CN115639842A - Inspection method and system using unmanned aerial vehicle - Google Patents
Inspection method and system using unmanned aerial vehicle Download PDFInfo
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Abstract
The invention relates to a method and a system for routing inspection by using an unmanned aerial vehicle, wherein the method comprises the following steps: determining a laser point cloud model based on laser point cloud data, wherein the laser point cloud data is point cloud data of a routing inspection area of an object to be detected; generating a routing inspection route for the unmanned aerial vehicle to inspect the object to be inspected based on the laser point cloud model; and utilizing the routing inspection route to realize the routing inspection of the object to be detected. The embodiment of the invention can improve the inspection efficiency of the unmanned aerial vehicle on the object to be detected.
Description
Technical Field
The invention relates to the technical field of automatic inspection, in particular to an inspection method and an inspection system using an unmanned aerial vehicle.
Background
In China, the inspection of the traditional power system generally adopts a manual inspection mode, although the method is simple, the efficiency is lower, the period is longer, a large amount of optical equipment and inspection personnel with high quality and rich experience need to be equipped, and the requirements on manpower and financial resources are higher, so that a novel inspection mode of unmanned aerial vehicle and manual inspection cooperation is gradually popularized at present. The ground and aerial inspection mode is popularized, the inspection efficiency and benefit are comprehensively improved, and the safe operation of the power system is ensured.
Along with unmanned aerial vehicle's popularization, unmanned aerial vehicle is applied to the electric power system and patrols and examines also more and more, relies on ground control personnel to accomplish the information acquisition to power equipment, and field technician can only use unmanned aerial vehicle to replace original telescope to obtain the image. But at present, inspection can be carried out only in a manual mode, the potential safety hazards are more in the inspection process in the manual mode, and the working efficiency is lower.
Therefore, it is needed to provide a method for routing inspection by an unmanned aerial vehicle, so as to improve the work efficiency of the unmanned aerial vehicle in the routing inspection process.
Disclosure of Invention
The invention provides a method and a system for routing inspection by using an unmanned aerial vehicle, which aim to solve the technical problem of working efficiency in the routing inspection process of the unmanned aerial vehicle.
The invention discloses a patrol method using an unmanned aerial vehicle, which comprises the following steps:
determining a laser point cloud model based on laser point cloud data, wherein the laser point cloud data is point cloud data of a routing inspection area of an object to be detected;
generating a routing inspection route for the unmanned aerial vehicle to inspect the object to be inspected based on the laser point cloud model;
and utilizing the routing inspection route to realize the routing inspection of the object to be detected.
Optionally, the method further includes:
sending the positioning data and the acquired data of the unmanned aerial vehicle to a patrol management control platform;
and utilizing the patrol inspection management control platform to display the positioning data and the collected data of the unmanned aerial vehicle in real time and output the defect identification analysis of the object to be detected.
Optionally, generating a routing inspection route for the unmanned aerial vehicle to inspect the object to be inspected based on the laser point cloud model includes:
performing data processing on laser point cloud data in the laser point cloud model to obtain target point cloud data, wherein the target point cloud data comprises ground points and non-ground points;
analyzing the obstacles in the target point cloud data according to the non-ground points to obtain the coordinates of the center points of the obstacles;
and generating a routing inspection route for the unmanned aerial vehicle to inspect the object to be inspected based on the central point coordinates of the barrier.
Optionally, the analyzing the obstacle in the target point cloud data according to the non-ground point to obtain a center point coordinate of the obstacle includes:
carrying out plane mesh segmentation on the non-ground points to obtain each non-ground segmentation region;
analyzing the connected components of each non-ground segmentation area to obtain a connected cluster map;
performing suspension line connection analysis based on the connected cluster map to obtain initial obstacle point cloud data;
and generating the coordinates of the center point of the obstacle by using the initial obstacle point cloud data.
Optionally, the connected component analysis includes: analyzing a connected domain; and/or, cluster analysis.
