Unmanned aerial vehicle-mounted active and passive imaging method and system for disaster risk detection

Abstract

The invention discloses an unmanned aerial vehicle-mounted active and passive imaging method and system for disaster risk detection, and belongs to the technical field of unmanned aerial vehicle-mounted laser radar and multispectral camera application and disaster risk detection. The method is based on an ARM (Advanced RISC Machines) processor with low power consumption, strong function directivity and a multi-interface protocol. The method is implemented through the contents of system integrated power supply design, system comprehensive control and time synchronization, active and passive imaging load field-of-view matching, active and passive imaging system and unmanned aerial vehicle platform interface adaptation, active and passive imaging system structure optimization, active and passive imaging system mounting flight calibration, ARM processor-based disaster risk detection algorithm implementation and the like. The problems that an acquisition system is large in weight, geometric / spectral information is not fused in real time, and earth surface elements cannot be calculated in real time are solved, and real-time acquisition of spatial dimension and spectral dimension information of the disaster interested area and rapid identification of the earth surface elements are achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) laser radar and multispectral camera application and disaster risk detection, and specifically relates to an unmanned aerial vehicle (UAV) active and passive imaging method and system for disaster risk detection. Background Art

[0002] Among various geological disaster monitoring methods, the use of remote sensing payloads of aerospace platforms for environmental monitoring and geological disaster identification and early warning has the advantages of large operating area, strong synchronization and high dynamics, and has been widely used. Among them, unmanned aerial vehicle platforms are often mounted with various payloads for flight and applied to multiple industries because of their small size, low cost, convenience and good terrain adaptability.

[0003] In the application of geological disaster risk detection, it is usually necessary to simultaneously obtain the spatial three-dimensional information and spectral radiation information of ground objects. In recent years, there have been ground observation methods that use two types of payloads, laser radar and multispectral imagers, on the same platform. Based on the data obtained by each, the corresponding feature information is extracted for fusion processing. However, due to the completely different imaging mechanisms of active laser radar and passive hyperspectral, the difference between the discreteness of laser point cloud data and the spatial continuity of multispectral images, and the fusion method between the target distance information reflected by the laser radar and the target electromagnetic spectrum reflection information attributes reflected by the multispectral, the multi-source data fusion processing accuracy and information extraction efficiency are greatly restricted.

[0004] In the process of carrying LiDAR and multispectral imagers on the same platform, the UAV platform needs to separately power and integrate the combined navigation, LiDAR scanner, multispectral camera and acquisition control system. The weight of the whole set of equipment is often 4-5kg, which greatly limits the flight radius, effective operation time and disaster risk detection and response capabilities of the UAV platform. According to data calculated by a UAV company, for rotary-wing UAVs, if the load is reduced from 5kg to 3kg, the effective flight time will increase by 42%, and if the load is reduced from 5kg to 2kg, the effective flight time will increase by 64%. Therefore, it is particularly critical to make the entire detection system smaller in size and lighter in weight while ensuring the load detection performance.

[0005] Integrating the ARM processor with low power consumption, strong functional directionality and multiple interface protocols with the POS / INS combined navigation, lidar scanner and multi-spectral camera can not only improve the collection and calculation capabilities of large amounts of data for the two detection payloads, but also increase the effective flight time by reducing the overall system weight of the payload, ultimately enhancing the active and passive imaging system's ability to detect disaster risks. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides an active and passive imaging method and system for UAV-borne disaster risk detection, which solves the problems of heavy system weight, low operating efficiency, single data type, no real-time fusion of geometric / spectral information, and inability to calculate surface elements in real time.

[0007] To achieve the above purpose, the technical solution adopted by the present invention is as follows: In one aspect, the present invention provides an active and passive imaging method for disaster risk detection carried out by an unmanned aerial vehicle, the method comprising the following steps: Step 1: System integrated power supply design: distribute the output power of the UAV platform to each active and passive imaging payload, POS / INS integrated navigation and ARM processor through a customized power supply board; Step 2: System integrated control and time synchronization: Based on the ARM processor, the communication control of each active and passive imaging payload is realized, and the time synchronization setting of the whole system is realized based on the POS / INS combined navigation; Step 3: Field of view matching of active and passive imaging payloads: Place the laser radar scanner and multispectral camera included in the active and passive imaging payloads in the same spatial imaging coordinate system for field of view matching; Step 4: UAV platform interface adaptation: matching the external communication protocol of the ARM processor and the power supply board with the interface protocol of the UAV platform; Step 5: System structure optimization: light-weight optimization design of the active and passive imaging system consisting of each active and passive imaging payload, POS / INS integrated navigation, ARM processor and the power supply board; Step 6: System mounting flight calibration: Use the optimized active and passive imaging system to match the laser footprint and ground pixel points through a long-distance ground target; Step 7. Implementation of disaster risk detection algorithm based on ARM processor: Based on the verified active and passive imaging system, using the ARM processor disaster risk factors, disaster risk detection in the area of ​​interest is achieved by analyzing the disaster factors.

