Unmanned aerial vehicle indoor positioning method, system and device and storage medium

Abstract

The invention provides an unmanned aerial vehicle indoor positioning method, system and device and a storage medium. The method comprises the steps of obtaining the height, the Z-axis speed and the Z-axis acceleration of an unmanned aerial vehicle; wherein the height of the unmanned aerial vehicle comprises a first measurement height zb obtained through the barometer module and a second measurement height zr obtained through the one-way ranging radar module; and fusing the height of the unmanned aerial vehicle, the Z-axis speed and the Z-axis acceleration by adopting Kalman filtering, and finally determining the height coordinate z of the unmanned aerial vehicle. According to the invention, the height of the unmanned aerial vehicle relative to the ground is determined based on the first measurement height zb obtained by the barometer module and the second measurement height zr obtained by the one-way ranging radar module instead of measuring the height by a single tool; and the measured heights are fused with the Z-axis speed and the Z-axis acceleration which are acquired by the inertial measurement unit module through Kalman filtering, and the output height coordinates of the unmanned aerial vehicle are more accurate through multiple iterations.

Description

Technical Field

[0001] The present invention relates to the technical field of indoor positioning of unmanned aerial vehicles, and in particular to an indoor positioning method, system, equipment and storage medium for unmanned aerial vehicles. Background Art

[0002] Drones have been widely used in various industries due to their outstanding efficiency and flexibility, powerful data collection and monitoring capabilities, excellent cost-effectiveness and innovation.

[0003] At present, indoor positioning can confirm the precise location of people or objects in buildings or closed spaces. In the indoor positioning technology of drones, there are various indoor positioning methods suitable for small drones, but they generally have some disadvantages. For example, the GPS positioning solution is affected by the external environment and has a very low positioning accuracy, and cannot complete accurate indoor positioning; the traditional camera positioning solution is easily affected by light, and it is easy to be inaccurate in positioning under poor lighting conditions; the binocular camera and host computer equipment are heavy and power-hungry, which are often difficult for small drones to carry, and the system assembly cost is high; UWB positioning has the problems of high power consumption, difficulty in assembly and high cost.

[0004] Existing UAV positioning methods all locate the two-dimensional coordinates of the UAV. When the UAV flies indoors, it is affected by the space and indoor objects. Using a single height measurement tool to obtain the height of the UAV makes the height positioning of the UAV inaccurate. Summary of the invention

[0005] In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a method, system, device and storage medium for indoor positioning of a drone, so as to solve the problem of inaccurate altitude positioning of a drone when flying indoors in the prior art.

[0006] To achieve the above objectives and other related objectives, the present invention provides a method for indoor positioning of a drone, comprising the following steps:

[0007] Obtain the height, Z-axis speed and Z-axis acceleration of the drone; wherein the height of the drone includes a first measured height z obtained by the barometer module b and the second measured height z obtained by the one-way ranging radar module r ;

[0008] The altitude, Z-axis velocity and Z-axis acceleration of the UAV are fused using Kalman filtering to finally determine the altitude coordinate z of the UAV.

[0009] Furthermore, a plurality of first feature modules are arranged along the height direction of the room. The UAV collects data of the first feature modules after multiple iterations of Kalman filter fusion, and calibrates the height coordinate z of the UAV, wherein the data of the first feature modules includes height data.

[0010] Furthermore, it also includes obtaining the position coordinates (x, y) of the drone based on ZigBee, then combining the height coordinate z of the drone, and finally outputting the position coordinates (x, y, z) of the drone; wherein obtaining the position coordinates (x, y) of the drone based on ZigBee specifically includes:

[0011] Set up a fixed point and multiple reference nodes indoors, and bind each reference node to the drone;

[0012] Based on ZigBee, the coordinates (x 1 ,y 1 );

[0013] The inertial measurement unit module is used to obtain the speed and acceleration of the X-axis and Y-axis of the drone, and the speed and acceleration of the X-axis and Y-axis of the drone are compared with the estimated target node D coordinate (x 1 ,y 1 ) is iterated through Kalman filtering to finally determine the position coordinates (x, y) of the UAV.