Optionally, based on the central point coordinate of barrier generates the route of patrolling and examining that unmanned aerial vehicle patrolled and examined the object to be measured, include:
marking out a safety region in the point cloud data based on the central point coordinates of the obstacle;
taking a safety area in the point cloud data as a route planning area, and generating inspection points for inspecting the object to be detected by the unmanned aerial vehicle by combining multidirectional positioning and the point cloud data of the object to be detected, wherein any inspection point has the corresponding unmanned aerial vehicle position, unmanned aerial vehicle orientation and camera angle;
and forming a routing inspection route for smoothly connecting all the routing inspection points based on the routing inspection points.
Optionally, the acquired data is image data acquired by the unmanned aerial vehicle in an inclined state.
Optionally, the object to be measured includes: insulator, electric power tower and wire.
In a second aspect, an embodiment of the present invention further provides an inspection system using an unmanned aerial vehicle, including:
the laser point cloud model generating unit is used for determining a laser point cloud model based on laser point cloud data, wherein the laser point cloud data are point cloud data of a routing inspection area of an object to be detected;
the routing inspection route generating unit is used for generating a routing inspection route for the unmanned aerial vehicle to inspect the object to be detected based on the laser point cloud data;
and the unmanned aerial vehicle is used for utilizing the routing inspection route to realize the routing inspection of the object to be detected.
Optionally, the method further includes:
the data transmission unit is used for sending the positioning data and the collected data of the unmanned aerial vehicle to the inspection management control platform;
and the inspection management control platform is used for displaying the acquired unmanned aerial vehicle positioning data and the image data in real time and outputting the defect identification analysis of the object to be detected.
It can be seen that, in the embodiments of the present invention, there is provided determining a laser point cloud model based on laser point cloud data; generating a routing inspection route for the unmanned aerial vehicle to inspect the object to be inspected based on the laser point cloud model; the routing inspection of the to-be-detected object by the unmanned aerial vehicle is realized by utilizing the routing inspection route, and the routing inspection route is sent to a routing inspection management control platform; utilize patrol and examine management control platform and show unmanned aerial vehicle's location data and data collection in real time to the technical scheme of the defect identification analysis of output to be measured object, under this technical scheme, utilize laser point cloud data can be for unmanned aerial vehicle plans out the route of patrolling and examining, need not the manual mode and patrol and examine, reduce unmanned aerial vehicle and patrol and examine the time of in-process manual control consumption, improved unmanned aerial vehicle and patrolled and examined efficiency to the object of awaiting measuring. Furthermore, the positioning data and the collected data of the unmanned aerial vehicle are displayed in real time through the inspection management control platform, the defect identification analysis of the object to be detected is output, the defect of the object to be detected is reflected through the defect identification analysis, the manual data analysis process of the object to be detected is avoided, the inspection scheme for automatic data analysis is provided, the analysis time of the collected data is greatly saved, and the inspection efficiency is further improved.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the invention.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic step diagram of a routing inspection method using an unmanned aerial vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a route inspection generation process disclosed in the embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a step of determining coordinates of a center point of an obstacle according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a step of generating a routing inspection route for the unmanned aerial vehicle to inspect the object to be detected in the embodiment of the present invention;
fig. 5 is an alternative block diagram of an inspection system utilizing a drone in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic step diagram of an inspection method using an unmanned aerial vehicle according to an embodiment of the present invention. As shown in fig. 1, the steps of the inspection method using the unmanned aerial vehicle may specifically include:
s11, determining a laser point cloud model based on laser point cloud data, wherein the laser point cloud data are inspection area data of an object to be detected;
the laser point cloud data can be obtained by three-dimensional reconstruction through a laser range finder or a visual device, and the patrol area data can comprise laser point cloud data of an object to be detected and laser point cloud data of the surrounding environment of the object to be detected.
The object to be measured can be electrical equipment, such as insulators, electrical towers, wires, foundations and base planes, grounding devices, insulators, cross arms and the like. Optionally, the lead may be specifically a pull wire, a drainage wire, a shield wire, a ground wire, or the like. At present, the object to be measured can also be other equipment that can adopt unmanned aerial vehicle to carry out data acquisition, perhaps state monitoring.
