CN115641450A - Point cloud data-based tower insulator extraction method and device and storage medium - Google Patents
Point cloud data-based tower insulator extraction method and device and storage medium Download PDFInfo
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Abstract
The invention discloses a method and a device for extracting a tower insulator based on point cloud data and a storage medium. The extraction method of the pole tower insulator based on the point cloud data comprises the following steps: removing non-ground points of point cloud data through a preset iterative filtering encryption algorithm to determine ground point data; according to the ground point data, ground point removing is carried out on the point cloud data, and non-ground point data are determined; and calculating non-ground point data according to a preset pole tower insulator model, and extracting a pole tower insulator. The technical problems that no relevant research exists in the prior art for extracting the tower insulator based on the point cloud data, and the efficient extraction of the tower insulator can not be carried out through the point cloud data are solved.
Description
Technical Field
The invention relates to the technical field of point cloud data extraction, in particular to a method and a device for extracting a tower insulator based on point cloud data and a storage medium.
Background
At present, the research on automatic extraction of the pole and tower insulators at home and abroad is less, relevant contents are identified based on infrared images, and no relevant research is currently available for automatic extraction of the pole and tower insulators based on laser point cloud data. For modeling of the pole and tower insulator, the number and quality of actual point cloud data of the pole and tower insulator, the geometric forms of a wire and an iron tower and account information of the pole and tower insulator need to be considered. The pole tower insulators are different in functions and shapes, so that the technical problem of how to efficiently extract the pole tower insulators through point cloud data exists at present.
Aiming at the technical problems that related research is not available in the prior art for extracting the pole tower insulator based on point cloud data, and efficient extraction of the pole tower insulator cannot be performed through the point cloud data, an effective solution is not provided at present.
Disclosure of Invention
The embodiment of the disclosure provides a method and a device for extracting a tower insulator based on point cloud data and a storage medium, so as to at least solve the technical problems that no relevant research exists in the prior art for extracting the tower insulator based on the point cloud data, and the efficient extraction of the tower insulator can not be carried out through the point cloud data.
According to one aspect of the embodiment of the disclosure, a method for extracting a tower insulator based on point cloud data is provided, which includes: removing non-ground points of point cloud data through a preset iterative filtering encryption algorithm to determine ground point data; according to the ground point data, ground point removing is carried out on the point cloud data, and non-ground point data are determined; and calculating the non-ground point data according to a preset pole tower insulator model, and extracting the pole tower insulator.
According to another aspect of the embodiments of the present disclosure, there is also provided a storage medium including a stored program, wherein the method of any one of the above is performed by a processor when the program is executed.
According to another aspect of the embodiments of the present disclosure, there is also provided a device for extracting a tower insulator based on point cloud data, including: the first determining module is used for removing non-ground points of point cloud data through a preset iterative filtering encryption algorithm to determine ground point data; the second determining module is used for removing ground points from the point cloud data according to the ground point data and determining non-ground point data; and the extraction module is used for calculating the non-ground point data according to a preset pole tower insulator model and extracting a pole tower insulator.
According to another aspect of the embodiments of the present disclosure, there is also provided a device for extracting a tower insulator based on point cloud data, including: a processor; and a memory coupled to the processor for providing instructions to the processor for processing the following processing steps: removing non-ground points of point cloud data through a preset iterative filtering encryption algorithm to determine ground point data; according to the ground point data, ground point removing is carried out on the point cloud data, and non-ground point data are determined; and calculating non-ground point data according to a preset pole tower insulator model, and extracting a pole tower insulator.
