CN113793296B - Point cloud data processing method and device - Google Patents
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- 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
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Abstract
The disclosure provides a point cloud data processing method, comprising the following steps: acquiring a plurality of groups of point cloud data of an antenna panel; registering every two sets of point cloud data in the plurality of sets of point cloud data to obtain a plurality of sets of registered point cloud data; sequentially filtering and fitting the multiple groups of registered point cloud data to obtain multiple groups of fitted point cloud data; and extracting deformation parameters of the plurality of groups of fitted point cloud data to obtain the deformation parameters of the antenna panel. According to the point cloud data processing method, the deformation data of the antenna panel are obtained efficiently through registration, filtering, vulnerability restoration and fitting processing of multiple groups of point cloud data under different acquired conditions. The disclosure also provides a point cloud data processing device.
Description
Technical Field
The disclosure relates to the technical field of antenna panel data processing, in particular to a point cloud data processing method and device.
Background
With the development of deep space exploration industry, the requirements on the antenna panel are higher and higher, the antenna size is larger and larger, and the requirements on the accuracy of the antenna panel are higher and higher. In the use process of the antenna, under the action of natural factors such as temperature, wind, frost, rain and snow and self gravity, the antenna structure inevitably deforms along with the time, and on the other hand, along with the change of a pitch angle, the antenna panel also deforms, so that the high-precision deformation monitoring of the antenna panel is the premise of ensuring the normal work of the antenna.
According to the development of the antenna panel measuring method, it can be classified into a conventional measuring method, an industrial measuring method and a radio hologram method. The traditional measurement method comprises a mechanical measurement method and an optical measurement method; the industrial measurement method can be classified into an optical theodolite measurement method, a total station measurement method, a three-dimensional laser scanner measurement method, a laser tracker measurement method, and a photogrammetry method according to the difference of instruments; the electro-optical hologram may be classified into a far-field electro-optical hologram and a near-field electro-optical hologram.
The traditional measuring method has the defects of smaller measuring range and complicated measuring process, and has higher requirements on the attitude of the antenna panel. The industrial measurement method has the characteristics of larger measuring range, higher precision, higher measurement speed and higher degree of automation in the process of measuring the antenna panel, but is greatly influenced by environment and has special requirements on the attitude of the antenna panel. The radio full system method has the advantages of unlimited range and higher precision, but has the characteristics of long data measuring time and certain requirements on the attitude of the antenna panel.
Based on the characteristics of the antenna panel deformation measurement method, a high-efficiency, high-precision and high-automation antenna panel data processing method needs to be provided.
Disclosure of Invention
In order to solve the problems in the prior art, the method and the device for processing the point cloud data provided by the embodiment of the disclosure adopt a high-precision three-dimensional laser scanner to scan an antenna panel to obtain the point cloud data under different conditions, register, filter, fit, bug repair and deformation data analysis are performed on the point cloud data, so that the point cloud data of a large-sized antenna panel is rapidly acquired and processed, and the deformation data of the antenna panel can be rapidly obtained.
A first aspect of the present disclosure provides a point cloud data processing method, including: acquiring a plurality of groups of point cloud data of an antenna panel; registering every two sets of point cloud data in the plurality of sets of point cloud data to obtain a plurality of sets of registered point cloud data; sequentially filtering and fitting the multiple groups of registered point cloud data to obtain multiple groups of fitted point cloud data; and extracting deformation parameters of the plurality of groups of fitted point cloud data to obtain the deformation parameters of the antenna panel.
Further, the method further comprises the steps of performing fitting processing on the plurality of groups of registered point cloud data to obtain a plurality of groups of fitted point cloud data: and performing vulnerability restoration processing on the plurality of groups of filtered point cloud data to obtain a plurality of groups of point cloud data of the complete antenna panel.
Further, acquiring multiple sets of point cloud data of the antenna panel includes: scanning the antenna panel by adopting a three-dimensional laser scanner under different pitching angles of the antenna panel, different temperature environments and different illumination conditions to obtain the plurality of groups of point cloud data; wherein, three-dimensional laser scanner sets up on antenna feed platform.
Further, performing registration processing on each two sets of point cloud data in the multiple sets of point cloud data to obtain multiple sets of registered point cloud data, including: extracting at least three characteristic point coordinates of each group of point cloud data by taking the characteristic coordinates of the cube base as reference points; calculating a transformation matrix of each two groups of point cloud data according to at least three characteristic point coordinates of each two groups of point cloud data; and rotating and translating at least three characteristic point coordinates of each group of point cloud data through a corresponding transformation matrix to obtain a plurality of groups of registered point cloud data.
