection measurement point cloud point-by-point weighted registration method, equipment and medium which consider the measurement precision of each matching point, and the overall registration precision of the point cloud is improved.
In order to achieve the purpose, the point-by-point weighted registration method of the fringe projection measurement point cloud comprises the following steps of:
step 1: based on a stripe projection three-dimensional measurement system, performing multi-view measurement on a target to obtain a sampling stripe diagram;
step 2: based on the fringe pattern obtained by sampling and calibration information of the three-dimensional measurement system, solving multi-view three-dimensional point cloud of the surface of the measured object by combining fringe pattern processing technology;
step 3: algebraic operation is carried out on the sampling fringe pattern to obtain a phase precision characteristic quantity;
step 4: constructing a point-by-point weighting factor positively correlated with the quality of the three-dimensional point cloud based on the phase precision characteristic quantity;
step 5: performing coarse registration on the multi-view point cloud to obtain a position initial value of fine registration;
step 6: performing point cloud fine registration by using the position initial value and the target point cloud, using the nearest point as a point cloud matching point, and giving different weights to the point cloud matching point pairs based on the constructed point-by-point weighting factors to construct a point cloud fine registration objective function;
step 7: and performing least square operation on the point cloud fine registration objective function to obtain an optimal translation matrix and an optimal rotation matrix, performing rotation translation pose adjustment on the point cloud, and performing iterative solution to realize point cloud point-by-point weighting fine registration.
Further, in step 3, the characteristic quantity characterizing the phase precision is
Wherein A is a stripe background item, B is stripe contrast, f 1 Representing a phase accuracy characterization function.
Further, the point-wise weighting factor ω in step 4 i Is defined as follows:
wherein ,is the phase precision characteristic quantity of origin cloud, +.>Is the phase precision characteristic quantity of the corresponding point of the target point cloud, f 2 Representing a point-wise weighting factor characterization function.
Further, in step 6, the point cloud fine registration objective function is:
wherein , and />Is the corresponding point in the source point cloud and the target point cloud, R * and t* Optimal rotational translation matrix for transforming origin cloud to target point cloud, ω i Is a weighting factor for the matching point pair.
Further, in step 7, an optimal translation matrix t for point cloud registration is calculated * The formula is:
definition:
wherein ,for the sum of the weighting factors of the matching point pairs, +.> and />Is a weighted average of the origin cloud and the target point cloud.
Further, in step 7, the optimal rotation matrix R for point cloud registration is calculated * The process is as follows:
singular value decomposition is performed on the matrix C:
C＝U∑V T
trace(RC)＝trace(RU∑V T )＝trace(∑V T RU)
when V is T When RU is a unit array, trace (RC) takes the maximum value, that is:
V T R * U＝I
R * ＝VU T
solving a point cloud registration optimal rotation matrix R through singular value decomposition of matrix C * 。
A computer device comprising a memory and a processor electrically connected, said memory having stored thereon a computer program executable on the processor, said processor implementing the steps of the above-described registration method when said computer program is executed.
A computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the registration method described above.
Compared with the prior art, the invention has at least the following beneficial technical effects:
the three-dimensional measurement of the stripe projection profilometry and the subsequent point cloud registration technology are fused into an organic whole; by fully considering the precision difference of different points to be matched, a point cloud registration function with more suitability is constructed, so that the adaptability to complex measurement point clouds is improved, and the overall registration precision is improved.
The phase precision in the fringe projection profilometry can be represented by indexes such as fringe background items, fringe contrast and the like, and the phase precision is positively correlated with the three-dimensional measurement precision, so that the indexes such as the fringe background items, the fringe contrast and the like can represent the three-dimensional point cloud data quality; and building a splicing weighting factor according to indexes such as a stripe background item, stripe contrast and the like, performing point-by-point weighted ICP registration, increasing the weight of high-precision points in the splicing process through the constraint function of point-by-point weighting, and improving the overall registration precision of the point cloud.