In the embodiment of the disclosure, since the non-ground points are relatively discrete, the point cloud data is extracted through the iterative filtering encryption algorithm, the ground point data can be efficiently extracted, and then the point cloud data is processed through the ground point data to obtain the non-ground point data, so as to obtain the non-ground point data with higher precision. By comprehensively investigating the shape characteristics of common pole tower insulators and analyzing the mathematical expression of the model, a perfect pole tower insulator model library is established for construction. The tower insulator is extracted from the non-ground point data with high precision, and the technical effect of effectively extracting the tower insulator is achieved. The technical problems that no relevant research exists in the prior art for extracting the tower insulator based on the point cloud data, and the efficient extraction of the tower insulator can not be carried out through the point cloud data are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:
fig. 1 is a hardware block diagram of a computing device for implementing the method according to embodiment 1 of the present disclosure;
fig. 2 is a schematic flow chart of a method for extracting a tower insulator based on point cloud data according to a first aspect of embodiment 1 of the present disclosure;
fig. 3 is a schematic diagram of non-ground point culling for point cloud data according to the first aspect of embodiment 1 of the present disclosure;
fig. 4 is a schematic diagram of a tower insulator extraction device based on point cloud data according to embodiment 2 of the disclosure; and
fig. 5 is a schematic diagram of an extraction device for a tower insulator based on point cloud data according to embodiment 3 of the present disclosure.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. It is to be understood that the described embodiments are merely exemplary of some, and not all, of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to the present embodiment, there is also provided an embodiment of a method for extracting a tower insulator based on point cloud data, where it is to be noted that the steps illustrated in the flowchart of the drawings may be executed in a computer system such as a set of computer executable instructions, and although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be executed in an order different from that here.
The method embodiments provided by the present embodiment may be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Fig. 1 shows a hardware structure block diagram of a computing device for implementing a method for extracting a tower insulator based on point cloud data. As shown in fig. 1, the computing device may include one or more processors (which may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, and a transmission device for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, the computing device may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
It should be noted that the one or more processors and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuitry may be a single, stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computing device. As referred to in the disclosed embodiments, the data processing circuit acts as a processor control (e.g., selection of a variable resistance termination path connected to the interface).
The memory may be used to store a software program and a module of application software, for example, a program instruction/data storage device corresponding to the extraction method for a tower insulator based on point cloud data in the embodiment of the present disclosure, and the processor executes various functional applications and data processing by operating the software program and the module stored in the memory, that is, implements the extraction method for a tower insulator based on point cloud data of the application program. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory located remotely from the processor, which may be connected to the computing device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device is used for receiving or sending data via a network. Specific examples of such networks may include wireless networks provided by communication providers of the computing devices. In one example, the transmission device includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computing device.
It should be noted here that in some alternative embodiments, the computing device shown in fig. 1 described above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that FIG. 1 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in a computing device as described above.
Under the operating environment, according to the first aspect of the embodiment, a method for extracting a tower insulator based on point cloud data is provided. Fig. 2 shows a flow diagram of the method, which, with reference to fig. 2, comprises:
s201: removing non-ground points of point cloud data through a preset iterative filtering encryption algorithm to determine ground point data;
s202: according to the ground point data, ground point elimination is carried out on the point cloud data, and non-ground point data are determined;
s203: and calculating the non-ground point data according to a preset pole tower insulator model, and extracting a pole tower insulator.
As described in the background art, at present, few researches on automatic extraction of pole and tower insulators are performed at home and abroad, relevant contents are identified based on infrared images, and no relevant researches are performed on automatic extraction of the pole and tower insulators based on laser point cloud data. For modeling of the pole and tower insulator, the number and quality of actual point cloud data of the pole and tower insulator, the geometric forms of a wire and an iron tower and account information of the pole and tower insulator need to be considered. The pole tower insulators are different in functions and shapes, so that the technical problem of how to efficiently extract the pole tower insulators through point cloud data exists at present.
In view of this, the present application provides a method for extracting a tower insulator based on point cloud data, where a computing device may remove non-ground points of point cloud data through a preset iterative filtering encryption algorithm to determine ground point data.
In particular, the point cloud data may be lidar point cloud data onboard a helicopter. At present, most classification algorithms are based on local similarity of point cloud during classification, for example, local gradient difference and elevation difference are needed to be compared, and the morphology of an object is needed to be compared. Therefore, an algorithm for uniformly subdividing the ground feature categories does not exist, a targeted algorithm principle is established according to the characteristics of different ground feature point clouds, and the method for effectively and quickly classifying most point clouds is a common classification mode. The lead, the pole tower, the building, the crossover line and the pole tower insulator are key elements forming a non-ground point, and therefore a rapid classification algorithm for the ground objects is provided.
