CN114004981A - Vehicle body R angle visual detection method and system under incomplete point cloud condition - Google Patents
Vehicle body R angle visual detection method and system under incomplete point cloud condition Download PDFInfo
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Abstract
The invention belongs to the technical field of visual detection, and particularly relates to a vehicle body R angle visual detection method and system under the condition of incomplete point cloud, wherein the method comprises the following steps: acquiring shot vehicle body R corner point cloud contour data; setting upper and lower extreme values of the R corner radius of the vehicle body; processing the acquired data; distinguishing core points, boundary points and outliers by using a variable-radius-based DBSCAN clustering algorithm to obtain the core points finally used for fitting the R angle; fitting the core points of the R angle by using a least square method based on circle center constraint to obtain the circle center and the radius of the R angle; and comparing the radius of the obtained R angle with the upper and lower extreme values of the radius of the R angle of the set vehicle body, and determining whether the R angle meets the requirement of the design specification. Compared with the existing method for measuring only the R angle, the method is more accurate, the whole measuring process does not need human intervention, and the measuring efficiency is higher.
Description
Technical Field
The invention belongs to the technical field of visual detection, and particularly relates to a vehicle body R angle visual detection method and system under the condition of incomplete point cloud.
Background
However, as the perceived quality of automobiles becomes more important, the appearance quality even becomes a decisive factor for customers to purchase the product. The geometric accuracy of the R angle of the white automobile body of the automobile is an important factor for ensuring the qualified assembly and the beautiful appearance of the automobile. The R cutin quantity is the key for realizing the high-efficiency and high-precision detection and control of the perception quality of the car body. However, currently, the R angle in the automobile industry is generally measured by using an R gauge, the measurement accuracy is affected by manual experience, and the measurement efficiency is low. With the continuous improvement of the measurement accuracy of the optical sensor, the line laser detection is gradually applied to the R angle measurement, however, for the incomplete circular arc of the sheet metal part on the vehicle body, the extracted characteristic information is less, and the fitting accuracy is not high, so that the research on the visual detection method of the R angle is urgently needed.
In the prior art, ginger, 38040and the like use a bull gear as a research object and provide a least square fitting method with radius difference constraint. The method comprises the steps of firstly calculating the radius of a patch point on a bull gear, then calculating the center of the bull gear by using a least square circle fitting method of radius constraint, and finally processing simulation data and example data by using a program, so that the fitting precision can be effectively improved by using the method. But the small needle arcs still have difficulty meeting the detection accuracy. CN 105716540A, a method for detecting the assembling property of the square shell of a secondary battery and the R angle of a top cover is provided, the tangents on the two sides of the contour line of the inner edge are detected, the two tangents are enabled to coincide with the X axis and the Y axis in a rectangular coordinate system through rotation and translation, so that the contour line of the R angle is placed in the rectangular coordinate system, and finally the R angle is fitted by using an algorithm, so that the detection precision can be effectively improved, but the method is only suitable for the R angle intersected with the two tangents, namely 1/4 round corners, the measurement of various arcs of the sheet metal part of a vehicle body cannot be met, and the efficiency is not high.
Disclosure of Invention
In order to solve the problems, the invention provides the vehicle body R angle visual detection method and the vehicle body R angle visual detection system under the incomplete point cloud condition, which have the advantages of higher precision, better repeatability and better reproducibility, and are used for meeting the high-efficiency and quick production requirements on an industrial assembly line. The specific technical scheme is as follows:
a vehicle body R angle visual detection method under the condition of incomplete point cloud comprises the following steps:
s1: shooting the R corner of the car body by using a line laser camera to obtain shot cloud contour data of the R corner of the car body;
s2: setting upper and lower extreme values of the R corner radius of the vehicle body;
s3: processing the acquired cloud outline data of the R corner points of the vehicle body;
s4: distinguishing core points, boundary points and outliers according to the processed cloud contour data of the R corner points of the car body by using a variable radius-based DBSCAN clustering algorithm to obtain the core points finally used for fitting the R corners;
s5: fitting the core points of the R angle by using a least square method based on circle center constraint to obtain the circle center and the radius of the R angle;
s6: and comparing the radius of the obtained R angle with the upper and lower extreme values of the radius of the R angle of the set vehicle body, and determining whether the R angle meets the requirement of the design specification.
Preferably, the angle range of the line laser camera incident angle and the vehicle body plane where the R angle is located in the step S1 is [ -30 °,30 ° ].