[0014] Furthermore, based on ZigBee, the coordinates (x 1 ,y 1 ), including:

[0015] Based on ZigBee, the received signal strength value RSSI at the target node D is obtained;

[0016] The distance between the target node and each reference node is calculated based on the received signal strength value RSSI at the target node;

[0017] Find the three reference nodes closest to the target node;

[0018] According to the coordinates of the three reference nodes and the distances between the target node and the three reference nodes, the coordinates of the target node D are estimated using the trilateral measurement method.

[0019] Furthermore, the speed and acceleration of the drone on the X and Y axes are compared with the estimated target node D coordinate (x 1 ,y 1 ) After the fusion iteration through Kalman filtering, it also includes:

[0020] Arrange several ZigBee transmitting modules in the indoor environment, and record the coordinates of each ZigBee transmitting module, each ZigBee transmitting module serves as a reference node;

[0021] A different second characteristic module is provided at each reference node;

[0022] The velocity and acceleration of the drone's X-axis and Y-axis are compared with the estimated target node D coordinate (x 1 ,y 1 ) After multiple fusion iterations through Kalman filtering, the UAV is located above one of the reference nodes to obtain the feature information coordinates of the corresponding reference node, and the collected coordinates are calibrated with the iterative coordinates of the UAV.

[0023] Furthermore, the received signal strength value RSSI value of each reference node is collected for multiple measurements and the average value of the multiple RSSI values ​​is calculated as the benchmark value, i.e., fingerprint, of each reference point. The fingerprints of all reference nodes form a fingerprint library. When the coordinates of the target node D are estimated, the received signal strength value RSSI value at the target node D is collected and compared with the value in the fingerprint library.

[0024] On the other hand, the present application also provides a drone indoor positioning system, comprising:

[0025] A barometer module is installed on the drone, and the barometer module is used to obtain a first measurement height z of the drone. b ;

[0026] A one-way ranging radar module is installed on the drone, and the one-way ranging radar module is used to obtain a second measurement height z r ;

[0027] Inertial measurement unit module, used to obtain the speed and acceleration of the drone's X-axis, Y-axis, and Z-axis;

[0028] A ZigBee module, including a ZigBee transmitting module and a ZigBee receiving module, wherein the ZigBee transmitting module is installed on each reference node indoors, and the ZigBee receiving module is installed on the drone, and the ZigBee receiving module is used to receive signals from the ZigBee transmitting module;

[0029] The first fusion module is used to obtain the height coordinate z of the drone by fusing the height, Z-axis vertical velocity and Z-axis vertical acceleration of the drone through Kalman filtering; wherein the height of the drone includes a first measured height z obtained by a barometer b and the second measured height z obtained by the one-way ranging radar r ;

[0030] The second fusion module is used to fuse the estimated coordinates of the drone based on the ZigBee module with the speed and acceleration of the drone measured by the inertial measurement unit to obtain the position coordinates (x, y) of the drone.

[0031] Furthermore, it also includes:

[0032] The first feature module is arranged along the direction of the building height and is used to mark the vertical height of the room;

[0033] A second feature module is provided at each reference node and is used to mark each reference node;

[0034] The identification module is used to identify the first feature module and the second feature module and output the reference height of the drone and the coordinate data of the corresponding reference node respectively.

[0035] On the other hand, the present application also provides an electronic device, which includes: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device implements the above-mentioned drone indoor positioning method.

[0036] On the other hand, the present application also provides a computer-readable storage medium, characterized in that a computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer executes the above-mentioned drone indoor positioning method.