The TIN-based filtering algorithm has the thought principle that an irregular triangular net can well fit to the detail information of the approximate terrain surface, and the gradient change between triangular surfaces in the TIN can well reflect the fluctuation change of the terrain surface. On the basis of the principle of abrupt elevation change, the fluctuation change between adjacent triangles should be continuous and smooth, and if the fluctuation is violent, the non-ground points exist in the area. Therefore, non-ground points can be effectively removed through an iterative filtering encryption algorithm.
Furthermore, the computing device can also perform ground point elimination on the point cloud data according to the ground point data to determine non-ground point data. By the method, the non-ground points of the point cloud data are effectively removed, so that the generated ground point data is high in precision. Therefore, the point cloud data are processed according to the ground point data, and the accuracy of the obtained non-ground point data is higher. Therefore, the effect of effectively extracting the non-ground point data is realized.
And further, calculating the non-ground point data according to a preset pole tower insulator model, and extracting pole tower insulators. Therefore, the obtained high-precision non-ground point data is calculated according to the pole tower insulator model, and the extraction precision of the pole tower insulator can be improved. The tower insulator model is constructed by comprehensively investigating the shape characteristics of common tower insulators and analyzing the mathematical expression of the model and establishing a perfect tower insulator model library.
Therefore, through the mode, the non-ground points are relatively discrete, the point cloud data are subjected to non-ground point extraction through the iterative filtering encryption algorithm, the ground point data can be efficiently extracted, then the point cloud data are processed through the ground point data, the non-ground point data are obtained, and the non-ground point data with high precision are further obtained. A perfect tower insulator model library is established for construction by comprehensively investigating the shape characteristics of common tower insulators and analyzing the mathematical expression of the model. The tower insulator is extracted from the non-ground point data with high precision, and the technical effect of effectively extracting the tower insulator is achieved. The technical problems that no relevant research exists in the prior art for extracting the tower insulator based on the point cloud data, and the efficient extraction of the tower insulator can not be carried out through the point cloud data are solved.
Optionally, the method further comprises: and denoising the point cloud data by using a preset denoising algorithm.
Specifically, point cloud denoising is to use a denoising algorithm based on point cloud local spatial distribution statistics to mark points with a large difference between local point density and overall point density as noise points. Therefore, the effect of effectively carrying out follow-up processing on the point cloud data is achieved.
Optionally, the operation of removing non-ground points of point cloud data and determining ground point data by using a preset iterative filtering encryption algorithm includes: judging whether the angular points in the point cloud data are non-ground points one by one; and under the condition that the angular points are non-ground points, removing the ground points and determining the ground point data.
Optionally, the operation of determining one by one whether an angular point in the point cloud data is a non-ground point includes: dividing the point cloud data according to a pre-selected seed area to generate seed point cloud data; building a triangulation network model at angular points in the seed point cloud data through a triangulation network generation rule; selecting three mutually adjacent lowest points in the seed point cloud data as initial ground points by using a triangulation network model, wherein the three lowest points are three vertexes of a triangle in the triangulation network model; selecting another vertex of the adjacent triangle which has a common side with the triangle as a candidate point; calculating the plane included angle between the triangle and the adjacent triangle and the distance from the alternative point to the triangle; and under the condition that the plane included angle and the distance are smaller than the preset judgment threshold value, the alternative point is a ground point, otherwise, the alternative point is a non-ground point.
Specifically, referring to fig. 3, the principle of the filter algorithm based on the filter algorithm of the TIN is that an irregular triangular network can well fit to the detailed information of the approximate terrain surface, and the gradient change between triangular surfaces in the TIN can well reflect the fluctuation change of the terrain surface. On the basis of the principle of abrupt elevation change, fluctuation changes between adjacent triangles should be continuously and smoothly, and if severe changes exist, non-ground points exist in the area of the adjacent triangles.