Drawings
FIG. 1 is a sinusoidal fringe diagram of two view angles of an acquired object under test;
FIG. 2 is an unwrapped phase diagram of two view angles of an object under test;
FIG. 3 is a three-dimensional point cloud of two views of an object under test;
FIG. 4 is a graph of weighting factors for two views of an object under test;
FIG. 5 is a rough registration diagram of two view point clouds of an object to be measured;
FIG. 6a is an unweighted fine registration diagram of two view point clouds of an object under test;
FIG. 6b is a view point cloud weighted fine registration chart of two view points of the measured object;
FIG. 7 is a root mean square error statistical graph of the weighted and non-weighted point cloud fine registration process;
FIG. 8 is a graph of the maximum common point set statistics for the weighted and non-weighted point cloud fine registration process;
fig. 9 is a schematic structural diagram of a computer device according to the present invention.
Detailed Description
In order to make the purpose and technical scheme of the invention clearer and easier to understand. The present invention will now be described in further detail with reference to the drawings and examples, which are given for the purpose of illustration only and are not intended to limit the invention thereto.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more. In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
The phase is a core parameter for three-dimensional reconstruction by fringe projection contour operation, and the precision directly determines the three-dimensional measurement precision. The phase is obtained by sampling a fringe pattern through two core processing steps of phase demodulation and phase unwrapping, and the precision of the phase can be represented by indexes such as fringe background items, fringe contrast, phase errors and the like. The phase accuracy is positively correlated with the three-dimensional reconstruction accuracy. In view of this, in the subsequent registration process of fringe projection measurement point cloud, a splicing weighting factor can be established by means of phase precision, point-by-point weighted ICP registration can be performed, the contribution value of points with good measurement precision to registration is improved, the influence of abnormal points on the splicing effect is reduced, and the point cloud registration precision is improved through the constraint effect of point-by-point weighting.
Example 1
The fringe projection measurement point cloud point-by-point weighting registration method comprises the following steps:
step 1: and based on the constructed fringe projection three-dimensional measurement system, performing multi-view measurement on the target, and sampling to obtain a fringe pattern.
And sequentially projecting a plurality of groups of sinusoidal fringe patterns with different frequencies generated by computer coding onto an object to be measured by a projection system, and acquiring deformed fringe images which are modulated by the height of the object by using an industrial camera, wherein the deformed fringe images are sinusoidal fringe patterns of two visual angles of the object as shown in figure 1.
Step 2: based on the fringe pattern obtained by sampling and calibration information of the three-dimensional measurement system, the multi-view three-dimensional point cloud of the surface of the measured object is obtained by combining fringe pattern processing technology.
Demodulating the acquired fringe pattern to obtain a wrapped phase, unwrapping the wrapped phase to obtain a real phase, solving the real phase pattern as shown in fig. 2, and solving the three-dimensional point cloud through the real phase combined with calibration parameters, wherein the solved three-dimensional point cloud is as shown in fig. 3.
Step 3: algebraic operation is carried out on the fringe pattern to obtain the characteristic quantity capable of representing the phase precision.
The phase is obtained by two processing steps of phase demodulation and phase unwrapping of the fringe pattern, and the phase precision is defined asIt may be characterized by indicators of streak background terms, streak contrast, etc.
Wherein A is a stripe background item, B is stripe contrast, f 1 Representing a phase accuracy characterization function.
Step 4: and constructing a point-by-point weighting factor positively correlated with the quality of the three-dimensional point cloud based on the phase precision characteristic quantity.
Based on phase accuracyEstablishing a point-by-point weighting factor omega i Is defined as follows:
wherein ,is the phase precision characteristic quantity of origin cloud, +.>Is the phase precision characteristic quantity of the corresponding point of the target point cloud, f 2 Representing a point-wise weighting factor characterization function.
Based on phase accuracyThe point-by-point weighting factors are established as shown in fig. 4, where different grayscales represent different weight values.
Step 5: and performing coarse registration on the multi-view point cloud data to obtain a position initial value of fine registration.
And performing coarse registration by taking the first view as the target point cloud and taking the second view as the origin cloud, wherein the result is shown in fig. 5, and the general position is registered, so that a better position initial value is provided for the next fine registration, and the follow-up fine registration is prevented from falling into a local optimal solution.