[0008] In another aspect, the present invention provides an active and passive imaging system for disaster risk detection carried by an unmanned aerial vehicle, comprising: The system integrated power supply design module is used to distribute the output power of the UAV platform to each active and passive imaging payload, POS / INS integrated navigation and ARM processor through a customized power supply board; System integrated control and time synchronization module, used to realize communication control of each active and passive imaging payload based on ARM processor, and realize time synchronization setting of the whole system based on POS / INS combined navigation; Active and passive imaging payload field of view matching module, used to place the laser radar scanner and multispectral camera included in the active and passive imaging payload in the same spatial imaging coordinate system for field of view matching; The UAV platform interface adapter module is used to match the external communication protocol of the ARM processor and the power supply board with the interface protocol of the UAV platform; A system structure optimization module, used for lightweight optimization design of the active and passive imaging system composed of each active and passive imaging payload, POS / INS integrated navigation, ARM processor and the power supply board; The system is equipped with a flight calibration module, which is used to match the laser footprint and ground pixel points of the optimized active and passive imaging system through long-distance ground targets; The disaster risk detection algorithm implementation module based on the ARM processor is used for active and passive imaging systems based on the verification, and utilizes the ARM processor disaster risk factors to realize disaster risk detection in the area of ​​interest by analyzing the disaster factors.

[0009] In a third aspect, the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned active and passive imaging method for unmanned aerial vehicles for disaster risk detection.

[0010] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned active and passive imaging method for unmanned aerial vehicles for disaster risk detection.

[0011] The beneficial effects of the present invention are: The present invention is based on an ARM processor and provides an active and passive imaging method and system for UAV-borne disaster risk detection, which can realize real-time acquisition of spatial and spectral information of disaster-interested areas and rapid identification of surface elements, in order to meet the urgent needs of monitoring and early warning, post-disaster emergency response, disaster situation assessment and post-disaster reconstruction, and provide important technical and methodological support for the current problems faced in disaster risk detection, monitoring and early warning, such as terrain survey, geological judgment, and personnel rescue. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a flow chart of an active and passive imaging method carried by an unmanned aerial vehicle for disaster risk detection according to the present invention. DETAILED DESCRIPTION

[0013] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0014] The present invention is aimed at the existing unmanned aerial vehicle laser radar and multispectral camera in the fields of disaster risk detection, real-time monitoring and early warning, post-disaster emergency response and post-disaster rescue, and deeply explores the relationship between the lightweight of the unmanned aerial vehicle platform and the effective flight time, so as to solve the problems of low operating efficiency due to the heavy weight of the system, single data type, no real-time fusion of geometric / spectral information, and inability to calculate surface elements in real time. Based on an ARM processor with low power consumption, strong functional directionality and multi-interface protocol, an unmanned aerial vehicle active and passive imaging method for disaster risk detection is realized through system integrated power supply design, system comprehensive control and time synchronization, active and passive imaging payload field of view matching, active and passive imaging system and unmanned aerial vehicle platform interface adaptation, active and passive imaging system structure optimization, active and passive imaging system mounting flight calibration and disaster risk detection algorithm implementation based on ARM processor.