Because the data volume of the directly obtained laser point cloud data is huge, the laser point cloud data needs to be simplified through a random sampling method, and then a laser point cloud model is generated based on the simplified data, and the point laser point cloud model is used for providing space structure information around the object to be detected and obstacle data needed by routing inspection path planning.
The laser point cloud model may be a three-dimensional mesh model composed of triangular patches, and certainly, may also be composed of patches of other shapes, such as polygons, and the process of constructing the three-dimensional mesh model may be constructed by using an algorithm in the prior art, such as an algorithm for reconstructing a three-dimensional mesh entity model based on a BP neural network, which is not described herein again.
Specifically, the laser point cloud model may be integrally manufactured by using a high-level modeling Polygon (Polygon) modeling method, and developed under a Unity engine.
And S12, generating a routing inspection route for the unmanned aerial vehicle to inspect the object to be inspected based on the laser point cloud model.
And S13, utilizing the routing inspection route to realize the routing inspection of the object to be detected.
The routing inspection route is a route for the unmanned aerial vehicle to inspect the object to be detected. In the embodiment of the invention, a safer routing inspection route can be generated based on the laser point cloud data of the surrounding environment of the object to be detected in the laser point cloud data in the laser point cloud model, so that the object to be detected is routed by using the unmanned aerial vehicle based on the routing inspection route, and automatic routing inspection is realized. Therefore, in the embodiment of the invention, the laser point cloud model can be used for planning the patrol route for the unmanned aerial vehicle, manual mode patrol is not needed, the time consumed by manual operation and control of the unmanned aerial vehicle in the patrol process is reduced, and the patrol efficiency of the unmanned aerial vehicle on the object to be detected is improved.
Fig. 2 is a schematic diagram of a step of generating a routing inspection route disclosed in the embodiment of the present invention. As shown in fig. 2, generating a routing inspection route for inspecting the object to be inspected by the unmanned aerial vehicle based on the laser point cloud model includes:
and S21, carrying out data processing on the laser point cloud data in the laser point cloud model to obtain target point cloud data, wherein the target point cloud data comprises ground points and non-ground points.
Optionally, the data processing process may specifically be a process of performing point cloud denoising and point cloud filtering on the laser point cloud data. The point cloud denoising process can be realized by different point cloud denoising algorithms, and the different point cloud denoising algorithms have different types of parameters to be adjusted and different numbers of parameters to be adjusted. Therefore, in the embodiment of the invention, the point cloud denoising can be a point cloud denoising algorithm of parameters to be substituted, and when the parameters are substituted into the point cloud denoising algorithm, initial point cloud data after point cloud denoising can be obtained, namely the point cloud denoising function of the laser point cloud data can be realized.
Further, the point cloud filtering process may use a morphological filter method to perform filtering to separate the ground points and the non-ground points. Of course, in other alternative embodiments, other filtering manners may also be adopted as long as the initial point cloud data can be filtered to obtain the target point cloud data, which is not limited herein.
It can be understood that the denoising result of the single point cloud denoising algorithm on the laser point cloud data may be rough, in order to improve the denoising effect of the point cloud denoising, the parameters of the point cloud denoising algorithm need to be adjusted again according to the actual result of the target point cloud data, and the denoising quality of the point cloud denoising algorithm on the laser point cloud data can be further optimized by changing the parameters of the point cloud denoising algorithm.
And S22, analyzing the obstacles in the target point cloud data according to the non-ground points to obtain the coordinates of the central point of the obstacles.
Further, the obstacle can be an essential building for bearing a power distribution network, such as a power cable and a telegraph pole. It should be noted that the power cable needs extra avoidance due to the high voltage, so as to prevent safety accidents. The high-voltage power transmission cable generally comprises a plurality of strands of secondary conductors, so that when modeling a power cable region, the plurality of strands of conductors need to be similar to one strand of conductor, a cylindrical region is formed around the cylindrical region by taking a certain safety distance as a radius, and a region outside the cylindrical region is taken as a safety inspection region, so that the center point coordinates of an obstacle need to be obtained to avoid the obstacle.
