CN111028221A - Airplane skin butt-joint measurement method based on linear feature detection - Google Patents
Airplane skin butt-joint measurement method based on linear feature detection Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/4808—Evaluating distance, position or velocity data
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Abstract
The invention discloses an aircraft skin butt seam measuring method based on linear characteristic detection, which comprises the following steps of: 1) utilizing aircraft skin butt joint point cloud data acquired by laser radar scanning equipment, and carrying out denoising treatment; 2) projecting the denoised butt joint point cloud onto a two-dimensional image, and generating an intensity image according to the reflection intensity of each point in the point cloud data; 3) carrying out LSD (linear stress distortion) linear detection on the intensity image, and extracting butt joint feature points; 4) and finally, calculating the three-dimensional coordinates of the butt joint characteristic points by utilizing the corresponding relation between the intensity image and the point cloud data, and calculating the butt joint gaps and the step difference values. The invention has the advantages that: compared with a skin butt-joint measurement system of structured light, the method has the advantages that complete skin butt-joint point cloud data are collected to measure gaps and step differences, overall measurement time is shortened, integrity of measurement results is improved, and automatic measurement of skin butt-joint is achieved.
Description
Technical Field
The invention relates to the technical field of digital measurement of aviation parts, in particular to an aircraft skin butt-joint measurement method based on linear feature detection.
Background
The aircraft skin part is an important component for constructing the aerodynamic appearance of the aircraft, the processing quality of the part directly influences the overall assembly precision of the aircraft, and the part is one of key factors for determining the overall performance of the aircraft. In the process of assembling aerospace parts, gaps are inevitably formed at the joint of the wing body, between the skin and the like.
The traditional measuring method for detecting the skin gap by using the feeler gauge has low efficiency, poor precision and large manual influence, so that a digital measuring mode is required for accurately measuring and analyzing the skin gap. Skin butt-seam detection technology research combined with digital measurement is carried out by various scholars at home and abroad, but at present, most of the methods are detection methods based on line structured light. The linear structure light can only collect data once from an angle to measure the skin butt seam, the size of the aircraft skin part is large, the single measurement result does not have integrity, the multiple linear structure light measurement needs more manpower and time, and the integral precision of the measurement result cannot be guaranteed.
In order to avoid the problems, the invention provides a skin butt-joint measurement method based on the linear feature detection of scattered point clouds, so as to realize high-speed and high-precision measurement of the matching assembly of the parts.
Disclosure of Invention
The invention aims to provide an aircraft skin butt-joint measuring method based on linear characteristic detection, which can eliminate background interference and bring convenience to skin butt-joint detection by effectively obtaining the butt-joint characteristic of point cloud data; the method solves the technical problems that the prior art adopts simple linear structured light to acquire data once and can only perform measurement once, and is difficult to perform complete seam alignment measurement, so that the measurement result has no integrity and the overall time efficiency is low; under the condition of ensuring the precision, the method reduces the labor consumption and the time consumption, has higher application value, and can better meet the requirement of measuring the aircraft skin.
In order to achieve the above purpose, with reference to fig. 1, the present invention provides an aircraft skin butt-seam measurement method based on linear feature detection, where the measurement method includes:
s1: acquiring aircraft skin butt seam point cloud data by using laser radar scanning equipment, and performing denoising treatment;
s2: projecting the denoised butt joint point cloud onto a two-dimensional image, and generating an intensity image according to the reflection intensity of each point in the point cloud data;
s3: carrying out LSD (linear stress distortion) linear detection on the intensity image, and extracting butt joint feature points;
s4: and calculating the three-dimensional coordinates of the butt joint characteristic points by utilizing the corresponding relation between the intensity image and the point cloud data, and calculating the butt joint gap and the step difference value.
In a further embodiment, in step S1, the denoising process includes the following steps:
s11: scanning the surface of the skin to be detected by adopting a laser radar to obtain scattered three-dimensional point cloud data of the skin to be detected;
s12: and denoising the point cloud data of the skin to be detected, and removing non-skin surface points to obtain the point cloud data only containing the skin surface.
In a further embodiment, in step S1, the obtaining of the aircraft skin butt-joint point cloud data by using the lidar scanning device means,
and placing the laser radar scanning equipment and the surface of the skin to be detected in parallel and then scanning.