[0015] First, the system is designed to integrate power supply. The output power of the UAV platform is distributed to each active and passive imaging payload, POS / INS integrated navigation and ARM processor through a customized power supply board. The voltage value of the UAV output power supply is adjusted to the input voltage value required by each active and passive imaging payload, POS / INS integrated navigation and ARM processor to ensure that each payload can work normally after system integration. Then, the system is comprehensively controlled and time synchronized, mainly including the communication control of each active and passive imaging payload based on the ARM processor and the time synchronization setting of the whole system based on the POS / INS integrated navigation. Then, the laser radar scanner and the multispectral camera are matched for field of view installation. The laser radar scanner and the multispectral camera are placed in the same spatial imaging coordinate system through the field of view angle of the laser radar scanner and the multispectral camera and the relationship between the optical pixel of the target object and the laser radar detector. Then, the active and passive imaging system is adapted to the universal interface of the UAV platform, and the external communication protocol of the ARM processor and the power supply board is matched with the interface protocol of the UAV platform, so that the active and passive imaging system can achieve interface adaptation of power supply, communication and structure with the UAV platform. At the same time, the system structure is optimized, the whole system is integrated as small as possible, and carbon fiber or aviation aluminum materials are used for processing and design. Then, the integrated system is matched with the laser footprint and ground pixel points through a long-distance ground target, and the active and passive imaging load installation structure is calibrated and the accuracy is tested. Finally, the disaster risk assessment factor algorithm is implemented on the ARM processor to realize the detection of disaster risks through lidar point cloud and multispectral image data. The present invention provides a new design method with high convenience and high timeliness for the rapid detection and identification of comprehensive remote sensing information in disaster areas.

[0016] like Figure 1 As shown, the specific steps of the active and passive imaging method for disaster risk detection carried out by an unmanned aerial vehicle of the present invention are as follows: Step 1: System integrated power supply design; The system integrated power supply design mainly realizes the distribution of the output power of the UAV platform to each active and passive imaging payload, POS / INS integrated navigation and ARM processor through a customized power supply board, and adjusts the voltage value of the UAV output power to the input voltage value required by each active and passive imaging payload, POS / INS integrated navigation and ARM processor, ensuring that each payload can work normally after system integration. Specifically, the output voltage of the UAV platform is a fixed value voltage, and the fixed value voltage input by the UAV platform is distributed to the ARM processor, POS / INS integrated navigation, lidar scanner and multispectral camera through circuit design based on the ARM processor and the customized power supply board.

[0017] Step 2: System comprehensive control and time synchronization; Step 2.1. Use the serial port and port of the ARM processor to realize the communication connection with the lidar scanner, multispectral camera and POS / INS combined navigation. The ARM processor sends the communication protocol instructions of each active and passive imaging payload to realize the functions of imaging payload power on and off, acquisition start, acquisition stop, acquisition parameter configuration and raw data storage, so as to realize the comprehensive control of the system based on the ARM processor.

[0018] Step 2.2: To ensure the time correspondence between laser ranging data, optical image data, and position and attitude data in subsequent data processing, it is also necessary to perform timing communication for active and passive imaging payloads based on the time information sent by the POS / INS combined navigation. Specifically, after receiving the flight speed, altitude, and pulse signals sent by the POS / INS combined navigation, the integrated control module in the ARM processor calculates the appropriate reference operating frequency, and synchronously sends the trigger signal after frequency division processing to the laser radar scanner and multispectral camera to achieve synchronous operation of each payload, and finally adjust each active and passive imaging system to the same time reference.

[0019] Step 3: Matching the field of view of active and passive imaging payloads; The active and passive imaging payloads mainly include lidar scanners and multispectral cameras. On the laboratory optical calibration platform, the lidar scanner and the multispectral camera are placed in the same spatial imaging coordinate system through the field of view of the lidar scanner and the multispectral camera and the relationship between the optical pixels of the target objects and the lidar detectors. The stability of the relative position relationship between the two payloads is ensured by using structural parts for fixed connection.

[0020] Step 4: Active and passive imaging systems are adapted to the UAV platform interface; The ARM processor is used to distribute instructions for communication, power supply, data storage, and real-time data solution for each payload. In order to make the active and passive imaging disaster risk detection system more applicable, the external communication protocol of the ARM processor and the power supply board is matched with the interface protocol of the UAV platform, so that the system can achieve power supply, communication and structural interface adaptation with the UAV platform.

[0021] Step 5: Active and passive imaging system structure optimization; An integrated structural optimization design is performed on the active and passive imaging system consisting of active and passive imaging payloads, POS / INS combined navigation, ARM processor and the power supply board, and carbon fiber or aviation aluminum materials are used as much as possible for hardware material selection.