[0037] As described above, the present invention has the following beneficial effects:

[0038] 1. When locating the height of the drone, the present invention is based on the first measured height z obtained by the barometer module. b and the second measured height z obtained by the one-way ranging radar module r Instead of using a single tool to measure the height to determine the height of the drone relative to the ground, these measured heights are fused with the Z-axis velocity and Z-axis acceleration collected by the inertial measurement unit module through Kalman filtering. Through multiple iterations, the output drone height coordinates are made more accurate.

[0039] 2. When positioning the two-dimensional coordinates of the drone indoors, ZigBee technology can be used to quickly and effectively locate the drone and has a lower cost than using UWB ultra-wideband indoor positioning technology.

[0040] 3. By positioning the coordinates of the drone in three directions, the fused coordinates will be calibrated by collecting data from the first feature module and the second feature module, making the final output coordinates more accurate.

[0041] 4. The indoor positioning method of the drone provided in this application obtains the three-dimensional coordinates of the drone indoors, which has a good guiding role in the flight of the drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Shown is a flow chart of the indoor positioning method of a drone provided by the present invention;

[0043] Figure 2 This is a specific flow chart of step S13.

[0044] Figure 3 It is a schematic diagram of the target point and the reference node;

[0045] Figure 4 Schematic diagram for reference node layout;

[0046] Figure 5 Schematic diagram of the structure of the UAV indoor positioning system. DETAILED DESCRIPTION

[0047] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0048] It should be noted that the diagram provided in the present embodiment only illustrates the basic concept of the present invention in a schematic manner, so the diagram only shows the components related to the present invention rather than drawing according to the number, shape and size of the components during actual implementation. The type, quantity and ratio of each component during actual implementation can be a random change, and the component layout type may also be more complicated. The structure, ratio, size, etc. illustrated in the drawings of the present specification are only used to match the content disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the limiting conditions that the present invention can implement, so they have no technical substantive significance. Any modification of the structure, change of the proportional relationship or adjustment of the size should still fall within the scope of the technical content disclosed by the present invention without affecting the effect that the present invention can produce and the purpose that can be achieved. At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in this specification are only for the convenience of narration, and are not used to limit the scope of the present invention. The change or adjustment of its relative relationship should also be regarded as the scope of the present invention without substantially changing the technical content.

[0049] Figure 1 Shown is a flow chart of the present invention, see Figure 1The present invention provides a method for indoor positioning of a drone, comprising the following steps:

[0050] S11, obtaining the altitude, Z-axis velocity and Z-axis acceleration of the drone, wherein the altitude of the drone includes a first measured altitude z obtained by a barometer module b and the second measured height z obtained by the one-way ranging radar module r .

[0051] The measured altitude of existing drones is generally obtained through a single barometer module or a single one-way ranging radar module, and then processed.

[0052] If only one method is used to measure the height of the drone, such as using only the barometer module to measure the height of the drone, since the indoor environment is usually a relatively closed space, the opening and closing of doors and windows and the operation of the air conditioning system will cause changes in local air pressure, which may interfere with the reading of the barometer and thus affect the accuracy of the height measurement. The temperature and humidity changes indoors are more frequent and drastic than those outdoors (for example, the heat generated by people's activities, the operation of humidifiers or dehumidifiers, etc.), and these factors will affect the atmospheric pressure, which in turn affects the measurement results of the barometer. In addition, if there is a powerful ventilation system or fan indoors, they can quickly change the air pressure distribution in the room, causing the barometer reading to be unstable. Another reason is that the air circulation in the indoor space is poor compared to the open outdoor environment, which may cause the barometer to respond slowly, especially when the drone moves quickly. For multi-story buildings, the air pressure difference between different floors is not large, making it difficult to distinguish subtle height changes by relying solely on the barometer.

[0053] If only a single one-way ranging radar module is used to obtain the height of the drone, if the indoor floor or ceiling material is not suitable for reflecting radar signals (such as carpets, cushions and other sound-absorbing materials), a reliable echo signal may not be obtained, thus affecting the accuracy of the height measurement. In a complex indoor environment, the radar signal may be reflected by walls or other objects, resulting in multiple paths to the receiver, which may lead to incorrect distance readings, especially when there are many obstacles and reflective surfaces. At the same time, electromagnetic interference or sound interference (for ultrasonic sensors) generated by other electronic devices may affect the performance of the radar, resulting in inaccurate data.

