CN116740060B - Method for detecting size of prefabricated part based on point cloud geometric feature extraction - Google Patents
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Abstract
The invention discloses a method for detecting the size of an assembled prefabricated part based on the extraction of geometrical characteristics of point clouds, which comprises the steps of firstly, finely dividing the point cloud foreground and the background of the assembled prefabricated part based on normal vectors, so that the point cloud of the assembled prefabricated part is divided from the environmental background; then, after the geometric characteristics of the point cloud of the prefabricated part are extracted, fitting the edge line of the prefabricated part according to the geometric characteristics of the point cloud, and calculating according to the fitted edge line to obtain the size information of the prefabricated part; and finally, adding 3D text labels of the size information of the prefabricated parts under the point cloud visual interface. According to the method, the edge line is fitted and the characteristic points are extracted based on the segmentation result of the cloud data of the real measurement points of the prefabricated components, so that the size of the prefabricated components is calculated, the purpose of automatic size detection is achieved, the detection efficiency is improved, and meanwhile, the accuracy of size detection is guaranteed.
Description
Technical Field
The invention relates to the field of assembly type building industry, in particular to an assembly type prefabricated part size detection method based on point cloud geometric feature extraction.
Background
The building is an important direction of development of the building industry, and is a building assembled by processing various types of building components in advance in a factory and then transporting the building components to a construction site through reliable connection. Compared with the existing cast-in-situ structure building, the cast-in-situ structure building has the advantages of large-scale production, high construction speed and low construction cost.
In an industrial construction prefabricated component production plant, a plurality of prefabricated components of different types are often produced according to the order requirements. In order to reduce the cost, the existing manufacturing enterprises generally adopt a mode that a plurality of types of components are produced by converting processes on the same production line. In production, different products have various technical categories, large operation process difference and complex index system. The assembled prefabricated components are large in size, various in types and complex in structure; this brings great difficulty to the design of the automated and intelligent technical scheme for the quality detection of the prefabricated components. In the existing automatic detection method, in the process of detecting the prefabricated components, the accuracy of a detection result is often difficult to meet the requirement, and even the detection result needs to be checked manually.
The existing quality detection methods are limited to human eye detection and steel ruler detection, the detection efficiency is low, the quality detection is easy to be influenced by human, and the quality detection result is required to be recorded by paper, so that the intelligent degree is low. The common component size detection and assembly modes all require a great deal of human intervention. Instruments such as a level gauge, a theodolite, a total station and the like can only observe a single point, the detection efficiency is low, and rapid and effective detection can not be carried out on multiple components and large-size components. Especially when measuring large components. In addition, factory-produced prefabricated components are susceptible to damage during transportation and assembly. In the manufacturing stage, the problems of component size errors, component position deviation, surface quality defects and the like may be caused by reasons of material problems, template manufacturing defects and the like.Existing 2The computer vision technology can automatically detect, but has large limitation, is easily influenced by shooting angles, and cannot visualize quality inspection results.
Disclosure of Invention
The invention aims to provide an assembled prefabricated part size detection method based on point cloud geometric feature extraction, which is used for fitting edge lines and extracting feature points based on a segmentation result of assembled prefabricated part real-point cloud data, further calculating the size of an assembled prefabricated part, realizing the purpose of automatic size detection of the assembled prefabricated part, improving the detection efficiency and ensuring the accuracy of size detection.
The technical scheme of the invention is as follows:
the method for detecting the size of the prefabricated part based on the point cloud geometric feature extraction specifically comprises the following steps:
(1) The method comprises the steps of carrying out segmentation on the point cloud foreground and the background of the prefabricated part based on the refinement of normal vector, so that the point cloud of the prefabricated part is segmented from the environment background;
(2) Extracting geometric features of the point cloud of the prefabricated part;
(3) Fitting the edge line of the prefabricated part according to the geometric characteristics of the point cloud of the prefabricated part, and calculating according to the fitted edge line to obtain the size information of the prefabricated part;
(4) 3 adding size information of prefabricated part under point cloud visualization interfaceText labels.
