CN115265402A - Prefabricated part overall dimension error detection method based on 3D laser scanner - Google Patents
Prefabricated part overall dimension error detection method based on 3D laser scanner Download PDFInfo
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- CN115265402A CN115265402A CN202210497369.2A CN202210497369A CN115265402A CN 115265402 A CN115265402 A CN 115265402A CN 202210497369 A CN202210497369 A CN 202210497369A CN 115265402 A CN115265402 A CN 115265402A
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- 238000012545 processing Methods 0.000 claims abstract description 9
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- 238000004519 manufacturing process Methods 0.000 abstract description 5
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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
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Abstract
The invention discloses a prefabricated part outline dimension error detection method based on a 3D laser scanner, which comprises the following steps: a chessboard target plate and a plurality of detection points which are arranged in a ring shape are arranged around the prefabricated part to be detected; scanning the prefabricated part to be detected and the chessboard target plate by using a 3D laser scanner at each detection point, and scanning at least three chessboard target plates which are not on the same plane and have different central points on the same straight line when two adjacent detection points are scanned so as to obtain a plurality of first point cloud data; splicing the plurality of first point cloud data, and then extracting second point cloud data of a prefabricated part area to be detected from the point cloud data generated by splicing; processing the second point cloud data by using standard point cloud data to obtain the external dimension error of the prefabricated part to be detected; the standard point cloud data is the point cloud data of the prefabricated part generated according to the design drawing. The automatic detection of the external dimension error of the oversized prefabricated part is realized, and the production is guided.
Description
Technical Field
The invention belongs to the technical field of prefabricated part detection, and particularly relates to a prefabricated part outline dimension error detection method based on a 3D laser scanner.
Background
At present, with the development of assembly type buildings in China, subway prefabricated assembly type stations are widely applied, and the subway prefabricated assembly type stations have the advantages that the stations can be quickly assembled after excavation is finished, the exposure time of foundation pits is shortened, the construction progress is accelerated, the road occupation time is shortened, and the like. However, the assembly requirement of the assembly station is high in component precision requirement, so that the detection of the overall dimension error of the prefabricated component and the guidance of production and construction according to the detection result are particularly critical. In recent years, with the development of a 3D laser scanning technology, a detection method based on a 3D scanner is conditionally developed for detecting the dimension error of an oversized prefabricated part, so that the detection is more convenient and efficient.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a prefabricated part outline dimension error detection method based on a 3D laser scanner.
The technical problem to be solved by the invention is realized by the following technical scheme:
a method for detecting the outline dimension error of a prefabricated part based on a 3D laser scanner comprises the following steps:
step (1): a chessboard target plate and a plurality of detection points which are arranged in a ring shape are arranged around the prefabricated component to be detected;
step (2): scanning the prefabricated part to be detected and the chessboard target plate by using a 3D laser scanner at each detection point, and scanning at least three chessboard target plates which are not on the same plane and have different central points on the same straight line when two adjacent detection points are scanned so as to obtain a plurality of first point cloud data;
and (3): splicing the plurality of first point cloud data, and then extracting second point cloud data of a prefabricated part area to be detected from the point cloud data generated by splicing; the splicing reference of the point cloud data is at least three chessboard target plates which are scanned by two adjacent detection points together and are not on the same plane, and the central points of the chessboard target plates are not on the same straight line;
and (4): processing the second point cloud data by using standard point cloud data to obtain the external dimension error of the prefabricated part to be detected; the standard point cloud data is the point cloud data of the prefabricated part generated according to a design drawing.
Further, the step (4) comprises:
step (4.1): performing coordinate rotation and contour fitting on the second point cloud data and the standard point cloud data to obtain third point cloud data;
step (4.2): and comparing the third point cloud data with the standard point cloud data to calculate to obtain the external dimension error of the prefabricated part to be detected.
Further, in the step (4.1), the coordinate rotation and the contour fitting of the second point cloud data and the standard point cloud data are performed by using an ICP algorithm and a least square method.
Further, in the step (4.2), the third point cloud data and the standard point cloud data are compared and calculated by using a section cutting method, so that the overall dimension error of the prefabricated part to be detected is obtained.
