CN113793296A - Point cloud data processing method and device - Google Patents
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Abstract
The invention 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 each two groups of point cloud data in the multiple groups of point cloud data to obtain multiple groups of registered point cloud data; sequentially filtering and fitting multiple groups of registered point cloud data to obtain multiple groups of fitted point cloud data; and carrying out deformation parameter extraction on the multiple groups of fitted point cloud data to obtain deformation parameters of the antenna panel. According to the point cloud data processing method, the multiple groups of point cloud data under different acquired conditions are subjected to registration, filtering, bug fixing and fitting processing, and deformation data of the antenna panel are acquired efficiently. 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, the requirements on antenna panels are higher and higher, the sizes of antennas are larger and larger, and the requirements on the accuracy of the antenna panels are higher and higher. In the use process of the antenna, the antenna structure inevitably deforms along with the lapse of time under the action of natural factors such as temperature, wind, frost, rain, snow and the like and self gravity, and on the other hand, the antenna panel also deforms along with the change of the pitch angle, 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, there are a conventional measuring method, an industrial measuring method, and a radio holography method. Conventional measurement methods include mechanical measurement methods, optical measurement methods; the industrial measurement method can be divided 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 different instruments; the radio holography can be classified into a far-field radio holography and a near-field radio holography.
The traditional measuring method has the defects of small 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 large measurement range, high precision, high measurement speed and high automation degree in the process of measuring the antenna panel, but is greatly influenced by the environment and has special requirements on the posture of the antenna panel. The radio system method has the advantages of unlimited measuring range and high precision, but has the characteristics of long data measuring time and certain requirements on the posture 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, according to the point cloud data processing method and device provided by the embodiment of the disclosure, the antenna panel is scanned by the high-precision three-dimensional laser scanner to obtain point cloud data under different conditions, and the point cloud data is subjected to registration, filtering, fitting, bug fixing and deformation data analysis, so that the point cloud data of the large-scale antenna panel is rapidly obtained 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 each two groups of point cloud data in the multiple groups of point cloud data to obtain multiple groups of registered point cloud data; sequentially filtering and fitting multiple groups of registered point cloud data to obtain multiple groups of fitted point cloud data; and carrying out deformation parameter extraction on the multiple groups of fitted point cloud data to obtain deformation parameters of the antenna panel.
Further, before fitting the plurality of sets of registered point cloud data to obtain the plurality of sets of fitted point cloud data, the method further includes: and carrying out bug repairing treatment on the multiple groups of filtered point cloud data to obtain multiple groups of complete point cloud data of the 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 antenna panel pitching angles, different temperature environments and different illumination conditions to obtain the plurality of groups of point cloud data; wherein, the three-dimensional laser scanner sets up on antenna feed platform.
Further, registering each two groups of point cloud data in the multiple groups of point cloud data to obtain multiple groups of registered point cloud data, including: taking the characteristic coordinates of the cubic base as reference points, and extracting at least three characteristic point coordinates of each group of point cloud data; 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 transformation matrix corresponding to the characteristic point coordinates to obtain a plurality of groups of point cloud data after registration.
Further, the filtering processing is performed on the multiple sets of registered point cloud data, and the filtering processing includes: statistical filtering is carried out on stray discrete point cloud data in a plurality of groups of registered point cloud data by adopting a Statistical out-line Remove algorithm to obtain a plurality of groups of point cloud data after primary filtering; and performing secondary filtering on the multiple groups of point cloud data subjected to the first filtering by adopting a Random Sample Consensus algorithm to obtain multiple groups of point cloud data subjected to the second filtering.
Further, the fitting process of the multiple sets of filtered point cloud data includes: and performing surface fitting on the three-dimensional coordinates of the multiple groups of point cloud data subjected to the second filtering to obtain multiple groups of point cloud data subjected to fitting.
Further, the multiple groups of point cloud data comprise multiple groups of main surface point cloud data and multiple groups of secondary surface point cloud data, and the multiple groups of point cloud data subjected to secondary filtering comprise multiple groups of main surface point cloud data subjected to secondary filtering and multiple groups of secondary surface point cloud data subjected to secondary filtering; the method for performing surface fitting on the three-dimensional coordinates of the multiple groups of point cloud data subjected to second filtering comprises the following steps: performing surface fitting on the multiple groups of secondarily filtered main face point cloud data through a ternary high-order polynomial to obtain multiple groups of fitted main face point cloud data; and performing surface fitting on the multiple groups of secondary surface point cloud data subjected to the second filtering through a Biconic function to obtain multiple groups of fitted secondary surface point cloud data.
