Unmanned aerial vehicle multi-source sensor positioning method, system, device and medium

Abstract

The invention discloses an unmanned aerial vehicle multi-source sensor positioning method, system and device and a medium, and relates to the technical field of unmanned aerial vehicle positioning, and the method comprises the steps: obtaining the laser point cloud information of a to-be-positioned object scanned by an unmanned aerial vehicle based on a laser scanner, and the first pose information of the unmanned aerial vehicle; determining a coordinate system rotation matrix according to coordinate systems where the laser point cloud information and the first pose information are located; determining second pose information of the unmanned aerial vehicle in a coordinate system corresponding to the laser point cloud information according to the first pose information and the coordinate system rotation matrix; and determining a pose information deviation according to the first pose information and the second pose information, determining actual point cloud information of the to-be-positioned object according to the pose information deviation and the laser point cloud information, and further determining positioning information of the to-be-positioned object. According to the invention, real-time acquisition of high-precision poses can be realized, and the pose precision, the positioning precision and the navigation precision of the unmanned aerial vehicle can be improved.

Description

Background Art

[0002] As a new type of technical equipment, drones can achieve rich functions by carrying a variety of sensors. They can play an important role in production and life. Drone sensors involve a variety of technologies and principles such as inertial navigation systems, GPS positioning systems, and laser radar systems. They work together in the flight control, positioning navigation, target recognition, and environmental perception of drones to ensure the safety, stability, and efficient operation of drones.

[0003] Since the time and space references of the data collected by various types of sensors carried on the UAV platform are inconsistent, it is necessary to solve the problems of time reference and space reference before fusion diagnosis and analysis of multi-source data, so as to improve the UAV's posture accuracy, positioning accuracy and navigation accuracy. Summary of the invention

[0004] The technical problem to be solved by the present invention is to address the deficiencies of the prior art, specifically to address the problems of inconsistent UAV sensor data benchmarks and low positioning accuracy, and specifically to provide a UAV multi-source sensor positioning method, system, device and medium, as follows:

[0005] 1) In the first aspect, the present invention provides a method for positioning a multi-source sensor of an unmanned aerial vehicle, and the specific technical solution is as follows:

[0006] S1, obtaining laser point cloud information in a laser coordinate system obtained by scanning an object to be positioned by a laser scanner of a drone; obtaining the first position information of the drone based on the drone coordinate system;

[0007] S2, determining a coordinate system rotation matrix according to the laser coordinate system and the UAV coordinate system; determining second posture information of the UAV in the laser coordinate system according to the first posture information and the coordinate system rotation matrix; determining a posture information deviation according to the first posture information and the second posture information, and determining actual point cloud information of the object to be located according to the posture information deviation and the laser point cloud information; determining the positioning information of the object to be located according to the actual point cloud information.

[0008] The beneficial effects of the multi-source sensor positioning method for unmanned aerial vehicles provided by the present invention are as follows:

[0009] By determining the rotation matrix of the coordinate system and unifying the coordinate standards of the radar laser scanner and the drone's posture, the positioning obtained by the drone's laser radar when scanning near-ground objects during flight can be more accurate, thereby achieving real-time acquisition of high-precision posture, and also improving the drone's posture accuracy, positioning accuracy, and navigation accuracy.

[0010] Based on the above solution, the present invention can also be improved as follows.

[0011] Further, the obtaining of the first position information of the drone is specifically as follows:

[0012] S101, obtaining first posture data collected by the drone based on an inertial processing unit sensor and second posture data collected based on a global positioning sensor;

[0013] S102, determining third posture information of the drone according to the first posture data; determining fourth posture information of the drone according to the second posture data;

[0014] S103, inputting the third posture information and the fourth posture information into a Kalman filter equation to obtain the first posture information of the drone.

[0015] Furthermore, the S101 further includes:

[0016] The process of determining the third posture information is specifically as follows:

[0017] The third posture information of the UAV is determined according to the first posture data and the error value, and the error value is obtained by the inertial processing unit sensor.