Step 6: performing point cloud fine registration of an ICP method by using the initial position value and the target point cloud: the ICP method takes the closest point as a matching point, different weights are given to point cloud matching point pairs based on the constructed point-by-point weighting factors, and an improved point cloud accurate registration objective function is constructed.
Based on the constructed point-wise weighting factors, the objective function of ICP point cloud registration can be optimized as:
wherein , and />Is the correspondence in the source point cloud and the target point cloudPoint, R * For the optimal rotation matrix of transforming the origin cloud to the target point cloud, t * Optimal translation matrix for transforming origin cloud to target point cloud,> and />For origin cloud and target point Yun Di i matching point pairs omega i For the weighting factor of the matching point pair, P s And R is the number of origin clouds, R is a rotation matrix of origin cloud-to-target point cloud conversion, and t is a translation matrix of origin cloud-to-target point cloud conversion.
Step 7: and performing least square operation on the objective function to obtain an optimal translation matrix and an optimal rotation matrix, performing rotation translation pose adjustment on the point cloud, and performing iterative solution to realize point-by-point weighting fine registration of the point cloud.
Calculating an optimal translation matrix of point cloud fine registration:
the optimal translation is obtained by calculating the weighted centroids of the origin cloud and the target point cloud, and the point cloud translation transformation is to translate the centroid positions of the point cloud translation transformation.
The calculation point cloud weighted centroid formula is as follows:
wherein ,for the sum of the weighting factors of the matching point pairs, +.> and />The weighted centroid of the origin cloud and the target point cloud is obtained, and N is the number of matching point pairs of the origin cloud and the target point cloud.
Calculating an optimal translation matrix of point cloud fine registration:
let n= |p s The objective function of the ICP point cloud registration is:
the method for deriving the data comprises the following steps:
to minimize the objective function and let its derivative be 0, there are: the optimal translation matrix formula for point cloud registration is as follows:
no matter what value is taken by R, we can find the optimal translation t for the objective function to get the minimum value according to the above * I.e. by calculating the centroids of the origin cloud and the target point cloud, an optimal translation is obtained.
After carrying out translation transformation on the point cloud, calculating an optimal rotation matrix for fine point cloud registration:
by deriving the optimal translation, the optimal translation can be obtained by calculating the mass centers of the origin cloud and the target point cloud, namely
Therefore, both the origin cloud and the target point cloud are converted into the centroid coordinate system, and then and />As an intermediate variable, the ICP point cloud registration objective function rewrites as:
expanding the expression:
since the coordinates of the points are deterministic, independent of R, minimizing the objective function is equivalent to:
namely, the following steps:
singular value decomposition is performed on the matrix C:
C＝U∑V T
trace(RC)＝trace(RU∑V T )＝trace(∑V T RU)
when V is T When RU is a unit array, trace (RC) takes the maximum value. Namely:
V T R * U＝I
R * ＝VU T
obtaining an orthogonal matrix R by singular value decomposition of the matrix C * However, this orthogonal matrix is divided into two cases, one is a satisfactory rotation matrix and one is an unsatisfactory reflection matrix, which needs to be corrected. Since the rotation matrix has a determinant of 1, the reflection matrix has a determinant value of-1, and the rotation matrix expression after correction is:
the integration is as follows:
from the above deductions, the optimal rotation matrix for point cloud registration is calculated by converting the origin cloud and the target point cloud into a centroid coordinate system, constructing a covariance matrix C, and obtaining the rotation matrix by Singular Value Decomposition (SVD) of C.
And after the optimal rotation translation matrix of the point cloud fine registration is obtained, pose adjustment is carried out on the origin cloud, and point-by-point weighting fine registration of the fringe projection measurement point cloud is realized through iterative solution. And meanwhile, carrying out unweighted point cloud registration on the fringe projection measurement point cloud, and comparing and evaluating the point-by-point weighted registration effect. The non-weighted fine registration result is shown in fig. 6a and the weighted fine registration result is shown in fig. 6 b. The registration effect is evaluated by two indexes of root mean square error (RSME) reflecting the overall registration error of the two point clouds and maximum common point set (LCP) reflecting the maximum matching degree, and the root mean square error and the maximum common point set of the accurate registration of the weighted and unweighted point clouds are respectively counted, as shown in fig. 7 and 8.
