A method, system, device and medium for positioning a multi-source sensor of a UAV

Abstract

The application discloses a kind of unmanned vehicle multi-source sensor positioning method, system, equipment and medium, it is related to unmanned vehicle positioning technical field, method includes: obtaining the laser point cloud information that unmanned vehicle is based on laser scanner to the object to be positioned for scanning obtains and the first pose information of unmanned vehicle;According to the coordinate system where laser point cloud information and first pose information respectively are located determines coordinate system rotation matrix;According to first pose information and coordinate system rotation matrix determine that unmanned vehicle is located in the second pose information of laser point cloud information corresponding coordinate system;According to first pose information and second pose information determine pose information deviation, according to pose information deviation and laser point cloud information determine the actual point cloud information of the object to be positioned, further determine the positioning information of the object to be positioned.The application can realize the real-time acquisition of high-precision pose, also can improve the pose accuracy, positioning accuracy and navigation accuracy of unmanned vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle positioning, and in particular to a multi-source sensor positioning method, system, device and medium for unmanned aerial vehicles. BACKGROUND

[0002] As a new type of technical equipment, unmanned aerial vehicles can realize rich functions by carrying various sensors, and can play an important role in production and life. Unmanned aerial vehicle sensors involve inertial navigation systems, GPS positioning systems, laser radar systems and other technologies and principles, which jointly act on the flight control, positioning and navigation, target recognition and environment perception of unmanned aerial vehicles to ensure the safe, stable and efficient operation of unmanned aerial vehicles.

[0003] Since the time and space reference of the data collected by various sensors on the unmanned aerial vehicle platform is inconsistent, the time reference and space reference need to be solved before the multi-source data is fused, diagnosed and analyzed, so as to improve the pose accuracy, positioning accuracy and navigation accuracy of the unmanned aerial vehicle. SUMMARY

[0004] The present application solves the technical problems of the prior art, specifically the problems of inconsistent sensor data reference and low positioning accuracy of unmanned aerial vehicles, and specifically provides a multi-source sensor positioning method, system, device and medium for unmanned aerial vehicles, as follows:

[0005] 1) In a first aspect, the present application provides a multi-source sensor positioning method for unmanned aerial vehicles, and the specific technical solutions are as follows:

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

[0007] S2, determining a coordinate system rotation matrix according to the laser coordinate system and the coordinate system of the unmanned aerial vehicle; determining second pose information of the unmanned aerial vehicle in the laser coordinate system according to the first pose information and the coordinate system rotation matrix; determining 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 determining positioning information of the to-be-positioned object according to the actual point cloud information.

[0008] The multi-source sensor positioning method for unmanned aerial vehicles provided by the present application has the following beneficial effects:

[0009] By determining the coordinate system rotation matrix, the coordinate standards of the radar laser scanner and the unmanned aerial vehicle pose are unified, so that the positioning obtained by the unmanned aerial vehicle laser radar scanning near-ground objects during flight movement is more accurate, so that real-time acquisition of high-precision pose is realized, and the pose accuracy, positioning accuracy and navigation accuracy of the unmanned aerial vehicle are improved.

[0010] Based on the above scheme, the application can be further improved as follows.

[0011] Further, the first pose information of the unmanned aerial vehicle is obtained, specifically:

[0012] S101, first pose data collected based on an inertial processing unit sensor and second pose data collected based on a global positioning sensor are obtained;

[0013] S102, third pose information of the unmanned aerial vehicle is determined according to the first pose data; fourth pose information of the unmanned aerial vehicle is determined according to the second pose data;

[0014] S103, the third pose information and the fourth pose information are input into a Kalman filtering equation to obtain the first pose information of the unmanned aerial vehicle.

[0015] Further, the S101 further comprises:

[0016] The determination process of the third pose information is specifically:

[0017] The third pose information of the unmanned aerial vehicle is determined according to the first pose data and an error value, and the error value is obtained by the inertial processing unit sensor.