[0018] Further, the error value includes:

[0019] The zero bias error, scale factor error, cross-coupling error and random noise caused by the accelerometer and gyroscope in the inertial processing unit sensor.

[0020] Further, the coordinate system rotation matrix is ​​determined according to the laser coordinate system and the drone coordinate system, specifically:

[0021] The laser coordinate system and the drone coordinate system are calibrated by the ICP nearest iterative point method to determine the coordinate system rotation matrices corresponding to the laser coordinate system and the drone coordinate system.

[0022] 2) In a second aspect, the present invention also provides a multi-source sensor positioning system for an unmanned aerial vehicle, and the specific technical scheme is as follows: comprising: an acquisition module and a determination module;

[0023] The acquisition module is used to acquire laser point cloud information in a laser coordinate system obtained by scanning the object to be positioned by a laser scanner of the drone; and acquire the first position information of the drone based on the drone coordinate system;

[0024] The determination module is used to determine the coordinate system rotation matrix according to the laser coordinate system and the UAV coordinate system; determine the second posture information of the UAV in the laser coordinate system according to the first posture information and the coordinate system rotation matrix; determine the posture information deviation according to the first posture information and the second posture information, determine the actual point cloud information of the object to be located according to the posture information deviation and the laser point cloud information; determine the positioning information of the object to be located according to the actual point cloud information.

[0025] Based on the above solution, the present invention can also be improved as follows.

[0026] Further, the obtaining of the first position information of the drone is specifically as follows:

[0027] Acquire the first posture data collected by the drone based on the inertial processing unit sensor and the second posture data collected based on the global positioning sensor;

[0028] Determine third posture information of the UAV according to the first posture data; determine fourth posture information of the UAV according to the second posture data;

[0029] The third posture information and the fourth posture information are input into the Kalman filter equation to obtain the first posture information of the UAV.

[0030] Further, the process of determining the third posture information is specifically as follows:

[0031] The third posture information of the UAV is determined according to the first posture data and the error value, and the error value is obtained by the inertial processing unit sensor.

[0032] Further, the error value includes:

[0033] The zero bias error, scale factor error, cross-coupling error and random noise caused by the accelerometer and gyroscope in the inertial processing unit sensor.

[0034] Further, the coordinate system rotation matrix is ​​determined according to the laser coordinate system and the drone coordinate system, specifically:

[0035] The laser coordinate system and the drone coordinate system are calibrated by the ICP nearest iterative point method to determine the coordinate system rotation matrices corresponding to the laser coordinate system and the drone coordinate system.

[0036] 3) In a third aspect, the present invention further provides a computer device, comprising a processor, wherein the processor is coupled to a memory, wherein the memory stores at least one computer program, and wherein the at least one computer program is loaded and executed by the processor so that the computer device implements any of the above methods.

[0037] 4) In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor so that a computer implements any of the above methods.

[0038] It should be noted that the beneficial effects achieved by the technical solutions of the second to fourth aspects of the present invention and the corresponding possible implementation methods can be found in the above-mentioned technical effects of the first aspect and its corresponding possible implementation methods, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0040] Figure 1 A schematic diagram of the steps of a multi-source sensor positioning method for an unmanned aerial vehicle according to an embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram of a process for obtaining drone posture information according to an inertial processing unit according to an embodiment of the present invention;

[0042] Figure 3 The figure is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0044] like Figure 1 As shown, a multi-source sensor positioning method for an unmanned aerial vehicle according to an embodiment of the present invention includes the following steps:

[0045] S1, obtaining laser point cloud information in a laser coordinate system obtained by scanning an object to be positioned by a laser scanner of the drone; obtaining the first position information of the drone based on the drone coordinate system;

[0046] S2, determine the coordinate system rotation matrix according to the laser coordinate system and the UAV coordinate system; determine the second pose information of the UAV in the laser coordinate system according to the first pose information and the coordinate system rotation matrix; determine the pose information deviation according to the first pose information and the second pose information, and determine the actual point cloud information of the object to be located according to the pose information deviation and the laser point cloud information; determine the positioning information of the object to be located according to the actual point cloud information.