Selecting a seed region for filtering, and establishing a TIN triangulation network model in the whole region by using the original discrete airborne laser radar corner points in the region by using a TIN triangulation network generation rule. Three mutually adjacent lowest points in the area are selected as initial ground points, and the three initial ground points are the vertexes of a triangle T in the TIN triangulation network. And (3) taking the vertex of the adjacent triangle which has a common side with the triangle T as an alternative point, and calculating the plane included angle between the two triangles and the distance from the point to the triangle T. If the included angle and the distance are smaller than the given judgment threshold value, accepting the alternative point as a ground point, selecting a triangle taking the newly accepted ground point and an adjacent ground point as vertexes, and judging the next point to be judged; otherwise, the non-ground is considered to be filtered, the judged non-ground points are removed from the TIN triangulation network, the TIN triangulation network with the non-ground points removed is generated, and the next alternative point is judged by the same method until all laser foot points are judged.
Optionally, the operation of calculating the non-ground point data and extracting the tower insulator according to a preset tower insulator model includes: determining a tower insulator preliminary model of the tower insulator according to the number of the transmission conductors near the tower insulator and the type of the power tower; optimizing the tower insulator preliminary model through a least square algorithm to determine a tower insulator optimal model; modeling the optimal model of the pole tower insulator according to the hanging point of the transmission conductor and the hanging point of the pole tower insulator, and determining the pole tower insulator model; and calculating the non-ground point data by using the pole tower insulator model, and extracting the pole tower insulator.
Particularly, at present, the research on automatic extraction of the pole and tower insulators at home and abroad is less, related contents are identified based on infrared images, and the automatic extraction research of the pole and tower insulators based on laser point cloud data is firstly adopted in the project. For modeling of the pole and tower insulator, the number and quality of actual point cloud data of the pole and tower insulator, the geometric forms of a wire and an iron tower and account information of the pole and tower insulator need to be considered. The pole tower insulators are different in function and shape, so that before modeling, the shape characteristics of common pole tower insulators are comprehensively investigated, the mathematical expression of the model is analyzed, and a perfect pole tower insulator model library is established.
The method comprises the steps of firstly determining a possible model of a tower insulator aiming at actual point cloud of the tower insulator, such as the number of transmission conductors near the tower insulator and the type of a power tower; then, determining an optimal model of the pole tower insulator by using a least square algorithm; and finally, referring to the wire hanging point and the pole tower insulator hanging point, thereby modeling the pole tower insulator.
Further, referring to fig. 1, according to a second aspect of the present embodiment, there is provided a storage medium. The storage medium comprises a stored program, wherein the method of any of the above is performed by a processor when the program is run.
Therefore, according to the embodiment, since the non-ground points are relatively discrete, the point cloud data is extracted through the iterative filtering encryption algorithm, the ground point data can be efficiently extracted, and then the point cloud data is processed through the ground point data to obtain the non-ground point data, so that the non-ground point data with higher precision is obtained. By comprehensively investigating the shape characteristics of common pole tower insulators and analyzing the mathematical expression of the model, a perfect pole tower insulator model library is established for construction. The tower insulator is extracted from the non-ground point data with high precision, and the technical effect of effectively extracting the tower insulator is achieved. The technical problems that no relevant research exists in the prior art for extracting the tower insulator based on the point cloud data, and the efficient extraction of the tower insulator can not be carried out through the point cloud data are solved.
It should be noted that for simplicity of description, the above-mentioned method embodiments are shown as a series of combinations of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.
Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method according to the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
Example 2
Fig. 4 shows an apparatus 400 for extracting a tower insulator based on point cloud data according to the embodiment, where the apparatus 400 corresponds to the method according to the first aspect of the embodiment 1. Referring to fig. 4, the apparatus 400 includes: the first determining module 410 is configured to remove non-ground points of point cloud data through a preset iterative filtering encryption algorithm to determine ground point data; the second determining module 420 is configured to perform ground point elimination on the point cloud data according to the ground point data, and determine non-ground point data; and the extracting module 430 is used for calculating the non-ground point data according to a preset pole tower insulator model and extracting a pole tower insulator.
