CN110502771B - Prefabricated part point cloud automatic die closing method based on particle swarm algorithm - Google Patents
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Abstract
The invention provides a prefabricated part point cloud automatic die closing method based on a particle swarm algorithm. The automatic mold closing method comprises the steps of inputting prefabricated part point cloud, retrieving a corresponding BIM model, discretely extracting contour point cloud data of the BIM model, resampling, roughly matching and judging, grading important parts of the BIM model, searching and matching points, optimizing spatial positions and the like. The invention provides a technical method for automatically closing a prefabricated part point cloud and a BIM design model, effectively solves the problem that a large amount of manual intervention is required for closing the prefabricated part and the BIM model during quality detection, and provides method support for later-stage size data estimation.
Description
Technical Field
The invention relates to the field of building safety management, in particular to a prefabricated part point cloud automatic die closing method based on a particle swarm algorithm.
Background
In the assembly building industry, in order to ensure successful installation of prefabricated components at the construction site, strict quality checks are required prior to transportation. The currently adopted quality detection method is a traditional manual measurement method. Manually measuring prefabricated parts has significant disadvantages, such as: in the detection process, the personnel consumption is high, the time and money cost is high, and once the workload is overlarge, the artificial detection error is easily caused. Therefore, when the number of prefabricated parts is large and the form of the prefabricated parts is complex, the three-dimensional laser scanning measurement technology can be used for effectively solving the problems.
At present, technicians usually adopt a method of mold closing detection when performing error detection on point cloud data of prefabricated parts. The mold closing detection refers to matching and combining the point cloud data of the prefabricated part with the BIM design model, so that the purpose of observing and comparing errors is achieved. In the current stage, the matching point needs to be manually specified in the process of mold closing detection, and as a result of manual selection, the matching point has an error, which leads to a more unreliable mold closing result.
Therefore, in order to avoid human intervention in the mold clamping process, it is necessary to develop an automatic mold clamping method that can automatically select the matching points and iteratively update the matching points.
Disclosure of Invention
The invention aims to provide a prefabricated part point cloud automatic die closing method based on a particle swarm algorithm, and aims to solve the problems in the prior art.
The technical scheme adopted for achieving the purpose of the invention is that the automatic die closing method for the point cloud of the prefabricated part based on the particle swarm algorithm comprises the following steps:
1) and inputting prefabricated part point cloud data.
2) And finding a BIM design model corresponding to the prefabricated part in the BIM database, and converting the BIM design model into the expected point cloud.
3) And acquiring a BIM design model contour point set.
4) Resampling the BIM model outline point set obtained in the step 3) and the prefabricated part point cloud data input in the step 1) by utilizing Max Leverage resampling algorithm
5) And carrying out coarse registration on the BIM model contour resampling point set and the prefabricated part resampling point set by utilizing a 4PCS algorithm. And calculating a space rigid body transformation matrix.
6) And carrying out coarse registration on the complete BIM model outline point set and the complete prefabricated part point cloud by utilizing the step space rigid body transformation matrix.
7) Dividing the BIM model contour point set in the step 6) into n levels according to the characteristics of the prefabricated parts. Calculating all levels of contained points according to the total number of the BIM model outline point sets, sampling all levels of points of the BIM model outline point sets by adopting a Max Leverage resampling algorithm, and setting different search neighborhoods to calculate matching points.
8) And iteratively calculating a translation vector and a quaternion between the BIM model outline point set and the prefabricated part point cloud data matching points by adopting a particle swarm algorithm, and performing coordinate transformation on the prefabricated part point cloud data according to the calculated translation vector and quaternion until the prefabricated part point cloud data is optimized for M times and then stopping.
9) And outputting a mold matching result of the BIM model outline point set and the prefabricated part point cloud data.
Further, in step 7), BIM modelThe total number of contour point sets is Num. The BIM model contour point set is divided into 4 levels according to the importance degree. Base search neighborhood radius is tr. The i-th level neighborhood has a radius of (5-i) · tr. Wherein i is 1,2,3, 4. According to three-dimensional coordinates (x, y, z) and unit normal vector (n) of each point in BIM model contour point setx,ny,nz) And coordinates, and circularly calculating the lever values of all the points and arranging the lever values from large to small. And taking out the maximum leverage point every time, judging whether the number of the taken-out points reaches the number of the grading data points of 0.25Num, and if so, updating in a grading way. Stopping when all points are fetched and the ranking is complete.
Further, in the step 7), a Kd-Tree data structure is established for the prefabricated part point cloud data. Searching each point in all BIM model contour point sets to the nearest neighbor point in the prefabricated part point cloud data, selecting the point with the nearest neighbor point distance smaller than the set hierarchical neighborhood radius of the point to construct a model-point cloud paired point pair, and screening the repeated model-point cloud paired point pairs to enable the paired points to be in one-to-one correspondence.
