CN112254664B - High-performance component point cloud contour analysis and evaluation method - Google Patents

High-performance component point cloud contour analysis and evaluation method Download PDF

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CN112254664B
CN112254664B CN202010719263.3A CN202010719263A CN112254664B CN 112254664 B CN112254664 B CN 112254664B CN 202010719263 A CN202010719263 A CN 202010719263A CN 112254664 B CN112254664 B CN 112254664B
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CN112254664A (en
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梅晗香
李佳航
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Nanjing Youdeng Technology Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
    • G06T7/85—Stereo camera calibration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G06T2207/10012—Stereo images

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Abstract

The invention discloses a high-performance component point cloud contour analysis and evaluation method, which comprises the following steps: step 1: calibrating a robot and a three-dimensional scanner; step 2: planning a robot path, scanning the surface of a component according to the planned path, and converting point clouds scanned by a three-dimensional laser scanner into a robot coordinate system for point cloud fusion by means of a calibration matrix of the robot and the three-dimensional scanner; and step 3: extracting cross-section point cloud data of any position of the fused point cloud by adopting a cross-section extraction method; and 4, step 4: performing three-dimensional space curve compliance on the cross-section point cloud data; and 5: and comparing the compliant measurement cross section space point cloud data with the design model outline, and determining the nesting relation between the measurement space point cloud data and the design outline by using a ray method. Through the mode, the whole large part can be scanned at high precision, single contour analysis is carried out on the obtained point cloud data, the dispersion of the parts is determined, and adjustment and repair are convenient for operators.

Description

High-performance component point cloud contour analysis and evaluation method
Technical Field
The invention relates to the technical field of computer vision, in particular to a high-performance component point cloud contour analysis and evaluation method.
Background
With the continuous upgrading of the transformation of the traditional industry and the rapid development of the strategic emerging industry, the measurement, processing and assembly of high-performance components are the key of the development of high-precision equipment, which represents the advanced level of the scientific and technological development of the country, and the measurement and data analysis technology is the important factor in the manufacture of the high-precision equipment. The high-performance component has the characteristics of large size, complex shape, thin wall and the like, designed geometric model parameters are often more ideal, and the performance indexes of the geometric parameters of the finally processed product cannot meet the requirements due to inevitable geometric and physical non-uniform deformation in the processing process, so that great challenges are brought to the manufacturing and assembling of the complex high-performance component.
At present, many students and companies research the three-dimensional laser scanner and carry out point cloud collection on the surface of an object, but the quantity of collected data points can reach millions, and an operator is required to manually hold the three-dimensional laser scanner to carry out point cloud collection, so that the operator easily generates fatigue to cause the increase of measurement errors. As any section point cloud data of a complex high-performance component needs to be obtained during product processing and then is compared with a design model, all data of the point cloud obtained by the three-dimensional laser scanner needs to be fused into a base coordinate system, and then geometric parameter analysis and out-of-tolerance analysis are carried out, so that certain difficulty is brought to measurement and analysis of the high-performance component.
Disclosure of Invention
The invention aims to provide a high-performance component point cloud contour analysis and evaluation method which can be used for scanning the whole large-scale part with high precision, performing single contour analysis on the obtained point cloud data, determining the dispersion of parts and components and facilitating adjustment and repair of operators.
In order to solve the technical problems, the invention adopts a technical scheme that: the method for analyzing and evaluating the point cloud contour of the high-performance part comprises the following steps:
step 1: calibrating a robot and a three-dimensional scanner;
and 2, step: planning a path of the robot, scanning the surface of the high-performance part by using a three-dimensional scanner, calibrating a matrix by using the robot and the three-dimensional scanner, and converting point clouds scanned by the three-dimensional laser scanner into a robot coordinate system for point cloud fusion;
and 3, step 3: extracting cross-section point cloud data of any position of the fused point cloud by adopting a cross-section extraction method;
and 4, step 4: performing three-dimensional space curve compliance on the cross section point cloud data to obtain compliant three-dimensional point cloud data of the measured cross section space;
and 5: and comparing the compliant measurement cross section space point cloud data with the design model outline, and determining the nesting relation between the measurement space point cloud data and the design outline by utilizing a ray intersection odd-even method.
