CN111815611A - Round hole feature extraction method for rivet hole measurement point cloud data - Google Patents
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Abstract
The invention discloses a round hole feature extraction method for rivet hole measurement point cloud data, and relates to the technical field of part detection in aircraft manufacturing; in order to solve the problem of complex operation; the method specifically comprises the following steps: extracting point cloud boundary points according to the distribution condition of k neighborhood points of each point in the point cloud; obtaining point cloud blocks belonging to different boundary characteristics through Euclidean distance clustering segmentation of boundary points; and extracting the rivet hole boundary in the cloud block of the segmentation point by an ellipse fitting method. The method replaces the traditional contact type rivet hole measuring method, can effectively overcome the defects of manual marking and manual cutting, is high in measuring efficiency and good in flexibility, simplifies the process compared with other rivet hole special extracting methods based on scattered point clouds, can reduce excessive manual participation in the extracting process, is high in automation degree, can eliminate other round holes which are not rivet holes in the point clouds and needs to be eliminated, calculates dc, and eliminates the extracting hole if the dc is smaller than a set threshold value, so that the accuracy is high.
Description
Technical Field
The invention relates to the technical field of part detection in aircraft manufacturing, in particular to a round hole feature extraction method for rivet hole measurement point cloud data.
Background
The hole forming precision of the rivet hole is of great significance to the quality of aircraft manufacturing and assembly, for the rivet hole with high precision requirement, the hole needs to be detected after hole forming, the traditional three-coordinate measuring machine detection method is low in efficiency and difficult to detect parts such as large-size auxiliary wallboards, the special rivet hole detection tool for manufacturing is long in time consumption and high in cost, the rivet hole is detected by adopting a non-contact scanning measurement mode gradually along with improvement of the rivet hole forming precision requirement of aircraft production and increase of hole detection requirements, the rivet hole is detected by adopting the non-contact scanning measurement mode, scattered point cloud of the rivet hole is obtained by the mode, and the point cloud data needs to be processed to extract the rivet hole characteristics in the point cloud.
Through retrieval, a patent with Chinese patent application number CN201710718764.8 discloses a rivet hole position detection method on an aircraft skin, which comprises the following steps: a. manufacturing a set of bracket matched with the covering part for supporting; b. making a reference hole on the bracket as a reference for measurement; c. after the skin part is placed on the bracket and fixed, positioning holes in the skin part and positioning holes in the bracket are positioned by positioning pins; d. inputting the coordinates of the reference hole into laser tracker equipment, and fitting the coordinates of the reference hole to enable the coordinates of the reference hole to coincide with theoretical coordinates designed by the die; e. measuring the hole position of the rivet hole by using a laser tracker with the reference hole as a reference; f. and comparing the coordinate value data measured by the rivet hole with the digital-analog middle coordinate value of the skin part to judge whether the rivet hole position meets the requirement. The method for detecting the positions of the rivet holes on the aircraft skin in the patent has the following defects: the operation is complicated, the measurement time is long, and the manual work is needed to participate in time.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a round hole feature extraction method facing rivet hole measurement point cloud data.
In order to achieve the purpose, the invention adopts the following technical scheme:
a round hole feature extraction method facing rivet hole measurement point cloud data comprises the following steps:
s1: extracting point cloud boundary points according to the distribution condition of k neighborhood points of each point in the point cloud;
s2: obtaining point cloud blocks belonging to different boundary characteristics through Euclidean distance clustering segmentation of boundary points;
s3: and extracting the rivet hole boundary in the cloud block of the segmentation point by an ellipse fitting method.