[0054] Therefore, when the present invention locates the height of the drone, the first measured height z is obtained by the barometer module. b and the second measured height z obtained by the one-way ranging radar module r By fusing these two measured heights, the problem of low accuracy due to a single measurement can be eliminated.

[0055] The present invention obtains the Z-axis velocity and Z-axis acceleration through an inertial measurement unit (IMU). The inertial measurement unit (IMU) contains sensors such as an accelerometer, a gyroscope, and a magnetometer. The accelerometer can measure the linear acceleration of the drone on three axes, including the influence of gravity. The accelerometer can be used to detect the static tilt angle and dynamic acceleration of the object. The gyroscope can measure the rotation rate of the object around three axes (pitch, roll, and yaw). The gyroscope provides information about the change in the attitude of the object, which is very important for determining the direction of the object. The magnetometer is used to measure the strength and direction of the geomagnetic field, which can help determine the absolute direction of the object, i.e., the heading angle.

[0056] S12, the measured altitude, Z-axis velocity and Z-axis acceleration of the UAV are fused by using Kalman filtering to finally determine the altitude coordinate z of the UAV.

[0057] The gyroscope and accelerometer in the inertial measurement unit (IMU) are used to measure the angular velocity and acceleration of the drone in real time; the magnetometer in the inertial measurement unit (IMU) is used to measure the geomagnetic field strength of the drone in real time. The angular velocity, angular acceleration and acceleration of the drone and the geomagnetic field strength in which it is located are used as state variables; the state equation is determined according to the change process of the state variables over time. Then, according to the state transfer matrix, the Kalman filter is used to fuse the measured values ​​of the drone's height, Z-axis velocity and Z-axis acceleration to determine the measurement equation, and then the measurement residual is used to correct the predicted state to obtain a more accurate state estimate, that is, to update the state quantity and error covariance matrix in the Kalman filter algorithm to determine the height coordinate z of the drone.

[0058] After the Kalman filter has been iteratively updated for many times, it is possible that a small error in the early stage will lead to a relatively large subsequent error. For example, if the dynamic model or observation model of the system does not match the actual situation, then the estimation of the Kalman filter will be biased. Therefore, in some embodiments, by setting multiple first feature modules along the height direction of the room, the first feature modules can be evenly set on the indoor wall, and each first feature module contains different feature information. The system records the height information corresponding to each first feature module. After the drone has been used for a certain period of time or has flown a certain distance, the drone can be controlled to fly to the position corresponding to the first feature module, and the first feature module is identified by the identification module to determine the current height of the drone, and then compared and calibrated with the iterative value, and the data of the drone is updated; thereby, the error generated after the iteration can be eliminated.

[0059] like Figure 2As shown, the two-dimensional coordinates of the drone in the room are obtained based on ZigBee technology, so it also includes S13, obtaining the position coordinates (x, y) of the drone based on ZigBee, and then combining the height coordinate z of the drone, and finally outputting the position coordinates (x, y, z) of the drone. The position coordinates (x, y) of the drone and the height coordinate z of the drone are obtained by simultaneous acquisition and fusion. After the two coordinates are obtained separately, they are integrated to obtain the position coordinates (x, y, z) of the drone and output them.

[0060] Among them, obtaining the position coordinates (x, y) of the drone based on ZigBee specifically includes:

[0061] S131. Set up a fixed point and multiple reference nodes indoors, and bind each reference node to the drone.

[0062] Specifically, multiple reference nodes are set on the ground indoors, the position coordinates of each reference node are known, a ZigBee transmitting module is installed on each reference node, and a ZigBee receiving module is installed on the drone.