Further, in the step 1, the robot clamps the three-dimensional laser scanner device, and the robot flange coordinate system and the three-dimensional laser scanner coordinate system are calibrated by using hand-eye calibration, so that a coordinate conversion matrix H between the three-dimensional laser scanner coordinate system and the robot flange coordinate system is obtained.
Further, the point cloud fusion in the step 2 is to convert the point cloud scanned by the three-dimensional laser scanner to the robot coordinate system by using the coordinate conversion matrix H and the coordinate conversion matrix F of the flange in the robot coordinate system for point cloud fusion.
Further, the section extraction method can obtain the measured section outline point cloud data by intersecting a series of parallel sections with the measured data, wherein the ith section FiCan be expressed as:
{fi-[f0+Δhn(i-1)]]}·n＝0
wherein f isiIs a point on the i-th cross section, f0Is a point on the initial section, delta h represents the distance used when the section is cut, the direction vector of the parallel section is n, and two offset sections are obtained after the left side and the right side of the ith section are offset by delta gamma (delta gamma is less than or equal to delta h)
Figure BDA0002599366320000021
And
Figure BDA0002599366320000022
Figure BDA0002599366320000031
set cross section FiAnd
Figure BDA0002599366320000032
point cloud set of P-Section FiAnd
Figure BDA0002599366320000033
point cloud set of P+The actual point p on the i-th cross section is calculated as follows (u,v)(ii) a In the set P-In randomly taking a point prGo through the set P+All points in (1), find prIs marked as puIn the same way, in set P-In (2) find puIs marked as pvA 1 is to puAnd pvSet as the nearest neighbor matching point pair(pu，pv) Repeat P-Determining a set of nearest neighbor matching point pairs, puAt a distance D from the i-th cross sectionu：
Du＝|{[f0+Δhn(i-1)]-pu}·n|
Nearest neighbor matching point pair (p)u，pv) The projection distance D of the formed line segment on the direction vector nu,vExpressed as:
Du,v＝|[pu-pv]·n|
actual point p on the i-th cross section(u,v)Expressed as:
Figure BDA0002599366320000034
further, the step 5 establishes a compliance criterion objective function ξ in a three-dimensional space:
Figure BDA0002599366320000035
wherein, K "jIs a polynomial discrete curvature KiOf second derivative, T'iIs polygonal discrete deflection
Figure BDA0002599366320000036
The first derivative of (a);
minimizing the latest position of ξ -determined points by a nonlinear optimization algorithm
Figure BDA0002599366320000037
By using new positions
Figure BDA0002599366320000038
Recalculating formula xi to obtain target function value xi*If | ξ*Xi | < τ (threshold τ ═ 1e-8 in the experiment), then use the latest position
Figure BDA0002599366320000039
Replacement of Pi。
Further, in the step 5, a nesting relation between the measurement point cloud and the design model contour is determined by using a parity method of ray intersection, the known design model is D, the point of the cross-sectional measurement contour is q, a ray in the right direction is made by using q as an end point, and whether the measurement point is inside or outside the design model contour is determined by determining the parity of intersection between the ray and the design model D:
Figure BDA0002599366320000041
Wherein flag represents a flag bit, and n represents the number of intersections between the ray and the design model D.
Further, the ray passes through a common point of the contour, and if two adjacent sides of the common point are positioned at two sides of the ray, n is equal to n + 1; if the two adjacent sides of the common point are on the same side of the ray, n is unchanged.
Further, when the ray coincides with one side of the contour, if two adjacent sides are on two sides of the ray, n is equal to n + 1; if the two adjoining sides are on the same side of the ray, n is unchanged.