Optionally, fig. 3 is a schematic diagram illustrating a step of determining coordinates of a center point of an obstacle in the embodiment of the present invention. Referring to fig. 3, analyzing the obstacle in the point cloud data according to the non-ground point to obtain the coordinates of the center point of the obstacle includes:
s31, carrying out plane mesh segmentation on the non-ground points to obtain each non-ground segmentation region;
and S32, analyzing the connected components of the non-ground segmentation areas to obtain a connected cluster map.
Optionally, the connected component analysis includes: analyzing a connected domain; and/or, cluster analysis.
Connected domain analysis is a more common and basic method in many application areas of image analysis processing. In this embodiment, connected component analysis may be used to analyze the non-ground points, so as to determine a connected cluster map corresponding to the non-ground points.
Clustering analysis is a set of statistical analysis techniques that classify study objects into relatively homogeneous groups and can be considered a supervised learning.
In an embodiment, the clustering analysis may be an improved binary Kmeans algorithm, the algorithm divides k initial central points according to the non-ground partition regions, divides the non-ground partition regions into k clusters by using the k initial central points, recalculates respective central points according to the classified data, continues to divide the non-ground partition regions with the k initial central points as centers if the central points change, and then performs convergence if the central points do not change, thereby obtaining a connected clustering map.
And S33, carrying out suspension wire connection analysis based on the connected cluster map to obtain initial obstacle point cloud data.
It should be noted that the obstacle is generally a line or a column, and for this purpose, the connected cluster map may be subjected to a suspension connection analysis to determine point cloud data of the initial obstacle, so as to identify the obstacle and coordinates of a center point thereof according to the point cloud data of the initial obstacle.
And S34, generating the center point coordinates of the obstacle by using the initial obstacle point cloud data.
And S23, generating a routing inspection route for the unmanned aerial vehicle to inspect the object to be inspected based on the central point coordinates of the barrier.
As described above, when the obstacle is the power cable, the electric field intensity in the surrounding area of the power cable is too high, which is not suitable for the unmanned aerial vehicle to approach, and once the unmanned aerial vehicle approaches, the safe operation of the power cable may be threatened. Therefore, there is a safety in the periphery of electric power cable and patrols and examines the region, can generate unmanned aerial vehicle in this safety is patrolled and examined the route of patrolling and examining to the determinand to can realize patrolling and examining the safety of determinand under this route of patrolling and examining.
The embodiment of the invention further provides a step schematic diagram for generating the routing inspection route for the unmanned aerial vehicle to inspect the object to be detected. As shown in fig. 4, the step of generating a routing inspection route for the unmanned aerial vehicle to inspect the object to be tested based on the coordinates of the center point of the obstacle may specifically include:
s41, marking out a safety area in the point cloud data based on the central point coordinates of the barrier;
step S42, taking a safety area in the point cloud data as a route planning area, and combining multidirectional positioning and the point cloud data of the object to be detected to generate inspection points for inspecting the object to be detected by the unmanned aerial vehicle, wherein any inspection point has the corresponding unmanned aerial vehicle position, unmanned aerial vehicle orientation and camera angle;
and S43, forming a routing inspection route smoothly connecting all routing inspection points based on the routing inspection points.
The inspection point can be a point for acquiring data of the object to be detected by the unmanned aerial vehicle, an airborne camera of the unmanned aerial vehicle is mounted on the three-dimensional holder, and the three-dimensional holder has a wide rotating angle, so that the view field of the camera at a single viewpoint is increased. The use of a three-dimensional pan-tilt contributes to reducing the number of viewpoints. After the unmanned aerial vehicle flies to reach a certain inspection point position, the tripod head horizontally and vertically rotates to take a plurality of photos, the photos contain information of power equipment in a certain area around an object to be detected, although the staying time at a single viewpoint at each time is slightly longer than that before, the total viewpoint number is greatly reduced, and the flying path is shortened, so the inspection efficiency is effectively improved by the method.
