Graph frequency adaptive BIM and point cloud registration method

Abstract

The invention discloses a graph frequency adaptive BIM and point cloud registration method, and relates to the technical field of intelligent buildings. The method at least comprises the following steps: S1, triangularizing a BIM component, and reserving triangularized vertexes and recording the triangularized vertexes as BIM point clouds; scanning a real component corresponding to the BIM, and recording a scanned point cloud as a component point cloud; s2, respectively calculating atlas cumulative distribution of the component point cloud and the BIM point cloud by adopting a corresponding method; s3, respectively calculating feature points of the component point cloud and the BIM point cloud by adopting corresponding methods; and S4, registering the BIM point cloud and the component point cloud by using an ICP (Inductively Coupled Plasma) algorithm according to the reserved feature points. According to the method, manually set feature point selection parameters are not needed, the spectrum cut-off frequency is automatically calculated according to the point cloud, the feature points for matching are adaptively generated, and the robustness when the point cloud is matched with the BIM is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent buildings, and in particular to a frequency-adaptive BIM and point cloud registration method. Background Art

[0002] Currently, the existing BIM and point cloud registration often directly selects all points or randomly selects some points, and then uses the iterative nearest neighbor algorithm for registration;

[0003] However, in actual scanning, the amount of point cloud data is often very large, and selecting all points for registration requires a lot of computing resources;

[0004] At the same time, since point cloud data are usually affected by scanning accuracy, environmental factors, scanning angle, etc., there may be noise, missing points or accuracy deviations. Randomly selecting some points cannot guarantee the registration effect.

[0005] To this end, it is necessary to comprehensively consider the characteristics of the component itself, adaptively select the points used for registration, and improve the accuracy of registration.

[0006] Therefore, it is necessary to propose a new solution to the above problems. Summary of the invention

[0007] The purpose of the present invention is to provide a BIM and point cloud registration method with adaptive graph frequency, which does not require artificially set feature point selection parameters, automatically calculates the graph cutoff frequency according to the point cloud, and adaptively generates matching feature points, thereby improving the robustness of point cloud and BIM matching, so as to solve the technical problems raised in the background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: a frequency-adaptive BIM and point cloud registration method, comprising at least the following steps:

[0009] S1: triangulate the BIM component and keep the triangulated vertices as BIM point cloud; scan the real component corresponding to the BIM and record the scanned point cloud as the component point cloud;

[0010] S2: Calculate the cumulative distribution of the component point cloud and BIM point cloud respectively using the corresponding methods;

[0011] S3: Calculate the feature points of the component point cloud and the BIM point cloud respectively using corresponding methods;

[0012] S4: Based on the retained feature points, the ICP algorithm is used to align the BIM point cloud and the component point cloud.

[0013] Furthermore, the S2 at least includes the following steps:

[0014] S2.1: Calculate point cloud The corresponding undirected weighted graph

[0015] Where: V is the vertex set of point cloud X, ε is the graph The edge set of is constructed by the nearest neighbor algorithm. is the weight matrix, point x i With neighbors x j The weight W i，j equal:

[0016]

[0017] where δ i For x i The indexes of the nearest M neighbors in X;

[0018] S2.2: Calculate the graph Laplacian matrix A = D -1 W;

[0019] Where: D is a diagonal matrix, and the diagonal elements satisfy:

[0020] Use the power iteration method to calculate the largest eigenvalue λ of A N and the minimum eigenvalue λ1;

[0021] Take the linear partition α1=λ1=0,…,α p , …, α P =λ N , use the window function to estimate the interval [0, α p ], refer to the following formula (1):

[0022]

[0023] in:

[0024]

[0025] z j ∈ is a standard normal distribution vector, Tr(·) represents the trace operation;

[0026] The last approximation sign in the above formula is that the trace of A can be estimated using the quadratic expectation of the multivariate standard normal distribution vector z:

[0027]

[0028] represents the expectation operation;

[0029] The window function in formula (1) First, the eigenvalue λ∈[0,αp] is Linearly scale to [-1, 1], and then use K-order Chebyshev polynomials to approximate the window function:

[0030]

[0031] in is the Chebyshev polynomial,

[0032] Coefficient c k Given by:

[0033]

[0034] Compensation factor g k Formula (2):

[0035]

[0036] S2.3: Record the frequency separation points and the number of normalized feature points as a coordinate series p∈[0, P], perform cubic interpolation on the coordinate series to obtain the cumulative distribution of the point cloud.