The invention has the beneficial effects that: the method for analyzing and evaluating the point cloud contour of the high-performance part has the following beneficial effects and advantages:
1. the method for analyzing and evaluating the point cloud profile of the high-performance part can be used for scanning the point cloud of the three-dimensional appearance of the large-scale high-performance part;
2. the point cloud contour analysis and evaluation method for the high-performance parts can realize the analysis of geometric parameters of large-scale high-performance parts.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is also possible for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a flow chart of a high-performance component point cloud contour analysis and evaluation method of the present invention;
FIG. 2 is a point cloud image obtained by a scanner according to the high-performance component point cloud profile analysis and evaluation method of the present invention;
FIG. 3 is a cross-section extraction method adopted by the high-performance component point cloud contour analysis and evaluation method of the invention;
FIG. 4 is a point cloud image obtained by a section extraction method of the high-performance component point cloud profile analysis and evaluation method.
Detailed Description
To make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in detail below with reference to the accompanying drawings. Examples of these preferred embodiments are illustrated in the accompanying drawings. The embodiments of the invention shown in the drawings and described in accordance with the drawings are exemplary only, and the invention is not limited to these embodiments.
It should be noted that, in order to avoid obscuring the present invention with unnecessary details, only the structures and/or processing steps closely related to the scheme according to the present invention are shown in the drawings, and other details not so relevant to the present invention are omitted.
Also, in the description of the present invention, the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on those shown in the drawings, only for convenience of description and simplification of description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Referring to fig. 1 to 4, an embodiment of the present invention includes: the method for analyzing and evaluating the point cloud contour of the high-performance part comprises the following specific steps:
step 1: the robot clamps the three-dimensional laser scanner device, and the robot flange coordinate system and the three-dimensional laser scanner coordinate system are calibrated by means of hand-eye calibration, so that a coordinate conversion matrix H between the three-dimensional laser scanner coordinate system and the robot flange coordinate system is obtained.
Step 2: and planning a path of the robot, scanning according to the planned path, and converting the point cloud scanned by the three-dimensional laser scanner into a robot coordinate system by using a coordinate conversion matrix H and a coordinate conversion matrix F of the flange in the robot coordinate system to perform point cloud fusion.
And step 3: and extracting the cross-section point cloud data of any position of the fused point cloud by adopting a cross-section extraction method.
As shown in FIG. 3, a series of parallel sections are intersected with the measurement data to obtain a measured section profile point cloud data, i-th section FiCan be expressed as:
{fi-[f0+Δhn(i-1)]]}·n＝0
wherein f isiIs a point on the i-th cross section, f0Is a point on the initial section, delta h represents the distance used when the section is cut, the direction vector of the parallel section is n, and two offset sections are obtained after the left side and the right side of the ith section are offset by delta gamma (delta gamma is less than or equal to delta h)
Figure BDA0002599366320000061
And
Figure BDA0002599366320000062
Figure BDA0002599366320000063
set cross section FiAnd
Figure BDA0002599366320000067
point cloud set of P-Section FiAnd
Figure BDA0002599366320000064
point cloud set of P+The actual point p on the i-th cross section is calculated as follows(u,v)(ii) a In the set P-In random getting a point prGo through the set P+All points in (1), find prIs marked as puIn the same way, in set P-In (2) find puIs marked as pvA 1 is to puAnd pvSet as the nearest neighbor matching point pair (p)u，pv) Repeat P-Determining a set of nearest neighbor matching point pairs, puAt a distance D from the i-th cross sectionu：
Du＝|{[f0+Δhn(i-1)]-pu}·n|
Nearest neighbor matching point pair (p)u，pv) The projection distance D of the formed line segment on the direction vector nu,vExpressed as:
Du,v＝|[pu-pv]·n|
actual point p on the i-th cross section(u,v)Expressed as:
Figure BDA0002599366320000065
and 4, step 4: and (4) performing three-dimensional space curve compliance on the cross section point cloud data to obtain the compliant three-dimensional point cloud data of the measurement cross section space.