[0022] Step 6: Flight calibration of active and passive imaging systems; The integrated active and passive imaging system for disaster risk detection matches the laser footprints and ground pixels through long-distance ground targets. Specifically, by flying over the target calibration area, the spatial coordinate solution equations of the laser footprints obtained by the active imaging of the lidar scanner and the spatial coordinate solution equations of the pixel points obtained by the passive imaging of the multispectral camera are combined to build a calibration and adjustment model on the same platform to perform calibration between the two payloads of the lidar scanner and the multispectral camera. A total of six relative position relationships were calibrated. They represent the linear offsets of the multispectral camera imaging center in the laser radar scanner body coordinate system and the laser radar scanner imaging center X, Y, and Z axes, respectively. They represent the angular rotation of the three axes of the multispectral camera and the three axes of the laser radar scanner in the laser radar scanner body coordinate system. After calibration, fly over the calibration area again, and use the precise position of the coordinates of the objects in the calibration area as the inspection point to inspect the object mapping accuracy of the active and passive imaging system.

[0023] Step 7: Implementation of disaster risk detection algorithm based on ARM processor; On the ARM processor, the spatial geometry and spectral characteristics of the lidar point cloud and multispectral image data are used to calculate the five disaster risk factors of the area of ​​interest, including slope, aspect, slope curvature, plane curvature and vegetation coverage. Through the comprehensive analysis of disaster factors, disaster risk detection in the area of ​​interest is achieved.

[0024] On the other hand, the present invention provides an active and passive imaging system for disaster risk detection carried by an unmanned aerial vehicle, wherein each module included in the system can implement each step of the aforementioned method, specifically including: The system integrated power supply design module is used to distribute the output power of the UAV platform to each active and passive imaging payload, POS / INS integrated navigation and ARM processor through a customized power supply board; System integrated control and time synchronization module, used to realize communication control of each active and passive imaging payload based on ARM processor, and realize time synchronization setting of the whole system based on POS / INS combined navigation; Active and passive imaging payload field of view matching module, used to place the laser radar scanner and multispectral camera included in the active and passive imaging payload in the same spatial imaging coordinate system for field of view matching; The UAV platform interface adapter module is used to match the external communication protocol of the ARM processor and the power supply board with the interface protocol of the UAV platform; A system structure optimization module, used for lightweight optimization design of the active and passive imaging system composed of each active and passive imaging payload, POS / INS integrated navigation, ARM processor and the power supply board; The system is equipped with a flight calibration module, which is used to match the laser footprint and ground pixel points of the optimized active and passive imaging system through long-distance ground targets; The disaster risk detection algorithm implementation module based on the ARM processor is used for active and passive imaging systems based on the verification, and utilizes the ARM processor disaster risk factors to realize disaster risk detection in the area of ​​interest by analyzing the disaster factors.

[0025] In a third aspect, the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned active and passive imaging method for unmanned aerial vehicles for disaster risk detection.

[0026] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0027] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0028] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0029] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0030] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An active and passive imaging method for disaster risk detection carried out by an unmanned aerial vehicle, characterized in that: The method comprises the following steps: Step 1: System integrated power supply design: distribute the output power of the UAV platform to each active and passive imaging payload, POS / INS integrated navigation and ARM processor through a customized power supply board; Step 2: System integrated control and time synchronization: Based on the ARM processor, the communication control of each active and passive imaging payload is realized, and the time synchronization setting of the whole system is realized based on the POS / INS combined navigation; Step 3: Field of view matching of active and passive imaging payloads: Place the laser radar scanner and multispectral camera included in the active and passive imaging payloads in the same spatial imaging coordinate system for field of view matching; Step 4: UAV platform interface adaptation: matching the external communication protocol of the ARM processor and the power supply board with the interface protocol of the UAV platform; Step 5: System structure optimization: light-weight optimization design of the active and passive imaging system consisting of each active and passive imaging payload, POS / INS integrated navigation, ARM processor and the power supply board; Step 6: System mounting flight calibration: Use the optimized active and passive imaging system to match the laser footprint and ground pixel points through a long-distance ground target; Step 7. Implementation of disaster risk detection algorithm based on ARM processor: Based on the verified active and passive imaging system, using the ARM processor disaster risk factors, disaster risk detection in the area of ​​interest is achieved by analyzing the disaster factors.