Further, filtering the plurality of sets of registered point cloud data, including: carrying out statistical filtering on stray discrete point cloud data in a plurality of groups of registered point cloud data by adopting STATISTICAL OUTLINE REMOVE algorithm to obtain a plurality of groups of point cloud data after primary filtering; and performing secondary filtering on the plurality of groups of first filtered point cloud data by adopting Random Sample Consensus algorithm to obtain a plurality of groups of second filtered point cloud data.
Further, fitting the plurality of groups of filtered point cloud data includes: and performing surface fitting on the three-dimensional coordinates of the plurality of groups of point cloud data subjected to the second filtering to obtain a plurality of groups of fitted point cloud data.
Further, the plurality of sets of point cloud data comprise a plurality of sets of primary face point cloud data and a plurality of sets of secondary face point cloud data, and the plurality of sets of second filtered point cloud data comprise a plurality of sets of second filtered primary face point cloud data and a plurality of sets of second filtered secondary face point cloud data; performing surface fitting on three-dimensional coordinates of a plurality of groups of point cloud data after second filtering, wherein the surface fitting comprises the following steps: performing surface fitting on the main surface point cloud data subjected to the second filtering by using a ternary high-order polynomial to obtain main surface point cloud data subjected to the fitting; and performing surface fitting on the plurality of groups of secondary surface point cloud data subjected to secondary filtering through a Biconic function to obtain a plurality of groups of fitted secondary surface point cloud data.
Further, extracting deformation parameters of the plurality of groups of fitted point cloud data to obtain deformation parameters of the antenna panel, wherein the method comprises the following steps: and performing difference analysis on the plurality of groups of fitted point cloud data, the historical point cloud data and the historical antenna design model by adopting a section line and regular point mode to obtain deformation parameters of the antenna panel.
Further, the deformation parameters of the antenna panel include an overall deformation average value, a standard deviation, a maximum value and a minimum value of the antenna panel.
A second aspect of the present disclosure provides a point cloud data processing apparatus, including: the data acquisition module is used for acquiring a plurality of groups of point cloud data of the antenna panel; the data registration module is used for carrying out registration processing on every two groups of point cloud data in the plurality of groups of point cloud data to obtain a plurality of groups of registered point cloud data; the data processing module is used for sequentially carrying out filtering and fitting processing on the plurality of groups of registered point cloud data to obtain a plurality of groups of fitted point cloud data; and the antenna deformation analysis module is used for extracting deformation parameters of the plurality of groups of fitted point cloud data to obtain the deformation parameters of the antenna panel.
A third aspect of the present disclosure provides an electronic device, comprising: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the point cloud data processing method provided by the first aspect of the disclosure.
A fourth aspect of the present disclosure provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the point cloud data processing method provided by the first aspect of the present disclosure.
A fifth aspect of the present disclosure provides a computer program product comprising a computer program which, when executed by a processor, implements the point cloud data processing method provided by the first aspect of the present disclosure.
Compared with the prior art, the method has the following advantages:
(1) The three-dimensional laser scanner can acquire the point cloud data of the complete antenna panel at high speed and high precision in a non-contact mode, and three-dimensional information of the antenna panel is rapidly determined in real time.
(2) The working state of the large antenna can be any pitching angle, the three-dimensional laser scanner is fixed on the antenna panel and is connected with the network cable through the router, so that the remote and high-automation measurement of the point cloud data acquisition of the antenna panel under any posture is realized.
(3) The three-dimensional laser scanning system obtains target information by actively transmitting laser signals and transmitting and receiving the transmitted laser signals through the reflecting prism, is not limited by conditions such as external illumination, air pressure and temperature, adopts the three-dimensional laser scanner to measure the point cloud data of the antenna panel, can measure the antenna panel in real time, and is not influenced by external environment.
(4) And carrying out rapid registration, filtering, vulnerability restoration, surface fitting and deformation parameter extraction on the obtained point cloud data of the antenna panel under different conditions by adopting a specific algorithm point cloud data, so as to realize high-efficiency acquisition of the deformation data of the antenna panel.
