CN111981983A - Scanner precision evaluation method for three-dimensional measurement of complex-morphology object - Google Patents
Scanner precision evaluation method for three-dimensional measurement of complex-morphology object Download PDFInfo
- Publication number
- CN111981983A CN111981983A CN202010875757.0A CN202010875757A CN111981983A CN 111981983 A CN111981983 A CN 111981983A CN 202010875757 A CN202010875757 A CN 202010875757A CN 111981983 A CN111981983 A CN 111981983A
- Authority
- CN
- China
- Prior art keywords
- scanner
- data
- dimensional
- detected
- scanning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 51
- 238000005259 measurement Methods 0.000 title claims abstract description 24
- 238000000034 method Methods 0.000 claims description 15
- 230000001788 irregular Effects 0.000 claims description 5
- 239000003086 colorant Substances 0.000 claims description 3
- 210000003477 cochlea Anatomy 0.000 claims description 2
- 238000012827 research and development Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 2
- 238000007499 fusion processing Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 238000010146 3D printing Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
Images
Classifications
-
- 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
- G01B11/002—Measuring arrangements characterised by the use of optical techniques for measuring two or more coordinates
- G01B11/005—Measuring arrangements characterised by the use of optical techniques for measuring two or more coordinates coordinate measuring machines
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention discloses a scanner precision evaluation method for three-dimensional measurement of a complex-shape object, which comprises the steps of carrying out three-dimensional scanning on a model to be detected by using a line laser scanner and a scanner to be detected to respectively obtain three-dimensional data D1 and D2; then, importing a three-dimensional processing software unified data format, simultaneously importing MIMICS software, and automatically registering the two groups of data; selecting a cuboid frame to arbitrarily intercept an area to be compared, wherein the measurement range is a common intercepted area; taking each parameter value measured by the scanning data of the line laser scanner as a standard value, and calculating the precision value of each evaluation element of the scanner to be detected; and introducing weighting factors of the evaluation elements and an overall precision evaluation value algorithm. The invention can quickly and accurately evaluate the scanning precision of the scanner to be detected, also considers the missing scanning area and can provide a precision reference value for the research and development and improvement of the scanner.
Description
Technical Field
The invention relates to a precision evaluation method of a three-dimensional scanner, in particular to a scanner precision evaluation method for three-dimensional measurement of objects with complex shapes.
Background
The three-dimensional reconstruction technology is widely applied to the fields of precision measurement, material increase manufacturing, medical treatment and the like, no corresponding regulation is provided in China at present for detecting the measurement precision of a three-dimensional scanner, precision evaluation usually refers to foreign manufacturers, few enterprises for research and development are provided in China, more mature enterprises are provided with Hangzhou, Xiamen reaches maintenance letter and the like, and software design is not shared. In order to improve the scanning efficiency, a surface structured light three-dimensional scanner based on a raster projection principle or a binocular three-dimensional scanner based on stereoscopic vision is mostly sold in the market, the influences of ambient light, lens distortion, noise interference and the like are considered, and the precision degree and the space coordinate accuracy of the measured point cloud data are relatively lower than those of the surface structured light or point scanning type three-dimensional scanner. Generally, the method for evaluating the accuracy of the scanner is as follows: selecting a scanning device with higher relative precision, measuring the same regular object with a scanner to be tested under the same environmental condition, and comparing the characteristic point spacing error, the point cloud contact ratio and the like of the two. However, for irregular objects with complex shapes, due to factors such as scanning path planning and mechanical design of the scanner, a visual blind area is caused, and the size of the missing scanning area of the characteristic points on different scanning surfaces is different, so that the condition is also an important evaluation factor for detecting the measurement accuracy of the scanner.