In a further embodiment, in step S2, the process of projecting the denoised butt-seam point cloud onto a two-dimensional image and generating an intensity image according to the reflection intensity of each point in the point cloud data includes the following steps:
s21: traversing the point cloud data, respectively recording the maximum value and the minimum value under the X coordinate and the Y coordinate in the point cloud coordinates (X, Y, z), and setting the maximum value and the minimum value of the X coordinate as Xmax、xminThe minimum value of the Y coordinate is Ymax、ymin；
S22: calculating the size of the projection image, wherein the width H of the projection image is defined as ymaxAnd yminDefining the height W of the intensity image as xmaxAnd xminA difference of (d);
s23: performing grid partitioning on the point cloud data, including:
s231: setting step length l, and dividing the projection image into regular grids;
s232: for any point (x, y, z), according to the formula
Wherein the content of the first and second substances,
calculating the corresponding row number r and column number c of each point in the grid for the operation of rounding downwards;
s233: recording the corresponding relation between the coordinates of each point and the row and column numbers;
s24: and traversing all grids in the partitioned grids to generate an intensity image, wherein (1) if the number of points in the grids is more than 0, the intensity of the grids is the average value of the intensities of all the points in the grids, and (2) if the number of the points in the grids is equal to 0, the intensity of the grids is set as a fixed value epsilon.
In a further embodiment, in step S3, the process of performing LSD line detection on the intensity image and extracting the seam feature points includes the following steps:
s31: extracting straight lines in the intensity image by using an LSD algorithm to obtain a straight line detection result graph;
s32: image binarization processing is carried out on the linear detection result graph,
s33: and performing horizontal projection and vertical projection of image pixels on the binarized line detection result image, and extracting butt joint feature points in the intensity image.
In a further embodiment, in step S4, the process of calculating three-dimensional coordinates of the butt seam feature points by using the correspondence between the intensity images and the point cloud data, and calculating the butt seam gaps and the step values includes the following steps:
s41: converting the two-dimensional coordinates of the butt joint feature points in the intensity image into three-dimensional coordinates by utilizing the corresponding relation between the coordinates of each point and the grid row number;
s42: fitting a butt seam characteristic line in a three-dimensional space by using the three-dimensional coordinates of the butt seam characteristic points;
s43: and calculating the butt seam gap and the step value according to the butt seam characteristic line.
In a further embodiment, in step S42, a Hough straight line fitting method is used to perform straight line fitting on the three-dimensional coordinates of the butt seam feature points.
Compared with the prior art, the technical scheme of the invention has the following remarkable beneficial effects:
(1) the method can effectively obtain the butt joint characteristics of the point cloud data, eliminate background interference and bring convenience to skin butt joint detection.
(2) The method solves the technical problems that in the prior art, the method for detecting the aircraft skin butt joint adopts simple linear structured light to acquire data once, and can only perform measurement once, and is difficult to measure the complete butt joint, so that the measurement result has no integrity, and the overall time efficiency is low.
(3) Under the condition of ensuring the precision, the method reduces the labor consumption and the time consumption, has higher application value, and can better meet the requirement of measuring the aircraft skin.
It should be understood that all combinations of the foregoing concepts and additional concepts described in greater detail below can be considered as part of the inventive subject matter of this disclosure unless such concepts are mutually inconsistent. In addition, all combinations of claimed subject matter are considered a part of the presently disclosed subject matter.
The foregoing and other aspects, embodiments and features of the present teachings can be more fully understood from the following description taken in conjunction with the accompanying drawings. Additional aspects of the present invention, such as features and/or advantages of exemplary embodiments, will be apparent from the description which follows, or may be learned by practice of specific embodiments in accordance with the teachings of the present invention.
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The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. Embodiments of various aspects of the present invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a schematic flow chart diagram of an aircraft skin butt-seam measurement method based on linear feature detection.
Fig. 2 is a schematic view of the skin butt-seam model of the present invention.
Fig. 3 is a schematic diagram of the correspondence between grid row and column numbers and point cloud data according to the present invention.
Fig. 4 is a schematic diagram of a straight line feature extraction result according to the present invention.
FIG. 5 is a schematic view of the step of the slit gap according to the present invention.
Detailed Description
In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings.
With reference to fig. 1, the invention provides an aircraft skin butt-seam measurement method based on linear feature detection, which includes the following steps:
(1) and point cloud data acquisition and pretreatment.
(2) An intensity image is generated.
(3) And extracting butt joint feature points.
(4) And calculating the butt gap and the step value.
In the step (1), the point cloud data acquisition and pretreatment mainly comprises the following substeps:
and (1-1) scanning the surface of the skin to be detected by adopting a laser radar to obtain scattered three-dimensional point cloud data of the skin to be detected.