Further, in step 8), the translation vector and quaternion between the paired points obtained in step 7) are calculated.
At any point p (x, y, z) according to any quaternion q (q)0,q1,q2,q3) Calculation rule of rotation:
wherein p' is the point obtained after rotation.
By translating the vector t (t)1,t2,t3) As 3 translation variables, the optimization objective function is set to:
q0,q1,q2,q3,t1,t2,t3∈(-1,1)
wherein R (q) represents the rotation translation calculation, N represents the number of matched points, and all variables are controlled between-1 and 1.
The technical effects of the invention are undoubted:
A. repeatedly optimizing and calculating matching points of a BIM model outline point set and prefabricated part point cloud data by utilizing a particle swarm algorithm so as to finish automatic die assembly;
B. the mold closing process comprises rough matching and optimized matching steps, rough matching completes approximate positioning of the point cloud data of the BIM and the prefabricated part, and optimized matching can perform matching point search in different degrees according to the importance degree of different parts in the BIM, so that the mold closing result is more accurate;
C. the method effectively solves the problems of unreliability and uncertainty of manually selecting matching points in the traditional die assembly process, so that the die assembly process is changed into automation and does not need manual intervention, and a method support is provided for the point cloud error comparison of the prefabricated part in the later period.
Drawings
FIG. 1 is a process flow diagram;
FIG. 2 is a schematic diagram of a prefabricated part point cloud data and a contour point set extracted by a BIM model;
FIG. 3 is a schematic diagram of Max Leverage 10% resampling effect;
FIG. 4 is a schematic diagram illustrating an automatic coarse matching effect using 4 PCS;
FIG. 5 is a schematic diagram illustrating the automatic ranking of the importance of the BIM contour point set;
FIG. 6 is a diagram showing the result of automatic mold clamping.
Detailed Description
The present invention is further illustrated by the following examples, but it should not be construed that the scope of the above-described subject matter is limited to the following examples. Various substitutions and alterations can be made without departing from the technical idea of the invention and the scope of the invention is covered by the present invention according to the common technical knowledge and the conventional means in the field.
Example 1:
referring to fig. 1, the embodiment discloses a point cloud automatic die closing method for a prefabricated part based on a particle swarm algorithm, aiming at the problems of low automation degree, low result reliability, no operability when the detection number is large and the like in the existing prefabricated part die closing detection technology, and comprising the following steps:
1) and inputting prefabricated part point cloud data.
2) And finding a BIM design model corresponding to the prefabricated part in the BIM database, and converting the BIM design model into the expected point cloud.
3) Referring to fig. 2, the BIM design model is discretized, and a set of contour points of the BIM design model is obtained. Wherein 2a is the point cloud data of the concrete external wall panel, and 2b is the outline point set of the BIM model.
4) And (3) respectively resampling the BIM model contour point set obtained in the step (3) and the prefabricated part point cloud data input in the step (1) by utilizing a Max Leverage resampling algorithm. The basic principle of the Max Leverage resampling algorithm is described as follows: according to the three-dimensional coordinates (x, y, z) of the point cloud and the unit normal vector coordinates (n)x,ny,nz) And circularly calculating lever values (Leverage values) of all the points, arranging the lever values from large to small, taking out the maximum lever value point every time, judging whether the taken-out point set reaches a sampling quantity standard, stopping circulation when the sampling quantity standard is reached, and outputting the point set after resampling.
In this embodiment, the resampled data amount accounts for 10% of the original data amount. And the matching point selection of rough matching can be reduced by taking a small amount of data, so that the calculation amount is reduced, and the calculation efficiency is improved. Referring to fig. 3, the sampling points are roughly the whole size corner range of the prefabricated parts.
5) And roughly registering the contour resampling Point Set of the BIM model and the resampling Point Set of the prefabricated part by using a 4PCS (4-Point Congreent Set, four-Point Congruent Set) algorithm. And calculating a space rigid body transformation matrix. The 4PCS algorithm rationale is described as follows: firstly, searching two pairs of space intersection data points in a template point set, and respectively calculating the length of each pair of data points; secondly, searching all data point pairs which meet the two groups of lengths and are intersected in the point matching set; and finally, carrying out space coordinate transformation on the model point set and the pairing point set, detecting whether pairing is successful or not, circulating for many times, and outputting an optimal space rigid body transformation matrix.
6) And carrying out coarse registration on the complete BIM model outline point set and the complete prefabricated part point cloud by utilizing the step space rigid body transformation matrix. As shown in FIG. 4, the rough matching result of the BIM model contour point set and the prefabricated part point cloud data by adopting the 4PCS algorithm is given. In the figure, 4a is a rough matching schematic diagram of the point cloud of the prefabricated part and the outline of the BIM model, and 4b and 4c are rough matching detailed schematic diagrams. 4b and 4c it can be seen that the contour point sets only match roughly at the long edges of the members, while the matching differences at the corners of the embedment or member are large. In actual production, there may be a difference in the dimension of the prefabricated part from the BIM model contour point set.