Drawings
For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
FIG. 1 schematically illustrates a flow chart of a method of point cloud data processing according to an embodiment of the present disclosure;
fig. 2 schematically illustrates a structural schematic of an antenna according to an embodiment of the present disclosure;
fig. 3 schematically illustrates a structural diagram of an antenna feed platform according to an embodiment of the present disclosure
Fig. 4 schematically illustrates an antenna panel point cloud data schematic according to an embodiment of the present disclosure;
Fig. 5 schematically illustrates a cube base schematic on an antenna feed platform according to an embodiment of the present disclosure;
FIG. 6 schematically illustrates a block diagram of a point cloud data processing apparatus according to an embodiment of the present disclosure;
Fig. 7 schematically shows a block diagram of an electronic device adapted to implement the method described above according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a convention should be interpreted in accordance with the meaning of one of skill in the art having generally understood the convention (e.g., "a system having at least one of A, B and C" would include, but not be limited to, systems having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a formulation similar to at least one of "A, B or C, etc." is used, in general such a formulation should be interpreted in accordance with the ordinary understanding of one skilled in the art (e.g. "a system with at least one of A, B or C" would include but not be limited to systems with a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
Some of the block diagrams and/or flowchart illustrations are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, when executed by the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). Additionally, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon, the computer program product being for use by or in connection with an instruction execution system.
The embodiment of the disclosure provides a point cloud data processing method, which comprises the following steps: acquiring a plurality of groups of point cloud data of an antenna panel; registering every two sets of point cloud data in the plurality of sets of point cloud data to obtain a plurality of sets of registered point cloud data; sequentially filtering and fitting the multiple groups of registered point cloud data to obtain multiple groups of fitted point cloud data; and extracting deformation parameters of the plurality of groups of fitted point cloud data to obtain the deformation parameters of the antenna panel.
The embodiment of the disclosure provides a point cloud data processing method, which is characterized in that point cloud data of an antenna panel under different conditions are obtained by adopting a non-contact three-dimensional laser scanner, the point cloud data of the antenna panel is measured in a range, the accuracy is high, the point cloud data of the complete antenna panel can be obtained at high speed and high accuracy, the real-time rapid measurement of three-dimensional information of the antenna panel is realized, and the method can be further applied to rapid monitoring of the deformation of the antenna panel; and the specific algorithm is adopted to perform rapid registration, filtering, vulnerability restoration, surface fitting and deformation parameter extraction on the point cloud data of the antenna panel, so that the deformation data of the antenna panel is obtained with high efficiency.
Fig. 1 schematically illustrates a flow chart of a point cloud data processing method according to an embodiment of the present disclosure. As shown in fig. 1, the method includes: steps S101 to S104.
In operation S101, a plurality of sets of point cloud data of an antenna panel are acquired.
In an embodiment of the present disclosure, as shown in fig. 2, the antenna 200 includes a main reflection panel 210, a sub reflection panel 220, and an antenna feed platform 230. Wherein the secondary reflecting panel 220 is erected above the primary reflecting panel 210 by an adjusting mechanism, and a plurality of cube bases 240 are arranged around above the antenna feed platform 230, as shown in fig. 3.
According to an embodiment of the present disclosure, acquiring multiple sets of point cloud data for an antenna panel includes: scanning the antenna panel by adopting a three-dimensional laser scanner under different pitching angles of the antenna panel, different temperature environments and different illumination conditions to obtain the plurality of groups of point cloud data; wherein the three-dimensional laser scanner is disposed on an antenna feed platform 230. The three-dimensional laser scanner can be selected as an S150-type three-dimensional laser scanner, the scanning mode is a phase type three-dimensional laser scanner, the ranging error is within 1mm, the longitudinal/transverse direction of a scanning visual field is 300 degrees/360 degrees, the scanning speed (unit point/second) can be 97w, 48w, 24w and 12w, the angular precision is 19 angular seconds, and the three-dimensional laser scanner can be connected through a WLAN and can be accessed through a user terminal with an HTML 5.