And (1-2) denoising the skin point cloud data to be detected, removing non-skin surface points and only containing the skin surface point cloud data.
In this embodiment, in step (1-1), in order to ensure the correspondence between the point cloud data and the projection image in the subsequent step, the lidar scanning device needs to scan in parallel with the object to be measured as much as possible, so that the coordinates of the points in the acquired scattered point cloud data are in the plane as much as possible. And (1-2) carrying out primary denoising treatment on the acquired point cloud data, and removing points on the non-skin surface in a man-machine interaction mode.
In the step (2), generating the intensity image of the point cloud data includes the following steps:
(2-1) traversing the point cloud data, and respectively recording the maximum value and the minimum value under the X coordinate and the Y coordinate in the point cloud coordinates (X, Y, z), wherein the maximum value and the minimum value of the X coordinate are Xmax、xminThe minimum value of the Y coordinate is Ymax、ymin。
(2-2) calculating the size of the projected image whose width H is ymaxAnd yminCalculating the height W of the intensity image as xmaxAnd xminThe difference of (a).
(2-3) grid partitioning is carried out on point cloud data, step length l is set, a projection image is divided into regular grids, and for any point (x, y, z), according to a formula
Calculating the corresponding row number r and column number c of each point in the grid, and recording the corresponding relation between the coordinates of each point and the row number and column number, wherein
Expressed as a floor function.
(2-4) generating an intensity image, traversing all grids in the partitioned grids, and if the number of points in the grids is more than 0, determining the intensity of the grids to be the average value of the intensities of all the points in the grids; if the number of points in the grid is equal to 0, then ε is set.
In this embodiment, in step (2-3), assuming that the step length l of the grid block is 1, after the grid block is divided, the coordinates of the corresponding point in each grid block in the grid are recorded, as shown in fig. 3, where r and c represent the row number and column number of each grid block, respectively, and p is the column number of each grid blockiExpressed as the coordinates of the ith point. In step (2-4), if the number of points in the grid is equal to 0, let ∈ be 0, that is, the intensity value of the grid is 0.
In the step (3), the step of extracting the butt joint feature points comprises the following steps:
and (3-1) extracting straight lines in the intensity image by using an LSD algorithm to obtain a straight line detection result graph.
And (3-2) carrying out image binarization processing on the straight line detection result graph.
And (3-3) performing horizontal projection and vertical projection of image pixels on the binarized straight line detection result image, and extracting butt joint feature points in the intensity image.
In this embodiment, the straight line detection result in step (3-1) includes a straight line of the butt seam feature and an edge straight line of the point cloud model, as shown in fig. 4. In the steps (3-2) and (3-3), an image processing method is utilized to extract the butt seam feature points in the straight line detection result graph, binarization processing is carried out on the straight line detection result graph, and according to the position relation of the edge straight line and the butt seam straight line in the image, horizontal projection operation and vertical projection operation are respectively carried out on pixels in the binarized image, so that the row and column number where the pixels forming the butt seam straight line are located in the image is extracted.
In the step (4), the step of calculating the gap and the valence difference value of the butt seam comprises the following steps:
and (4-1) converting the two-dimensional coordinates of the butt joint feature points in the intensity image into three-dimensional coordinates by utilizing the corresponding relation between the point cloud data and the grid row number in the step (2-3).
And (4-2) fitting the butt seam characteristic line in the three-dimensional space by using the three-dimensional coordinates of the butt seam characteristic points in the step (4-1).
And (4-3) calculating the butt seam gap and the step value according to the butt seam characteristic line.
In this embodiment, in the step (4-1), the three-dimensional coordinates of the butt-joint feature points in the point cloud data are obtained by combining the pixel row number and the pixel column number of the butt-joint feature points obtained in the step (3-3) with the correspondence between the coordinates of each point of the point cloud data obtained in the root step (2-3) and the row number and the column number of the grid. In this embodiment, in step (4-2), the Hough line fitting method is used to perform line fitting on the butt joint feature points in the three-dimensional space. In the step (4-3), the gap and the step value between the butt seams are calculated according to the spatial position relationship of the butt seam characteristic lines, as shown in fig. 5.
In this disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the present disclosure are not necessarily defined to include all aspects of the invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in greater detail below, may be implemented in any of numerous ways, as the disclosed concepts and embodiments are not limited to any one implementation. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.