Optionally, the apparatus 400 further comprises: and the denoising module is used for denoising the point cloud data by utilizing a preset denoising algorithm.
Optionally, the first determining module 410 includes: the judgment sub-module is used for judging whether the angular points in the point cloud data are non-ground points one by one; and the first determining submodule is used for removing ground points and determining ground point data under the condition that the angular points are non-ground points.
Optionally, the determining sub-module includes: the generating unit is used for dividing the point cloud data according to a pre-selected seed area to generate seed point cloud data; the establishing unit is used for establishing a triangulation network model at the angular points in the seed point cloud data through a triangulation network generating rule; the first selection unit is used for selecting three mutually adjacent lowest points in the seed point cloud data as initial ground points by utilizing a triangulation network model, wherein the three lowest points are three vertexes of a triangle in the triangulation network model; the second selecting unit is used for selecting another vertex of the adjacent triangle which has a common side with the triangle as a candidate point; the calculating unit is used for calculating the plane included angle between the triangle and the adjacent triangle and the distance from the alternative point to the triangle; and the judging unit is used for judging that the alternative points are ground points under the condition that the plane included angle and the distance are smaller than the preset judging threshold value, and otherwise, the alternative points are non-ground points.
Optionally, the extracting module 430 includes: the second determining submodule is used for determining a tower insulator preliminary model of a tower insulator according to the number of transmission conductors near the tower insulator and the type of the power tower; the third determining submodule is used for optimizing the tower insulator preliminary model through a least square algorithm and determining a tower insulator optimal model; the fourth determining submodule is used for modeling the optimal model of the pole tower insulator according to the hanging point of the transmission conductor and the hanging point of the pole tower insulator and determining the pole tower insulator model; and the extraction submodule is used for calculating the non-ground point data by using the pole and tower insulator model and extracting the pole and tower insulator.
Therefore, according to the embodiment, since the non-ground points are relatively discrete, the point cloud data is extracted through the iterative filtering encryption algorithm, the ground point data can be efficiently extracted, and then the point cloud data is processed through the ground point data to obtain the non-ground point data, so that the non-ground point data with higher precision is obtained. By comprehensively investigating the shape characteristics of common pole tower insulators and analyzing the mathematical expression of the model, a perfect pole tower insulator model library is established for construction. The tower insulator is extracted from the non-ground point data with high precision, and the technical effect of effectively extracting the tower insulator is achieved. The technical problems that no relevant research exists in the prior art for extracting the tower insulator based on the point cloud data, and the efficient extraction of the tower insulator can not be carried out through the point cloud data are solved.
Example 3
Fig. 5 shows an apparatus 500 for extracting a tower insulator based on point cloud data according to the present embodiment, where the apparatus 500 corresponds to the method according to the first aspect of embodiment 1. Referring to fig. 5, the apparatus 500 includes: a processor 510; and a memory 520 coupled to the processor 510 for providing instructions to the processor 510 to process the following process steps: removing non-ground points of point cloud data through a preset iterative filtering encryption algorithm to determine ground point data; according to the ground point data, ground point removing is carried out on the point cloud data, and non-ground point data are determined; and calculating the non-ground point data according to a preset pole tower insulator model, and extracting the pole tower insulator.
Optionally, the memory 520 is further configured to provide the processor 510 with instructions to process the following process steps: and denoising the point cloud data by using a preset denoising algorithm.
Optionally, the operation of removing non-ground points of point cloud data and determining ground point data by using a preset iterative filtering encryption algorithm includes: judging whether the angular points in the point cloud data are non-ground points one by one; and under the condition that the angular points are non-ground points, removing the ground points and determining the ground point data.