Preferably, the shot vehicle body R corner point cloud contour data is obtained and then derived from the camera, the CSV format document is converted into the TXT format document, and invalid data in the document is removed.
Preferably, the step S1 includes performing bilateral filtering on the cloud contour data of the R corner points of the vehicle body.
Preferably, the processing of the acquired cloud contour data of the R corner point of the vehicle body in the step S3 includes the following steps:
s31: preliminarily screening the cloud outline data of the R corner points of the vehicle body by using the curvature, setting a curvature value range, calculating the curvature of the cloud outline data of the R corner points of the vehicle body, removing the data of which the curvature is smaller than the minimum curvature value or larger than the maximum curvature value, and finishing the preliminary screening of the cloud outline data of the R corner points of the vehicle body;
s32: step-variable grouping is carried out on the screened vehicle body R corner point cloud contour data according to the size of the data volume, and a plurality of groups of vehicle body R corner point cloud contour data with different data volumes are obtained;
s33: and performing circle fitting on the cloud contour data of the R corner point of the vehicle body of each group by using a least square method based on circle center constraint to obtain the circle center coordinates and the radius of each group, screening the data again according to the upper extreme value and the lower extreme value of the radius of the R corner point of the set vehicle body, and removing the data exceeding the threshold value set by the radius to obtain the processed cloud contour data of the R corner point of the vehicle body.
Preferably, the step S31 specifically includes:
s311: grouping the cloud outline data of the R corner points of the vehicle body, and taking three data points with the same data interval as a group;
s312, fitting a curve by using three data points of each group to obtain a parameter equation;
s313: after a parameter equation is obtained, the curvature of a curve represented by the group of three data points is calculated by using a curvature formula, and the cloud contour data of the R corner point of the vehicle body, of which the curvature is greater than or less than a curvature value range threshold value, are removed.
Preferably, the curvature of the middle data point of each set of data is used as the curvature estimation value of the curve fitted by the set of data, and the specific calculation process is as follows:
let each group of data include (x)1,y1)、(x2,y2)、(x3,y3) Three data points, with the middle data point (x)2,y2) As an estimate of the curvature of the curve fitted to the set of data, the parametric equation is as follows:
the length of two segments of vectors is used as a value range:
t in the parameter equation satisfies the following condition:
then there are:
and:
writing in matrix form:
and:
the abbreviation is:
find (a)1,a2,a3) And (b)1,b2,b3) With the analytical equation of the curve, the first derivative and the second derivative of the variable are calculated to obtain:
the final curvature k of the curve fitted to the set of 3 data points is calculated as:
when the curvature k is larger or smaller than the curvature value range threshold, the middle data point (x) of the group of 3 data points is selected2,y2) And (5) removing.
Preferably, the curvature has a range of values
Wherein
The average value of the upper extreme value and the lower extreme value of the set R corner radius of the vehicle body is obtained.
A visual detection system for an R angle of a vehicle body under the condition of incomplete point cloud comprises a data acquisition module, an R angle setting module, a data processing module, an R angle fitting module and an R angle detection module; the data acquisition module, the data processing module, the R angle fitting module and the R angle detection module are sequentially connected; the R angle setting module is connected with the R angle detection module;
the data acquisition module is used for acquiring cloud contour data of R corners of the vehicle body;
the R angle setting module is used for setting the tolerance limit and the upper and lower extreme values of the R angle of the vehicle body;
the data processing module is used for processing the acquired cloud contour data of the R corner points of the vehicle body; the R angle fitting module is used for fitting according to the processed cloud contour data of the R corner point of the vehicle body to obtain the circle center and the radius of the R angle; and the R angle detection module is used for comparing the circle center and the radius of the R angle obtained by fitting according to the set upper and lower extreme values of the radius of the R angle of the vehicle body, and determining whether the R angle meets the requirement of the design specification.
The invention has the beneficial effects that: the method comprises the steps of photographing the R angle of the vehicle body to be detected, extracting laser line data, screening effective data of the R angle of the vehicle body, and calculating the size of the fitted R angle to judge whether the R angle of the white vehicle body meets the manufacturing quality requirement. Compared with the existing method for measuring only the R angle, the method is more accurate, the whole measuring process does not need human intervention, and the measuring efficiency is higher.
The invention judges whether the R angle of the automobile body-in-white meets the assembly requirement or not through point cloud contour shooting and subsequent computer processing, and has good repeatability and reproducibility because the whole detection process is rarely influenced by human factors.