In the embodiment of the disclosure, taking an antenna with the Tianjin diameter of 70 meters of a three-dimensional laser scanner to obtain a plurality of groups of point cloud data of an antenna panel as an example, placing the three-dimensional laser scanner on a tripod on an antenna feed source platform 230, sending out wireless signals by the three-dimensional laser scanner to be connected with a router, connecting the router to the ground through a network cable, connecting the network cable with a user terminal such as a desktop computer, a notebook computer and the like through a switching port, setting scanning parameters of the three-dimensional laser scanner through the user terminal, and remotely controlling the three-dimensional laser scanner to realize data acquisition, wherein the point cloud data amount of a single station is two tens of millions, and the single station measurement time is about 1 to 2 minutes. The three-dimensional laser scanner is utilized to scan different panel pitching angles, different temperature environments and different illumination conditions of the antenna to obtain multiple groups of electric cloud data, wherein the data obtained by scanning the main reflection panel 210 is main surface point cloud data, the data obtained by scanning the auxiliary reflection panel 220 is auxiliary surface point cloud data, and a group of point cloud data schematic diagrams obtained after scanning the antenna are shown in fig. 4. Preferably, the pitch angle range is 10-90 degrees, the different temperature environment ranges can be within the range of-5-40 ℃, the wind speed is not more than 28 m/s, and the illumination condition is not limited.
In operation S102, registering each two sets of point cloud data in the plurality of sets of point cloud data to obtain a plurality of sets of registered point cloud data.
In the embodiment of the disclosure, the registering processing is performed on each two sets of point cloud data in the plurality of sets of point cloud data acquired in the step S101, which may be that each two sets of point cloud data are respectively registered, or one set of point cloud data is used as reference data, and other point cloud data are registered with the reference data as reference.
Specifically, in order to realize high-precision point cloud data registration, point cloud data registration under different conditions is realized by adopting a feature cube base as a common point. Registering each two sets of point cloud data in the plurality of sets of point cloud data to obtain a plurality of sets of registered point cloud data, wherein the registering comprises the following steps: extracting at least three feature point coordinates of each set of point cloud data by taking the feature coordinates of the cube base 240 as reference points; calculating a transformation matrix of each two groups of point cloud data according to at least three characteristic point coordinates of each two groups of point cloud data; and rotating and translating at least three characteristic point coordinates of each group of point cloud data through a corresponding transformation matrix to obtain a plurality of groups of registered point cloud data. The cube bases 240 are located around the upper side of the antenna feed source platform 230 and are distributed at equal intervals, the number of the cube bases 240 arranged on the antenna feed source platform 230 is related to the size of the antenna, the number of the cube bases measured by effective energy is guaranteed to be more than 3, and generally 10-15 cube bases 240 are arranged, so that the number of the measured effective bases is guaranteed to be more than 8. The characteristic point coordinates of every two sets of point cloud data are the point coordinates of the same characteristic object of every two sets of point cloud data, and the registration of the point cloud data can be completed within a few minutes.
In an embodiment of the present disclosure, a feature coordinate extraction process of a cube base includes the steps of:
1) Selecting the top of the cube base in an interactive mode to obtain a top laser point cloud coordinate;
2) The top laser point cloud coordinate is taken as the circle center, the length of the corner line of the cube base is taken as the radius, a radius search algorithm is adopted to obtain the near point cloud data of the top surface of the cube base, and as shown in fig. 5, the upper left plane data is the near point cloud data of the top surface of the cube base;
3) Setting a top surface elevation threshold according to the elevation of the selected point, and carrying out high Cheng Guolv to obtain high-quality top surface point cloud data;
4) Acquiring top surface near point cloud data by adopting a random sampling consistency algorithm of a plane model, and further optimizing the top surface near point cloud data to acquire segmented top surface point cloud data;
5) Fitting plane model parameters by adopting a least square algorithm of a plane model, and obtaining an optimal top surface point cloud and the plane model parameters through multiple iterations;
6) Calculating the coordinates of the corner points according to the principle that two planes intersect to form points; according to the intersection of two planes into a line, an intersection line of the top surface and the back surface is obtained, and the center of the line is taken as the characteristic coordinate of the cube base;
7) Repeating the steps 1) to 6) at least twice, and selecting at least three characteristic coordinates of the cube base for registering the point cloud data of the actually measured antenna panel and the theoretical model of the antenna panel.
And solving a transformation matrix according to three pairs of point cloud data of every two groups, wherein the transformation matrix comprises a rotation matrix and a translation matrix, and finally realizing the registration of the point cloud data through a cube base. For example, the reference point coordinates are: x0 (1.566,2.008, -1.658), X1 (0.393,1.945, -1.658), X2 (-1.889, -1.179, -1.658), and X3 (-0.886, -3.256, -1.660). The three-dimensional coordinates of the point cloud data to be registered are as follows: and x0 (3.035,0.00,3.266), x1 (2.808,1.152,3.266), x2 (-0.605,2.974,3.266) and x3 (-2.5211.689,3.266), calculating to obtain a rotation matrix and a translation matrix of each two groups of point cloud data of the four groups of point cloud data, and then rotating and translating three characteristic point coordinates x 0-x 3 through a transformation matrix corresponding to the rotation matrix to obtain registered point cloud data. The four points are selected for each group of point cloud data to be more than one pair of characteristic point coordinates, so that registration accuracy evaluation is better.