Preferably: the method for extracting the point cloud boundary points comprises the following steps:
s11: establishing a kd-Tree structure of scattered point cloud by using a kd-Tree method to obtain a point PiAnd its k neighborhood point Nj(j =0,1,2 … k-1) to obtain P by least squares fittingiA local tangent plane to the point;
s12: establishing a plane coordinate system on the tangent plane by PiTo the tangent plane projection point PiAs origin of coordinates, PiPoint sum N0Projected point of points
Constructed vector
As the X-axis of the coordinate system, normal to the plane
With vectors
Ride across
×
As the Y axis of the coordinate system;
s13: converting the three-dimensional coordinate of each point in the point set X to the plane coordinate system to obtain a two-dimensional coordinate set of the point set X
；
S14: to be provided with
In (1)
The point is used as the starting point of the vector,
taking the point as a vector end point to obtain a plane vector set
(j=0,1,2…k-1), calculating
The included angle alpha j from each vector to the X axis of the local coordinate system and the included angle beta j from the Y axis;
s15: the boundary points are identified from the maximum value θ max among the boundary points θ j.
Preferably: if β j in said S14>Pi/2, then α j = αj + pi, then arranging the alpha j in ascending order to obtain an angle sequence eta j, and calculating an included angle theta j = eta j-1 between adjacent angles of the eta j, wherein
。
Preferably: in the step S15, when the maximum included angle θ max in the adjacent angle sequence θ j exceeds the maximum included angle threshold θ, the point Pi is a boundary point, otherwise, the point Pi is a non-boundary point and the threshold is set
The size of the threshold is determined according to the cloud distribution situation, and the threshold is generally set
Set to pi/2.
Preferably: the boundary point Euclidean distance clustering segmentation is that different boundary features in the boundary point cloud have the characteristic of continuous distance between adjacent points in the same boundary feature, namely, the distance between adjacent points in the same boundary feature is small, and the distance between the closest points of different boundary features is large.
Preferably: the method for partitioning the Euclidean distance clusters of the boundary points comprises the following steps:
s31: a kd tree point cloud structure is established for the boundary point cloud X, so that subsequent point neighborhood searching is facilitated;
s32: creating an empty cluster set C and a point set Q;
s33: for any pointP i 
Processing the X;
s34: when each point in the boundary point cloud X is subjected to the step S33, a cluster set C is obtained;
s35: deleting fewer points in the cluster set C thann minAnd (4) obtaining a final cluster set C by cloud block point, and completing segmentation.
Preferably: for any point in the S33P i 
X, performing the following steps:
s41: handleP i Adding into Q;
s42: for each pointP i 
Q, executing the following steps:
a. k neighbor search algorithm through boundary point kd treeP i The k neighborhood of (2) is put into the point set;
b. for each one
If, if
Is not in Q, and
andp j of Euclidean distance rj<d th Then add Q and remove from X;
s43: and when the Q cannot find new points to be added, putting the Q into the cluster set C, and emptying the Q.
Preferably: the method for extracting the rivet hole boundary in the cloud block of the segmentation point by the ellipse fitting method comprises the following steps:
s51: cloud block with dividing pointsc i 
C least square fitting plane, constructing local plane two-dimensional coordinate system, and blocking point cloudc i Converting the three-dimensional coordinates of all the points into a constructed local plane two-dimensional coordinate system to obtain two-dimensional coordinates
；
S52: to plane two-dimensional coordinate
Least square fitting the plane ellipse to obtain an ellipse equation Ax 2+Bxy+Cy 2+ Dx + Ey + F =0, center of ellipse
And major and minor axis radii
And
；
s53: and extracting the rivet hole boundary characteristics in the boundary point cloud blocks according to the ellipse fitting Error, the ratio of the major axis to the minor axis, the maximum radius threshold rmax, the minimum radius threshold rmin and the circle center position threshold d.
Preferably: the local plane two-dimensional coordinate system is constructed by any vector on the plane
Normal to a plane, as the X-axis of the coordinate system
And
cross product of (a) as a coordinate system Y-axis, point cloud blockc i Any point inp 0Projected points on a plane
As the origin of the coordinate system.
The invention has the beneficial effects that:
1. the method has the advantages that the traditional contact type rivet hole measuring method is replaced, the defects of manual marking and manual cutting can be effectively overcome, the measuring efficiency is high, the flexibility is good, compared with other rivet hole special extracting methods based on scattered point clouds, the process is simplified, excessive manual participation in the extracting process can be reduced, and the automation degree is high.