The specific mode for carrying out the point cloud foreground-background segmentation of the prefabricated component based on the normal vector refinement comprises the following steps:
s11, refining normal vector: traversing each point in the point cloud of the prefabricated component, acquiring adjacent point sets of each point, and calculating curvature change and normal difference between a normal vector of each point and a normal vector of a current point of any point in each adjacent point set; when the curvature change and the normal direction difference exceed the set threshold values, taking the curvature change and the normal direction difference as residual error items, constructing a linear equation set, and calculating an adjustment value of an algorithm vector by solving the linear equation set; updating the normal vector of the current point according to the adjustment value of the normal vector to obtain a refined normal vector, repeating the steps, iterating for a plurality of times to adjust and optimize the normal vector of each point until the preset iteration times are reached, outputting the optimized normal vector as a new normal vector, and orienting the new normal vector to realize the refinement of the normal vector;
s12, performing image segmentation based on region growth refined by normal vector, performing secondary region growth segmentation when segmentation is not complete, and passing through a threshold value based on the condition of over-segmentationThe plane segmentation method is used for circularly recursively acquiring the maximum bin of the over-segmentation result, and finally, the point cloud of the prefabricated component is segmented from the environment background.
The said threshold valueThe plane segmentation method specifically comprises the following steps:
a. establishing a plane model: randomly selecting three points in the point cloud data to establish an initial plane model;
b. single plane fitting: after a plane model is built and a fitting plane is built, calculating the distance between each point in the point cloud and the fitting plane, and counting the number of internal points;
c. multi-plane fitting: judging whether the current fitted plane model is reasonable or not according to the number of the internal points, and when the number of the internal points reaches a preset threshold value, considering that the current plane model is better to fit, and taking the current plane model as a final segmentation result; when the number of the inner points is insufficient, reconstructing the inner points into a new point cloud, and executing the steps on the new point cloudObtaining a new oneDividing the new division result with the step +.>And (3) comparing the segmentation results of the cells, and selecting the group with the largest surface element as the final segmentation result.
The specific mode for extracting the geometric features of the prefabricated part point cloud is edge extraction of the prefabricated part point cloud, and the specific steps are as follows:
point-to-pointNeighborhood point->Fitting the tangent plane by least squares, +.> =1，2，…，/>The method comprises the steps of carrying out a first treatment on the surface of the The general expression of the plane equation is shown in formula (1):
（1）；
the least square equation corresponding to each neighborhood point is shown in formula (2):
（2）；
i.e.（3）；
Solving the equation of the above formula (3) to obtain parameters、/>And->Carrying out formula (1) and obtaining a plane equation;
point to PointNeighborhood point->Projection onto tangential plane, point->The projection point in the tangential plane is，/>And->The parametric equation of (2) is shown in the following formula (4):
（4）；
will beSubstituting tangential plane equation to obtain +.>：
（5）；
Wherein,,、/>、/>、/>are constants in the tangential plane equation, and the projection point coordinates ++can be obtained by substituting the formula (5) into the formula (4)>；
In dotsProjection points of +.>For starting point, & lt + & gt>Defining a vector for the endpoint +.>Wherein any vector is taken>The vector product of the vector and the normal vector of the tangential plane is calculated as a vector +.>Then sequentially finding the vector +.>Respectively and vector->Vector->Included angle->、/>When->Included angle->The value of (2) is replaced by->The method comprises the steps of carrying out a first treatment on the surface of the For formula (6)>Sorting, selecting the maximum +.>As maximum angle->；
（6）；
Comparing adjacent two vectorsMaximum included angle->With the magnitude of the set angle threshold, when the maximum included angle +>If the angle is larger than the set angle threshold, then the point +.>Added to the set of boundary points.
The fitting of the edge line of the prefabricated part is to fit a straight line in the boundary point set by randomly selecting a group of points, and evaluate the fitting effect by calculating the distance from other points to the straight line.
When the edge of one plane of the prefabricated component is rectangular, grouping the edge straight lines through the geometric features of the straight lines when fitting the edge lines, and then selecting a group of points after grouping to fit the straight lines.