Further, carry out deformation parameter extraction to the point cloud data after the multiunit is fitted, obtain the deformation parameter of antenna panel, include: 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 the deformation parameters of the antenna panel.
Further, the deformation parameters of the antenna panel include an average value, a standard deviation, a maximum value and a minimum value of the overall deformation 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 registering every two groups of point cloud data in the multiple groups of point cloud data to obtain multiple groups of registered point cloud data; the data processing module is used for sequentially filtering and fitting the multiple groups of registered point cloud data to obtain multiple 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 on which a computer program is stored, 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 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 disclosure.
Compared with the prior art, the method has the following beneficial effects:
(1) the three-dimensional laser scanner can acquire complete point cloud data of the antenna panel at high speed and high precision in a non-contact mode, and three-dimensional information of the antenna panel can be rapidly measured 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 connected with the network cable through the router, and the point cloud data of the antenna panel under any posture can be remotely and automatically measured.
(3) The three-dimensional laser scanning system transmits and receives the laser signals transmitted back through the reflecting prism to obtain target information through actively transmitting the laser signals, 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 performing rapid registration, filtering, bug repairing, surface fitting and deformation parameter extraction on the acquired point cloud data of the antenna panel under different conditions by adopting a specific algorithm, thereby realizing 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 shows a flow diagram of a point cloud data processing method 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 is a schematic diagram illustrating an antenna feed platform according to an embodiment of the present disclosure
FIG. 4 schematically illustrates an antenna panel point cloud data schematic in accordance with an embodiment of the present disclosure;
fig. 5 schematically illustrates a schematic diagram of a cubic pedestal 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 above described method 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 illustrative only 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 disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not 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 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 is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have 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 convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have 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 block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, 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, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon 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 each two groups of point cloud data in the multiple groups of point cloud data to obtain multiple groups of registered point cloud data; sequentially filtering and fitting multiple groups of registered point cloud data to obtain multiple groups of fitted point cloud data; and carrying out deformation parameter extraction on the multiple 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, wherein a non-contact mode three-dimensional laser scanner is adopted to obtain point cloud data of an antenna panel under different conditions, the point cloud data of the antenna panel is measured in a measuring range, the method has the characteristic of high precision, the point cloud data of the complete antenna panel can be obtained at high speed and high precision, the three-dimensional information of the antenna panel can be rapidly measured in real time, and the method can be further applied to rapid monitoring of deformation of the antenna panel; and a specific algorithm is adopted to carry out rapid registration, filtering, leak repairing, surface fitting and deformation parameter extraction on the point cloud data of the antenna panel, so that the deformation data of the antenna panel can be efficiently obtained.
Fig. 1 schematically shows 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 the embodiment of the present disclosure, as shown in fig. 2, which is a schematic structural diagram of an antenna, the antenna 200 includes a main reflective panel 210, a sub-reflective panel 220, and an antenna feed platform 230. The sub-reflecting panel 220 is disposed above the main reflecting panel 210 through an adjusting mechanism, and a plurality of cubic bases 240 are disposed around the antenna feed platform 230, as shown in fig. 3.
According to the embodiment of the disclosure, acquiring multiple groups of point cloud data of an antenna panel comprises: scanning the antenna panel by adopting a three-dimensional laser scanner under different antenna panel pitching angles, 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 the antenna feed platform 230. The three-dimensional laser scanner may be selected from a S150 type three-dimensional laser scanner, which scans in a phase mode, has a range error within 1mm, a scanning field of view of 300 °/360 ° in a longitudinal/transverse direction, a scanning speed (unit point/second) of 97w, 48w, 24w, and 12w, and an angular precision of 19 arc seconds, and is connectable via a WLAN and accessible via a user terminal with HTML 5.