[0047] The beneficial effects of the multi-source sensor positioning method for unmanned aerial vehicles provided by the present invention are as follows:

[0048] By determining the rotation matrix of the coordinate system and unifying the coordinate standards of the radar laser scanner and the drone's posture, the positioning obtained by the drone's laser radar when scanning near-ground objects during flight can be more accurate, thereby achieving real-time acquisition of high-precision posture, and also improving the drone's posture accuracy, positioning accuracy, and navigation accuracy.

[0049] S1, obtain the laser point cloud information in the laser coordinate system obtained by scanning the object to be positioned by the UAV based on the laser scanner; obtain the first position information of the UAV based on the UAV coordinate system. Among them:

[0050] Drones refer to drones equipped with multiple sensors such as inertial navigation systems, GPS positioning systems, and lidar systems.

[0051] Laser scanner refers to an instrument on a drone that has a laser scanning function, which can be a laser radar or LiDAR scanner, etc.

[0052] Objects to be located refer to objects that need to be located in near-ground observations after the drone takes off.

[0053] Laser point cloud information refers to the point cloud information obtained by a laser scanner scanning the object to be located.

[0054] The laser coordinate system can be a coordinate system constructed with the position of the laser scanning radar platform at the time of shooting as the origin, the geographic north as the y-axis, and the geographic east as the x-axis.

[0055] The coordinate system of the drone can be a coordinate system constructed with the location of the inertial processing unit in the drone's inertial navigation system (INS) as the origin, geographic north as the y-axis, and geographic east as the x-axis. It should be noted that the difference between the laser coordinate system and the coordinate system of the drone is that the drone usually maintains a flying state during low-altitude ground observation, which makes the laser radar always in motion during the operation process and cannot obtain a real three-dimensional point cloud. The inertial navigation system is an autonomous navigation system that does not rely on external information and does not radiate energy to the outside. By combining the two, the laser radar can be provided with high-precision position and attitude information at every moment through inertial navigation and global positioning, so that the point cloud projection can be converted into a real three-dimensional space.

[0056] The first posture information refers to: the posture information of the drone.

[0057] In another embodiment of the present invention, the first position information of the drone is obtained, specifically:

[0058] S101, obtaining first posture data collected by the drone based on an inertial processing unit sensor and second posture data collected based on a global positioning sensor;

[0059] S102, determining third posture information of the drone according to the first posture data; determining fourth posture information of the drone according to the second posture data;

[0060] S103, input the third posture information and the fourth posture information into the Kalman filter equation to obtain the first posture information of the drone.

[0061] Inertial processing unit refers to: IMU (Inertial measurement unit).

[0062] The first pose data refers to the pose data collected by the inertial processing unit sensor, which usually includes the speed, position, orientation, acceleration, angular rate, etc. of the drone, and the third pose information of the drone is calculated based on this. The inertial processing unit is a prior art and will not be described in detail.

[0063] Global positioning refers to: GPS (Global Positioning System).

[0064] The second posture data refers to: according to the posture data collected by the global positioning sensor, the fourth posture information of the drone is determined through interaction with the positioning satellite.

[0065] In another embodiment of the present invention, the determination process of the third posture information is specifically as follows: determining the third posture information of the drone according to the first posture data and the error value, and the error value is obtained by the inertial processing unit sensor. The error value includes: zero bias error, proportional factor error, cross-coupling error and random noise caused by the accelerometer and gyroscope in the inertial processing unit sensor. Wherein:

[0066] The error of the inertial processing unit sensor can be modeled as a first formula, which is as follows:

[0067]

[0068] in, and represents the true value of the IMU sensor accelerometer and gyroscope measurement, b a and b g They represent the bias errors of the accelerometer and gyroscope of the inertial processing unit sensor, Gg represents the g-related bias. This error is due to the fact that the accelerations in the three axes may affect the same gyroscope measurement value, recorded as Gg; wa and wg represent the random noise of the accelerometer and gyroscope, M a and M g represents the scale factor and cross-coupling error of the accelerometer and gyroscope, which is determined by the second formula, which is as follows:

[0069]

[0070] Among them, (S a,x ,S a,y ,S a,z ) represents the scale factor of the accelerometer, (S g,x ,S g,y ,S g,z ) represents the scale factor of the gyroscope.