The fitting of the edge line of the prefabricated part is to perform a straight line fitting algorithm for a plurality of times, so that all boundary points of the plane of the prefabricated part are fitted, and a plurality of fitting straight lines are obtained; calculating the size of the prefabricated part, namely calculating the distance between every two fitting straight lines, and obtaining the size information of the prefabricated part by solving the shortest distance between the two fitting straight lines; comparing the calculated size information of the prefabricated part with the size of the standard prefabricated part, namely calculating by the formula (7) to obtain measurement accuracy：
（7）；
In the formula (7), the amino acid sequence of the compound,for the dimensional information of prefabricated components of the assembly type, +.>Is the size of a standard prefabricated part.
The invention has the advantages that:
(1) According to the method for detecting the size of the prefabricated part based on the point cloud geometric feature extraction, which is provided by the invention, the point cloud segmentation part provides a method for segmenting the point cloud foreground and the background of the prefabricated part based on the normal vector refinement, and the image segmentation is performed based on the region growth of the normal vector refinement, so that the accuracy of segmenting the point cloud foreground and the background is improved, and better initial data is provided for the automatic size detection of the subsequent prefabricated part.
(2) When the geometric features of the point cloud of the prefabricated component are extracted, the boundary feature extraction algorithm based on the normal line improved by the search strategy is adopted, and the boundary feature extraction algorithm based on the normal line improved by the search strategy is implemented by comparing one point with the neighborhood point thereofVector included angle projected in tangential plane to judge whether the point is boundary point or not, the search strategy improvement means that the radius of normal calculation is searched and calculatedThe combination of neighbor searches becomes a hybrid search, thereby balancing the search speed and accuracy; compared with the traditional edge feature extraction algorithm, the method has higher detection precision, is more flexible in detection mode for components with different shapes, breaks the bottleneck of traditional detection, and enables quality detection to be paperless, intelligent and visual.
(3) The invention is based on 3Visual technique, 2->Vision means that by taking a picture of a plane with a camera and then identifying the object by image analysis or comparison, the features on the object plane can be seen, which can be used for absence/presence detection, and 3Visually acquired data are mainly 3 +.>The three-dimensional point cloud data collected by the sensor not only can sense whether the components exist in the scene, but also can accurately detect the size and the position orientation of the components, and the detection data is more comprehensive and accurate.
Detailed Description
The following description will clearly and fully describe the technical solutions of the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The method for detecting the size of the prefabricated part based on the point cloud geometric feature extraction specifically comprises the following steps:
(1) And the point cloud foreground and the background of the prefabricated component are segmented based on the refinement of the normal vector, so that the point cloud of the prefabricated component is segmented from the environment background (prefabricated factory), and the specific mode is as follows:
s11, refining normal vector: traversing each point in the point cloud of the prefabricated component, acquiring adjacent point sets of each point, and calculating curvature change and normal difference between a normal vector of each point and a normal vector of a current point of any point in each adjacent point set; when the curvature change and the normal direction difference exceed the set threshold values, taking the curvature change and the normal direction difference as residual error items, constructing a linear equation set, and calculating an adjustment value of an algorithm vector by solving the linear equation set; updating the normal vector of the current point according to the adjustment value of the normal vector to obtain a refined normal vector, repeating the steps, iterating for a plurality of times to adjust and optimize the normal vector of each point until the preset iteration times are reached, outputting the optimized normal vector as a new normal vector, and orienting the new normal vector to realize the refinement of the normal vector;
s12, performing image segmentation based on region growth refined by normal vector, performing secondary region growth segmentation when segmentation is not complete, and passing through a threshold value based on the condition of over-segmentationThe plane segmentation method comprises the steps of circularly recursively obtaining the maximum bin of an over-segmentation result, and finally, segmenting the point cloud of the prefabricated component from an environmental background;
wherein, based on threshold valueThe plane segmentation method specifically comprises the following steps:
a. establishing a plane model: randomly selecting three points in the point cloud data to establish an initial plane model;
b. single plane fitting: after a plane model is built and a fitting plane is built, calculating the distance between each point in the point cloud and the fitting plane, and counting the number of internal points;