In the embodiment of the disclosure, use three-dimensional laser scanner Tianjin diameter 70 meters antenna to acquire antenna panel's multiunit point cloud data as an example, place three-dimensional laser scanner on the tripod on antenna feed platform 230, three-dimensional laser scanner sends radio signal and links to each other with the router, the router passes through the net twine and is connected to ground, the net twine passes through the switching mouth and connects user terminal, for example desktop, notebook computer etc., set up three-dimensional laser scanner's scanning parameter through user terminal, remote control three-dimensional laser scanner realizes data acquisition, wherein the point cloud data volume of single station is two million, single station measuring time is about 1 ~ 2 minutes. A three-dimensional laser scanner is used to scan the antenna at different panel pitch angles, in different temperature environments and under different illumination conditions to obtain multiple sets of electrical cloud data, where the data obtained by scanning the main reflective panel 210 is main surface point cloud data, the data obtained by scanning the auxiliary reflective panel 220 is auxiliary surface point cloud data, and fig. 4 is a schematic diagram of a set of point cloud data obtained after scanning the antenna. Preferably, the pitching angle range is 10-90 degrees, the range of different temperature environments can be-5-40 ℃, the wind speed does not exceed 28 m/s, and the illumination condition is not limited.
In operation S102, each two sets of point cloud data in the multiple sets of point cloud data are registered to obtain multiple sets of registered point cloud data.
In the embodiment of the present disclosure, each two groups of point cloud data in the multiple groups of point cloud data obtained in step S101 are subjected to registration processing, where each two groups of point cloud data are respectively subjected to registration processing, or one group of point cloud data is used as reference data, and other point cloud data are registered with reference to the reference data.
Specifically, in order to realize high-precision point cloud data registration, the feature cube base is used as a common point, and point cloud data registration under different conditions is realized. Registering each two groups of point cloud data in the multiple groups of point cloud data to obtain multiple groups of registered point cloud data, wherein the registering process comprises the following steps: taking the characteristic coordinates of the cube base 240 as reference points, and extracting at least three characteristic point coordinates of each group of point cloud data; 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 transformation matrix corresponding to the characteristic point coordinates to obtain a plurality of groups of point cloud data after registration. Wherein, cube base 240 is located around the antenna feed platform 230 top and equidistant distribution, and the quantity of the cube base 240 that sets up on the antenna feed platform 230 is relevant with the antenna size, and the cube base of guaranteeing that the effective energy is measured can more than 3, and cube base 240 sets up 10 ~ 15 under the general condition, and it can guarantee that the effective base of measuring is more than 8. The characteristic point coordinates of every two groups of point cloud data are the point coordinates of the same characteristic objects of every two groups of point cloud data, and the point cloud data can be registered within minutes.
In the embodiment of the disclosure, the characteristic coordinate extraction process of the cube base includes the following steps:
1) selecting the top of the cubic base in an interactive mode to obtain a top laser point cloud coordinate;
2) using the top laser point cloud coordinate as the center of a circle, using the length of the angular line of the cubic base as the radius, and adopting a radius search algorithm to obtain point cloud data near the top surface of the cubic base, wherein the top left plane data is the point cloud data near the top surface of the cubic base as shown in fig. 5;
3) setting a top surface elevation threshold according to the elevation of the selected point, and performing elevation filtering to obtain high-quality top surface point cloud data;
4) acquiring point cloud data near the top surface by adopting a random sampling consistency algorithm of a plane model, further optimizing the point cloud data near the top surface, and acquiring the partitioned point cloud data of the top surface;
5) fitting plane model parameters by adopting a least square algorithm of a plane model, and obtaining optimal top surface point cloud and plane model parameters through multiple iterations;
6) calculating the coordinates of the angular points according to the principle that the two planes are intersected into points; obtaining an intersection line of the top surface and the back surface according to the intersection line of the two planes, and taking the center of the line as a characteristic coordinate of the cubic base;
7) and repeating the steps 1) to 6) at least twice, and selecting the characteristic coordinates of at least three cubic bases for registration of 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 each 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: 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), wherein a rotation matrix and a translation matrix of each two groups of point cloud data of the four groups of point cloud data are obtained through calculation, and then the three characteristic point coordinates x 0-x 3 are rotated and translated through a corresponding transformation matrix to obtain the registered point cloud data. The four points are selected so that each group of point cloud data has one more pair of feature point coordinates, and registration accuracy evaluation is better performed.