[0071] The process of determining the third posture information of the drone based on the first posture data and the error value is as follows:

[0072] The first pose data is used to initialize and align the inertial processing unit, providing initial position, speed and pose information for the subsequent determination of the first pose information. The alignment methods include static alignment, dynamic alignment and transfer rotation, etc. Since the alignment process is a prior art, it will not be described here.

[0073] like Figure 2 As shown, the acceleration and angular rate information output by the inertial processing unit is used, and the result of the alignment of the first posture information is used as the initial value for solution. According to the Newtonian laws of mechanics for moving objects on the earth, the position, velocity and attitude information of the UAV, i.e., the third posture information, is solved.

[0074] Input the third and fourth pose information into the Kalman filter equation to obtain the first pose information of the drone. Specifically:

[0075] The third and fourth posture information are used as measurement information and input into the Kalman filter equation to obtain the first posture information of the drone. At the same time, the error of inertial navigation can also be obtained. Based on the error of inertial navigation, the attitude error and the inertial processing unit error can be indirectly estimated. Based on the attitude error and the inertial processing unit error, the data is fed back and corrected to make the calculation result more accurate.

[0076] S2, determine the coordinate system rotation matrix according to the laser coordinate system and the UAV coordinate system; determine the second pose information of the UAV in the laser coordinate system according to the first pose information and the coordinate system rotation matrix; determine the pose information deviation according to the first pose information and the second pose information, and determine the actual point cloud information of the object to be located according to the pose information deviation and the laser point cloud information; determine the positioning information of the object to be located according to the actual point cloud information.

[0077] In another embodiment of the present invention, the laser coordinate system and the drone coordinate system are calibrated by the ICP nearest iterative point method to determine the coordinate system rotation matrix corresponding to the laser coordinate system and the drone coordinate system. Specifically:

[0078] 1) When the UAV is stationary, collect the original data of the calibration object under inertial navigation and the point cloud data under the lidar;

[0079] 2) Calculate the attitude angle of inertial navigation based on the original data, calculate the conversion matrix between the attitude angle and the inertial navigation coordinates, and obtain the coordinates of the calibration object under inertial navigation;

[0080] 3) Determine the point cloud coordinates of the calibration object under the laser radar coordinates;

[0081] 4) Correlate the coordinates of the calibration object in the inertial navigation coordinate system with the coordinates in the lidar coordinate system to obtain the coordinate system rotation matrix.

[0082] In addition, the coordinates of the calibration object under inertial navigation are changed according to the coordinate system rotation matrix, and the result is compared with the coordinates under the laser radar coordinate system to obtain the analysis error. The coordinate system rotation matrix is ​​corrected according to the analysis error to make the subsequent calculation results more accurate and further improve the positioning accuracy.

[0083] The actual point cloud information refers to an information group including positioning information, speed, angular rate and other information.

[0084] After determining the rotation matrix of the coordinate system, the process of determining the second pose information based on the first pose information is a pure geometric calculation problem. The pose information deviation is determined based on the first pose information and the second pose information, and the actual point cloud information of the object to be located is determined based on the pose information deviation and the laser point cloud information; the positioning information of the object to be located can be determined based on the actual point cloud information.