c. multi-plane fitting: judging whether the current fitted plane model is reasonable or not according to the number of the internal points, and when the number of the internal points reaches a preset threshold value, considering that the current plane model is better to fit, and taking the current plane model as a final segmentation result; when the number of the inner points is insufficient, reconstructing the inner points into a new point cloud, and executing the steps on the new point cloudObtaining a new segmentation result, and combining the new segmentation result with the step +.>Comparing the segmentation results of the (a), and selecting a group with the largest surface element as a final segmentation result;
(2) Extracting geometric features of the point cloud of the prefabricated part, wherein the specific mode is edge extraction of the point cloud of the prefabricated part, and the specific steps are as follows:
point-to-pointNeighborhood point->Fitting the tangent plane by least squares, +.> =1，2，…，/>The method comprises the steps of carrying out a first treatment on the surface of the The general expression of the plane equation is shown in formula (1):
（1）；
the least square equation corresponding to each neighborhood point is shown in formula (2):
（2）；
i.e.（3）；
Solving the equation of the above formula (3) to obtain parameters、/>And->Carrying out formula (1) and obtaining a plane equation;
point to PointNeighborhood point->Projection onto tangential plane, point->The projection point in the tangential plane is，/>And->The parametric equation of (2) is shown in the following formula (4):
（4）；
will beSubstituting tangential plane equation to obtain +.>：
（5）；
Wherein,,、/>、/>、/>are constants in the tangential plane equation, and the projection point coordinates ++can be obtained by substituting the formula (5) into the formula (4)>；
In dotsProjection points of +.>For starting point, & lt + & gt>Defining a vector for the endpoint +.>Wherein any vector is taken>The vector product of the vector and the normal vector of the tangential plane is calculated as a vector +.>Then sequentially finding the vector +.>Respectively and vector->Vector->Included angle->、/>When->Included angle->The value of (2) is replaced by->The method comprises the steps of carrying out a first treatment on the surface of the For formula (6)>Sorting, selecting the maximum +.>As maximum angle->；
（6）；
Comparing adjacent two vectorsMaximum included angle->With the magnitude of the set angle threshold, when the maximum included angle +>If the angle is larger than the set angle threshold, then the point +.>Adding the boundary points into the boundary point set;
(3) Fitting the edge line of the prefabricated part according to the geometric characteristics of the point cloud of the prefabricated part, and calculating according to the fitted edge line to obtain the size information of the prefabricated part;
fitting of the edge lines of the prefabricated part is that a plurality of edge lines are fitted simultaneously: fitting straight lines by randomly selecting a group of points in the boundary point set, fitting all boundary points of the plane of the prefabricated part by executing a straight line fitting algorithm for a plurality of times, thereby obtaining a plurality of fitting straight lines, calculating the distance between every two fitting straight lines, obtaining the size information of the prefabricated part by solving the shortest distance between the two fitting straight lines, comparing the calculated size information of the prefabricated part with the size of a standard prefabricated part, namely obtaining the measurement precision by calculating according to a formula (7)：
（7）；
In the formula (7), the amino acid sequence of the compound,for the dimensional information of prefabricated components of the assembly type, +.>Is the size of a standard prefabricated part;
when the edge of one plane of the prefabricated component is rectangular, grouping the edge straight lines through geometric features (such as the start point, the end point, the direction vector and the like of the straight lines) of the straight lines when fitting the edge lines, and then selecting a group of points after grouping to fit the straight lines;
(4) 3 adding size information of prefabricated part under point cloud visualization interfaceText labels.
In order to verify the validity of the invention, the embodiment adopts an experimental method to verify the validity and accuracy of the size detection method of the assembled prefabricated part, and the specific experimental process comprises the following steps: the method comprises the steps of segmenting a foreground and a background of a component point cloud, extracting geometric features of the component point cloud, and automatically detecting the size and displaying a detection result.
1. Component point cloud foreground and background segmentation:
the improved effect of the experimental algorithm is that the indoor data set is disclosedAnd (3) performing verification, wherein two six classes of boxes, stacked boxes, shielding objects, cylindrical objects, mixed objects and complex scenes are selected. Through experiments, the invention has good accuracy and recall rate in object segmentation.
2. Extracting geometric features of component point clouds:
the embodiment mainly adopts the traditional technologyThe algorithm, the traditional boundary feature extraction algorithm based on the normal and the boundary feature extraction algorithm based on the normal with improved search strategy are compared, so that the effectiveness of the invention is embodied. By carrying out experiments on point clouds of 14 members with different shapes, compared with other two algorithms, the algorithm provided by the invention has the greatest number of extracted effective boundary points.