Patent CN109238197A discloses a precision evaluation method for an oral three-dimensional scanner, which is to process a measurement standard component by 3D printing, use a three-dimensional measuring instrument as standard three-dimensional data S1, measure three-dimensional data S2 by the three-dimensional scanner to be measured, import geogical 2013 software, intercept the same area after coordinate normalization, and compare the average distance and standard deviation of two groups of point clouds. The standard data selection three-coordinate measuring instrument of the method has certain limitation, and the mechanical contact method is not suitable for objects with complex surface appearance and low material hardness; the method only considers the mutual average distance of the integral point clouds in a certain common area, namely the integral contact ratio, and does not relate to the comparison of the characteristics of the two point clouds, such as finding the distance between two characteristic points in S2, comparing the distances between two corresponding characteristic points in a standard point cloud, or comparing the normal vector characteristics, curvature characteristics and the like of a pair of matching points; the point cloud of the missed scanning part is not considered in the scheme, and the scheme has fewer evaluation elements on the whole and has no comprehensive precision evaluation system or numerical description.
By combining the documents and the current situations of research, development and application of the domestic three-dimensional scanner, the three-dimensional scanner precision evaluation system has multiple evaluation elements embodied by numerical values, considers the condition of missing scanning point cloud, has important reference significance for the research, development and improvement of the scanner.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a scanner precision evaluation method for three-dimensional measurement of objects with complex shapes.
In order to achieve the technical purpose and achieve the technical effect, the invention is realized by the following technical scheme:
a scanner precision evaluation method for three-dimensional measurement of objects with complex shapes comprises the following steps:
step 1) selecting an irregular object with complex morphology characteristics as a model to be detected;
step 2) three-dimensionally scanning the model to be detected by using a line laser scanner to obtain three-dimensional data D1, and three-dimensionally scanning the model to be detected by using a scanner to be detected to obtain three-dimensional data D2;
step 3) importing the three-dimensional data D1 and the three-dimensional data D2 into three-dimensional processing software, converting the three-dimensional processing software into a unified format, simultaneously importing MIMICS software, automatically registering two groups of data, overlapping matching points together, distinguishing different groups of data with colors, and checking two groups of data display items and operations;
step 4) selecting a cuboid frame to randomly intercept an area to be compared, hiding an unselected area, and measuring and comparing data to be further performed, wherein the measurement range is a common intercepted area and can be fitted into an open-loop contour line or a closed-loop surface characteristic area along adjacent continuous key points, and the evaluation elements to be measured comprise: the method comprises the following steps of (1) intercepting the point cloud number, the grid number, the volume and the total surface area of an area, drawing a data comparison table, and filling data;
and 5) taking each parameter value measured by the scanning data of the line laser scanner as a standard value, and calculating the precision value of each evaluation element of the scanner to be detected, wherein the precision value is calculated as follows:
in the formula, alpha is a standard value, and beta is a measured value of each evaluation element of the scanner to be detected;
step 6) introducing weighting factors of the evaluation elements, giving the weighting factors to the evaluation elements, and performing an overall precision evaluation value algorithm:
＝Σσi·λi
where σ is a total accuracy evaluation valueiFor each evaluation element precision value, lambdaiWeighting factors for each evaluation element.
Further, the comparison of the point feature, the contour feature and the region feature of the two sets of data in step 4) is based on selecting one or more corresponding feature points matched with the point feature, the contour feature and the region feature.
Further, the scanning of the model to be detected by the line laser scanner comprises line laser scanning, drain region compensation scanning and point cloud splicing of each surface.
Further, the three-dimensional processing software is WINDOS10 self-contained viewing software or SolidWorks software.
Further, the unified format is STL format, and the three-dimensional data D1 and the three-dimensional data D2 are imported into the MIMICS software in part form.
Further, the model to be tested is a human body cochlea model or a human image model.
Further, the model to be tested is designed automatically, printed in 3D mode or processed in numerical control mode.