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In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a schematic flow chart of a visual inspection method for an R corner of a vehicle body under an incomplete point cloud condition according to the invention;
FIG. 2 is a diagram illustrating the effect of the fitting of the present invention;
fig. 3 is a schematic diagram of a vehicle body R corner visual inspection system under an incomplete point cloud condition.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As shown in fig. 1, the present embodiment provides a vehicle body R corner visual inspection method under the incomplete point cloud condition, which includes the following steps:
s1: shooting the R corner of the car body by using a line laser camera to obtain shot cloud contour data of the R corner of the car body; the angle range of the incidence angle of the line laser camera and the plane of the vehicle body where the R angle is located is [ -30 degrees, 30 degrees ]. Due to errors of the laser sensor and environmental interference, the obtained data is generally disordered, and part of laser points are not reflected back to the sensor. And only the data of the X coordinate without the Z coordinate, or the data measured when the range of the line laser camera is exceeded or the data measured when the gap exists in the vehicle body are invalid data, wherein the range of the line laser camera is exceeded, namely the distance between the line laser camera and the R angle exceeds the range of the line laser camera. Therefore, the photographed vehicle body R corner point cloud contour data is required to be obtained, then the vehicle body R corner point cloud contour data is derived from the camera, the CSV format document is converted into the TXT format document, invalid data is removed, data transposition is carried out, and the transversely arranged data is converted into a longitudinally arranged form, so that the data effectiveness can be improved, and the later fitting precision can be improved.
The laser measurement area has a plane and a cambered surface, and laser data has certain fluctuation and can interfere the calculation of later-stage curvature, so that the data needs to be filtered, invalid data is removed, then vehicle body R corner point cloud profile data is filtered, specifically, bilateral filtering is adopted for point cloud filtering, the position of a current sampling point is corrected by taking weighted average of adjacent sampling points, the surface of a space three-dimensional model is effectively denoised, geometric characteristic information in the point cloud data is kept, the three-dimensional point cloud data is prevented from being transited smoothly, the influence of geometric factors on results is reduced, and distortion is prevented.
S2: setting upper and lower extreme values of the R corner radius of the vehicle body; in order to ensure the fitting accuracy, the upper and lower extreme values of the radius of the R angle of the vehicle body are required to be input as reference data to be compared with the size of the fitted R angle.
S3: processing the acquired cloud outline data of the R corner points of the vehicle body; the processing of the acquired cloud outline data of the R corner points of the vehicle body comprises the following steps:
s31: preliminarily screening the cloud outline data of the R corner points of the vehicle body by using the curvature, setting a curvature value range, calculating the curvature of the cloud outline data of the R corner points of the vehicle body, removing the data of which the curvature is smaller than the minimum curvature value or larger than the maximum curvature value, and finishing the preliminary screening of the cloud outline data of the R corner points of the vehicle body; the method specifically comprises the following steps:
s311: grouping the cloud outline data of the R corner points of the vehicle body, and taking three data points with the same data interval as a group; for example: 30 points are arranged in the whole curve, the 30 points are grouped and rearranged, the point interval is 10, the first group is the No. 1 point, the No. 11 point and the No. 21 point; the second group is point No. 2, point No. 12 and point No. 22; and so on).
S312, fitting a curve by using three data points of each group to obtain a parameter equation;
s313: after a parameter equation is obtained, the curvature of a curve represented by the group of three data points is calculated by using a curvature formula, and the cloud contour data of the R corner point of the vehicle body, of which the curvature is greater than or less than a curvature value range threshold value, are removed.
The curvature of the middle data point of each group of data is used as the curvature estimation value of the curve fitted by the group of data, and the specific calculation process is as follows:
let each group of data include (x)1,y1)、(x2,y2)、(x3,y3) Three data points, with the middle data point (x)2,y2) As an estimate of the curvature of the curve fitted to the set of data, the parametric equation is as follows:
the length of two segments of vectors is used as a value range:
t in the parameter equation satisfies the following condition:
then there are:
and:
writing in matrix form:
and:
the abbreviation is:
find (a)1,a2,a3) And (b)1,b2,b3) With the analytical equation of the curve, the first derivative and the second derivative of the variable are calculated to obtain:
the final curvature k of the curve fitted to the set of 3 data points is calculated as:
when the curvature k is larger or smaller than the curvature value range threshold, the middle data point (x) of the group of 3 data points is selected2,y2) And (5) removing. The curvature value range is
Wherein
The average value of the upper extreme value and the lower extreme value of the set R corner radius of the vehicle body is obtained.