In a further optional implementation, the acquisition state of the unmanned aerial vehicle can also be an inclined state, that is, the unmanned aerial vehicle is in the inclined state, a plurality of photos are taken by utilizing the three-dimensional holder through horizontal and vertical rotation, the photos are used as image data acquired in the embodiment of the invention, and the image data can increase the abundance of data acquired by the inspection point, so that a good data base can be provided for the defect identification and analysis of a subsequent object to be detected.
In addition, because every inspection point is independent, for this reason, can with the inspection point is connected to form the route of patrolling and examining of each inspection point of smooth connection, under this route of patrolling and examining, unmanned aerial vehicle can carry out all-round data collection to the determinand based on the corresponding unmanned aerial vehicle position of patrolling and examining, unmanned aerial vehicle orientation and camera angle, thereby realizes holistic process of patrolling and examining.
In a further optional implementation of the embodiment of the present application, as shown with continued reference to fig. 1, the step of using the inspection method for the unmanned aerial vehicle may further include:
s14, sending the positioning data and the collected data of the unmanned aerial vehicle to a patrol management control platform;
and S15, displaying the positioning data and the collected data of the unmanned aerial vehicle in real time by using the inspection management control platform, and outputting the defect identification analysis of the object to be detected.
The positioning data is used to represent a current position of the drone. The staff can be according to unmanned aerial vehicle's location data real-time supervision unmanned aerial vehicle is located the position to in time know the job schedule. The acquired data is image data acquired by the unmanned aerial vehicle in an inclined state. The collected data can provide a good data base for a subsequent defect identification process.
The defect identification analysis is used for representing the defects of the object to be detected. It should be noted that the patrol inspection management control platform can be a platform with data processing capability, and can process positioning data and collected data of the unmanned aerial vehicle, so as to output defect identification and analysis of an object to be detected. The defect identification analysis can embody the defect of the object to be detected, avoids the manual data analysis process of the object to be detected, provides an automatic inspection and automatic analysis inspection scheme, greatly saves the analysis time of data acquisition, and further improves the inspection efficiency.
It can be seen that, in the embodiments of the present invention, there is provided determining a laser point cloud model based on laser point cloud data; generating a routing inspection route for the unmanned aerial vehicle to inspect the object to be inspected based on the laser point cloud model; the routing inspection of the to-be-detected object by the unmanned aerial vehicle is realized by utilizing the routing inspection route, and the routing inspection route is sent to a routing inspection management control platform; utilize patrol and examine management control platform and show unmanned aerial vehicle's location data and data collection in real time to the technical scheme of the defect identification analysis of output to be measured object, under this technical scheme, utilize laser point cloud data can be for unmanned aerial vehicle plans out the route of patrolling and examining, need not the manual mode and patrol and examine, reduce unmanned aerial vehicle and patrol and examine the time of in-process manual control consumption, improved unmanned aerial vehicle and patrolled and examined efficiency to the object of awaiting measuring. Furthermore, the positioning data and the collected data of the unmanned aerial vehicle are displayed in real time through the inspection management control platform, the defect identification analysis of the object to be detected is output, the defect of the object to be detected is reflected through the defect identification analysis, the manual data analysis process of the object to be detected is avoided, the inspection scheme for automatic data analysis is provided, the analysis time of the collected data is greatly saved, and the inspection efficiency is further improved.
Correspondingly, the embodiment of the present invention further provides an inspection system using an unmanned aerial vehicle, and as shown in fig. 5, the system specifically includes:
a laser point cloud model generating unit 51, configured to determine a laser point cloud model based on laser point cloud data, where the laser point cloud data is point cloud data of a routing inspection area of an object to be detected;
the routing inspection route generating unit 52 is used for generating a routing inspection route for the unmanned aerial vehicle to inspect the object to be detected based on the laser point cloud model;
and the unmanned aerial vehicle 53 is used for utilizing the routing inspection route to realize the routing inspection of the object to be detected.
Continuing to refer to fig. 5, the inspection system using the drone further includes:
the data transmission unit 54 is used for sending the positioning data and the collected data of the unmanned aerial vehicle to the inspection management control platform;
and the inspection management control platform 55 is used for displaying the acquired unmanned aerial vehicle positioning data and image data in real time and outputting the defect identification analysis of the object to be detected.