3. Automated size detection:
the experiment is to model six different types of components (the numbers are respectively、/>、、/>、/>、/>) Wherein->、/>、/>The length is the same as that of the real component, belonging to the qualified component, < ->、/>Setting an error of +5mm, < > on the real member>Setting error-1 mm. Tests were performed on six different types of components to determine if the invention is able to accurately detect if the dimensions of the components meet quality standards. To further verify the accuracy of the dimensions of the detection member of the present invention, the experiment was divided into three steps:
(1) And verifying the influence of the distance for acquiring the point cloud data on the experimental result. Express boxes (150 mm×290 mm) similar to the shape of the members were measured at different distances, and the experimental results are shown in table 1 below:
table 1 error table of measured data for different distances
；
(2) The error of the collected point cloud is about 2mm within the distance range of 600mm-1600mm, which is obtained from the above table 1, and the measurement is performedThe precision reaches 98 percent. Thus, 1400mm was chosen as the reference distance when the component point cloud was acquired. Reference to national standard1, allowing deviation of the size of the prefabricated part to be +/-5 mm, adding a risk assessment mechanism, setting the deviation to be low risk within the range of 1mm, and considering the deviation to be qualified without taking any measures; setting the deviation within the range of 1-3mm as a stroke risk, considering that the deviation is possibly unqualified, and suggesting a secondary spot check of a quality inspector; for deviations in the range of 3-5mm, setting the deviation as a high risk, considering that the deviation is high in possibility of failure, releasing warning, and reminding a quality inspector to focus on manual rechecking of the components.
（3）、、/>And->The component is detected as a qualified component,>and->The component is at high risk and needs to be re-checked manually for the second time,/->The component is a medium risk, and the sampling inspection is reminded to be performed again.
After comparing with the manual recheck, the invention meets the quality inspection standard in the detection accuracy of the yard in the factory or outside the factory, realizes the preliminary screening work of the quality detection link, realizes the automation, improves the detection efficiency and ensures the detection accuracy.
Because the number of the point clouds is large, the time is too long when the edge extraction is carried out, and the down-sampling treatment is carried out on the point clouds by adopting an octree sampling method, so that the edge extraction time is reduced, and the efficiency is improved.
4. And (3) displaying a detection result:
selecting an area needing size detection from the point cloud object, selecting a marked point in the area, visualizing the point cloud by using a visualization tool, selecting the marked point by means of mouse interaction and the like, correlating the detected size information with the point cloud data, and displaying the size information at the marked point.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. The method for detecting the size of the prefabricated part based on the point cloud geometric feature extraction is characterized by comprising the following steps of: the method specifically comprises the following steps:
(1) The method comprises the steps of carrying out segmentation on the point cloud foreground and the background of the prefabricated part based on the refinement of normal vector, so that the point cloud of the prefabricated part is segmented from the environment background;
the specific mode for segmenting the point cloud foreground and the background of the prefabricated component based on the normal vector refinement comprises the following steps:
s11, refining normal vector: traversing each point in the point cloud of the prefabricated component, acquiring adjacent point sets of each point, and calculating curvature change and normal difference between a normal vector of each point and a normal vector of a current point of any point in each adjacent point set; when the curvature change and the normal direction difference exceed the set threshold values, taking the curvature change and the normal direction difference as residual error items, constructing a linear equation set, and calculating an adjustment value of an algorithm vector by solving the linear equation set; updating the normal vector of the current point according to the adjustment value of the normal vector to obtain a refined normal vector, repeating the steps, iterating for a plurality of times to adjust and optimize the normal vector of each point until the preset iteration times are reached, outputting the optimized normal vector as a new normal vector, and orienting the new normal vector to realize the refinement of the normal vector;
s12, performing image segmentation based on region growth with refined normal vector, performing secondary region growth segmentation when segmentation is not complete, and circularly recursively acquiring a maximum bin of an over-segmentation result through a RANSAC plane segmentation method based on a threshold value when over-segmentation exists, and finally segmenting a point cloud of the prefabricated component from an environmental background;