In an embodiment of the present disclosure, the registration accuracy evaluation specifically includes: and after the point cloud data to be registered are transformed through a translation matrix and a rotation matrix, subtracting the point cloud data from the reference points to finally calculate the relative difference value of four point coordinate points, and then calculating the average value to finally obtain the registration precision. The final registration accuracy of the data is 0.140627 mm, so that higher registration accuracy is achieved. In general, the registration accuracy is evaluated at the mm level, and the smaller the registration accuracy is, the better the registration accuracy is.
In operation S103, filtering and fitting are sequentially performed on the multiple sets of registered point cloud data, so as to obtain multiple sets of fitted point cloud data.
The three-dimensional laser scanner can generate certain errors or errors due to the existence of various influencing factors in the process of acquiring the data, for example, the three-dimensional laser scanner can generate point cloud data beyond the range of the antenna panel in the process of measuring the antenna panel data; noise generated by reflected beams returned by different objects may be received by one emitted beam due to the dispersion of the laser beams; the factors such as noise caused by vibration, wind and temperature in the measuring process can generate unnecessary point cloud data and the like, so that the point cloud data of the antenna panel obtained by final measurement comprise noise point cloud data, other point cloud data exceeding the range, point cloud data of an antenna panel platform and a secondary reflecting surface bracket and point cloud data of the antenna panel. Therefore, aiming at the characteristics of different point cloud data of the antenna panel, different algorithms are needed to be adopted to carry out filtering processing on the point cloud data.
According to an embodiment of the present disclosure, filtering a plurality of sets of registered point cloud data includes: carrying out statistical filtering on stray discrete point cloud data in a plurality of groups of registered point cloud data by adopting STATISTICAL OUTLINE REMOVE algorithm to obtain a plurality of groups of point cloud data after primary filtering; and performing secondary filtering on a plurality of groups of first filtered point cloud data by adopting Random Sample Consensus algorithm to obtain a plurality of groups of second filtered point cloud data, wherein the plurality of groups of second filtered point cloud data are high-quality electric cloud data, and the filtering of the point cloud data can be completed within a few minutes.
Specifically, the filtering processing of the point cloud data specifically includes:
First filtering: the point cloud data of the antenna panel can generate some stray discrete point cloud data in the measuring process, and the points are easy to filter based on STATISTICAL OUTLINE REMOVE algorithm. For each point cloud data, calculating the average distance standard deviation from each point cloud data to all adjacent points, wherein the points with the distances outside the standard deviation range can be defined as outliers and deleted, so that a statistical filtering-based mode is achieved.
And (3) secondary filtering: and adopting Random Sample Consensus algorithm to randomly sample a plurality of groups of small samples of the first filtered point cloud data, fitting, and taking a model with the minimum error as an optimal model, namely establishing the point cloud data without outliers in the minimum samples. After the establishment of the optimal model, setting a threshold value, adopting the optimal model to verify a plurality of groups of first filtered point cloud data, and if the difference between the calculated value and the accurate value of the sample point is greater than the threshold value, considering the sample point as an error sample and rejecting the error sample so as to achieve the secondary filtering effect.
According to an embodiment of the present disclosure, after performing fitting processing on a plurality of sets of registered point cloud data to obtain a plurality of sets of filtered point cloud data, before the plurality of sets of fitted point cloud data, the method further includes: and performing vulnerability restoration processing on the plurality of groups of filtered point cloud data to obtain a plurality of groups of point cloud data of the complete antenna panel. In the measurement process, as the point cloud data of each group of registered antenna panel cannot be obtained due to the existence of the partially shielded antenna panel point cloud data, the filtered point cloud data also has the partial point cloud data not to be obtained, and the scanning loopholes formed in the scanning process are filled to obtain the complete antenna panel point cloud data. Specifically, for any grid of the point cloud data of the antenna panel, the fairing processing is performed on the point cloud data, wherein the fairing processing conditions comprise the edges of the grid and the points of the grid, and in order to achieve high-quality point cloud data bug fixes, control points are needed to be added into the grid so as to ensure better restoration of geometric information, and finally achieve efficient bug fixes of the point cloud data of the antenna panel.