Optionally, the operation of determining one by one whether an angular point in the point cloud data is a non-ground point includes: dividing the point cloud data according to a pre-selected seed area to generate seed point cloud data; building a triangulation network model at angular points in the seed point cloud data through a triangulation network generation rule; selecting three mutually adjacent lowest points in the seed point cloud data as initial ground points by using a triangulation network model, wherein the three lowest points are three vertexes of a triangle in the triangulation network model; selecting another vertex of the adjacent triangle which has a common side with the triangle as a candidate point; calculating the plane included angle between the triangle and the adjacent triangle and the distance from the alternative point to the triangle; and under the condition that the plane included angle and the distance are smaller than the preset judgment threshold value, the alternative point is a ground point, otherwise, the alternative point is a non-ground point.
Optionally, according to a preset pole tower insulator model, calculating non-ground point data, and extracting an operation of a pole tower insulator, including: determining a tower insulator preliminary model of a tower insulator according to the number of transmission conductors near the tower insulator and the type of the power tower; optimizing the preliminary model of the tower insulator by a least square algorithm to determine an optimal model of the tower insulator; according to the hanging point of the transmission conductor and the hanging point of the tower insulator, modeling the optimal model of the tower insulator, and determining a tower insulator model; and calculating the non-ground point data by using the pole tower insulator model, and extracting the pole tower insulator.
Therefore, according to the embodiment, since the non-ground points are relatively discrete, the point cloud data is extracted through the iterative filtering encryption algorithm, the ground point data can be efficiently extracted, and then the point cloud data is processed through the ground point data to obtain the non-ground point data, so that the non-ground point data with higher precision is obtained. By comprehensively investigating the shape characteristics of common pole tower insulators and analyzing the mathematical expression of the model, a perfect pole tower insulator model library is established for construction. The pole tower insulator is extracted from the non-ground point data with high precision, and the technical effect of effectively extracting the pole tower insulator is achieved. The technical problems that no relevant research exists in the prior art for extracting the tower insulator based on the point cloud data, and the efficient extraction of the tower insulator can not be carried out through the point cloud data are solved.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present invention, it should be understood that the disclosed technical contents can be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit 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 embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (12)
1. A method for extracting a tower insulator based on point cloud data is characterized by comprising the following steps:
removing non-ground points of point cloud data through a preset iterative filtering encryption algorithm to determine ground point data;
according to the ground point data, ground point elimination is carried out on the point cloud data, and non-ground point data are determined;
and calculating the non-ground point data according to a preset pole tower insulator model, and extracting a pole tower insulator.
2. The method of claim 1, further comprising:
and denoising the point cloud data by using a preset denoising algorithm.
3. The method of claim 1, wherein the operation of determining ground point data by removing non-ground points of point cloud data through a preset iterative filtering encryption algorithm comprises:
judging whether the angular points in the point cloud data are non-ground points one by one;
and under the condition that the angular points are the non-ground points, the ground points are removed, and the ground point data are determined.
4. The method of claim 3, wherein the operation of determining one by one whether the corner points in the point cloud data are non-ground points comprises:
dividing the point cloud data according to a pre-selected seed area to generate seed point cloud data;
building a triangulation network model at angular points in the seed point cloud data through a triangulation network generation rule;
selecting three mutually adjacent lowest points in the seed point cloud data as initial ground points by using the triangulation network model, wherein the three lowest points are three vertexes of a triangle in the triangulation network model;
selecting another vertex of an adjacent triangle having a common side with the triangle as a candidate point;
calculating the plane included angle between the triangle and the adjacent triangle and the distance from the alternative point to the triangle;
and under the condition that the plane included angle and the distance are both smaller than a preset judgment threshold value, the alternative point is a ground point, otherwise, the alternative point is a non-ground point.