According to the method, the curvature value range is adopted to screen the data, so that corresponding straight line contour data points in the collected vehicle body R corner point cloud contour data can be effectively removed, points with large fluctuation are removed, R corner arc line contour data points in the collected vehicle body R corner point cloud contour data are reserved, the effectiveness of fitting data is improved, and therefore the fitting precision is improved.
S32: carrying out variable-step grouping on the screened vehicle body R corner point cloud contour data according to the size of the data volume, namely carrying out variable-step grouping on the vehicle body R corner point cloud contour data after removing the data points with curvatures not conforming to the curvature value range threshold to obtain a plurality of groups of vehicle body R corner point cloud contour data with different data volumes; for example: the total amount of data is 28, 10 data are grouped into groups, the interval step is 4, the number of the first group of data is 1-10, the number of the second group of data is 5-14, the number of the third group of data is 9-18, the number of the fourth group of data is 13-22, the number of the fifth group of data is 17-26, the number of the fifth group of data is 21-28, the number of the sixth group of data is 25-28, the amount of the previous data in each group is the same, and the amount of the last data is different, so that the purpose of grouping is achieved, and more data are provided for the following fitting. In order to prevent the data quantity from being too small and the fitting effect from being poor, the number of the residual point clouds after the curvature data screening is not less than 150.
S33: and performing circle fitting on the cloud contour data of the R corner point of the vehicle body of each group by using a least square method based on circle center constraint to obtain the circle center coordinates and the radius of each group, screening the data again according to the upper extreme value and the lower extreme value of the radius of the R corner point of the set vehicle body, and removing the whole group of data exceeding the radius set threshold to obtain the processed cloud contour data of the R corner point of the vehicle body. Further optimization of the fit is required because the small groups of data within the threshold range are retained, where there are more than one group of small groups within the threshold range.
S4: and distinguishing core points, boundary points and outliers according to the processed cloud contour data of the R corner points of the vehicle body by using a variable radius-based DBSCAN clustering algorithm to obtain the core points finally used for fitting the R corners.
The variable radius is embodied in that the data left after the previous step of screening is not a group, and the radius is not the same, so that each group of data finally used for fitting, which is screened out in the previous step, is clustered, algorithm processing is performed based on the coordinates of the circle center screened out in the previous step and the radius, in the DBSCAN algorithm, initially, all objects in a given data set D (the data points left after the previous step of screening) are marked as "unvisited", the DBSCAN randomly selects an object p which is not accessed (randomly selects a point), the mark p is marked as "visited", and whether the epsilon-field of p at least contains MinPts objects is checked. If not, p is marked as a noise point. Otherwise, a new cluster C (set of core points) is created for p and all objects in the ε -field of p are placed in the candidate set N. DBSCAN iteratively adds objects in N that do not belong to other clusters to C. In this process, corresponding to object p ' marked as "unvisited" in N, DBSCAN marks it as "visited" and examines its ε -realm, and if the ε -realm of p ' contains at least MinPts objects, then all the objects in the ε -realm of p ' are added to N. DBSCAN continues to add objects to C until C cannot expand, i.e., until N is empty. At this time, the cluster C is generated and output. To find the next cluster, DBSCAN randomly selects an object that has not been accessed from the remaining objects. The clustering process continues until all objects are accessed, which is the core point data in C. The method comprises the following specific steps:
the Else flag p is noise;
until has no objects marked as univisited.
S5: fitting the core points of the R angle screened by the DBSCAN algorithm by using a least square method based on circle center constraint to obtain the circle center and the radius of the R angle; the effect of the fitting is shown in fig. 2.
S6: and comparing the radius of the obtained R angle with the upper and lower extreme values of the radius of the R angle of the set vehicle body, and determining whether the R angle meets the requirement of the design specification.
As shown in fig. 3, the vehicle body R angle vision inspection system under the incomplete point cloud condition in the embodiment of the present invention includes a data acquisition module, an R angle setting module, a data processing module, an R angle fitting module, and an R angle detection module; the data acquisition module, the data processing module, the R angle fitting module and the R angle detection module are sequentially connected; the R angle setting module is connected with the R angle detection module;
the data acquisition module is used for acquiring the cloud contour data of the R corner points of the vehicle body;
the R angle setting module is used for setting the tolerance limit and the upper and lower extreme values of the R angle of the vehicle body;
the data processing module is used for processing the acquired cloud contour data of the R corner points of the vehicle body; the R angle fitting module is used for fitting according to the processed cloud contour data of the R angular point of the vehicle body to obtain the circle center and the radius of the R angle; the R angle detection module is used for comparing the circle center and the radius of the R angle obtained by fitting according to the set upper and lower extreme values of the radius of the R angle of the vehicle body, and determining whether the R angle meets the requirement of the design specification.