In the embodiment of the present disclosure, the registration accuracy evaluation specifically includes: and after the point cloud data to be registered is transformed by the translation matrix and the rotation matrix, subtracting the reference point to finally calculate the relative difference value of the coordinate points of the four points, and then calculating the average value to finally obtain the registration precision. The final registration accuracy of the above data is calculated to be 0.140627 mm, so that higher registration accuracy is achieved. It should be noted that, in general, the registration accuracy is evaluated at mm level, and the smaller the registration accuracy, the better the registration accuracy.
In operation S103, filtering and fitting the multiple sets of registered point cloud data sequentially to obtain multiple sets of fitted point cloud data.
Due to the existence of various influence factors in the data acquisition process of the three-dimensional laser scanner, certain errors or errors can be generated in the acquired point cloud data, for example, the point cloud data beyond the range of an antenna panel can be generated in the process of measuring the data of the antenna panel by the three-dimensional laser scanner; due to the discreteness of the laser beams, one emitted light beam may receive noise generated by the reflected light beams returned by different objects; factors such as noise caused by vibration, wind and temperature in the measurement process can generate unnecessary point cloud data and the like, so that the point cloud data of the antenna panel obtained through final measurement comprise noise point cloud data, other point cloud data beyond the range, point cloud data of an antenna panel platform and an auxiliary reflecting surface support and point cloud data of the antenna panel. Therefore, different algorithms are required to be adopted to filter the point cloud data according to the characteristics of different point cloud data of the antenna panel.
According to the embodiment of the disclosure, the filtering processing is performed on the multiple groups of point cloud data after registration, and the filtering processing comprises the following steps: statistical filtering is carried out on stray discrete point cloud data in a plurality of groups of registered point cloud data by adopting a Statistical out-line Remove algorithm to obtain a plurality of groups of point cloud data after primary filtering; and performing secondary filtering on multiple groups of first filtered point cloud data by adopting a Random Sample Consensus algorithm to obtain multiple groups of second filtered point cloud data, wherein the multiple 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 minutes.
Specifically, the filtering processing on the point cloud data specifically includes:
and (3) first filtering: the point cloud data of the antenna panel can generate stray discrete point cloud data in the measuring process, and the points are easily filtered based on a Statistical Outline Remove algorithm. And calculating the average standard deviation of the distances from each point cloud data to all the adjacent points of the point cloud data, wherein the points with the distances outside the standard deviation range can be defined as outliers and deleted, and a statistical filtering-based mode is achieved.
And (3) second filtering: and randomly sampling small samples of the multiple groups of first filtered point cloud data by adopting a Random Sample Consensus algorithm, fitting, and taking the model with the minimum error as an optimal model, namely establishing point cloud data without outliers in the minimum samples. Setting a threshold value after the optimal model is established, verifying multiple groups of first filtered point cloud data by adopting the optimal model, and if the difference between the calculated value and the accurate value of the sample point is greater than the threshold value, determining that the sample point is an error sample and removing the error sample to achieve the secondary filtering effect.
According to the embodiment of the disclosure, after the multiple sets of registered point cloud data are fitted to obtain the multiple sets of filtered point cloud data and before the multiple sets of fitted point cloud data, the method further comprises: and carrying out bug repairing treatment on the multiple groups of filtered point cloud data to obtain multiple groups of complete point cloud data of the antenna panel. In the measurement process, because each group of point cloud data after registration cannot be obtained due to the fact that part of the antenna panel point cloud data which is partially shielded exists, part of the point cloud data which is not obtained exists in the filtered point cloud data, and a scanning leak formed in the scanning process needs to be filled to obtain complete antenna panel point cloud data. Specifically, for any grid of the antenna panel point cloud data, fairing processing is performed on the any grid, wherein the fairing processing conditions include edges of the grid and points of the grid, and in order to realize high-quality point cloud data vulnerability repair, control points need to be added into the grid to ensure better reduction of geometric information, and finally, efficient vulnerability repair of the antenna panel point cloud data is realized.
According to the embodiment of the disclosure, the fitting process of the multiple groups of filtered point cloud data includes: and performing surface fitting on the three-dimensional coordinates of the multiple groups of point cloud data subjected to the second filtering to obtain multiple groups of point cloud data subjected to fitting.