The invention has the beneficial effects that:
1. according to the invention, by means of a 3D laser scanning technology, detection on an oversized prefabricated part can be realized to obtain point cloud data of the oversized prefabricated part, and the outline dimension error between the oversized prefabricated part and a designed part is obtained by analyzing and processing the point cloud data, so that production is guided, and the precision requirement of an assembled subway station on the prefabricated part is met;
2. the detection method can greatly improve the detection precision, shorten the detection speed and reduce the detection cost, and the detection method and the operation are faster and more efficient;
3. the automatic detection of the damage deformation of the oversized prefabricated part is realized by using the 3D laser scanner, the production and processing quality of the prefabricated part is evaluated, and the instructive suggestion is improved for the subsequent production and construction through the detection result.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic structural view of a chessboard target plate;
FIG. 3 is a schematic diagram of a plurality of first point cloud data after splicing;
FIG. 4 is a schematic diagram of second point cloud data;
FIG. 5 is a schematic diagram of the effect of the second point cloud data before coordinate rotation and contour fitting with the standard point cloud data;
FIG. 6 is a schematic diagram showing the effect of the second point cloud data after coordinate rotation and contour fitting with the standard point cloud data;
fig. 7 is a schematic diagram of the dimensional error of the prefabricated part to be detected.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
The embodiment of the invention provides a prefabricated part outline dimension error detection method based on a 3D laser scanner, and please refer to fig. 1-7, the method specifically comprises the following steps:
step (1): a plurality of chessboard target plates and a plurality of detection points which are arranged in a ring shape are arranged around the prefabricated component to be detected. The chessboard target plate is fixed on the periphery of a prefabricated part to be detected.
When the detection point is selected, the complete point cloud data of the component to be detected can be obtained finally.
Step (2): and scanning the prefabricated part to be detected and the chessboard target plate by using a 3D laser scanner at each detection point, and scanning at least three chessboard target plates which are not on the same plane and have different central points on the same straight line together when two adjacent detection points are scanned so as to obtain a plurality of first point cloud data.
The chessboard target plates are shown in fig. 2, when two adjacent detection points are scanned, at least three chessboard target plates which are not on the same plane and have different central points on the same straight line are shared, and the central points of the chessboard target plates which are not on the same plane and have different central points on the same straight line are connected to form a plane which is mainly used for positioning, and the positions of the chessboard target plates for positioning before and after measurement are kept unchanged. Software matched with the 3D laser scanner can identify the central point of the chessboard target plate, and if the software can identify three coplanar and non-collinear central points, the splicing of point cloud data of adjacent detection points can be realized.
The first point cloud data in the embodiment of the invention refers to three-dimensional coordinates of the prefabricated part obtained by scanning, is part of information of the measurement data of the 3D laser scanner, can be directly extracted, does not need to be matched with instruments such as a total station and the like during measurement, and is convenient to operate.
If the cloud data of a plurality of first points obtained by the measurement of the 3D laser scanner is too large, the point cloud can be diluted by adopting a conventional algorithm under the condition of meeting the requirement of measurement precision, so that the algorithm efficiency of subsequent registration fitting is improved.
In addition, the KD-Tree method can be adopted to perform noise reduction processing on the first point cloud data, so that redundant useless information can be removed.
Before the 3D laser scanner is measured, tripod leveling and instrument measuring range reasonable adjustment need be carried out, thereby improving measuring efficiency.
And (3): splicing the plurality of first point cloud data, and then extracting second point cloud data of a prefabricated part area to be detected from the point cloud data generated by splicing; the splicing reference of the point cloud data is at least three chessboard target plates which are not in the same plane and have center points not in the same straight line and are scanned by two adjacent detection points together.
Reading all the first point cloud data, splicing all the first point cloud data by using point cloud data formed by the chessboard target plates as a reference, namely, overlapping three-dimensional coordinates formed by the chessboard target plates scanned jointly in two groups of first point cloud data obtained by scanning two adjacent detection points by the 3D laser scanner, then extracting second point cloud data of the prefabricated part to be detected from the overlapped point cloud data, wherein the extraction process is completed by software carried by the 3D laser scanner.