[0018] Further, the error value comprises:

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

[0020] Further, the determination of the coordinate system rotation matrix according to the laser coordinate system and the unmanned aerial vehicle coordinate system is specifically:

[0021] The laser coordinate system and the unmanned aerial vehicle coordinate system are calibrated by the ICP nearest iteration point method to determine the corresponding coordinate system rotation matrix of the laser coordinate system and the unmanned aerial vehicle coordinate system.

[0022] 2) In a second aspect, the application further provides a multi-source sensor positioning system of 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 configured to acquire laser point cloud information in a laser coordinate system obtained by scanning an object to be positioned by a laser scanner based on a UAV; and acquire first position information of the UAV based on a UAV coordinate system;

[0024] The determination module is configured to determine a coordinate system rotation matrix based on the laser coordinate system and the UAV coordinate system; determine second position information of the UAV in the laser coordinate system based on the first position information and the coordinate system rotation matrix; determine position information deviation based on the first position information and the second position information; determine actual point cloud information of the object to be positioned based on the position information deviation and the laser point cloud information; and determine positioning information of the object to be positioned based on the actual point cloud information.

[0025] Based on the above solutions, the application can be further improved as follows.

[0026] Further, the first position information of the UAV is acquired in the following manner:

[0027] The first position data collected by an inertial processing unit sensor and the second position data collected by a global positioning sensor are acquired based on the UAV;

[0028] The third position information of the UAV is determined based on the first position data; and the fourth position information of the UAV is determined based on the second position data.

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

[0030] Further, the determination process of the third position information is as follows:

[0031] The third position information of the UAV is determined based on the first position data and an error value, and the error value is obtained by the inertial processing unit sensor.

[0032] Further, the error value includes:

[0033] Zero bias error, proportional factor error, cross-coupling error and random noise caused by an accelerometer and a gyroscope in the inertial processing unit sensor.

[0034] Further, the coordinate system rotation matrix is determined based on the laser coordinate system and the UAV coordinate system in the following manner:

[0035] The laser coordinate system and the UAV coordinate system are calibrated by an ICP nearest iteration point method to determine the coordinate system rotation matrix corresponding to the laser coordinate system and the UAV coordinate system.

[0036] 3) In a third aspect, the present invention also provides a computer device, the computer device including a processor coupled to a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor to enable the computer device to implement any of the above methods.

[0037] 4) In a fourth aspect, the present invention also provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to enable a computer to implement any of the above methods.

[0038] It should be noted that the beneficial effects of the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementations can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here. Attached Figure Description

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

[0040] Figure 1 This is a flowchart illustrating the steps of a UAV multi-source sensor localization method according to an embodiment of the present invention.

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

[0042] Figure 3 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Detailed Implementation

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

[0044] like Figure 1 As shown, an embodiment of the present invention provides a UAV multi-source sensor localization method, which includes the following steps:

[0045] S1, acquire laser point cloud information in the laser coordinate system obtained by the UAV scanning the object to be positioned based on the laser scanner; acquire the first pose information of the UAV based on the UAV coordinate system.

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

[0047] The unmanned aerial vehicle multi-source sensor positioning method provided by the application has the following beneficial effects:

[0048] Through the determination of the coordinate system rotation matrix, the coordinate standards of the radar laser scanner and the UAV pose are unified, so that the positioning obtained by the UAV laser radar when scanning the near-ground object during the flight movement is more accurate, thereby realizing real-time acquisition of high-precision pose, and improving the pose accuracy, positioning accuracy and navigation accuracy of the UAV.

[0049] S1, obtain laser point cloud information in a laser coordinate system obtained by scanning an object to be positioned by a laser scanner based on a UAV; and obtain first pose information based on a UAV coordinate system.

[0050] The UAV refers to a UAV carrying multiple sensors such as an inertial navigation system, a GPS positioning system and a laser radar system.

[0051] The laser scanner refers to an instrument with a laser scanning function on the UAV, which can be a laser radar or a LiDAR scanner.

[0052] The object to be positioned refers to an object that needs to be positioned in near-ground observation after the UAV takes off.

[0053] The laser point cloud information refers to point cloud information obtained by scanning the object to be positioned by the laser scanner.