In the inspection system using an unmanned aerial vehicle according to the embodiment of the present invention, the inspection route generating unit 52 is configured to generate an inspection route for inspecting an object to be inspected by the unmanned aerial vehicle based on the laser point cloud model, and includes:
performing data processing on laser point cloud data in the laser point cloud model to obtain target point cloud data, wherein the target point cloud data comprises ground points and non-ground points;
analyzing the obstacles in the target point cloud data according to the non-ground points to obtain the coordinates of the center points of the obstacles;
and generating a routing inspection route for the unmanned aerial vehicle to inspect the object to be inspected based on the central point coordinates of the obstacle.
In the inspection system using an unmanned aerial vehicle according to the embodiment of the present invention, the inspection route generating unit 52 is configured to analyze the obstacle in the target point cloud data according to the non-ground point, so as to obtain the coordinates of the center point of the obstacle, and includes:
carrying out plane mesh segmentation on the non-ground points to obtain each non-ground segmentation area;
analyzing the connected components of each non-ground segmentation area to obtain a connected cluster map;
performing suspension line connection analysis based on the connected cluster map to obtain initial obstacle point cloud data;
and generating the coordinates of the center point of the obstacle by using the initial obstacle point cloud data.
In the inspection system using the unmanned aerial vehicle according to the embodiment of the present invention, the connected component analysis includes: analyzing a connected domain; and/or, cluster analysis.
In the inspection system using an unmanned aerial vehicle according to the embodiment of the present invention, the inspection route generating unit 52 is configured to generate an inspection route for the unmanned aerial vehicle to inspect an object to be inspected based on the coordinates of the center point of the obstacle, and includes:
marking out a safety region in the point cloud data based on the coordinates of the center point of the obstacle;
taking the safety area in the point cloud data as a route planning area, and combining multidirectional positioning and the point cloud data of the object to be detected to generate inspection points for the unmanned aerial vehicle to inspect the object to be detected, wherein any inspection point has the corresponding unmanned aerial vehicle position, unmanned aerial vehicle orientation and camera angle;
and forming a routing inspection route for smoothly connecting all the routing inspection points based on the routing inspection points.
In the inspection system using the unmanned aerial vehicle according to the embodiment of the invention, the acquired data is image data acquired by the unmanned aerial vehicle in an inclined state.
In the inspection system using the unmanned aerial vehicle according to the embodiment of the present invention, the object to be measured includes: insulator, electric power tower and wire.
In the inspection system using the unmanned aerial vehicle, provided by the embodiment of the invention, the inspection route can be planned for the unmanned aerial vehicle by using the laser point cloud data, manual mode inspection is not needed, the time consumed by manual operation of the unmanned aerial vehicle in the inspection process is reduced, and the inspection efficiency of the unmanned aerial vehicle on the object to be detected is improved. Furthermore, the positioning data and the collected data of the unmanned aerial vehicle are displayed in real time through the inspection management control platform, the defect identification analysis of the object to be detected is output, the defect of the object to be detected is reflected through the defect identification analysis, the manual data analysis process of the object to be detected is avoided, the inspection scheme for automatic data analysis is provided, the analysis time of the collected data is greatly saved, and the inspection efficiency is further improved.
Those of skill in the art will further appreciate that the various illustrative logical blocks, units, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, elements, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design requirements of the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present embodiments.
The various illustrative logical blocks, or elements, or devices described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be located in a user terminal. In the alternative, the processor and the storage medium may reside in different components in a user terminal.
In one or more exemplary designs, the functions described above in connection with the embodiments of the invention may be implemented in hardware, software, firmware, or any combination of the three. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media that facilitate transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, such computer-readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store program code in the form of instructions or data structures and which can be read by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Additionally, any connection is properly termed a computer-readable medium, and, thus, is included if the software is transmitted from a website, server, or other remote source via a coaxial cable, fiber optic cable, twisted pair, digital Subscriber Line (DSL), or wirelessly, e.g., infrared, radio, and microwave. Such discs (disk) and disks (disc) include compact disks, laser disks, optical disks, DVDs, floppy disks and blu-ray disks, where magnetic discs generally reproduce data magnetically, while disks generally reproduce data optically with lasers. Combinations of the above may also be included in the computer-readable medium.