As can be seen from fig. 7 and 8, the root mean square error of the weighted and unweighted point cloud fine registration iterative process is basically the same, but on the maximum common point set, the weighted registration is superior to the non-dissuaded registration, and at the end of the iteration, a part of points with better matching can be added, so that the maximum matching degree of the two pieces is improved and the registration precision is improved through the constraint effect of point-by-point weighting.
Example 2
The computer device provided by the invention, as shown in fig. 9, comprises a memory and a processor which are electrically connected, wherein the memory stores a computing program which can be run on the processor, and the processor realizes the steps of the registration method when executing the computing program.
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present invention.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer device may include, but is not limited to, a processor, a memory.
The processor may be a central processing unit (CentralProcessingUnit, CPU), but may also be other general purpose processors, digital signal processors (DigitalSignalProcessor, DSP), application specific integrated circuits (ApplicationSpecificIntegratedCircuit, ASIC), off-the-shelf programmable gate arrays (Field-ProgrammableGateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like.
The memory may be used to store the computer program and/or module, and the processor may implement various functions of the terminal device by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
Example 3
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (5)
1. The fringe projection measurement point cloud point-by-point weighting registration method is characterized by comprising the following steps of:
step 1: based on a stripe projection three-dimensional measurement system, performing multi-view measurement on a target to obtain a sampling stripe diagram;
step 2: based on the fringe pattern obtained by sampling and calibration information of the three-dimensional measurement system, solving multi-view three-dimensional point cloud of the surface of the measured object by combining fringe pattern processing technology;
step 3: algebraic operation is carried out on the sampling fringe pattern to obtain a phase precision characteristic quantity;
step 4: constructing a point-by-point weighting factor positively correlated with the quality of the three-dimensional point cloud based on the phase precision characteristic quantity;
step 5: performing coarse registration on the multi-view point cloud to obtain a position initial value of fine registration;
step 6: performing point cloud fine registration by using the position initial value and the target point cloud, using the nearest point as a point cloud matching point, and giving different weights to the point cloud matching point pairs based on the constructed point-by-point weighting factors to construct a point cloud fine registration objective function;
step 7: performing least square operation on the point cloud fine registration objective function to obtain an optimal translation matrix and an optimal rotation matrix, performing rotation translation pose adjustment on the point cloud, and performing iterative solution to realize point cloud point-by-point weighting fine registration;
in the step 3, the characteristic quantity representing the phase precision is that the phase precision is
Wherein A is a stripe background item, B is stripe contrast, f 1 Representing a phase precision characterization function;
the point-by-point weighting factor omega in the step 4 i Is defined as follows:
wherein ,is the phase precision characteristic quantity of origin cloud, +.>Is the phase precision characteristic quantity of the corresponding point of the target point cloud, f 2 Representing a point-by-point weighting factor characterization function;
in the step 6, the point cloud fine registration objective function is:
wherein , and />Is the corresponding point in the source point cloud and the target point cloud, R * and t* Optimal rotational translation matrix for transforming origin cloud to target point cloud, ω i Is a weighting factor for the matching point pair.
2. The method according to claim 1, wherein in step 7, an optimal translation matrix t for point cloud registration is calculated * The formula is:
definition:
wherein ,for the sum of the weighting factors of the matching point pairs, +.> and />Is a weighted average of the origin cloud and the target point cloud.
3. The method according to claim 1, wherein in step 7, an optimal rotation matrix R for point cloud registration is calculated * The process is as follows:
singular value decomposition is performed on the matrix C:
C＝U∑V T
trace(RC)＝trace(RU∑V T )＝trace(∑V T RU)
when V is T When RU is a unit array, trace (RC) takes the maximum value, that is:
V T R * U＝I
R * ＝VU T
solving a point cloud registration optimal rotation matrix R through singular value decomposition of matrix C * 。
4. A computer device comprising an electrically connected memory and a processor, the memory having stored thereon a computing program executable on the processor, when executing the computing program, performing the steps of the method of any of claims 1-3.
5. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any of claims 1-3.
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