Establishing a compliance criterion objective function xi in a three-dimensional space:
Figure BDA0002599366320000066
wherein, K "jIs a polynomial discrete curvature KiOf second derivative, T'iIs polygonal discrete deflection
Figure BDA0002599366320000071
The first derivative of (a).
By non-linearityOptimization algorithm minimizes the latest position of xi determination points
Figure BDA0002599366320000072
By using new positions
Figure BDA0002599366320000073
Recalculating formula xi to obtain target function value xi*If | ξ*Xi | < τ (threshold τ ═ 1e-8 in the experiment), then use the latest position
Figure BDA0002599366320000074
Substitution of Pi。
And 5: and comparing the compliant measurement cross section space point cloud data with the design model outline, determining the relationship between the compliant measurement cross section space point cloud and the design model outline by using a ray method, and judging the concave-convex shape.
And determining the position relation between the measuring point cloud and the design model outline by utilizing a ray intersection odd-even method. The design model is known as D, the point of the cross-section measurement profile is known as q, a ray in the right direction is made by taking q as an end point, and the parity of the intersection between the ray and the design model D is determined to judge whether the measurement point is in the interior or the exterior of the design model profile
Figure BDA0002599366320000075
Wherein flag represents a flag bit, and n represents the number of intersections between the ray and the design model D.
There are two special cases that need to be dealt with:
(1) the ray passes through the common point of the contour, if the adjacent edges of the two sides of the common point are positioned at the two sides of the ray, n is n + 1; if the adjacent edges at two sides of the common point are positioned at the same side of the ray, n is unchanged;
(2) when the ray is coincident with one side of the outline, if two adjacent sides are on two sides of the ray, n is equal to n + 1; if the two adjoining sides are on the same side of the ray, n is unchanged.
The method for analyzing and evaluating the point cloud contour of the high-performance part has the following beneficial effects and advantages:
1. The point cloud outline analysis and evaluation method for the high-performance parts can be used for carrying out three-dimensional appearance point cloud scanning on large high-performance parts;
2. the point cloud outline analysis and evaluation method for the high-performance parts can realize the geometric parameter analysis of the large high-performance parts.
Furthermore, it should be noted that, in the present specification, "include" or any other variation thereof is intended to cover a non-exclusive inclusion, so that a process, a method, an article or an apparatus including a series of elements includes not only those elements but also other elements not explicitly listed, or further includes elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
It should be understood that although the specification describes embodiments, not every embodiment includes only a single embodiment, and such description is for clarity purposes only, and it will be understood by those skilled in the art that the specification as a whole and the embodiments may be suitably combined to form other embodiments as will be apparent to those skilled in the art.

Claims (8)

1. A high-performance component point cloud contour analysis and evaluation method is characterized by comprising the following steps: the method comprises the following steps:
step 1: calibrating a robot and a three-dimensional scanner;
step 2: planning a path of the robot, scanning the surface of the high-performance part by using a three-dimensional scanner, calibrating a matrix by using the robot and the three-dimensional scanner, and converting point clouds scanned by the three-dimensional laser scanner into a robot coordinate system for point cloud fusion;
and step 3: extracting cross-section point cloud data of any position of the fused point cloud by adopting a cross-section extraction method;
and 4, step 4: performing three-dimensional space curve compliance on the cross section point cloud data to obtain compliant three-dimensional point cloud data of the measured cross section space;
and 5: and comparing the compliant measurement cross section space point cloud data with the design model outline, and determining the nesting relation between the measurement space point cloud data and the design outline by utilizing a ray intersection odd-even method.
2. The method for analyzing and evaluating the point cloud contour of a high-performance part according to claim 1, wherein: step 1, the robot clamps the three-dimensional laser scanner device, and a robot flange coordinate system and a three-dimensional laser scanner coordinate system are calibrated by means of hand-eye calibration, so that a coordinate conversion matrix H between the three-dimensional laser scanner coordinate system and the robot flange coordinate system is obtained.