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

[0055] The UAV coordinate system can be a coordinate system with the position of an inertial processing unit in an inertial navigation system (INS) of the UAV 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 UAV is that the UAV usually maintains a flight state in low-altitude observation, which makes the laser radar always maintain a motion state during operation and cannot obtain real three-dimensional point clouds. 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 inertial navigation and global positioning, high-precision position and attitude information of the laser radar at each moment is provided, so that the point cloud projection can be converted to a real three-dimensional space.

[0056] The first pose information refers to the pose information of the UAV.

[0057] In another embodiment of the application, the first pose information of the UAV is obtained, specifically:

[0058] S101, first pose data collected by an inertial processing unit sensor of the UAV and second pose data collected by a global positioning sensor are obtained;

[0059] S102, third pose information of the UAV is determined according to the first pose data, and fourth pose information of the UAV is determined according to the second pose data;

[0060] S103, the third pose information and the fourth pose information are input into a Kalman filtering equation to obtain the first pose information of the UAV. Wherein:

[0061] The inertial processing unit refers to an 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 UAV, and the third pose information of the UAV is calculated accordingly. The inertial processing unit is prior art and will not be described again.

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

[0064] The second pose data refers to the pose data collected by the global positioning sensor, and the fourth pose information of the UAV is determined by interaction with positioning satellites.

[0065] In another embodiment of the present invention, the process of determining the third pose information specifically involves: determining the third pose information of the UAV based on the first pose data and the error value, wherein the error value is obtained through the inertial processing unit sensor. The error value includes: zero-bias error, scaling factor error, cross-coupling error, and random noise caused by the accelerometer and gyroscope within the inertial processing unit sensor. Wherein:

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

[0067]

[0068] in, and This represents the true values ​​of the IMU sensor accelerometer and gyroscope measurements, b a and b g These represent the zero-bias errors of the accelerometer and gyroscope in the inertial processing unit, respectively. Gg represents the g-correlated zero-bias, which is caused by the fact that accelerations along all three axes can affect the same gyroscope measurement value, denoted as Gg; wa and wg represent the random noise of the accelerometer and gyroscope, respectively. a and M g The scaling factor and cross-coupling error of the accelerometer and gyroscope are represented, and are determined by the second formula, which is as follows:

[0069]

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

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

[0072] The inertial processing unit (IMU) is initialized and aligned using the first pose data, providing initial position, velocity, and attitude information for subsequent determination of the first pose information. Alignment methods include static alignment, dynamic alignment, and transfer rotation. Since the alignment process is existing technology, it will not be elaborated upon here.

[0073] like Figure 2 As shown, the acceleration and angular rate information output by the inertial processing unit are used, and the result of the first attitude information alignment is used as the initial value for calculation. The position, velocity and attitude information of the UAV, i.e. the third attitude information, are calculated according to Newton's laws of motion for moving objects on Earth.

[0074] Input the third pose information and the fourth pose information into the Kalman filtering equation to obtain the first pose information of the unmanned aerial vehicle, and specifically:

[0075] Input the third pose information and the fourth pose information as measurement information into the Kalman filtering equation to obtain the first pose information of the unmanned aerial vehicle. At the same time, the error of the inertial navigation can also be obtained, based on which the attitude error and the inertial processing unit error can be indirectly estimated, and the data can be feedback corrected based on the attitude error and the inertial processing unit error, so that the calculation result is more accurate.

[0076] S2, determine the coordinate system rotation matrix according to the laser coordinate system and the unmanned aerial vehicle coordinate system; determine the second pose information of the unmanned aerial vehicle 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, determine the actual point cloud information of the object to be positioned according to the pose information deviation and the laser point cloud information; and determine the positioning information of the object to be positioned according to the actual point cloud information.