The invention has the beneficial effects that:
the invention adopts the data measured by the line laser scanner as reference, and introduces the weighting factors of all evaluation elements by importing the MIMICS software data for automatic registration, thereby rapidly and accurately evaluating the scanning precision of the scanner to be detected, and the precision also considers the missing scanning area, namely the investigation of the scanning recall ratio, and can provide precision reference values for the research and development and improvement of the scanner.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings. The detailed description of the present invention is given in detail by the following examples and the accompanying drawings.
Drawings
FIG. 1 is a diagram illustrating the data display of the MIMICS software importing the three-dimensional data D1 and the three-dimensional data D2 according to the present invention;
FIG. 2 is a three-dimensional display of FIG. 1 after registration and pose normalization;
FIG. 3 is a diagram of the file work area of FIG. 2 within the MIMICS software;
FIG. 4 is a graph showing two sets of data measurements;
FIG. 5 is an explanatory diagram of the principle of triangulation with surface area as an evaluation element;
FIG. 6 is a two-dimensional cross-sectional explanatory view of a surface area as an evaluation element;
FIG. 7 is a diagram illustrating evaluation elements and weighting factors;
fig. 8 is a flow chart of the operation of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Referring to fig. 1 and 8, a method for evaluating the accuracy of a scanner for three-dimensional measurement of an object with a complex morphology includes the following steps:
step 1) selecting an irregular object with complex appearance characteristics as a model to be detected, wherein the model to be detected can be designed, 3D printed or numerically controlled, and a portrait model is selected as the model to be detected;
step 2) three-dimensionally scanning the model to be detected by using a line laser scanner to obtain three-dimensional data D1, three-dimensionally scanning the model to be detected by using a scanner to be detected to obtain three-dimensional data D2, wherein the scanning comprises line laser scanning, drain region supplementary scanning and point cloud splicing of each surface;
step 3) importing the three-dimensional data D1 and the three-dimensional data D2 into SolidWorks software, converting the SolidWorks software into an STL format, simultaneously importing the two sets of data into MIMICS software in a part form, automatically registering the two sets of data, overlapping matching points, distinguishing different sets of data by colors, displaying as shown in FIG. 2, and viewing two sets of data display items and operations at the file working area as shown in FIG. 3;
step 4) selecting a cuboid frame to randomly intercept an area to be compared, hiding an unselected area, and measuring and comparing data to be further performed, wherein the measurement range is a common intercepted area and can be fitted into an open-loop contour line or a closed-loop surface characteristic area along adjacent continuous key points, and the evaluation elements to be measured comprise: the point distance, the point curvature radius, the angle formed by connecting a point with other two points, the feature area grid number, the point cloud number, the surface area and the perimeter, the area point cloud number, the grid number, the volume and the total surface area are intercepted, a data comparison table is drawn, data is filled, the data measurement result and the display are shown in figure 4, and the comparison of the point feature, the outline feature and the area feature of two groups of data is based on the selection of one or more corresponding feature points matched with the two groups of data;
and 5) taking each parameter value measured by the scanning data of the line laser scanner as a standard value, and calculating the precision value of each evaluation element of the scanner to be detected, wherein the precision value is calculated as follows:
in the formula, alpha is a standard value, and beta is a measured value of each evaluation element of the scanner to be detected;
step 6) introducing weighting factors of the evaluation elements, as shown in fig. 7, changing the weight of each evaluation element according to the requirement, giving the weighting factors to each evaluation element, and performing an overall precision evaluation value algorithm:
＝∑σi·λi
where σ is a total accuracy evaluation valueiFor each evaluation element precision value, lambdaiWeighting factors for each evaluation element.