If the amount of the second point cloud data is too large, the point cloud dilution processing can be performed by adopting a conventional algorithm under the condition that the requirement of measurement precision is met, so that the algorithm efficiency of subsequent registration fitting is improved, and in addition, the KD-Tree method can also be used for performing noise reduction processing on the second point cloud data to remove redundant useless information.
And (4): processing the second point cloud data by using standard point cloud data to obtain the external dimension error of the prefabricated part to be detected; the standard point cloud data is the point cloud data of the prefabricated part generated according to a design drawing.
Further, the step (4) comprises the following steps:
step (4.1): performing coordinate rotation and contour fitting on the second point cloud data and standard point cloud data to obtain third point cloud data;
specifically, the coordinate rotation and the contour fitting of the second point cloud data and the standard point cloud data are performed by using an ICP algorithm and a least square method.
And the second point cloud data is transformed to a coordinate system which is the same as the standard point cloud data by solving a rotational translation matrix between the two point cloud data, so that subsequent calculation is facilitated, and the least square method is used for calculating errors in the fitting process, so that the contour fitting result is more accurate.
Step (4.2): and comparing the third point cloud data with the standard point cloud data to calculate to obtain the overall dimension error of the prefabricated part to be detected.
Specifically, the third point cloud data and the standard point cloud data are compared and calculated by using a section cutting method, so that the contour dimension error of the prefabricated part to be detected is obtained and marked on the drawing.
The section cutting method comprises the specific steps of setting the distance between cutting surfaces along the X, Y, Z axis and the third point cloud data of the cutting component, namely the measurement point cloud data and the standard point cloud data; then projecting each obtained cutting surface into a corresponding parallel coordinate plane; sequentially calling the measured point cloud data and the standard point cloud data on each section along the axis, and calculating the error value of each measured point cloud of each section by using a method of searching nearest neighbor points through a KD-tree; setting an error threshold allowed by the quality standard, and marking and recording the measurement point cloud data with the error value of each section larger than the allowed error threshold; finally, summarizing the measured point cloud data of which the error value of each section is greater than the allowable error threshold value, and marking the measured three-dimensional point cloud, namely the third point cloud data, of the component; and directing the modification of subsequent components based on the marked points.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (4)
1. A prefabricated part outline dimension error detection method based on a 3D laser scanner is characterized by comprising the following steps:
step (1): a chessboard target plate and a plurality of detection points which are arranged in a ring shape are arranged around the prefabricated part to be detected;
step (2): scanning the prefabricated part to be detected and the chessboard target plate by using a 3D laser scanner at each detection point, and scanning at least three chessboard target plates which are not on the same plane and have different central points on the same straight line when two adjacent detection points are scanned so as to obtain a plurality of first point cloud data;
and (3): splicing the plurality of first point cloud data, and then extracting second point cloud data of a prefabricated part area to be detected from the point cloud data generated by splicing; the splicing reference of the point cloud data is at least three chessboard target plates which are not in the same plane and have center points not in the same straight line and are scanned by two adjacent detection points together;
and (4): processing the second point cloud data by using standard point cloud data to obtain the external dimension error of the prefabricated part to be detected; the standard point cloud data is the point cloud data of the prefabricated part generated according to the design drawing.
2. The 3D laser scanner-based prefabricated part dimension error detection method according to claim 1, wherein the step (4) comprises:
step (4.1): performing coordinate rotation and contour fitting on the second point cloud data and the standard point cloud data to obtain third point cloud data;
step (4.2): and comparing the third point cloud data with the standard point cloud data to calculate to obtain the overall dimension error of the prefabricated part to be detected.
3. The method for detecting the errors in the outer dimensions of the prefabricated parts based on the 3D laser scanner as set forth in claim 2, wherein in the step (4.1), the coordinate rotation and the contour fitting of the second point cloud data and the standard point cloud data are performed by using an ICP algorithm and a least square method.
4. The method for detecting the external dimension error of the prefabricated part based on the 3D laser scanner as claimed in claim 2, wherein in the step (4.2), the external dimension error of the prefabricated part to be detected is obtained by comparing the third point cloud data with the standard point cloud data by using a cross-section cutting method.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114322899A (en) * | 2021-12-27 | 2022-04-12 | 苏州方石科技有限公司 | Terrace detection method, storage medium and electronic device |
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