[0077] In another embodiment of the application, the laser coordinate system and the unmanned aerial vehicle coordinate system are calibrated by the ICP nearest iteration point method to determine the coordinate system rotation matrix corresponding to the laser coordinate system and the unmanned aerial vehicle coordinate system. Specifically as follows:

[0078] 1) When the unmanned aerial vehicle is fixed, collect the original data of the calibration object under the inertial navigation and the point cloud data under the laser radar;

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

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

[0081] 4) Correlate and calculate the coordinates of the calibration object under the inertial navigation coordinate and the coordinates under the laser radar coordinate system to obtain the coordinate system rotation matrix.

[0082] In addition, it also includes changing the coordinates of the calibration object under the inertial navigation according to the coordinate system rotation matrix, comparing the obtained result with the coordinates under the laser radar coordinate system to obtain the analysis error. According to the analysis error, the coordinate system rotation matrix is corrected, so that the subsequent calculation result is more accurate, and the positioning accuracy is further improved.

[0083] The actual point cloud information refers to a group of information including positioning information, speed, angular velocity and the like.

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

[0085] The advantages of the above scheme are:

[0086] 1) Using a LiDAR scanner combined with an inertial navigation system and a global positioning system, high-density laser point cloud data in a specified coordinate system can be obtained. This scanner can overcome the influence of long flight time, large maneuvering, bad weather conditions, dangerous environment, etc., and make up for the vacancy of three-dimensional point cloud information in the area of interest that cannot be obtained due to weather and time;

[0087] 2) The scheme in the present application sets multiple errors, and compensates for the measurement and calculation data using the errors to achieve online calibration and improve the attitude accuracy of the navigation system combined filter;

[0088] 3) Through the coordinate conversion between the inertial navigation system, the GPS positioning system and the laser radar system, the sensor data reference is unified, so that the real-time navigation measurement information is more accurate; through high-dimensional, anti-disturbance and non-linear inertial combined navigation solution, real-time acquisition of high-precision pose is realized.

[0089] In the above embodiments, although the steps are numbered S1, S2, etc., it is only a specific embodiment given by the present application, and those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, which is within the protection scope of the present application. It can be understood that in some embodiments, some or all of the above embodiments can be included.

[0090] The present application also provides a multi-source sensor positioning system for an unmanned aerial vehicle, and the specific technical solutions are as follows:

[0091] It comprises an acquisition module and a determination module.

[0092] The 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 based on an unmanned aerial vehicle; and acquire first pose information in an unmanned aerial vehicle coordinate system based on an unmanned aerial vehicle.

[0093] The determining module is used for determining a coordinate system rotation matrix according to the laser coordinate system and the UAV coordinate system, determining second pose information of the UAV in the laser coordinate system according to the first pose information and the coordinate system rotation matrix, determining pose information deviation according to the first pose information and the second pose information, determining actual point cloud information of the object to be positioned according to the pose information deviation and the laser point cloud information, and determining positioning information of the object to be positioned according to the actual point cloud information.

[0094] Based on the above scheme, the application can be further improved as follows.

[0095] Further, the first pose information of the UAV is acquired, specifically:

[0096] The first pose data collected by the inertial processing unit sensor and the second pose data collected by the global positioning sensor are acquired;

[0097] The third pose information of the UAV is determined according to the first pose data, and the fourth pose information of the UAV is determined according to the second pose data.

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

[0099] Further, the determination process of the third pose information is specifically as follows:

[0100] The third pose information of the UAV is determined according to the first pose data and the error value, and the error value is obtained by the inertial processing unit sensor.

[0101] Further, the error value includes:

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

[0103] Further, the coordinate system rotation matrix is determined according to the laser coordinate system and the UAV coordinate system, specifically:

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

[0105] It should be noted that the beneficial effects of the UAV multi-source sensor positioning system provided in the above embodiments are the same as those of the UAV multi-source sensor positioning method described above, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical 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 the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0106] like Figure 3 As shown, an embodiment of the present invention provides a computer device 300, which includes a processor 320 coupled to a memory 310. The memory 310 stores at least one computer program 330, which is loaded and executed by the processor 320 to enable the computer device 300 to implement any of the above-described methods. Specifically:

[0107] The computer device 300 can vary considerably due to differences in configuration or performance. It may include one or more processors 320 (Central Processing Units, CPUs) and one or more memories 310. The one or more memories 310 store at least one computer program 330, which is loaded and executed by the one or more processors 320 to enable the computer device 300 to implement the UAV multi-source sensor localization method provided in the above embodiments. Of course, the computer device 300 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The computer device 300 may also include other components for implementing device functions, which will not be elaborated here.