2. The kd-Tree method is adopted to establish the kd-Tree structure of the scattered point cloud, thereby being convenient to find out each point in the point cloudP i K neighborhood point set ofN j (j=0,1,2…k-1)。
3. And if the dc is smaller than a set threshold value, the extraction hole is removed, and the accuracy is high.
Drawings
FIG. 1 is a schematic flow chart of a round hole feature extraction method for rivet hole measurement point cloud data according to the present invention;
FIG. 2 is a rivet hole point cloud data schematic diagram of a round hole feature extraction method for rivet hole measurement point cloud data according to the present invention;
FIG. 3 is a schematic diagram of a local plane coordinate system established by the round hole feature extraction method for rivet hole measurement point cloud data according to the present invention;
FIG. 4 is a schematic diagram of an included angle between adjacent vectors calculated by the round hole feature extraction method for rivet hole measurement point cloud data according to the present invention;
fig. 5 is a schematic diagram of boundary point identification of the round hole feature extraction method for rivet hole measurement point cloud data according to the present invention.
Detailed Description
The technical solution of the present patent will be described in further detail with reference to the following embodiments.
Reference will now be made in detail to embodiments of the present patent, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present patent and are not to be construed as limiting the present patent.
In the description of this patent, it is to be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in the orientations and positional relationships indicated in the drawings for the convenience of describing the patent and for the simplicity of description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting of the patent.
In the description of this patent, it is noted that unless otherwise specifically stated or limited, the terms "mounted," "connected," and "disposed" are to be construed broadly and can include, for example, fixedly connected, disposed, detachably connected, disposed, or integrally connected and disposed. The specific meaning of the above terms in this patent may be understood by those of ordinary skill in the art as appropriate.
A round hole feature extraction method facing rivet hole measurement point cloud data is disclosed, as shown in FIGS. 1-4, and comprises the following steps:
s1: extracting point cloud boundary points according to the distribution condition of k neighborhood points of each point in the point cloud;
s2: obtaining point cloud blocks belonging to different boundary characteristics through Euclidean distance clustering segmentation of boundary points;
s3: and extracting the rivet hole boundary in the cloud block of the segmentation point by an ellipse fitting method.
The method for extracting the point cloud boundary points comprises the following steps:
s11: establishing a kd-Tree structure of scattered point cloud by using a kd-Tree method to obtain a point PiAnd its k neighborhood point Nj(j =0,1,2 … k-1) to obtain P by least squares fittingiA local tangent plane to the point;
s12: establishing a plane coordinate system on the tangent plane by PiTo the tangent plane projection point PiAs origin of coordinates, PiPoint sum N0Projected point of points
Constructed vector
As the X-axis of the coordinate system, normal to the plane
With vectors
Ride across
×
As the Y axis of the coordinate system;
s13: converting the three-dimensional coordinate of each point in the point set X to the plane coordinate system to obtain a two-dimensional coordinate set of the point set X
；
S14: to be provided with
In (1)
The point is used as the starting point of the vector,
taking the point as a vector end point to obtain a plane vector set
(j=0,1,2…k-1), calculating
The included angle alpha j from each vector to the X axis of the local coordinate system and the included angle beta j from the Y axis;
s15: the boundary points are identified from the maximum value θ max among the boundary points θ j.
Specifically, if β j in the above-mentioned S14>Pi/2, then alpha j = alpha j + pi, then alpha j is arranged in ascending order to obtain an angle sequence eta j, and an included angle theta j = eta j-1 between adjacent angles of eta j is calculated, wherein
。
Specifically, in S15, when the maximum included angle θ max in the adjacent angle sequence θ j exceeds the maximum included angle threshold θ, the point Pi is a boundary point, otherwise, the point Pi is a non-boundary point and the threshold is set
The size of the threshold is determined according to the cloud distribution situation, and the threshold is generally set
Set to pi/2.
The boundary point Euclidean distance clustering segmentation is that different boundary features in the boundary point cloud have the characteristic of continuous distance between adjacent points in the same boundary feature, namely, the distance between adjacent points in the same boundary feature is small, and the distance between the closest points of different boundary features is large.