[0085] The benefits of adopting the above solution are:

[0086] 1) Using LiDAR scanners in conjunction with inertial navigation systems and global positioning systems can obtain high-density laser point cloud data in a specified coordinate system. The scanner can not only overcome the impact of long flight time, large maneuvers, adverse weather conditions, dangerous environments, etc., but also make up for the lack of 3D point cloud information in the area of ​​interest that cannot be obtained due to weather and time;

[0087] 2) The scheme in the present invention sets multiple errors and uses the errors to compensate for the measurement and calculation data to achieve the purpose of online calibration and improve the attitude accuracy of the combined filtering of the navigation system;

[0088] 3) By converting the coordinates between the inertial navigation system, GPS positioning system and lidar system, the sensor data benchmark is unified, making the real-time navigation measurement information more accurate; through high-dimensional, anti-disturbance, nonlinear inertial combined navigation solution, real-time acquisition of high-precision posture is achieved.

[0089] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0090] The present invention also provides a multi-source sensor positioning system for unmanned aerial vehicles, and the specific technical solution is as follows:

[0091] Including: acquisition module and determination module;

[0092] An acquisition module is used to acquire laser point cloud information in a laser coordinate system obtained by scanning an object to be positioned by a laser scanner of the drone; and to acquire the first position information of the drone based on the drone coordinate system;

[0093] A determination module is used to determine a coordinate system rotation matrix according to a laser coordinate system and a UAV coordinate system; determine a second posture information of the UAV in the laser coordinate system according to the first posture information and the coordinate system rotation matrix; determine a posture information deviation according to the first posture information and the second posture information, determine the actual point cloud information of the object to be located according to the posture information deviation and the laser point cloud information; and determine the positioning information of the object to be located according to the actual point cloud information.

[0094] Based on the above solution, the present invention can also be improved as follows.

[0095] Further, obtain the first position information of the drone, specifically:

[0096] Obtaining the first posture data collected by the drone based on the inertial processing unit sensor and the second posture data collected based on the global positioning sensor;

[0097] Determine the third posture information of the UAV according to the first posture data; determine the fourth posture information of the UAV according to the second posture data;

[0098] The third posture information and the fourth posture information are input into the Kalman filter equation to obtain the first posture information of the UAV.

[0099] Furthermore, the process of determining the third posture information is specifically as follows:

[0100] The third posture information of the UAV is determined based on the first posture data and the error value, and the error value is obtained through the inertial processing unit sensor.

[0101] Furthermore, the error values ​​include:

[0102] The zero bias error, scale factor error, cross-coupling error and random noise caused by the accelerometer and gyroscope in the inertial processing unit sensor.

[0103] Furthermore, the coordinate system rotation matrix is ​​determined according to the laser coordinate system and the drone coordinate system, specifically:

[0104] The laser coordinate system and the UAV coordinate system are calibrated by the ICP nearest iterative point method to determine the coordinate rotation matrix corresponding to the laser coordinate system and the UAV coordinate system.

[0105] It should be noted that the beneficial effects of the multi-source sensor positioning system for unmanned aerial vehicles provided in the above embodiment are the same as the beneficial effects of the multi-source sensor positioning method for unmanned aerial vehicles provided in the above embodiment, and will not be described in detail here. In addition, when the system provided in the above embodiment realizes its functions, it only uses the division of the above functional modules as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be described in detail here.

[0106] like Figure 3 As shown, a computer device 300 according to an embodiment of the present invention includes a processor 320, the processor 320 is coupled to a memory 310, and the memory 310 stores at least one computer program 330, and the at least one computer program 330 is loaded and executed by the processor 320, so that the computer device 300 implements any of the above methods, specifically:

[0107] The computer device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors 320 (Central Processing Units, CPU) and one or more memories 310, wherein at least one computer program 330 is stored in the one or more memories 310, and the at least one computer program 330 is loaded and executed by the one or more processors 320, so that the computer device 300 implements a multi-source sensor positioning method for unmanned aerial vehicles provided in the above embodiment. Of course, the computer device 300 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the computer device 300 may also include other components for realizing the functions of the device, which will not be repeated here.

[0108] A computer-readable storage medium according to an embodiment of the present invention stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that a computer implements any one of the above methods.