[0037] Furthermore, the S3 at least includes the following steps:

[0038] S3.1: Take a differential operation on the cumulative frequency distribution of the point cloud and calculate the spectrum probability density P(λ);

[0039] S3.2: In the spectrum frequency range The cutoff feature λ is found according to the following heuristic algorithm c , where λ N is the maximum eigenvalue of A; λ1 is the minimum eigenvalue λ1 of A;

[0040] Assumptions The corresponding spectrum probability densities are and P(λ N ),

[0041] when If there is a trough in the , the eigenvalue corresponding to the trough is taken as λ c ;

[0042] when There is no trough in is the cut-off frequency λ c ;

[0043] Design the following high-pass filter:

[0044]

[0045] The interval [λ c ,λ N]After linear transformation Transformed to [-1, 1], the point cloud X passes through the high-pass filter h H (λ) is transformed into:

[0046]

[0047] Where A is the Laplace matrix, is a Chebyshev polynomial:

[0048]

[0049] c k are the coefficients of the polynomial approximation:

[0050]

[0051] Compensation factor g k For the value, refer to formula (2);

[0052] Solve the following optimization problem to obtain the feature points, refer to formula (3):

[0053]

[0054] in, It is a diagonal matrix, and the diagonal elements represent the sampling probability of each point;

[0055] Solve equation (3) and retain a predetermined proportion of points as feature points.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] The present invention does not require artificially set feature point selection parameters, automatically calculates the spectrum cutoff frequency according to the point cloud, and adaptively generates feature points for matching, thereby improving the robustness of point cloud and BIM matching. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0059] Figure 1 A schematic diagram of obtaining the point cloud of a component of the present invention;

[0060] Figure 2 It is a schematic diagram of the cumulative distribution of the point cloud of the present invention;

[0061] Figure 3 It is a schematic diagram of probability density of point cloud atlas of the present invention;

[0062] Figure 4 This is a schematic diagram of the cutoff frequency of the point cloud of the present invention;

[0063] Figure 5 It is a schematic diagram of the final registration result of the present invention. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0065] See also Figure 1-Figure 5 , a frequency-adaptive BIM and point cloud registration method, comprising at least the following steps:

[0066] S1: triangulate the BIM component and keep the triangulated vertices as BIM point cloud; scan the real component corresponding to the BIM and record the scanned point cloud as the component point cloud;

[0067] S2: Use corresponding methods to calculate the cumulative atlas density distribution of component point cloud and BIM point cloud respectively;

[0068] S2.1: Calculate point cloud The corresponding undirected weighted graph

[0069] Where: V is the vertex set of point cloud X, ε is the graph The edge set of is constructed by the nearest neighbor algorithm. is the weight matrix, point x i With neighbors x j The weight W i，j equal:

[0070]

[0071] where δ i For X i The indexes of the nearest M neighbors in X;

[0072] S2.2: Calculate the graph Laplacian matrix A = D -1 W;

[0073] Where: D is a diagonal matrix, and the diagonal elements satisfy:

[0074] Use the power iteration method to calculate the largest eigenvalue λ of A N and the minimum eigenvalue λ1;

[0075] Take the linear partition α1=λ1=0,…,α p, …, α P =λ N , use the window function to estimate the interval [0, α p ], refer to the following formula (1):

[0076]

[0077] in:

[0078]

[0079] z j ∈ is a standard normal distribution vector, Tr(·) represents the trace operation;

[0080] The last approximation sign in the above formula is that the trace of A can be estimated using the quadratic expectation of the multivariate standard normal distribution vector z:

[0081]

[0082] represents the expectation operation;

[0083] The window function in formula (1) First, the eigenvalue λ∈[0,αp] is Linearly scale to [-1, 1], and then use K-order Chebyshev polynomials to approximate the window function:

[0084]

[0085] in is the Chebyshev polynomial,

[0086] Coefficient c k Given by:

[0087]

[0088] Compensation factor g k Formula (2):

[0089]

[0090] S2.3: Record the frequency separation points and the number of normalized feature points as a coordinate series p∈[0, P], perform cubic interpolation on the coordinate series to obtain the cumulative distribution of the point cloud.