Further, the method for euclidean distance cluster segmentation of the boundary points comprises the following steps:
s31: a kd tree point cloud structure is established for the boundary point cloud X, so that subsequent point neighborhood searching is facilitated;
s32: creating an empty cluster set C and a point set Q;
s33: for any pointP i 
Processing the X;
s34: when each point in the boundary point cloud X is subjected to the step S33, a cluster set C is obtained;
s35: deleting fewer points in the cluster set C thann minAnd (4) obtaining a final cluster set C by cloud block point, and completing segmentation.
Further, the arbitrary point in S33P i 
X, performing the following steps:
s41: handleP i Adding into Q;
s42: for each pointP i 
Q, executing the following steps:
a. k neighbor search algorithm through boundary point kd treeP i The k neighborhood of (2) is put into the point set;
b. for each one
If, if
Is not in Q, and
andp j of Euclidean distance rj<d th Then add Q and remove from X;
s43: and when the Q cannot find new points to be added, putting the Q into the cluster set C, and emptying the Q.
Still further, thed th A threshold is partitioned for clustering.
Still further, then minIs the minimum threshold of the number of clustering points.
The method for extracting the rivet hole boundary in the cloud block of the segmentation point by the ellipse fitting method comprises the following steps:
s51: cloud block with dividing pointsc i 
C least square fitting plane, constructing local plane two-dimensional coordinate system, and blocking point cloudc i Three-dimensional coordinates of all points in the three-dimensional coordinate transformation table are converted into a constructed local plane two-dimensional coordinateUnder the standard system, obtaining two-dimensional coordinates
；
S52: to plane two-dimensional coordinate
Least square fitting the plane ellipse to obtain an ellipse equation Ax 2+Bxy+Cy 2+ Dx + Ey + F =0, center of ellipse
And major and minor axis radii
And
；
s53: and extracting the rivet hole boundary characteristics in the boundary point cloud blocks according to the ellipse fitting Error, the ratio of the major axis to the minor axis, the maximum radius threshold rmax, the minimum radius threshold rmin and the circle center position threshold d.
Further, the local plane two-dimensional coordinate system is constructed by any vector on the plane
Normal to a plane, as the X-axis of the coordinate system
And
cross product of (a) as a coordinate system Y-axis, point cloud blockc i Any point inp 0Projected points on a plane
As the origin of the coordinate system.
The ellipse equation Ax 2+Bxy+Cy 2Solution of + Dx + Ey + F =0 as follows:
order to
And
then the optimization goal is: min
=
，s.t.
>0, due to
When =0, W has a scaling factor such that all W' = aW also meet the optimization goal, so it is possible to let W
=1, then the optimization objective becomes: min
=
，s.t.
=1, construct lagrange function for equation: l (W, λ) =
-λ(
-1), deriving, order
Is obtained by
-λHW=0→
λ HW, let S =
SW = λ HW, 6 possible solutions W are obtained by solving the generalized inverse matrix, λ is the positive definite matrix since S is>0, therefore can be used
=1 and λ>0 to obtain the final qualified solution
。
Wherein H =
，
>0 is the ellipse fitting parameter constraint 4AC-
>0。
Calculating the center of the ellipse
And major and minor axis radii
And
：
calculating the ratio of the ellipse fitting Error to the major and minor axes:
wherein
F (p) is a set of grid surfaces, and n is a point cloud block ci
Number of points C, if Error>e, e is fitting Error threshold, the cloud block is non-elliptical, i.e. non-rivet hole, if Error is<e, comparing the ratio of the long axis to the short axis; if 1-r<ratio<1-r, wherein r is a long-short axis ratio threshold value, the cloud block roundness of the division point is high, namely the boundary of the rivet hole, and otherwise, the cloud block roundness is the boundary of the non-rivet hole; if the radius of the rivet hole is required, a maximum radius threshold value rmax and a minimum radius threshold value rmin can be set, the mean value of the long axis and the short axis is compared with rmax and rmin to extract the rivet hole meeting the radius requirement, other round holes which are not rivet holes in the point cloud can be eliminated, the spatial distance dc between the theoretical center of the non-rivet round hole and the center of the fitting ellipse of the algorithm is calculated, and if the dc is smaller than the set threshold value, the extracted hole is eliminated.