The data acquisition module comprises a line laser camera, an invalid data removing unit and a filtering unit, wherein the angle range of the line laser camera and the plane of the vehicle body where the R angle is located is [ -30 degrees, 30 degrees ]. The invalid data removing unit is used for removing invalid data in the shot vehicle body R corner point cloud contour data. The filtering unit is used for filtering the vehicle body R corner point cloud contour data after the invalid data are removed by adopting bilateral filtering.
The data processing module comprises a curvature primary screening unit, a variable step grouping unit, a radius screening unit and a clustering unit; the curvature preliminary screening unit is used for grouping the vehicle body R corner point cloud contour data, calculating the curvature of the middle data points in each group of data, and rejecting the corresponding middle data points with curvatures not conforming to the curvature value range according to the set curvature value range to obtain the vehicle body R corner point cloud contour data after preliminary screening; the variable-step grouping unit is used for performing variable-step grouping on the preliminarily screened vehicle body R corner point cloud contour number to obtain vehicle body R corner point cloud contour data after the variable-step grouping; the radius screening unit is used for calculating the radius of the corresponding group of the vehicle body R corner point cloud contour data after variable step length grouping, and eliminating the whole group of data of which the calculation result does not accord with the threshold value set by the radius according to the threshold value set by the radius to obtain the screened vehicle body R corner point cloud contour data. The clustering unit is used for obtaining core points for the R-angle fitting module to fit the R-angle by using a variable radius-based DBSCAN clustering algorithm for the screened cloud contour data of the R-angle point of the vehicle body.
Table 1 shows the data of the R angle measurement of the incomplete circular arc of the standard part where R is 1, and table 2 shows the data of the R angle measurement of the incomplete circular arc of the standard part where R is 2, which shows that the present invention has high accuracy in the R angle measurement.
Table 1 table of data for measuring R angle of incomplete circular arc of standard part
Table 2 standard part incomplete circular arc R angle measurement data table
And (3) performing algorithm processing fitting on the point cloud data with the theoretical R angle value of 1mm, and comparing the result with the result of measurement by adopting an R gauge and three coordinates, wherein the comparison result is shown in the following table 3 and is in unit mm.
TABLE 3 comparison of the inventive measurements with the three-coordinate measurement, the R gauge measurement and the inventive measurement
Wherein: CMM _ R is a three-coordinate measurement, R1 is an R gauge measurement, R0 is a measurement of the present invention, Δ 1 is a difference between the R gauge measurement and the three-coordinate, and Δ 2 is a difference between the line laser measurement and the three-coordinate. It can be seen that the inventive measurements are clearly due to the conventional R gauge measurements.
Those of ordinary skill in the art will appreciate that the elements of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the components of the examples have been described above generally in terms of their functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present application, it should be understood that the division of the unit is only one division of logical functions, and other division manners may be used in actual implementation, for example, multiple units may be combined into one unit, one unit may be split into multiple units, or some features may be omitted.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (9)
1. A vehicle body R angle visual detection method under the condition of incomplete point cloud is characterized by comprising the following steps: the method comprises the following steps:
s1: shooting the R corner of the car body by using a line laser camera to obtain shot cloud contour data of the R corner of the car body;
s2: setting upper and lower extreme values of the R corner radius of the vehicle body;
s3: processing the acquired cloud outline data of the R corner points of the vehicle body;
s4: distinguishing core points, boundary points and outliers according to the processed cloud contour data of the R corner points of the car body by using a variable radius-based DBSCAN clustering algorithm to obtain the core points finally used for fitting the R corners;
s5: fitting the core points of the R angle by using a least square method based on circle center constraint to obtain the circle center and the radius of the R angle;
s6: and comparing the radius of the obtained R angle with the upper and lower extreme values of the radius of the R angle of the set vehicle body, and determining whether the R angle meets the requirement of the design specification.
2. The method for visually detecting the R angle of the vehicle body under the sparse point cloud condition according to claim 1, wherein the method comprises the following steps: the angle range of the line laser camera incident angle and the vehicle body plane where the R angle is located in the step S1 is [ -30 degrees, 30 degrees ].