[0063] like Figure 4 As shown in the figure, the indoor reference nodes are arranged in a rectangular shape, and the distances between adjacent reference nodes are the same. A take-off point is set next to the reference node, and the drone stops at the take-off point after each use. Record the coordinate position (X i , Y i , Z i ), since the room is basically horizontal, the Z of each reference node can be i The value is set to a fixed value, for example, 10m.

[0064] S132, estimate the coordinates (x) of the target node D based on ZigBee 1 ,y 1 ).

[0065] First, the received signal strength value RSSI at the target node D is obtained based on ZigBee.

[0066] Before the actual flight of the aircraft, a fingerprint library of the received signal strength value RSSI must be established first. Specifically, the received signal strength value RSSI at a specific location is collected to establish a fingerprint library, and then these feature fingerprint libraries are used to estimate the unknown location.

[0067] Specifically, the received signal strength value RSSI value of each reference node can be collected for multiple measurements and the average of the multiple RSSI values ​​can be calculated as the benchmark value, i.e., fingerprint, of each reference point. The fingerprints of all reference nodes form a fingerprint library. When the coordinates of the target node D are estimated, the received signal strength value RSSI value at the target node D is collected and compared with the value in the fingerprint library.

[0068] Then, the distance between the target node and each reference node is calculated according to the received signal strength value RSSI at the target node. The distance between the target node and the reference node is calculated using the Euclidean distance formula in three-dimensional space.

[0069] After calculating the distance between the target node and each reference node, find the three reference nodes closest to the target node. The coordinates of these three reference points are known. Based on the coordinates of the three reference nodes and the distances between the target node and the three reference nodes, the coordinates of the target node D (x 1 ,y 1 , z 1 ).

[0070] like Figure 3 As shown, the three reference nodes are calculated as A(x a ,y a , z a )、B(x b ,y b , z b ) and C(x c ,y c , z c ), the distance between the target node and the reference node A is d a , the distance from the reference node B is d b , the distance from the reference node C is d c , where the coordinate of the Z axis is obtained by the above method. Here, we only need to calculate x 1 ,y 1 .

[0071]

[0072] S133, obtain the speed and acceleration of the X-axis and Y-axis of the drone through the inertial measurement unit module, and compare the speed and acceleration of the X-axis and Y-axis of the drone with the estimated target node D coordinate (x 1 ,y 1 ) is iterated through Kalman filtering to finally determine the position coordinates (x, y) of the UAV.

[0073] The coordinates of the target node D (x 1 ,y1 ), and then compare it with the X-axis velocity, acceleration, angular velocity, angular acceleration and Y-axis velocity, acceleration, angular velocity, angular acceleration obtained by the inertial measurement unit IMU, and the estimated coordinates of the target node D (x 1 ,y 1 ) are fused through Kalman filtering to finally determine the position coordinates (x, y) of the drone.

[0074] Similarly, errors may occur after multiple fusion iterations. Therefore, a second feature module is set at each reference node. The drone can be located at one of the reference nodes after a certain flight time or a certain distance. The coordinate information of the corresponding reference node is collected, and the collected coordinates are compared with the iterated coordinates to calibrate the coordinate data of the drone.

[0075] The feature information of each first feature module and the second feature module is different. The feature information of the first feature module and the second feature module can be collected by the recognition module on the drone, and the coordinate information corresponding to each feature module is stored in the system. The first feature module and the second feature module can be color modules or QR code modules.

[0076] On the other hand, the present invention also provides a drone indoor positioning system, comprising:

[0077] A barometer module is installed on the drone, and the barometer module is used to obtain a first measurement height z of the drone. b .

[0078] A one-way ranging radar module is installed on the drone, and the one-way ranging radar module is used to obtain a second measurement height z r .

[0079] The inertial measurement unit module IMU is used to obtain the velocity, acceleration, angular velocity and angular acceleration of the drone's X-axis, Y-axis and Z-axis.