7) And calculating the lever values of the BIM model contour point set, arranging the lever values from large to small, dividing the lever value grades according to different component characteristics, and setting different search neighborhoods to calculate matching points.
And (4) grading the importance degree of the contour point set into 1-4 grades according to the number Num of the contour point sets of the BIM model.
Setting the radius of a basic search neighborhood as trThe neighborhood radius of each level is (5-i) · trAs shown in FIG. 5, different colors represent different gradations, t in this embodimentr＝5mm。
According to three-dimensional coordinates (x, y, z) and unit normal vector (n) of each point in BIM model contour point setx,ny,nz) Coordinates, circularly calculating lever values of all points, and arranging the lever values from large to small; and taking out the maximum leverage point every time, judging whether the number of the taken-out points reaches the number of the grading data points of 0.25Num, if so, updating the grading, and stopping when all the points are taken out and grading is finished.
Establishing a Kd-Tree data structure for the prefabricated part point cloud data, searching each point in all BIM model contour point sets to the nearest neighbor point in the prefabricated part point cloud data, selecting the point with the nearest neighbor point distance smaller than the set hierarchical neighborhood radius of the point to construct a model-point cloud paired point pair, and screening the repeated model-point cloud paired point pairs to enable the paired points to be in one-to-one correspondence.
8) And iteratively calculating a translation vector and a quaternion between the BIM model outline point set and the prefabricated part point cloud data matching points by adopting a PSO (Particle Swarm Optimization), and performing coordinate transformation on the prefabricated part point cloud data according to the calculated translation vector and quaternion until the prefabricated part point cloud data is optimized for M times and then stopping.
Calculating translation vectors and quaternions between the paired points obtained in the step 7). Substituting quaternions for the rotation matrix can reduce 9 unknown variables of the rotation matrix to 4 unknown variables, greatly reducing the amount of computation.
At any point p (x, y, z) according to any quaternion q (q)0,q1,q2,q3) Calculation rule of rotation:
wherein p' is the point obtained after rotation.
By translating the vector t (t)1,t2,t3) As 3 translation variables, the optimization objective function can therefore be set to:
q0,q1,q2,q3,t1,t2,t3∈(-1,1)
wherein R (q) represents the rotation translation calculation, N represents the number of matched points, and all variables are controlled between-1 and 1.
The basic principle of the particle swarm optimization is as follows: the optimization problem is solved by adopting a simple speed-position model and through cooperation and competition of individuals among the populations.
The problem is that a search space is set to be a 7-dimensional space, and the number of particle swarms is Np：
The position of any particle i is represented by Xi＝(xi1,xi2,...,xiD)；
The flight velocity of any particle i is: vi＝(vi1,vi2,...,viD)；
The historical optimal positions of any particle i are: pi＝(pi1,pi2,...,piD)；
Global optimal position of particle swarm: pg＝(pg1,pg2,...,pgD)；
And (3) updating the speed:
vij(t+1)＝ωvij(t)
+c1×Rand()×(pij(t)-xij(t))+c2×Rand()(pgj(t)-xij(t))
where ω its inertial weight, c1And c2Is a learning factor;
and (3) updating the position: x is the number ofij(t+1)＝xij(t)+vij(t+1)；
The constraint condition is controlled to be-1 ≤ vijX is not less than 1 and-1 is not less thanij≤1；
History best and global best update:
and (3) specifying optimization times M, obtaining a translation vector and a quaternion through each optimization, directly converting the coordinates of the point cloud data of the prefabricated part, repeating the steps 7) and 8) through each optimization, updating the pairing points and the coordinate positions, and stopping after the objective function is optimized for M times. As shown in FIG. 6, the final mold clamping result is given, and the number N of the population is set in the embodimentpThe number of iterations is 3000 at 50, and the number of optimization M is 20. 6a is a mold closing result of the point cloud of the prefabricated part and the BIM model outline, and 6b and 6c are detailed schematic diagrams of mold closing. From the view of the integral mold closing condition, the mold closing condition of the point cloud data of the prefabricated part and the outline data of the BIM model is good, and the lever value grading method of the BIM model is combined to attach importance to the corner part in the point cloud dataAnd the embedded part is used for reducing the importance degree of the data of the middle section of the component, so that the attraction of different model areas to the point cloud data of the prefabricated component is different, and the die closing process is more in line with the detection requirement of the prefabricated component. Fig. 7 shows the iterative convergence result of the algorithm. Fig. 7a shows an optimization evaluation curve in which the abscissa indicates the number of optimization times, the ordinate indicates the evaluation function value, and the final output result is the result when the evaluation value is the highest and the number of optimization times is 18. FIG. 7b shows the convergence accuracy of each optimization of 20 sub-optimizations, the ordinate represents the convergence accuracy, and the abscissa represents the population number multiplied by the iteration number, which shows that the convergence accuracy of each optimization can reach 10-4. The algorithm of the present embodiment is effective.