Specifically, the fitting process is respectively performed on the acquired multiple groups of main surface point cloud data and multiple groups of auxiliary surface point cloud data, and the fitting process comprises the following steps: performing surface fitting on the multiple groups of secondarily filtered main face point cloud data through a ternary high-order polynomial to obtain multiple groups of fitted main face point cloud data; and performing surface fitting on the multiple groups of secondary surface point cloud data subjected to the second filtering through a Biconic function to obtain multiple groups of fitted secondary surface point cloud data. In the data fitting process, the main surface point cloud data and the sub surface point cloud data are fitted to obtain a main surface equation and a sub surface equation of the antenna panel, and the deformation parameters of each point on the antenna panel can also be obtained by analyzing the main surface equation and the sub surface equation.
In operation S104, deformation parameters of the plurality of groups of fitted point cloud data are extracted to obtain deformation parameters of the antenna panel.
In the embodiment of the disclosure, a cross-hatching and regular point mode is adopted, and difference analysis is performed on the multiple groups of fitted point cloud data obtained in the step S103, the historical point cloud data and the historical antenna design model to obtain the deformation parameters of the antenna panel. Specifically, the point cloud data obtained under different conditions can be subjected to single comparative 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 integral deformation average value, a standard deviation, a maximum value and a minimum value of the antenna panel.
Further, the average value, the standard deviation, the maximum value and the minimum value of the overall deformation of the antenna panel are further analyzed by combining with the primary surface equation and the secondary surface 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 above data analysis can process point cloud data under a single condition, for example, point cloud data at different temperatures, at the same pitch angle, and at the same illumination intensity; the point cloud data with multiple condition changes can also be processed, for example, the point cloud data with different temperatures, different pitch angles and the same illumination intensity is analyzed and processed to obtain deformation parameters of the antenna panel under different conditions, and finally, the deformation result is output to realize the visual analysis of the deformation of the antenna panel; the point cloud data of the antenna panels of a plurality of different stations can be analyzed and processed simultaneously to obtain deformation parameters of the antenna panels of different stations.
Fig. 6 schematically shows 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: 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.
And the data acquisition module 610 is used for acquiring multiple groups of point cloud data of the antenna panel. According to an embodiment of the present disclosure, the data obtaining module 610 may be configured to perform the step S101 described above with reference to fig. 1, for example, and is not described herein again.
And the data registration module 620 is configured to perform registration processing on each two groups of point cloud data in the multiple groups of point cloud data to obtain multiple groups of point cloud data after registration. According to an embodiment of the present disclosure, the data registration module 620 may be used to perform the step S102 described above with reference to fig. 1, for example, and is not described herein again.
And a data processing module 630, configured to sequentially perform filtering and fitting processing on the multiple sets of registered point cloud data to obtain multiple sets of fitted point cloud data. According to an embodiment of the present disclosure, the data processing module 630 may be configured to perform the step S103 described above with reference to fig. 1, for example, and is not described herein again.
And the antenna deformation analysis module 640 is configured to perform deformation parameter extraction on the multiple sets of fitted point cloud data to obtain deformation parameters of the antenna panel. The antenna deformation analysis module 640 may be configured to perform the step S104 described above with reference to fig. 1, for example, and is not described herein again.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of 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 a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any plurality 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 and implemented in one module, or any one of the modules may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment 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 partially as a hardware circuit, 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 by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or implemented by a suitable combination of any several of them. Alternatively, 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 above described method according to an embodiment of the present disclosure. The electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 7, the electronic device 700 described in this embodiment includes: a processor 701, which can perform 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 associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. 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 the different actions of the method flows according to embodiments of the present disclosure.
In the RAM703, various programs and data necessary for the operation of the system 700 are stored. The processor 701, the ROM 702, and the RAM703 are connected to each other by a bus 704. The processor 701 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 702 and/or the RAM 703. It is noted that the programs may also be stored in one or more memories other than the ROM 702 and RAM 703. The processor 701 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. 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 containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program, when executed by the processor 701, performs the above-described functions defined in the system of the embodiment of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
An embodiment of the present invention further provides a computer-readable storage medium, which may be included in the apparatus/device/system described in the foregoing embodiment; or may exist separately and not be assembled into the device/apparatus/system. The above-mentioned computer-readable storage medium carries one or more programs which, 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 embodiments of the 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, a computer-readable storage medium may include the ROM 702 and/or the RAM703 and/or one or more memories other than the ROM 702 and the RAM703 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. When the computer program product runs in a computer system, the program code is used for causing the computer system to realize the point cloud data processing method provided by the embodiment of the disclosure.