[0080] The ZigBee module includes a ZigBee transmitting module and a ZigBee receiving module. The ZigBee transmitting module is installed at each reference node on each reference node indoors. The ZigBee receiving module is installed on the drone. The ZigBee receiving module is used to receive the signal of the ZigBee transmitting module.

[0081] The first fusion module is used to obtain the height coordinate z of the drone by fusing the height, Z-axis vertical velocity and Z-axis vertical acceleration of the drone through Kalman filtering; wherein the height of the drone includes a first measured height z obtained by a barometer b and the second measured height z obtained by the one-way ranging radar r.

[0082] The second fusion module is used to fuse the estimated coordinates of the drone based on the ZigBee module with the speed and acceleration of the drone measured by the inertial measurement unit to obtain the position coordinates (x, y) of the drone.

[0083] It also includes a first characteristic module, which is arranged along the direction of the building height and is used to mark the vertical height of the room.

[0084] The second feature module is set at each reference node and is used to mark each reference node.

[0085] The first feature module and the second feature module include coordinate information of each point, and the first feature module can be installed on a wall indoors.

[0086] The first feature module and the second feature module can be a color module, a QR code module or other. In some embodiments, the color module is used, which is more cost-effective. The first feature module can be evenly arranged along the height direction of the indoor wall, and can also be pasted on the wall or set up in a separate rack.

[0087] The recognition module is used to identify the first feature module and output the reference height of the drone, and the recognition module is used to identify the coordinate data of the reference node corresponding to the second feature module.

[0088] The present invention uses the data of the barometer module, the one-way ranging radar module, and the inertial measurement unit module IMU to locate the altitude information of the drone through Kalman filtering, and uses the ZigBee module and the inertial measurement unit module IMU to locate the two-dimensional coordinates of the drone indoors through Kalman filtering, and finally outputs the three-dimensional positioning of the drone indoors. In order to prevent errors in the Kalman filtering process, both the altitude coordinates and the two-dimensional coordinates need to be calibrated at regular intervals or at fixed distances to prevent the iterative errors from becoming larger and larger.

[0089] The first fusion module and the second fusion module are both connected to the control module, and the control module integrates the first fusion module and the second fusion module to output the three-dimensional coordinates of the drone.

[0090] On the other hand, the present invention also provides an electronic device, which includes one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device implements the above-mentioned drone indoor positioning method.

[0091] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor of a computer, the computer executes the above-mentioned drone indoor positioning method.

[0092] It should be noted that the computer-readable storage medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The 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 of the above. More specific examples of computer-readable storage media may include, but are not limited to: 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 of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0093] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0094] The units involved in the embodiments of the present invention may be implemented by software or hardware, and the units described may also be arranged in a processor. The names of these units do not, in some cases, limit the units themselves. The units or modules described may also be arranged in a processor, for example, may be described as: a processor includes a data acquisition module, a first processing module, a second processing module, and a result generation module. The names of these units or modules do not, in some cases, limit the units or modules themselves.

[0095] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other.

Claims

1. A method for indoor positioning of a drone, characterized in that: The following steps are involved: Obtain the height, Z-axis speed and Z-axis acceleration of the drone; wherein the height of the drone includes a first measured height z obtained by the barometer module b and the second measured height z obtained by the one-way ranging radar module r ; The altitude, Z-axis velocity and Z-axis acceleration of the UAV are fused using Kalman filtering to finally determine the altitude coordinate z of the UAV.

2. The indoor positioning method of a UAV according to claim 1, characterized in that: Several first feature modules are arranged along the height direction of the room. The UAV collects data of the first feature modules after multiple iterations of Kalman filter fusion, and calibrates the height coordinate z of the UAV, wherein the data of the first feature modules includes height data.