9) And outputting a mold matching result of the BIM model outline point set and the prefabricated part point cloud data.
It should be noted that in the present embodiment, classification is performed according to the lever values of the contour points of the BIM model, so that the importance degrees of different parts of the component can be automatically distinguished, and the mold closing process is more accurate. After the 4PCS automatic coarse matching is completed, a larger search domain is set at a position (corner, etc.) where the lever value of the BIM model is larger, so that the search capability of the matching point in the whole world is increased. And automatically adjusting the position of the model by utilizing a particle swarm algorithm, continuously updating the matching points, and performing iterative optimization. The final objective is to have the BIM contour point set in a suitable position in the preform point cloud for later size comparison. In the embodiment, a heuristic search algorithm is adopted to optimize the matching problem, the mold closing result of the point cloud data of the prefabricated part and the BIM model outline point set is obtained, the automatic mold closing of the point cloud data of the prefabricated part is realized, more manual operations are avoided, the unreliability of mold closing by a manual method is solved, and therefore method support is provided for the size error evaluation of the prefabricated part.
Claims (2)
1. A prefabricated part point cloud automatic die closing method based on a particle swarm algorithm is characterized by comprising the following steps:
1) inputting prefabricated part point cloud data;
2) finding a BIM design model corresponding to the prefabricated part in a BIM database, and converting the BIM design model into expected point cloud;
3) acquiring a BIM design model outline point set;
4) resampling the BIM model outline point set obtained in the step 3) and the prefabricated part point cloud data input in the step 1) by utilizing Max Leverage resampling algorithm
5) Performing coarse registration on the BIM model contour resampling point set and the prefabricated part resampling point set by using a 4PCS algorithm; calculating a space rigid body transformation matrix;
6) roughly registering the complete BIM model outline point set and the complete prefabricated part point cloud by utilizing the spatial rigid body transformation matrix;
7) dividing the BIM model outline point set in the step 6) into n levels according to the characteristics of the prefabricated parts; calculating all levels of contained points according to the total number of the BIM model outline point sets, sampling all levels of points of the BIM model outline point sets by adopting a Max Leverage resampling algorithm, and setting different search neighborhoods to calculate matching points;
wherein, the number of the lumped points of the BIM model contour points is Num; the BIM model contour point set is divided into 4 levels according to the importance degree; base search neighborhood radius is tr(ii) a The i-th level neighborhood has a radius of (5-i) · tr(ii) a Wherein i is 1,2,3, 4; according to three-dimensional coordinates (x, y, z) and unit normal vector (n) of each point in BIM model contour point setx,ny,nz) Coordinates, circularly calculating lever values of all points, and arranging the lever values from large to small; taking out the maximum leverage point each time, judging whether the number of the taken-out points reaches 0.25Num of the number of the grading data points, and if so, updating in a grading way; stopping when all points are taken out and grading is finished;
establishing a Kd-Tree data structure for the prefabricated part point cloud data; searching each point in all BIM model contour point sets to the nearest neighbor point in the prefabricated part point cloud data; selecting points in the nearest neighbor points, wherein the distance from the corresponding point of the BIM model contour point set to the corresponding point is smaller than the radius of the set hierarchical neighborhood of the corresponding point to construct a model-point cloud paired point pair; screening repeated model-point cloud pairing point pairs to enable the pairing points to correspond one to one;
8) iteratively calculating a translation vector and a quaternion between a BIM model outline point set and a prefabricated part point cloud data matching point by adopting a particle swarm algorithm, and performing coordinate transformation on the prefabricated part point cloud data according to the calculated translation vector and quaternion until the prefabricated part point cloud data is optimized for M times and then stopping;
9) and outputting a mold matching result of the BIM model outline point set and the prefabricated part point cloud data.
2. The automatic die closing method for the prefabricated part point cloud based on the particle swarm algorithm is characterized in that: in step 8), calculating translation vectors and quaternions between the paired points obtained in step 7); at any point p (x, y, z) according to any quaternion q (q)0,q1,q2,q3) The calculation rule of the rotation is as follows:
wherein p' is the point obtained after rotation;
by translating the vector t (t)1,t2,t3) As 3 translation variables, the optimization objective function is set to:
wherein R (q) represents the calculation of the rotational translation, N represents the number of matched points, and all variables are controlled between-1 and 1.
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