Although the embodiments of the present invention have been disclosed, the present invention is not limited thereto. Various changes and modifications may be effected by one skilled in the art without departing from the spirit and scope of the invention, as defined in the appended claims.
Claims (10)
1. A patrol method using an unmanned aerial vehicle is characterized by comprising the following steps:
determining a laser point cloud model based on laser point cloud data, wherein the laser point cloud data is point cloud data of a routing inspection area of an object to be detected;
generating a routing inspection route for the unmanned aerial vehicle to inspect the object to be inspected based on the laser point cloud model;
and utilizing the routing inspection route to realize the routing inspection of the object to be detected.
2. The inspection method according to claim 1, further comprising:
sending the positioning data and the collected data of the unmanned aerial vehicle to a patrol management control platform;
and utilizing the patrol inspection management control platform to display the positioning data and the collected data of the unmanned aerial vehicle in real time and output the defect identification analysis of the object to be detected.
3. The inspection method according to claim 1, wherein the generating of the inspection route for the unmanned aerial vehicle to inspect the object to be inspected based on the laser point cloud model comprises:
performing data processing on laser point cloud data in the laser point cloud model to obtain target point cloud data, wherein the target point cloud data comprises ground points and non-ground points;
analyzing the obstacles in the target point cloud data according to the non-ground points to obtain the coordinates of the center points of the obstacles;
and generating a routing inspection route for the unmanned aerial vehicle to inspect the object to be inspected based on the central point coordinates of the barrier.
4. The inspection method according to claim 3, wherein analyzing the obstacles in the target point cloud data according to the non-ground points to obtain center point coordinates of the obstacles comprises:
carrying out plane mesh segmentation on the non-ground points to obtain each non-ground segmentation region;
analyzing the connected components of each non-ground segmentation area to obtain a connected cluster map;
performing suspension line connection analysis based on the connected cluster map to obtain initial obstacle point cloud data;
and generating the coordinates of the center point of the obstacle by using the initial obstacle point cloud data.
5. The inspection method according to claim 4, wherein the connected component analysis includes: analyzing the connected domain; and/or, cluster analysis.
6. The inspection method according to claim 4 or 5, wherein the generating of the inspection route for the unmanned aerial vehicle to inspect the object to be inspected based on the coordinates of the center point of the obstacle comprises:
marking out a safety region in the point cloud data based on the central point coordinates of the obstacle;
taking the safety area in the point cloud data as a route planning area, and combining multidirectional positioning and the point cloud data of the object to be detected to generate inspection points for the unmanned aerial vehicle to inspect the object to be detected, wherein any inspection point has the corresponding unmanned aerial vehicle position, unmanned aerial vehicle orientation and camera angle;
and forming a routing inspection route for smoothly connecting all the routing inspection points based on the routing inspection points.
7. The inspection method according to claim 2, wherein the collected data is image data collected by the unmanned aerial vehicle in an inclined state.
8. The inspection method according to claim 1, wherein the object to be tested includes: insulator, electric power tower and wire.
9. The utility model provides an utilize unmanned aerial vehicle's system of patrolling and examining which characterized in that includes:
the laser point cloud model generating unit is used for determining a laser point cloud model based on laser point cloud data, wherein the laser point cloud data are point cloud data of a routing inspection area of an object to be detected;
the inspection route generating unit is used for generating an inspection route for inspecting the object to be detected by the unmanned aerial vehicle based on the laser point cloud data;
and the unmanned aerial vehicle is used for utilizing the routing inspection route to realize the routing inspection of the object to be detected.
10. The inspection system according to claim 9, further comprising:
the data transmission unit is used for sending the positioning data and the collected data of the unmanned aerial vehicle to the inspection management control platform;
and the inspection management control platform is used for displaying the acquired unmanned aerial vehicle positioning data and the image data in real time and outputting the defect identification analysis of the object to be detected.
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