3. The indoor positioning method of a UAV according to claim 1 or 2, characterized in that: The method also includes obtaining the position coordinates (x, y) of the drone based on ZigBee, and then combining the height coordinate z of the drone to finally output the position coordinates (x, y, z) of the drone; wherein obtaining the position coordinates (x, y) of the drone based on ZigBee specifically includes: Set up a fixed point and multiple reference nodes indoors, and bind each reference node to the drone; Estimate the coordinates (x1, y1) of the target node D based on ZigBee; The velocity and acceleration of the X-axis and Y-axis of the drone are obtained through the inertial measurement unit module, and the velocity and acceleration of the X-axis and Y-axis of the drone are fused and iterated with the estimated target node D coordinate (x1, y1) through Kalman filtering to finally determine the position coordinates (x, y) of the drone.

4. The method for indoor positioning of a UAV according to claim 3, characterized in that: in, The coordinates (x1, y1) of the target node D are estimated based on ZigBee, including: Obtain the received signal strength value RSSI at the target node D based on ZigBee; The distance between the target node and each reference node is calculated based on the received signal strength value RSSI at the target node; Find the three reference nodes closest to the target node; According to the coordinates of the three reference nodes and the distances between the target node and the three reference nodes, the coordinates of the target node D are estimated using the trilateral measurement method.

5. The method for indoor positioning of a UAV according to claim 4, characterized in that: in, After fusing the X-axis and Y-axis speed and acceleration of the drone with the estimated target node D coordinate (x1, y1) through Kalman filtering, it also includes: Arrange several ZigBee transmitting modules in the indoor environment, and record the coordinates of each ZigBee transmitting module, each ZigBee transmitting module serves as a reference node; A different second characteristic module is provided at each reference node; After the velocity and acceleration of the drone's X-axis and Y-axis are fused and iterated multiple times with the estimated target node D coordinate (x1, y1) through Kalman filtering, the drone is located above one of the reference nodes to obtain the feature information coordinates of the corresponding reference node, and the collected coordinates are calibrated with the iterative coordinates of the drone.

6. The indoor positioning method of a UAV according to claim 4 or 5, characterized in that: The received signal strength value RSSI value of each reference node is collected for multiple measurements and the average value of multiple RSSI values ​​is calculated as the benchmark value of each reference point, i.e., the fingerprint. The fingerprints of all reference nodes form a fingerprint library. When the coordinates of the target node D are estimated, the received signal strength value RSSI value at the target node D is collected and compared with the value in the fingerprint library.

7. An indoor positioning system for unmanned aerial vehicles, characterized in that: include: A barometer module is installed on the drone, and the barometer module is used to obtain a first measured height z of the drone. b ; A one-way ranging radar module is installed on the drone, and the one-way ranging radar module is used to obtain a second measurement height z r ; Inertial measurement unit module, used to obtain the speed and acceleration of the drone's X-axis, Y-axis, and Z-axis; A ZigBee module, including a ZigBee transmitting module and a ZigBee receiving module, wherein the ZigBee transmitting module is installed on each reference node indoors, and the ZigBee receiving module is installed on the drone, and the ZigBee receiving module is used to receive signals from the ZigBee transmitting module; The first fusion module is used to obtain the height coordinate z of the drone by fusing the height, Z-axis vertical velocity and Z-axis vertical acceleration of the drone through Kalman filtering; wherein the height of the drone includes a first measured height z obtained by a barometer b and the second measured height z obtained by the one-way ranging radar r ; The second fusion module is used to fuse the estimated coordinates of the drone based on the ZigBee module with the speed and acceleration of the drone measured by the inertial measurement unit to obtain the position coordinates (x, y) of the drone.

8. The UAV indoor positioning system according to claim 7, characterized in that: Also includes: The first feature module is arranged along the direction of the building height and is used to mark the vertical height of the room; A second feature module is provided at each reference node and is used to mark each reference node; The identification module is used to identify the first feature module and the second feature module and output the reference height of the drone and the coordinate data of the corresponding reference node respectively.

9. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device implements the drone indoor positioning method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is caused to execute the indoor positioning method for a drone as described in any one of claims 1 to 6.

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