5. The method of claim 1, wherein the operation of computing the non-ground point data and extracting tower insulators according to a pre-set tower insulator model comprises:
determining a tower insulator preliminary model of the tower insulator according to the number of the transmission conductors near the tower insulator and the type of the power tower;
optimizing the tower insulator preliminary model through a least square algorithm to determine a tower insulator optimal model;
according to the hanging point of the transmission conductor and the hanging point of the pole tower insulator, modeling the pole tower insulator optimal model, and determining the pole tower insulator sub-model;
and calculating the non-ground point data by using the pole tower insulator model, and extracting the pole tower insulator.
6. A computer-readable storage medium, characterized in that the storage medium comprises a stored program, wherein the method of any of claims 1 to 5 is performed by a processor when the program is run.
7. The utility model provides a shaft tower insulator's extraction element based on point cloud data which characterized in that includes:
the first determining module is used for removing non-ground points of point cloud data through a preset iterative filtering encryption algorithm to determine ground point data;
the second determining module is used for removing ground points from the point cloud data according to the ground point data and determining non-ground point data;
and the extracting module is used for calculating the non-ground point data according to a preset pole tower insulator model and extracting pole tower insulators.
8. The apparatus of claim 7, further comprising:
and the denoising module is used for denoising the point cloud data by utilizing a preset denoising algorithm.
9. The apparatus of claim 7, wherein the first determining module comprises:
the judgment sub-module is used for judging whether the angular points in the point cloud data are non-ground points one by one;
and the first determining submodule is used for removing the ground points and determining the ground point data under the condition that the angular points are the non-ground points.
10. The apparatus of claim 9, wherein the determining sub-module comprises:
the generating unit is used for dividing the point cloud data according to a pre-selected seed area to generate seed point cloud data;
the establishing unit is used for establishing a triangulation network model at the angular points in the seed point cloud data through a triangulation network generating rule;
a first selecting unit, configured to select, by using the triangulation network model, three lowest points that are adjacent to each other in the seed point cloud data as initial ground points, where the three lowest points are three vertices of a triangle in the triangulation network model;
the second selecting unit is used for selecting another vertex of an adjacent triangle which has a common side with the triangle as a candidate point;
the calculating unit is used for calculating the plane included angle between the triangle and the adjacent triangle and the distance from the alternative point to the triangle;
and the judging unit is used for judging that the alternative points are ground points under the condition that the plane included angle and the distance are smaller than a preset judging threshold value, otherwise, the alternative points are non-ground points.
11. The apparatus of claim 7, wherein the extraction module comprises:
the second determining submodule is used for determining a tower insulator preliminary model of the tower insulator according to the number of the transmission conductors near the tower insulator and the type of the power tower;
the third determining submodule is used for optimizing the tower insulator preliminary model through a least square algorithm and determining a tower insulator optimal model;
the fourth determining submodule is used for modeling the optimal model of the tower insulator according to the hanging point of the transmission conductor and the hanging point of the tower insulator and determining the tower insulator submodel;
and the extraction submodule is used for calculating the non-ground point data by using the pole and tower insulator model and extracting the pole and tower insulator.
12. The utility model provides a shaft tower insulator's extraction element based on point cloud data which characterized in that includes:
a processor; and
a memory coupled to the processor for providing instructions to the processor for processing the following processing steps:
removing non-ground points of point cloud data through a preset iterative filtering encryption algorithm to determine ground point data;
according to the ground point data, ground point elimination is carried out on the point cloud data, and non-ground point data are determined;
and calculating the non-ground point data according to a preset pole tower insulator model, and extracting a pole tower insulator.
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| CN202211279130.4A CN115641450A (en) | 2022-10-19 | 2022-10-19 | Point cloud data-based tower insulator extraction method and device and storage medium |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115841568A (en) * | 2023-02-16 | 2023-03-24 | 北京华科智行科技有限公司 | Transmission tower insulator reconstruction method based on standing book data |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115841568A (en) * | 2023-02-16 | 2023-03-24 | 北京华科智行科技有限公司 | Transmission tower insulator reconstruction method based on standing book data |
| CN115841568B (en) * | 2023-02-16 | 2023-04-21 | 北京华科智行科技有限公司 | Method for reconstructing transmission tower insulator based on standing book data |
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