The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 701. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In one embodiment, the computer program may be hosted 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 in the form of a signal on a network medium, distributed, downloaded and installed via the communication section 709, and/or installed from the removable medium 711. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program, when executed by the processor 701, performs the above-described functions defined in the system of the embodiment of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, 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., through 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 are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be substantially or partially embodied in the form of a software product, or all or part of the technical solution that contributes to the prior art.
The flowchart 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 various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
While the 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 disclosure as defined by the appended claims and their equivalents. Accordingly, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.
Claims (10)
1. A point cloud data processing method is characterized by comprising the following steps:
acquiring a plurality of groups of point cloud data of an antenna panel;
registering each two groups of point cloud data in the multiple groups of point cloud data to obtain multiple groups 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 carrying out deformation parameter extraction on the plurality of groups of fitted point cloud data to obtain deformation parameters of the antenna panel.
2. The method of processing point cloud data according to claim 1, wherein before fitting 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 carrying out bug repairing treatment on the multiple groups of filtered point cloud data to obtain multiple groups of complete point cloud data of the antenna panel.
3. The method of claim 1, wherein the obtaining multiple sets of point cloud data for an antenna panel comprises:
scanning the antenna panel by adopting a three-dimensional laser scanner under different antenna panel pitching angles, different temperature environments and different illumination conditions to obtain the plurality of groups of point cloud data; wherein, the three-dimensional laser scanner sets up on antenna feed platform.
4. The point cloud data processing method of claim 1, wherein the 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 comprises:
taking the characteristic coordinates of the cubic base as reference points, and extracting at least three characteristic point coordinates of each group of point cloud data; 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 transformation matrix corresponding to the characteristic point coordinates to obtain the multiple groups of point cloud data after registration.
5. The point cloud data processing method of claim 1, wherein the filtering the plurality of sets of registered point cloud data comprises:
statistical filtering is carried out on the scattered point cloud data in the multiple groups of registered point cloud data by adopting a Statistical out-line Remove algorithm to obtain multiple groups of point cloud data after primary filtering;
and performing secondary filtering on the multiple groups of first filtered point cloud data by adopting a Random Sample Consensus algorithm to obtain multiple groups of second filtered point cloud data.
6. The point cloud data processing method of claim 5, wherein fitting the plurality of sets of filtered point cloud data comprises:
and performing surface fitting on the three-dimensional coordinates of the multiple groups of point cloud data subjected to the second filtering to obtain multiple groups of point cloud data subjected to fitting.
7. The point cloud data processing method of claim 6, wherein the plurality of sets of point cloud data includes a plurality of sets of primary surface point cloud data and a plurality of sets of secondary surface point cloud data, the plurality of sets of second filtered point cloud data including a plurality of sets of second filtered primary surface point cloud data and a plurality of sets of second filtered secondary surface point cloud data; performing surface fitting on the three-dimensional coordinates of the multiple groups of point cloud data subjected to the second filtering, including:
performing surface fitting on the multiple groups of secondarily filtered main face point cloud data through a ternary high-order polynomial to obtain multiple groups of fitted main face point cloud data;
and performing surface fitting on the multiple groups of secondary surface point cloud data subjected to the second filtering through a Biconic function to obtain multiple groups of fitted secondary surface point cloud data.
8. The point cloud data processing method of claim 1, wherein the extracting deformation parameters of the plurality of groups of fitted point cloud data to obtain the deformation parameters of the antenna panel comprises:
and performing difference analysis on the plurality of groups of fitted point cloud data, historical point cloud data and historical antenna design models by adopting a section line and regular point mode to obtain deformation parameters of the antenna panel.
9. The point cloud data processing method of claim 1, wherein the deformation parameters of the antenna panel include an average value, a standard deviation, a maximum value, and a minimum value of the overall deformation of the antenna panel.
10. 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 registering each 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 filtering and fitting the multiple groups of registered point cloud data to obtain multiple 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.
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