Claims (7)
1. An aircraft skin butt-joint measurement method based on linear feature detection is characterized by comprising the following steps:
s1: acquiring aircraft skin butt seam point cloud data by using laser radar scanning equipment, and performing denoising treatment;
s2: projecting the denoised butt joint point cloud onto a two-dimensional image, and generating an intensity image according to the reflection intensity of each point in the point cloud data;
s3: carrying out LSD (linear stress distortion) linear detection on the intensity image, and extracting butt joint feature points;
s4: and calculating the three-dimensional coordinates of the butt joint characteristic points by utilizing the corresponding relation between the intensity image and the point cloud data, and calculating the butt joint gap and the step difference value.
2. The method for measuring aircraft skin butt-seam based on straight-line feature detection according to claim 1, wherein in step S1, the denoising process comprises the following steps:
s11: scanning the surface of the skin to be detected by adopting a laser radar to obtain scattered three-dimensional point cloud data of the skin to be detected;
s12: and denoising the point cloud data of the skin to be detected, and removing non-skin surface points to obtain the point cloud data only containing the skin surface.
3. The method for measuring aircraft skin butt-joint based on straight line feature detection according to claim 1, wherein in step S1, the obtaining of the aircraft skin butt-joint point cloud data by using the laser radar scanning device means,
and placing the laser radar scanning equipment and the surface of the skin to be detected in parallel and then scanning.
4. The method for measuring aircraft skin butt-seam based on straight-line feature detection according to claim 1, wherein in step S2, the process of projecting the de-noised butt-seam point cloud onto the two-dimensional image and generating an intensity image according to the reflection intensity of each point in the point cloud data comprises the following steps:
s21: traversing the point cloud data, respectively recording the maximum value and the minimum value under the X coordinate and the Y coordinate in the point cloud coordinates (X, Y, z), and setting the maximum value and the minimum value of the X coordinate as Xmax、xminThe minimum value of the Y coordinate is Ymax、ymin；
S22: calculating the size of the projection image, wherein the width H of the projection image is defined as ymaxAnd yminDefining the height W of the intensity image as xmaxAnd xminA difference of (d);
s23: performing grid partitioning on the point cloud data, including:
s231: setting step length l, and dividing the projection image into regular grids;
s232: for any point (x, y, z), according to the formula
Wherein
Calculating the corresponding row number r and column number c of each point in the grid for the operation of rounding downwards;
s233: recording the corresponding relation between the coordinates of each point and the row and column numbers;
s24: and traversing all grids in the partitioned grids to generate an intensity image, wherein (1) if the number of points in the grids is more than 0, the intensity of the grids is the average value of the intensities of all the points in the grids, and (2) if the number of the points in the grids is equal to 0, the intensity of the grids is set as a fixed value epsilon.
5. The aircraft skin butt-seam measuring method based on the straight-line feature detection according to claim 4, wherein in step S3, the LSD straight-line detection is performed on the intensity image, and the process of extracting the butt-seam feature points comprises the following steps:
s31: extracting straight lines in the intensity image by using an LSD algorithm to obtain a straight line detection result graph;
s32: image binarization processing is carried out on the linear detection result graph,
s33: and performing horizontal projection and vertical projection of image pixels on the binarized line detection result image, and extracting butt joint feature points in the intensity image.
6. The method for measuring aircraft skin butt-joint according to claim 5, wherein in step S4, the process of calculating three-dimensional coordinates of butt-joint feature points by using the correspondence between the intensity images and the point cloud data, and calculating the butt-joint gaps and the step values comprises the following steps:
s41: converting the two-dimensional coordinates of the butt joint feature points in the intensity image into three-dimensional coordinates by utilizing the corresponding relation between the coordinates of each point and the grid row number;
s42: fitting a butt seam characteristic line in a three-dimensional space by using the three-dimensional coordinates of the butt seam characteristic points;
s43: and calculating the butt seam gap and the step value according to the butt seam characteristic line.
7. The aircraft skin butt-seam measurement method based on the straight line feature detection according to claim 6, wherein in step S42, a Hough straight line fitting method is adopted to perform straight line fitting on the three-dimensional coordinates of the butt-seam feature points.
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| CN114626470B (en) * | 2022-03-18 | 2024-02-02 | 南京航空航天大学深圳研究院 | Aircraft skin key feature detection method based on multi-type geometric feature operator |
| CN114627177A (en) * | 2022-03-25 | 2022-06-14 | 南京航空航天大学 | Aircraft skin gap and step difference measuring method based on image segmentation |
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