3. The method for visually detecting the R angle of the vehicle body under the incomplete point cloud condition according to claim 1, wherein the method comprises the following steps: and after obtaining the shot cloud outline data of the R corner point of the vehicle body, deriving the cloud outline data of the R corner point of the vehicle body from the camera, converting the CSV-format document into a TXT-format document, and removing invalid data.
4. The method for visually detecting the R angle of the vehicle body under the incomplete point cloud condition according to claim 1, wherein the method comprises the following steps: the step S1 includes performing bilateral filtering on the cloud contour data of the R corner points of the vehicle body.
5. The method for visually detecting the R angle of the vehicle body under the incomplete point cloud condition according to claim 1, wherein the method comprises the following steps: the step S3 of processing the acquired vehicle body R corner cloud contour data includes the following steps:
s31: preliminarily screening the cloud outline data of the R corner points of the vehicle body by using the curvature, setting a curvature value range, calculating the curvature of the cloud outline data of the R corner points of the vehicle body, removing the data of which the curvature is smaller than the minimum curvature value or larger than the maximum curvature value, and finishing the preliminary screening of the cloud outline data of the R corner points of the vehicle body;
s32: step-variable grouping is carried out on the screened vehicle body R corner point cloud contour data according to the size of the data volume, and a plurality of groups of vehicle body R corner point cloud contour data with different data volumes are obtained;
s33: and performing circle fitting on the cloud contour data of the R corner point of the vehicle body of each group by using a least square method based on circle center constraint to obtain the circle center coordinates and the radius of each group, screening the data again according to the upper extreme value and the lower extreme value of the radius of the R corner point of the set vehicle body, and removing the data exceeding the threshold value set by the radius to obtain the processed cloud contour data of the R corner point of the vehicle body.
6. The method for visually detecting the R angle of the vehicle body under the incomplete point cloud condition according to claim 5, wherein the method comprises the following steps: the step S31 specifically includes:
s311: grouping the cloud outline data of the R corner points of the vehicle body, and taking three data points with the same data interval as a group;
s312, fitting a curve by using three data points of each group to obtain a parameter equation;
s313: after a parameter equation is obtained, the curvature of a curve represented by the group of three data points is calculated by using a curvature formula, and the cloud contour data of the R corner point of the vehicle body, of which the curvature is greater than or less than a curvature value range threshold value, are removed.
7. The method for visually detecting the R angle of the vehicle body under the incomplete point cloud condition according to claim 6, wherein the method comprises the following steps: the curvature of the middle data point of each group of data is used as the curvature estimation value of the curve fitted by the group of data, and the specific calculation process is as follows:
let each group of data include (x)1,y1)、(x2,y2)、(x3,y3) Three data points, with the middle data point (x)2,y2) As an estimate of the curvature of the curve fitted to the set of data, the parametric equation is as follows:
the length of two segments of vectors is used as a value range:
t in the parameter equation satisfies the following condition:
then there are:
and:
writing in matrix form:
and:
the abbreviation is:
find (a)1,a2,a3) And (b)1,b2,b3) With the analytical equation of the curve, the first derivative and the second derivative of the variable are calculated to obtain:
the final curvature k of the curve fitted to the set of 3 data points is calculated as:
when the curvature k is larger or smaller than the curvature value range threshold, the middle data point (x) of the group of 3 data points is selected2,y2) And (5) removing.
8. The method for visually detecting the R angle of the vehicle body under the incomplete point cloud condition according to claim 6, wherein the method comprises the following steps: the curvature value range is
Wherein
The average value of the upper extreme value and the lower extreme value of the set R corner radius of the vehicle body is obtained.
9. The utility model provides a car body R angle vision detection system under incomplete point cloud condition which characterized in that: the device comprises a data acquisition module, an R angle setting module, a data processing module, an R angle fitting module and an R angle detection module; the data acquisition module, the data processing module, the R angle fitting module and the R angle detection module are sequentially connected; the R angle setting module is connected with the R angle detection module;
the data acquisition module is used for acquiring cloud contour data of R corners of the vehicle body;
the R angle setting module is used for setting the tolerance limit and the upper and lower extreme values of the R angle of the vehicle body;
the data processing module is used for processing the acquired cloud contour data of the R corner points of the vehicle body; the R angle fitting module is used for fitting according to the processed cloud contour data of the R corner point of the vehicle body to obtain the circle center and the radius of the R angle; and the R angle detection module is used for comparing the circle center and the radius of the R angle obtained by fitting according to the set upper and lower extreme values of the radius of the R angle of the vehicle body, and determining whether the R angle meets the requirement of the design specification.
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