[0091] S3: Calculate the feature points of the component point cloud and the BIM point cloud respectively using corresponding methods;

[0092] S3.1: Take a differential operation on the cumulative distribution of the point cloud spectrum and calculate the probability density of the spectrum P(λ);

[0093] S3.2: In the spectrum frequency range The cutoff feature λ is found according to the following heuristic algorithm c , where λ N is the maximum eigenvalue of A and λ1 is the minimum eigenvalue of A;

[0094] Assumptions The corresponding spectrum probability densities are and P(λ N ),

[0095] when If there is a trough in the , the eigenvalue corresponding to the trough is taken as λ c ;

[0096] when There is no trough in is the cut-off frequency λ c ;

[0097] Design the following high-pass filter:

[0098]

[0099] The interval [λ c ,λ N ]After linear transformation Transformed to [-1, 1], the point cloud X passes through the high-pass filter h H (λ) is transformed into:

[0100]

[0101] Where A is the Laplace matrix, is a Chebyshev polynomial:

[0102]

[0103] c k are the coefficients of the polynomial approximation:

[0104]

[0105] Compensation factor g k For the value, refer to formula (2);

[0106] Solve the following optimization problem to obtain the feature points, refer to formula (3):

[0107]

[0108] in, It is a diagonal matrix, and the diagonal elements represent the sampling probability of each point;

[0109] Solve equation (3) and retain a predetermined proportion of points as feature points.

[0110] S4: Based on the retained feature points, the ICP algorithm is used to align the BIM point cloud and the component point cloud.

[0111] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A frequency-adaptive BIM and point cloud registration method, characterized by: At least the following steps are included: S1: triangulate the BIM component and keep the triangulated vertices as BIM point cloud; scan the real component corresponding to the BIM and record the scanned point cloud as the component point cloud; S2: Calculate the cumulative distribution of the component point cloud and BIM point cloud respectively using the corresponding methods; S3: Calculate the feature points of the component point cloud and the BIM point cloud respectively using corresponding methods; S4: Based on the retained feature points, the ICP algorithm is used to align the BIM point cloud and the component point cloud.

2. The method for BIM and point cloud registration based on image frequency self-adaptation according to claim 1, characterized in that: The S2 at least comprises the following steps: S2.1: Calculate point cloud The corresponding undirected weighted graph in: is the vertex set of point cloud X, ε is the graph The edge set of is constructed by the nearest neighbor algorithm. is the weight matrix, point x i With neighbors x j The weight W i,j equal: where δ i For x i The indexes of the nearest M neighbors in X; S2.2: Calculate the graph Laplacian matrix A = D -1 W; Where: D is a diagonal matrix, and the diagonal elements satisfy: Use the power iteration method to calculate the largest eigenvalue λ of A N and the minimum eigenvalue λ1; Take the linear partition α1=λ1=0,…,α P ,…,α P =λ N , use the window function to estimate the interval [0,α p ], refer to the following formula (1): in: z j ∈ is a standard normal distribution vector, Tr(·) represents the trace operation; The last approximation sign in the above formula is that the trace of A can be estimated using the quadratic expectation of the multivariate standard normal distribution vector z: represents the expectation operation; The window function in formula (1) First, the eigenvalue λ∈[0,α p ]according to Linearly scale to [-1,1], and then use K-order Chebyshev polynomials to approximate the window function: in is the Chebyshev polynomial, Coefficient c k Given by: Compensation factor g k Formula (2): S2.3: Record the frequency separation points and the number of normalized feature points as a coordinate series p∈[0,P], perform cubic interpolation on the coordinate series to obtain the cumulative distribution of the point cloud.

3. The method for BIM and point cloud registration based on image frequency self-adaptation according to claim 2, characterized in that: The S3 at least includes the following steps: S3.1: Take a differential operation on the cumulative frequency distribution of the point cloud and calculate the spectrum probability density P(λ); S3.2: In the spectrum frequency range The cutoff feature λ is found according to the following heuristic algorithm c , where: N is the maximum eigenvalue of A; λ1 is the minimum eigenvalue λ1 of A; Assumptions The corresponding spectrum probability densities are and P(λ N ), when If there is a trough in the , the eigenvalue corresponding to the trough is taken as λ c ; when There is no trough in is the cut-off frequency λ c ; Design the following high-pass filter: The interval [λ c ,λ N ]After linear transformation Transformed to [-1,1], the point cloud X passes through the high-pass filter h H (λ) is transformed into: Where A is the Laplace matrix, is a Chebyshev polynomial: c k are the coefficients of the polynomial approximation: Compensation factor g k For the value, refer to formula (2); Solve the following optimization problem to obtain the feature points, refer to formula (3): in, It is a diagonal matrix, and the diagonal elements represent the sampling probability of each point; Solve equation (3) and retain a predetermined proportion of points as feature points.

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