(2) Extracting geometric features of the point cloud of the prefabricated part, wherein the specific mode is edge extraction of the point cloud of the prefabricated part, and the specific steps are as follows:
point-to-pointNeighborhood point->Fitting the tangent plane by least squares, +.>2, …, k; the general expression of the plane equation is shown in formula (1):
（1）；
the least square equation corresponding to k neighborhood points is shown in formula (2):
（2）；
i.e.（3）；
Solving the equation of the above formula (3) to obtain parameters、/>And->Carrying out formula (1) and obtaining a plane equation;
point to PointNeighborhood point->Projection onto tangential plane, point->The projection point in the tangential plane is，/>And->The parametric equation of (2) is shown in the following formula (4):
（4）；
will beSubstituting the tangential plane equation to obtain t:
（5）；
wherein A, B, C, D is a constant in the tangential plane equation, and the projection point coordinates can be obtained by substituting formula (5) into formula (4)；
In dotsProjection points of +.>For starting point, & lt + & gt>Defining a vector for the endpoint +.>Wherein any vector is taken>The vector product of the vector and the normal vector of the tangential plane is obtained as a vector v, and then the vector +.>Respectively and vector->Included angle ∈v>、/>When->Included angle->The value of (2) is replaced by->The method comprises the steps of carrying out a first treatment on the surface of the For formula (6)>Sorting, selecting the maximum +.>As maximum angle->；
（6）；
Comparing adjacent two vectorsMaximum included angle->With the set angle threshold, when the maximum included angle isIf the angle is larger than the set angle threshold, then the point +.>Adding the boundary points into the boundary point set;
(3) Fitting the edge line of the prefabricated part according to the geometric characteristics of the point cloud of the prefabricated part, and calculating according to the fitted edge line to obtain the size information of the prefabricated part;
(4) And adding 3D text labels of the size information of the prefabricated parts under the point cloud visual interface.
2. The method for detecting the size of the prefabricated part based on the point cloud geometrical feature extraction according to claim 1, wherein the method comprises the following steps of: the RANSAC plane segmentation method based on the threshold value specifically comprises the following steps:
a. establishing a plane model: randomly selecting three points in the point cloud data to establish an initial plane model;
b. single plane fitting: after a plane model is built and a fitting plane is built, calculating the distance between each point in the point cloud and the fitting plane, and counting the number of internal points;
c. multi-plane fitting: judging whether the current fitted plane model is reasonable or not according to the number of the internal points, and when the number of the internal points reaches a preset threshold value, considering that the current plane model is better to fit, and taking the current plane model as a final segmentation result; and c, when the number of the inner points is insufficient, reconstructing the inner points into a new point cloud, executing the step b on the new point cloud to obtain a new segmentation result, comparing the new segmentation result with the segmentation result of the step b, and selecting a group with the largest bin as a final segmentation result.
3. The method for detecting the size of the prefabricated part based on the point cloud geometrical feature extraction according to claim 1, wherein the method comprises the following steps of: the fitting of the edge line of the prefabricated part is to fit a straight line in the boundary point set by randomly selecting a group of points, and evaluate the fitting effect by calculating the distance from other points to the straight line.
4. The method for detecting the size of the prefabricated part based on the point cloud geometrical feature extraction according to claim 3, wherein the method comprises the following steps of: when the edge of one plane of the prefabricated component is rectangular, grouping the edge straight lines through the geometric features of the straight lines when fitting the edge lines, and then selecting a group of points after grouping to fit the straight lines.
5. The method for detecting the size of the prefabricated part based on the point cloud geometrical feature extraction according to claim 3, wherein the method comprises the following steps of: the fitting of the edge line of the prefabricated part is to perform a straight line fitting algorithm for a plurality of times, so that all boundary points of the plane of the prefabricated part are fitted, and a plurality of fitting straight lines are obtained; calculating the size of the prefabricated part, namely calculating the distance between every two fitting straight lines, and obtaining the size information of the prefabricated part by solving the shortest distance between the two fitting straight lines; comparing the calculated size information of the prefabricated part with the size of the standard prefabricated part, namely calculating by the formula (7) to obtain measurement precision E:
（7）；
in the formula (7), the amino acid sequence of the compound,for the dimensional information of prefabricated components of the assembly type, +.>Is the size of a standard prefabricated part.
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