When the method is used, a kd tree structure of scattered point cloud is established by adopting a kd tree method, so that each point in the point cloud is found outP i K neighborhood point set ofN j (j=0,1,2…k-1), replace the method of traditional contact measurement rivet hole, can effectively overcome the shortcoming of manual marking and manual cutting.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (9)
1. A round hole feature extraction method facing rivet hole measurement point cloud data is characterized by comprising the following steps:
s1: extracting point cloud boundary points according to the distribution condition of k neighborhood points of each point in the point cloud;
s2: obtaining point cloud blocks belonging to different boundary characteristics through Euclidean distance clustering segmentation of boundary points;
s3: and extracting the rivet hole boundary in the cloud block of the segmentation point by an ellipse fitting method.
2. The method for extracting the features of the round hole facing the rivet hole measurement point cloud data according to claim 1, wherein the method for extracting the boundary points of the point cloud comprises the following steps:
s11: establishing a kd-Tree structure of scattered point cloud by using a kd-Tree method to obtain a point PiAnd its k neighborhood point Nj(j =0,1,2 … k-1) to obtain P by least squares fittingiA local tangent plane to the point;
s12: establishing a plane coordinate system on the tangent plane by PiTo the tangent plane projection point PiAs origin of coordinates, PiPoint sum N0Projected point of points
Constructed vector
As the X-axis of the coordinate system, normal to the plane
With vectors
Ride across
×
As the Y axis of the coordinate system;
s13: converting the three-dimensional coordinates of each point in the point set X toOn the plane coordinate system, a two-dimensional coordinate set of a point set X is obtained
；
S14: to be provided with
In (1)
The point is used as the starting point of the vector,
taking the point as a vector end point to obtain a plane vector set
(j=0,1,2…k-1), calculating
The included angle alpha j from each vector to the X axis of the local coordinate system and the included angle beta j from the Y axis;
s15: the boundary points are identified from the maximum value θ max among the boundary points θ j.
3. The method for extracting features of a circular hole facing to rivet hole measurement point cloud data as claimed in claim 2, wherein if β j in S14>Pi/2, then alpha j = alpha j + pi, then alpha j is arranged in ascending order to obtain an angle sequence eta j, and an included angle theta j = eta j-1 between adjacent angles of eta j is calculated, wherein
。
4. The method for extracting features of a circular hole facing rivet hole measurement point cloud data as claimed in claim 3, wherein in step S15, when a maximum included angle θ max in an adjacent angle sequence θ j exceeds a maximum included angle threshold θ, a point Pi is a boundaryPoint, otherwise point Pi is a non-boundary point, threshold
The size of the threshold is determined according to the cloud distribution situation, and the threshold is generally set
Set to pi/2.
5. The method for extracting circular hole features from point cloud data measured by rivet holes according to claim 1, wherein the Euclidean distance clustering segmentation of the boundary points is that different boundary features in the point cloud have the characteristic of continuous distance between adjacent points in the same boundary feature, that is, the distance between adjacent points in the same boundary feature is small, and the distance between the closest points in different boundary features is large.
6. The method for extracting the circular hole features from the rivet hole measurement point cloud data according to claim 5, wherein the Euclidean distance clustering segmentation method for the boundary points comprises the following steps:
s31: a kd tree point cloud structure is established for the boundary point cloud X, so that subsequent point neighborhood searching is facilitated;
s32: creating an empty cluster set C and a point set Q;
s33: for any pointP i 
Processing the X;
s34: when each point in the boundary point cloud X is subjected to the step S33, a cluster set C is obtained;
s35: deleting fewer points in the cluster set C thann minAnd (4) obtaining a final cluster set C by cloud block point, and completing segmentation.