2. The method for active and passive imaging of disaster risk detection carried out by an unmanned aerial vehicle according to claim 1, characterized in that: The step 1 also includes: adjusting the voltage value of the output power supply of the UAV platform to the input voltage value required by each active and passive imaging load, POS / INS integrated navigation and ARM processor.

3. The method for active and passive imaging of a UAV for disaster risk detection according to claim 2, characterized in that: The voltage value of the output power supply of the UAV platform is a fixed value.

4. The method for active and passive imaging of unmanned aerial vehicles for disaster risk detection according to claim 1, characterized in that: The step 2 comprises: Step 2.1, realize the communication connection between the ARM processor and each active and passive imaging payload and POS / INS integrated navigation through the serial port and port on the ARM processor, and the ARM processor realizes the comprehensive control of each active and passive imaging payload and POS / INS integrated navigation by sending the communication protocol instructions corresponding to each active and passive imaging payload; Step 2.2: Based on the time information of the signal sent by the POS / INS integrated navigation, the active and passive imaging payloads are timed and communicated, and the time of each active and passive imaging payload and the ARM processor is unified to the same reference.

5. The method for active and passive imaging of unmanned aerial vehicles for disaster risk detection according to claim 1, characterized in that: The step 3 includes: on the optical calibration platform, the laser radar scanner and the multispectral camera are fixedly connected in the same spatial imaging coordinate system through the relationship between the field of view angle of the laser radar scanner and the multispectral camera, the optical pixel of the target object and the laser radar detector.

6. The method for active and passive imaging of unmanned aerial vehicles for disaster risk detection according to claim 1, characterized in that: The step 6 includes: mounting the optimized active and passive imaging system on the target calibration area of ​​the UAV, combining the laser footprint spatial coordinate solution equation obtained by the active imaging of the laser radar scanner and the pixel point spatial coordinate solution equation obtained by the passive imaging of the multispectral camera, building a calibration adjustment model on the same platform, and calculating the difference between the two payloads of the laser radar scanner and the multispectral camera. The value of can be used to calibrate the active and passive imaging systems. They represent the linear offsets of the imaging center of the multispectral camera in the laser radar scanner body coordinate system and the imaging center of the laser radar scanner in the X, Y, and Z axes. They respectively represent the angular rotation of the multispectral camera in the laser radar scanner body coordinate system and the laser radar scanner on the X, Y, and Z axes.

7. The method for active and passive imaging of unmanned aerial vehicles for disaster risk detection according to claim 1, characterized in that: The step 7 includes: the ARM processor processes the lidar point cloud and multispectral image data, and the disaster risk factors include the slope, slope aspect, slope curvature, plane curvature and vegetation coverage of the area of ​​interest.

8. An active and passive imaging system for disaster risk detection carried by unmanned aerial vehicles, characterized in that: include: The system integrated power supply design module is used to distribute the output power of the UAV platform to each active and passive imaging payload, POS / INS integrated navigation and ARM processor through a customized power supply board; System integrated control and time synchronization module, used to realize communication control of each active and passive imaging payload based on ARM processor, and realize time synchronization setting of the whole system based on POS / INS integrated navigation; Active and passive imaging payload field of view matching module, used to place the laser radar scanner and multispectral camera included in the active and passive imaging payload in the same spatial imaging coordinate system for field of view matching; The UAV platform interface adapter module is used to match the external communication protocol of the ARM processor and the power supply board with the interface protocol of the UAV platform; A system structure optimization module, used for lightweight optimization design of the active and passive imaging system composed of each active and passive imaging payload, POS / INS integrated navigation, ARM processor and the power supply board; The system is equipped with a flight calibration module, which is used to match the laser footprint and ground pixel points of the optimized active and passive imaging system through long-distance ground targets; The disaster risk detection algorithm implementation module based on the ARM processor is used for active and passive imaging systems based on the verification, and utilizes the ARM processor disaster risk factors to realize disaster risk detection in the area of ​​interest by analyzing the disaster factors.

9. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; Among them, when one or more programs are executed by the one or more processors, the one or more processors implement the active and passive imaging method for disaster risk detection carried out by an unmanned aerial vehicle as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Executable instructions are stored thereon, which, when executed by a processor, enable the processor to implement an active and passive imaging method for disaster risk detection carried out by an unmanned aerial vehicle as described in any one of claims 1 to 7.

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