[0108] An embodiment of the present invention provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to enable a computer to implement any of the above-described methods.

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

[0110] In an example embodiment, a computer program product or computer program is also provided, which includes computer instructions 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 one of the above unmanned aerial vehicle multi-source sensor positioning methods.

[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 do not represent a specific order or sequence. The order of use of similar objects can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described.

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

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

[0114] Although the embodiments of the present application have been shown and described above, it should be understood that the above-described embodiments are exemplary, and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-described embodiments within the scope of the present application.

Claims

1. A method for positioning a multi-source sensor of a UAV, characterized in that, The method comprises the steps of: S1, obtaining laser point cloud information in a laser coordinate system obtained by a UAV scanning a to-be-positioned object based on a laser scanner; obtaining first pose information of the UAV based on a UAV coordinate system; S2, determining a coordinate system rotation matrix according to the laser coordinate system and the UAV coordinate system; determining second pose information of the UAV in the laser coordinate system according to the first pose information and the coordinate system rotation matrix; determining 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 determining positioning information of the to-be-positioned object according to the actual point cloud information; The first pose information of the UAV is obtained in the following manner: S101, obtaining first pose data collected by an inertial processing unit sensor and second pose data collected by a global positioning sensor based on the UAV; S102, determining third pose information of the UAV according to the first pose data; and determining fourth pose information of the UAV according to the second pose data; S103, inputting the third pose information and the fourth pose information into a Kalman filtering equation to obtain the first pose information of the UAV; The third pose information is determined in the following manner: The third pose information of the UAV is determined according to the first pose data and an error value, and the error value is obtained by the inertial processing unit sensor. 2.The method of claim 1, wherein, The error value comprises: Zero bias error, proportional factor error, cross-coupling error and random noise caused by the accelerometer and the gyroscope in the inertial processing unit sensor. 3.The method of claim 1, wherein, The coordinate system rotation matrix is determined in the following manner: The laser coordinate system and the UAV coordinate system are calibrated by an ICP nearest iteration point method to determine the coordinate system rotation matrix corresponding to the laser coordinate system and the UAV coordinate system.

4. A multi-source sensor positioning system for unmanned aerial vehicles, comprising: The method comprises the steps of: an obtaining module and a determining module; The obtaining module is configured to obtain laser point cloud information in a laser coordinate system obtained by a UAV scanning a to-be-positioned object based on a laser scanner; obtaining first pose information of the UAV based on a UAV coordinate system; The determining module is configured to determine a coordinate system rotation matrix according to the laser coordinate system and the UAV coordinate system; determine second pose information of the UAV in the laser coordinate system according to the first pose information and the coordinate system rotation matrix; determine pose information deviation according to the first pose information and the second pose information, determine actual point cloud information of the to-be-positioned object according to the pose information deviation and the laser point cloud information; and determine positioning information of the to-be-positioned object according to the actual point cloud information; The first pose information of the UAV is obtained in the following manner: obtaining first pose data collected by an inertial processing unit sensor and second pose data collected by a global positioning sensor based on the UAV; According to the first pose data, third pose information of the UAV is determined; and according to the second pose data, fourth pose information of the UAV is determined; The third pose information and the fourth pose information are input into a Kalman filtering equation to obtain the first pose information of the UAV; The third pose information is determined according to the first pose data and an error value, and the error value is obtained by the inertial processing unit sensor. The computer device includes a processor coupled with a memory, and the memory stores at least one computer program, which is loaded and executed by the processor, so that the computer device implements the method according to any one of claims 1 to 3.

5. A computer device, comprising: The computer readable storage medium stores at least one computer program, which is loaded and executed by the processor, so that the computer implements the method according to any one of claims 1 to 3.

6. A computer readable storage medium characterized by, ​

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