7. The method for extracting features of circular holes from rivet hole measuring point cloud data according to claim 6, wherein the step S33 is performed on any pointP i 
X, performing the following steps:
s41: handleP i Adding into Q;
s42: for each pointP i 
Q, executing the following steps:
a. k neighbor search algorithm through boundary point kd treeP i The k neighborhood of (2) is put into the point set;
b. for each one
If, if
Is not in Q, and
andp j of Euclidean distance rj<d th Then add Q and remove from X;
s43: and when the Q cannot find new points to be added, putting the Q into the cluster set C, and emptying the Q.
8. The method for extracting the characteristics of the round hole facing the cloud data of the rivet hole measurement point as claimed in any one of claims 1 to 7, wherein the method for extracting the rivet hole boundary in the cloud block of the segmentation point by the ellipse fitting method comprises the following steps:
s51: cloud block with dividing pointsc i 
C least square fitting plane, constructing local plane two-dimensional coordinate system, and blocking point cloudc i Three-dimensional coordinates of all points in the image are converted into two-dimensional coordinates of a constructed local planeObtaining two-dimensional coordinates under a coordinate system
；
S52: to plane two-dimensional coordinate
Least square fitting the plane ellipse to obtain an ellipse equation Ax 2+Bxy+Cy 2+ Dx + Ey + F =0, center of ellipse
And major and minor axis radii
And
；
s53: and extracting the rivet hole boundary characteristics in the boundary point cloud blocks according to the ellipse fitting Error, the ratio of the major axis to the minor axis, the maximum radius threshold rmax, the minimum radius threshold rmin and the circle center position threshold d.
9. The method for extracting features of a circular hole facing to rivet hole measurement point cloud data according to claim 8, wherein the local plane two-dimensional coordinate system is constructed by any vector on a plane
Normal to a plane, as the X-axis of the coordinate system
And
cross product of (a) as a coordinate system Y-axis, point cloud blockc i Any point inp 0Projected points on a plane
As the origin of the coordinate system.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113129445A (en) * | 2020-11-30 | 2021-07-16 | 南京航空航天大学 | Large-scale three-dimensional rivet concave-convex amount visualization method based on multi-level fitting |
| CN113390357A (en) * | 2021-07-08 | 2021-09-14 | 南京航空航天大学 | Rivet levelness measuring method based on binocular multi-line structured light |
| CN114022617A (en) * | 2021-11-18 | 2022-02-08 | 中国科学院长春光学精密机械与物理研究所 | Method for discriminating scattered point cloud hole boundary |
| CN114897110A (en) * | 2022-07-15 | 2022-08-12 | 成都飞机工业（集团）有限责任公司 | Group hole measurement swing angle planning method, readable medium and equipment |
| CN115346019A (en) * | 2022-09-06 | 2022-11-15 | 南京航空航天大学 | Method, device and system for measuring geometrical parameters of point cloud circular hole |
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| CN113129445A (en) * | 2020-11-30 | 2021-07-16 | 南京航空航天大学 | Large-scale three-dimensional rivet concave-convex amount visualization method based on multi-level fitting |
| CN113390357A (en) * | 2021-07-08 | 2021-09-14 | 南京航空航天大学 | Rivet levelness measuring method based on binocular multi-line structured light |
| CN114022617A (en) * | 2021-11-18 | 2022-02-08 | 中国科学院长春光学精密机械与物理研究所 | Method for discriminating scattered point cloud hole boundary |
| CN114022617B (en) * | 2021-11-18 | 2024-04-30 | 中国科学院长春光学精密机械与物理研究所 | Method for judging hole boundaries of scattered point cloud |
| CN114897110A (en) * | 2022-07-15 | 2022-08-12 | 成都飞机工业（集团）有限责任公司 | Group hole measurement swing angle planning method, readable medium and equipment |
| CN114897110B (en) * | 2022-07-15 | 2022-11-18 | 成都飞机工业（集团）有限责任公司 | Group hole measurement swing angle planning method, readable medium and equipment |
| CN115346019A (en) * | 2022-09-06 | 2022-11-15 | 南京航空航天大学 | Method, device and system for measuring geometrical parameters of point cloud circular hole |
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