According to an embodiment of the present disclosure, fitting a plurality of sets of filtered point cloud data includes: and performing surface fitting on the three-dimensional coordinates of the plurality of groups of point cloud data subjected to the second filtering to obtain a plurality of groups of fitted point cloud data.
Specifically, fitting processing is performed on the obtained multiple groups of main surface point cloud data and multiple groups of auxiliary surface point cloud data respectively, including: performing surface fitting on the main surface point cloud data subjected to the second filtering by using a ternary high-order polynomial to obtain main surface point cloud data subjected to the fitting; and performing surface fitting on the plurality of groups of secondary surface point cloud data subjected to secondary filtering through a Biconic function to obtain a plurality of groups of fitted secondary surface point cloud data. In the data fitting process, the principal plane equation and the auxiliary plane equation of the antenna panel can be obtained by fitting the principal plane point cloud data and the auxiliary plane point cloud data, and the deformation parameters of each point on the antenna panel can also be obtained by analyzing the principal plane equation and the auxiliary plane equation.
In operation S104, deformation parameters of the antenna panel are obtained by extracting deformation parameters of the plurality of sets of fitted point cloud data.
In the embodiment of the disclosure, a section line and a regular point mode are adopted, and the point cloud data obtained in the step S103 after multiple groups of fitting is subjected to differential analysis with historical point cloud data and a historical antenna design model to obtain deformation parameters of an antenna panel. Specifically, the obtained point cloud data under different conditions can be subjected to independent comparison analysis or multi-condition analysis to obtain deformation parameters of the antenna panel under different conditions, wherein the deformation parameters of the antenna panel at least comprise an overall deformation average value, a standard deviation, a maximum value and a minimum value of the antenna panel.
Further, the overall deformation average value, standard deviation, maximum value and minimum value of the antenna panel are further analyzed by combining the main equation and the auxiliary equation of the antenna panel, so that the deformation statistics of a single antenna panel and the equation change of the section line of the antenna panel can be obtained, and the adjustment quantity of each panel of the large antenna is obtained and is output as a deformation result.
It should be noted that, the data analysis may process the point cloud data under a single condition, for example, process the point cloud data with the same pitch angle and the same illumination intensity at different temperatures; the multi-condition change point cloud data can be processed, such as the point cloud data with different temperatures, different pitch angles and the same illumination intensity are analyzed and processed to obtain deformation parameters of the antenna panel under different conditions, and finally the deformation results are output to realize visual analysis of the deformation of the antenna panel; and the point cloud data of the antenna panels of a plurality of different sites can be analyzed and processed simultaneously to obtain the deformation parameters of the antenna panels of different sites.
Fig. 6 schematically illustrates a block diagram of a point cloud data processing apparatus according to an embodiment of the present disclosure.
As shown in fig. 6, the point cloud data processing apparatus 600 includes: the system comprises a data acquisition module 610, a data registration module 620, a data processing module 630 and an antenna deformation analysis module 640. The system 600 may be used to implement the point cloud data processing method described with reference to fig. 1.
The data acquisition module 610 is configured to acquire multiple sets of point cloud data of the antenna panel. The data acquisition module 610 may be used, for example, to perform the step S101 described above with reference to fig. 1 according to an embodiment of the present disclosure, which is not described herein.
The data registration module 620 is configured to perform registration processing on each two sets of point cloud data in the multiple sets of point cloud data, so as to obtain multiple sets of registered point cloud data. The data registration module 620 may be used, for example, to perform the step S102 described above with reference to fig. 1, according to an embodiment of the present disclosure, which is not described herein.
The data processing module 630 is configured to sequentially perform filtering and fitting processing on the multiple sets of registered point cloud data, so as to obtain multiple sets of fitted point cloud data. The data processing module 630 may be used, for example, to perform step S103 described above with reference to fig. 1 according to an embodiment of the present disclosure, which is not described herein.
And the antenna deformation analysis module 640 is configured to extract deformation parameters of the plurality of sets of fitted point cloud data, so as to obtain deformation parameters of the antenna panel. The antenna deformation analysis module 640 may be used, for example, to perform the step S104 described above with reference to fig. 1, which is not described herein.