The theory of surface area as an evaluation element is based on the following explanation: because the surface of the complex model is provided with a plurality of concave-convex areas and has complex lines, in the scanning process, the movement path planning of the model placement platform to be detected is not good enough, and the influence of ambient light, the vibration of a scanning device and the like causes the scanner to generate a visual blind area or a miss-scanning area on the surface of the object to be detected, from the point cloud angle, the surface area calculation can be based on the triangle division principle, as shown in fig. 5, A, B, C is three scanned points, D is a miss-scanning point, the theoretical surface area is the sum of the areas of the surfaces M, N, Q, the actual area is calculated as the area of P, for the scanning instrument with higher precision, the more the number of the obtained single-side point clouds is; macroscopically, for the missing scanning area of the scanner, the image fusion is completed by a smooth surface, if a plurality of missing scanning objects exist, a large amount of fusion processing is needed, and the precision is greatly reduced. As shown in fig. 6, it is a two-dimensional cross-sectional view of the irregular model, wherein the solid line portion E is an ideal cross-sectional view, and the dotted line portion F is a result of the scanner failing to obtain the model concave information fusion processing, so the actually measured surface area is smaller than the ideal value.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A scanner precision evaluation method for three-dimensional measurement of objects with complex shapes is characterized by comprising the following steps:
step 1) selecting an irregular object with complex morphology characteristics as a model to be detected;
step 2) three-dimensionally scanning the model to be detected by using a line laser scanner to obtain three-dimensional data D1, and three-dimensionally scanning the model to be detected by using a scanner to be detected to obtain three-dimensional data D2;
step 3) importing the three-dimensional data D1 and the three-dimensional data D2 into three-dimensional processing software, converting the three-dimensional processing software into a unified format, simultaneously importing MIMICS software, automatically registering two groups of data, overlapping matching points together, distinguishing different groups of data with colors, and checking two groups of data display items and operations;
step 4) selecting a cuboid frame to randomly intercept an area to be compared, hiding an unselected area, and measuring and comparing data to be further performed, wherein the measurement range is a common intercepted area and can be fitted into an open-loop contour line or a closed-loop surface characteristic area along adjacent continuous key points, and the evaluation elements to be measured comprise: the method comprises the following steps of (1) intercepting the point cloud number, the grid number, the volume and the total surface area of an area, drawing a data comparison table, and filling data;
and 5) taking each parameter value measured by the scanning data of the line laser scanner as a standard value, and calculating the precision value of each evaluation element of the scanner to be detected, wherein the precision value is calculated as follows:
in the formula, alpha is a standard value, and beta is a measured value of each evaluation element of the scanner to be detected;
step 6) introducing weighting factors of the evaluation elements, giving the weighting factors to the evaluation elements, and performing an overall precision evaluation value algorithm:
＝∑σi·λi
where σ is a total accuracy evaluation valueiFor each evaluation element precision value, lambdaiWeighting factors for each evaluation element.
2. The method for evaluating the accuracy of a scanner for three-dimensional measurement of an object with a complex morphology according to claim 1, characterized in that: and 4) comparing the point characteristics, the contour characteristics and the region characteristics of the two groups of data in the step 4) on the basis of selecting one or more matched corresponding characteristic points.
3. The method for evaluating the accuracy of a scanner for three-dimensional measurement of an object with a complex morphology according to claim 2, characterized in that: and the scanning of the model to be detected by the line laser scanner comprises line laser scanning, drain region compensation scanning and point cloud splicing of each surface.
4. The method for evaluating the accuracy of a scanner for three-dimensional measurement of an object with a complex morphology according to claim 3, characterized in that: the three-dimensional processing software is WINDOS10 self-contained viewing software or SolidWorks software.
5. The method for evaluating the accuracy of a scanner for three-dimensional measurement of an object with a complex morphology according to claim 4, characterized in that: the unified format is STL format, and the three-dimensional data D1 and the three-dimensional data D2 are imported into MIMICS software in part form.
6. The method for evaluating the accuracy of a scanner for three-dimensional measurement of an object with a complex morphology according to claim 5, characterized in that: the model to be tested is a human body cochlea model or a human image model.