3. The high-performance part point cloud profile analysis and evaluation method of claim 1, wherein: and 2, point cloud fusion, namely converting the point cloud scanned by the three-dimensional laser scanner into the robot coordinate system by using the coordinate conversion matrix H and the coordinate conversion matrix F of the flange in the robot coordinate system for point cloud fusion.
4. The high-performance part point cloud profile analysis and evaluation method of claim 1, wherein: the section extraction method comprises the steps of intersecting a series of parallel sections with measurement data to obtain point cloud data of a profile of the measurement section, wherein the ith section FiExpressed as:
{fi-[f0+△hn(i-1)]]}·n＝0
wherein, fiIs a point on the i-th cross section, f0Is a point on the initial section, delta h represents the distance used in cutting the section, the direction vector of the parallel section is n, and two offset sections are obtained after the left side and the right side of the ith section are offset delta gamma not more than delta h
Figure FDA0003578653270000021
And
Figure FDA0003578653270000022
Figure FDA0003578653270000023
set cross section FiAnd
Figure FDA0003578653270000024
point cloud set of P-Section FiAnd
Figure FDA0003578653270000025
point cloud set of P+The actual point p on the i-th cross section is calculated as follows(u,v)(ii) a In the set P-In random getting a point prGo through the set P+All points in (1), find prIs marked as puIn the same way, in set P -In (2) find puIs marked as pvA 1 is to puAnd pvSet as the nearest neighbor matching point pair (p)u，pv) Repeat P-Determining a set of nearest neighbor matching point pairs, puAt a distance D from the i-th cross sectionu：
Du＝|{[f0+△hn(i-1)]-pu}·n|
Nearest neighbor matching point pair (p)u，pv) The projection distance D of the formed line segment on the direction vector nu,vExpressed as:
Du,v＝|[pu-pv]·n|
actual point p on the i-th cross section(u,v)Expressed as:
Figure FDA0003578653270000026
5. the method for analyzing and evaluating the point cloud contour of a high-performance part according to claim 1, wherein: step 5, establishing a compliance criterion objective function xi in a three-dimensional space:
Figure FDA0003578653270000027
wherein, K ″)jIs a polynomial discrete curvature KiOf second derivative, T'iIs polygonal discrete deflection
Figure FDA0003578653270000028
The first derivative of (a);
minimizing the latest position of ξ -determined points by a nonlinear optimization algorithm
Figure FDA0003578653270000029
By using new positions
Figure FDA00035786532700000210
Recalculating formula xi to obtain target function value xi*If | ξ*-ξ|<If the threshold value tau is 1e-8 in tau experiment, the latest position is used
Figure FDA00035786532700000211
Replacement of Pi。
6. The method for analyzing and evaluating the point cloud contour of a high-performance part according to claim 1, wherein: step 5, determining the nesting relation between the measurement point cloud and the design model contour by using a ray intersection odd-even method, wherein the known design model is D, the point of the cross section measurement contour is q, the q is used as an end point to make a ray in the right direction, and whether the measurement point is in the interior or the exterior of the design model contour is judged by determining the intersection odd-even between the ray and the design model D:
Figure FDA0003578653270000031
Wherein flag represents a flag bit, and n represents the number of intersections between the ray and the design model D.
7. The method for analyzing and evaluating the point cloud contour of a high-performance part according to claim 6, wherein: the ray passes through a common point of the contour, and if the adjacent edges of the two sides of the common point are positioned at the two sides of the ray, n is n + 1; if the two adjacent sides of the common point are on the same side of the ray, n is unchanged.
8. The method for analyzing and evaluating the point cloud contour of a high-performance part according to claim 6, wherein: when the ray is coincident with one side of the outline, if two adjacent sides are on two sides of the ray, n is equal to n + 1; if the two adjoining sides are on the same side of the ray, n is unchanged.
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