[0109] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0110] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs any of the above-mentioned multi-source sensor positioning methods for unmanned aerial vehicles.

[0111] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and represent the definition of a specific order or sequence. The order of use of similar objects can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0112] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0113] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0114] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A multi-source sensor positioning method for unmanned aerial vehicles, characterized in that: include: S1, obtaining laser point cloud information in a laser coordinate system obtained by scanning an object to be positioned by a laser scanner of a UAV; Obtaining the first position information of the drone based on the drone coordinate system; S2, determining a coordinate system rotation matrix according to the laser coordinate system and the UAV coordinate system; determining second posture information of the UAV in the laser coordinate system according to the first posture information and the coordinate system rotation matrix; determining a posture information deviation according to the first posture information and the second posture information, and determining actual point cloud information of the object to be located according to the posture information deviation and the laser point cloud information; determining the positioning information of the object to be located according to the actual point cloud information.

2. The method for positioning a multi-source sensor of an unmanned aerial vehicle according to claim 1, characterized in that: The obtaining of the first position information of the drone is specifically as follows: S101, obtaining first posture data collected by the drone based on an inertial processing unit sensor and second posture data collected based on a global positioning sensor; S102, determining third posture information of the drone according to the first posture data; determining fourth posture information of the drone according to the second posture data; S103, inputting the third posture information and the fourth posture information into a Kalman filter equation to obtain the first posture information of the drone.

3. The method for positioning a multi-source sensor of an unmanned aerial vehicle according to claim 2, characterized in that: The process of determining the third posture information is specifically as follows: The third posture information of the UAV is determined according to the first posture data and the error value, and the error value is obtained by the inertial processing unit sensor.

4. The method for positioning a multi-source sensor of an unmanned aerial vehicle according to claim 3, characterized in that: The error values ​​include: The zero bias error, scale factor error, cross-coupling error and random noise caused by the accelerometer and gyroscope in the inertial processing unit sensor.

5. The method for positioning a multi-source sensor of an unmanned aerial vehicle according to claim 1, characterized in that: The coordinate system rotation matrix is ​​determined according to the laser coordinate system and the drone coordinate system, specifically: The laser coordinate system and the drone coordinate system are calibrated by the ICP nearest iterative point method to determine the coordinate system rotation matrices corresponding to the laser coordinate system and the drone coordinate system.

6. A multi-source sensor positioning system for unmanned aerial vehicles, characterized in that: include: Get module and determine module; The acquisition module is used to acquire laser point cloud information in a laser coordinate system obtained by scanning the object to be positioned by the UAV based on a laser scanner; Obtaining the first position information of the drone based on the drone coordinate system; The determination module is used to determine the coordinate system rotation matrix according to the laser coordinate system and the UAV coordinate system; determine the second posture information of the UAV in the laser coordinate system according to the first posture information and the coordinate system rotation matrix; determine the posture information deviation according to the first posture information and the second posture information, determine the actual point cloud information of the object to be located according to the posture information deviation and the laser point cloud information; determine the positioning information of the object to be located according to the actual point cloud information.

7. The multi-source sensor positioning system for unmanned aerial vehicles according to claim 6, characterized in that: The obtaining of the first position information of the drone is specifically as follows: Acquire the first posture data collected by the drone based on the inertial processing unit sensor and the second posture data collected based on the global positioning sensor; Determine third posture information of the UAV according to the first posture data; determine fourth posture information of the UAV according to the second posture data; The third posture information and the fourth posture information are input into the Kalman filter equation to obtain the first posture information of the UAV.

8. The multi-source sensor positioning system for unmanned aerial vehicles according to claim 7, characterized in that: The process of determining the third posture information is specifically as follows: The third posture information of the UAV is determined according to the first posture data and the error value, and the error value is obtained by the inertial processing unit sensor.

9. A computer device, characterized in that: The computer device comprises a processor, the processor is coupled to a memory, at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the computer device implements the method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that a computer implements the method according to any one of claims 1 to 5.

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