Any number of modules, sub-modules, units, sub-units, or at least some of the functionality of any number of the sub-units according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented as split into multiple modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or in any other reasonable manner of hardware or firmware that integrates or encapsulates the circuit, or in any one of or a suitable combination of three of software, hardware, and firmware. Or one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be at least partially implemented as computer program modules, which, when executed, may perform the corresponding functions.
For example, any of the data acquisition module 610, the data registration module 620, the data processing module 630, and the antenna deformation analysis module 640 may be combined in one module to be implemented, or any of the modules may be split into multiple modules. Or at least some of the functionality of one or more of the modules may be combined with, and implemented in, at least some of the functionality of other modules. According to embodiments of the present disclosure, at least one of the data acquisition module 610, the data registration module 620, the data processing module 630, and the antenna deformation analysis module 640 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging the circuitry, or in any one of or a suitable combination of three of software, hardware, and firmware. Or at least one of the data acquisition module 610, the data registration module 620, the data processing module 630 and the antenna deformation analysis module 640 may be at least partially implemented as a computer program module which, when executed, may perform the corresponding functions.
Fig. 7 schematically shows a block diagram of an electronic device adapted to implement the method described above, according to an embodiment of the disclosure. The electronic device shown in fig. 7 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 7, the electronic device 700 described in the present embodiment includes: a processor 701 which can execute various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. The processor 701 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 701 may also include on-board memory for caching purposes. The processor 701 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flows according to embodiments of the disclosure.
In the RAM 703, various programs and data required for the operation of the system 700 are stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. The processor 701 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 702 and/or the RAM 703. Note that the program may be stored in one or more memories other than the ROM 702 and the RAM 703. The processor 701 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the present disclosure, the electronic device 700 may further include an input/output (I/O) interface 705, the input/output (I/O) interface 705 also being connected to the bus 704. The system 700 may also include one or more of the following components connected to the I/O interface 705: an input section 706 including a keyboard, a mouse, and the like; an output portion 707 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read therefrom is mounted into the storage section 708 as necessary.
According to embodiments of the present disclosure, the method flow according to embodiments of the present disclosure may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 701. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
Embodiments of the present invention also provide a computer-readable storage medium that may be included in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs that, when executed, implement a point cloud data processing method according to an embodiment of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In an embodiment of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 702 and/or RAM703 and/or one or more memories other than ROM 702 and RAM703 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code, when executed in a computer system, is configured to cause the computer system to implement the point cloud data processing method provided by the embodiments of the present disclosure.
The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 701. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed over a network medium in the form of signals, downloaded and installed via the communication section 709, and/or installed from the removable medium 711. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 701. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
It should be noted that, each functional module in each embodiment of the present disclosure may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such an understanding, the technical solution of the invention may be embodied essentially or partly in the form of a software product or in part in addition to the prior art.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be combined in various combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.
While the present disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure as defined by the appended claims and their equivalents. The scope of the disclosure should, therefore, not be limited to the above-described embodiments, but should be determined not only by the following claims, but also by the equivalents of the following claims.
Claims (7)
1. The point cloud data processing method is characterized by comprising the following steps of:
Acquiring a plurality of groups of point cloud data of an antenna panel;
registering every two sets of point cloud data in the plurality of sets of point cloud data to obtain a plurality of sets of registered point cloud data;
Sequentially performing filtering and fitting treatment on the multiple groups of registered point cloud data to obtain multiple groups of fitted point cloud data; the filtering processing of the plurality of groups of registered point cloud data comprises the following steps: carrying out statistical filtering on stray discrete point cloud data in the plurality of groups of registered point cloud data by adopting STATISTICAL OUTLINE REMOVE algorithm to obtain a plurality of groups of point cloud data after primary filtering; performing secondary filtering on the plurality of groups of point cloud data subjected to primary filtering by adopting Random Sample Consensus algorithm to obtain a plurality of groups of point cloud data subjected to secondary filtering; fitting the plurality of groups of filtered point cloud data, including: performing surface fitting on the three-dimensional coordinates of the plurality of groups of point cloud data subjected to the second filtering to obtain a plurality of groups of point cloud data subjected to fitting; the plurality of sets of point cloud data comprise a plurality of sets of primary face point cloud data and a plurality of sets of secondary face point cloud data, and the plurality of sets of second filtered point cloud data comprise a plurality of sets of second filtered primary face point cloud data and a plurality of sets of second filtered secondary face point cloud data; performing surface fitting on the three-dimensional coordinates of the plurality of sets of point cloud data after the second filtering includes: performing surface fitting on the multiple groups of main surface point cloud data subjected to secondary filtering through a ternary high-order polynomial to obtain multiple groups of fitted main surface point cloud data; performing surface fitting on the plurality of groups of secondary surface point cloud data subjected to secondary filtering through a Biconic function to obtain a plurality of groups of fitted secondary surface point cloud data;
and extracting deformation parameters of the plurality of groups of fitted point cloud data to obtain the deformation parameters of the antenna panel.