7. The method for evaluating the accuracy of a scanner for three-dimensional measurement of an object with a complex morphology according to claim 6, characterized in that: the model to be tested is designed automatically, printed in 3D mode or processed in numerical control mode.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010875757.0A CN111981983A (en) | 2020-08-27 | 2020-08-27 | Scanner precision evaluation method for three-dimensional measurement of complex-morphology object |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010875757.0A CN111981983A (en) | 2020-08-27 | 2020-08-27 | Scanner precision evaluation method for three-dimensional measurement of complex-morphology object |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111981983A true CN111981983A (en) | 2020-11-24 |
Family
ID=73439843
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010875757.0A Withdrawn CN111981983A (en) | 2020-08-27 | 2020-08-27 | Scanner precision evaluation method for three-dimensional measurement of complex-morphology object |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111981983A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112634458A (en) * | 2021-01-06 | 2021-04-09 | 广西科技大学 | Oral cavity model point cloud simplification method based on characteristic significance evaluation |
| CN113777590A (en) * | 2021-08-31 | 2021-12-10 | 同济大学 | Complex lunar surface simulation field for soft landing obstacle detection and verification method |
-
2020
- 2020-08-27 CN CN202010875757.0A patent/CN111981983A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112634458A (en) * | 2021-01-06 | 2021-04-09 | 广西科技大学 | Oral cavity model point cloud simplification method based on characteristic significance evaluation |
| CN113777590A (en) * | 2021-08-31 | 2021-12-10 | 同济大学 | Complex lunar surface simulation field for soft landing obstacle detection and verification method |
| CN113777590B (en) * | 2021-08-31 | 2024-01-26 | 同济大学 | Complex lunar surface simulation field for soft landing obstacle detection and verification method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109443209B (en) | Line structured light system calibration method based on homography matrix | |
| CN104331896B (en) | A kind of system calibrating method based on depth information | |
| CN109520436B (en) | Butterfly spring three-dimensional size automatic measurement system based on machine vision and measurement method thereof | |
| CN104266608B (en) | Field calibration device for visual sensor and calibration method | |
| CN103535960B (en) | Human body three-dimensional measurement method based on digital images | |
| CN110443879B (en) | Perspective error compensation method based on neural network | |
| CN109323650A (en) | Image visual transducer and the unified approach for putting ligh-ranging sensor measurement coordinate system | |
| CN111981983A (en) | Scanner precision evaluation method for three-dimensional measurement of complex-morphology object | |
| CN103948196A (en) | Human body data measuring method | |
| CN103191958B (en) | A kind of optical detecting method of sheet forming resilience | |
| CN106403838A (en) | Field calibration method for hand-held line-structured light optical 3D scanner | |
| CN107560541A (en) | The measuring method and device of picture centre deviation | |
| Liu et al. | Measuring method for micro-diameter based on structured-light vision technology | |
| CN111504182B (en) | Object volume vision measurement method using two-dimensional code for auxiliary calibration | |
| CN113310433A (en) | Virtual binocular stereo vision measuring method based on line structured light | |
| CN110942506A (en) | Object surface texture reconstruction method, terminal device and system | |
| CN105627935A (en) | Product material thickness three-dimensional analysis system and method | |
| CN106767433B (en) | A kind of method and system measuring foot sizing | |
| CN114004981B (en) | Vehicle body R angle visual detection method and system under incomplete point cloud condition | |
| CN109506629B (en) | Method for calibrating rotation center of underwater nuclear fuel assembly detection device | |
| JP4052489B2 (en) | Plane shape measurement analysis method | |
| CN106942813A (en) | The contactless convenient measuring method for obtaining somatic data | |
| CN113888641A (en) | Stumpage breast diameter measurement method based on machine vision and deep learning | |
| CN102829744B (en) | Method for measuring circular arc end tooth | |
| CN116465335A (en) | Automatic thickness measurement method and system based on point cloud matching |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20201124 |
|
| WW01 | Invention patent application withdrawn after publication |