2. The method for processing point cloud data according to claim 1, wherein before performing fitting processing on the plurality of sets of registered point cloud data to obtain a plurality of sets of fitted point cloud data, the method further comprises:
And performing vulnerability restoration processing on the plurality of groups of filtered point cloud data to obtain a plurality of groups of complete point cloud data of the antenna panel.
3. The method of claim 1, wherein the obtaining a plurality of sets of point cloud data for an antenna panel comprises:
Scanning the antenna panel by adopting a three-dimensional laser scanner under different pitching angles of the antenna panel, different temperature environments and different illumination conditions to obtain the plurality of groups of point cloud data; wherein, three-dimensional laser scanner sets up on antenna feed platform.
4. The method for processing point cloud data according to claim 1, wherein performing registration processing on every two sets of point cloud data in the plurality of sets of point cloud data to obtain a plurality of sets of registered point cloud data includes:
Extracting at least three characteristic point coordinates of each group of point cloud data by taking the characteristic coordinates of the cube base as reference points; wherein the cube base is positioned on the antenna feed source platform;
calculating a transformation matrix of each two groups of point cloud data according to at least three characteristic point coordinates of each two groups of point cloud data;
and rotating and translating at least three characteristic point coordinates of each group of point cloud data through a corresponding transformation matrix to obtain a plurality of groups of registered point cloud data.
5. The method for processing point cloud data according to claim 1, wherein the extracting deformation parameters of the plurality of sets of fitted point cloud data to obtain deformation parameters of the antenna panel includes:
and performing difference analysis on the plurality of groups of fitted point cloud data, the historical point cloud data and the historical antenna design model by adopting a section line and regular point mode to obtain deformation parameters of the antenna panel.
6. The method of claim 1, wherein the deformation parameters of the antenna panel include an overall deformation average, standard deviation, maximum, and minimum of the antenna panel.
7. A point cloud data processing apparatus, comprising:
The data acquisition module is used for acquiring a plurality of groups of point cloud data of the antenna panel;
The data registration module is used for carrying out registration processing on every two groups of point cloud data in the plurality of groups of point cloud data to obtain a plurality of groups of registered point cloud data;
The data processing module is used for sequentially carrying out filtering and fitting processing on the plurality of groups of registered point cloud data to obtain a plurality of groups of fitted point cloud data; the filtering processing of the plurality of groups of registered point cloud data comprises the following steps: carrying out statistical filtering on stray discrete point cloud data in the plurality of groups of registered point cloud data by adopting STATISTICAL OUTLINE REMOVE algorithm to obtain a plurality of groups of point cloud data after primary filtering; performing secondary filtering on the plurality of groups of point cloud data subjected to primary filtering by adopting Random Sample Consensus algorithm to obtain a plurality of groups of point cloud data subjected to secondary filtering; fitting the plurality of groups of filtered point cloud data, including: performing surface fitting on the three-dimensional coordinates of the plurality of groups of point cloud data subjected to the second filtering to obtain a plurality of groups of point cloud data subjected to fitting; the plurality of sets of point cloud data comprise a plurality of sets of primary face point cloud data and a plurality of sets of secondary face point cloud data, and the plurality of sets of second filtered point cloud data comprise a plurality of sets of second filtered primary face point cloud data and a plurality of sets of second filtered secondary face point cloud data; the performing surface fitting on the three-dimensional coordinates of the plurality of sets of point cloud data after the second filtering includes: performing surface fitting on the multiple groups of main surface point cloud data subjected to secondary filtering through a ternary high-order polynomial to obtain multiple groups of fitted main surface point cloud data; performing surface fitting on the plurality of groups of secondary surface point cloud data subjected to secondary filtering through a Biconic function to obtain a plurality of groups of fitted secondary surface point cloud data;
And the antenna deformation analysis module is used for extracting deformation parameters of the plurality of groups of fitted point cloud data to obtain the deformation parameters of the antenna panel.
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