An unmanned aerial vehicle-based integrated image control point measurement method and system

Abstract

The present disclosure provides a kind of unmanned aerial vehicle-based integrated image control point measurement method and system, it is related to surveying and mapping engineering technical field.The method comprises: obtaining flight route data and image control point layout parameters;Image control point layout parameters include: the layout coordinates corresponding to each image control point device, based on flight route data and each layout coordinates, control unmanned aerial vehicle to drop each image control point device, determine the network state corresponding to each image control point device, based on network state and each image control point device, determine the accurate three-dimensional coordinates corresponding to each image control point device, based on the accurate three-dimensional coordinates corresponding to each image control point device, control unmanned aerial vehicle to recycle each image control point device.Through the above method, the drop of image control point device, network state determination, accurate three-dimensional coordinate generation and automatic recycling are realized, thereby effectively solve the problem of low efficiency and potential safety hazard of manual layout and recycling image control point device, improve the efficiency and safety of integrated image control point measurement.

Description

Technical Field

[0001] This disclosure relates to the field of surveying and mapping engineering technology, and in particular to an integrated image control point measurement method and system based on unmanned aerial vehicles (UAVs). Background Technology

[0002] With the development of surveying and mapping engineering technology, integrated geocontrol point surveying is being used more and more widely in fields such as emergency rescue and disaster relief, and special terrain surveying. Integrated geocontrol point surveying includes: the deployment of geocontrol point devices, coordinate measurement, and retrieval.

[0003] Currently, integrated geocontrol point surveying mainly relies on a manual process. First, in the deployment phase, staff need to go to the work area to locate each marker point, and then place, affix, or paint the geocontrol point devices. Next, in the coordinate measurement phase, staff need to carry equipment such as Global Navigation Satellite System (GNSS) receivers or total stations to each deployed marker point to collect and record coordinates. Finally, in the retrieval phase, staff need to go to each marker point to retrieve reusable equipment.

[0004] The above methods require workers to travel back and forth in complex environments multiple times, resulting in low work efficiency and high costs. Setting up and measuring points in dangerous environments such as cliffs and swamps poses a safety threat to workers. Furthermore, in order to avoid danger or reduce labor intensity, workers tend to choose easily accessible locations rather than the theoretically best locations with the best aerial view and the most favorable conditions for uniform accuracy distribution. At the same time, errors are introduced in the process of centering, measuring antenna height, and manually recording data.

[0005] In summary, the main problems to be solved in integrated image control point measurement are low efficiency, high risk, and limited accuracy. Summary of the Invention

[0006] To overcome the problems existing in related technologies, this disclosure provides an integrated image control point measurement method and system based on unmanned aerial vehicles (UAVs).

[0007] According to a first aspect of the present disclosure, an integrated image control point measurement method based on an unmanned aerial vehicle (UAV) is provided, the method comprising: Acquire flight path data and control point deployment parameters; the control point deployment parameters include: the deployment coordinates corresponding to each control point device; Based on the flight path data and each deployment coordinate, the UAV is controlled to deploy each image control point device. The network state corresponding to each image control point device is determined, the corresponding coordinate measurement method is selected based on the network state, and the precise three-dimensional coordinates corresponding to each image control point device are determined based on the coordinate measurement method. Based on the precise three-dimensional coordinates corresponding to each image control point device, the UAV is controlled to recover each image control point device.

[0008] In one possible design, controlling the UAV to deploy each image control point device based on the flight path data and each deployment coordinate includes: Extract the flight path corresponding to the UAV from the flight path data; Control the UAV to fly to the area where the ground control points are deployed according to the flight path, and determine the terrain data corresponding to the area where the ground control points are deployed; Based on the terrain data and the physical parameters corresponding to each image control point device, the preset deployment height and preset deployment angle corresponding to each image control point device are determined; Based on the preset deployment height and preset deployment angle corresponding to each image control point device, the UAV is controlled to deploy each image control point device.

[0009] In one possible design, determining the network state corresponding to each image control point device, selecting a corresponding coordinate measurement method based on the network state, and determining the precise three-dimensional coordinates corresponding to each image control point device based on the coordinate measurement method includes: If the network status of each image control point device is network connected, then based on the satellite signals received by each image control point device, the precise three-dimensional coordinates corresponding to each image control point device are generated; If the network status of each image control point device is disconnected and it is in an optically responsive state, then the precise three-dimensional coordinates corresponding to each image control point device are generated based on the optical signals emitted by the UAV.

[0010] In one possible design, generating the precise three-dimensional coordinates corresponding to each image control point device based on the satellite signals received by each image control point device includes: Receive the wake-up confirmation signal corresponding to each image control point device, and send a position acquisition command to each image control point device in parallel, so that each image control point device acquires satellite signals based on the timestamp in the corresponding position acquisition command; When a confirmation signal confirming the completion of data acquisition from all image control point devices is received, the precise three-dimensional coordinates calculated based on the satellite signals are received from each image control point device.

[0011] In one possible design, generating the precise three-dimensional coordinates corresponding to each image control point device based on the optical signals emitted by the UAV includes: The UAV is controlled to transmit a first optical signal of a specific wavelength to each of the image control point devices; The drone is controlled to receive the second optical signal reflected by each image control point device; Determine the propagation time between the UAV transmitting the first optical signal and receiving the second optical signal, and determine the incident angle of the first optical signal; The drone's location information is determined, and based on the propagation duration, the incident angle, and the drone's location information, the precise three-dimensional coordinates corresponding to each image control point device are determined.

[0012] In one possible design, controlling the UAV to recover each image control point device based on its precise three-dimensional coordinates includes: If the network status of each image control point device is network connected, then based on the positioning signal sent by each image control point device, the UAV is controlled to retrieve each image control point device; If the network status of each image control point device is disconnected, the UAV is controlled to collect environmental images of the image control point device, and the image control point device is retrieved based on the environmental images.

[0013] In one possible design, controlling the UAV to retrieve each of the image control points based on the positioning signals sent by each image control point includes: The drone is controlled to receive positioning signals sent by each of the image control point devices; The device identifier and current real-time location of each image control point device are parsed from the positioning signal. Based on the device identifier, determine the initial deployment position of the corresponding image control point device, and calculate the deviation value between the initial deployment position and the current real-time position; Based on the deviation value, the UAV is controlled to recover each image control point device.

[0014] In one possible design, controlling the UAV to recover each image control point device based on the deviation value includes: If the deviation value is less than the preset deviation value, then control the UAV to fly to the current real-time position to retrieve the image control point device; If the deviation value is not less than the preset deviation value, the UAV is controlled to acquire environmental images of the image control point device, and the image control point device is retrieved based on the environmental images of the image control point device.

[0015] According to a second aspect of the present disclosure, an integrated image control point measurement system based on an unmanned aerial vehicle (UAV) is provided, applied to an UAV system, comprising: The data acquisition module is used to acquire flight path data and control point deployment parameters; the control point deployment parameters include: the deployment coordinates corresponding to each control point device; The device deployment module is used to control the UAV to deploy each image control point device based on the flight path data and each deployment coordinate; The coordinate determination module is used to determine the network state corresponding to each image control point device, select the corresponding coordinate measurement method based on the network state, and determine the precise three-dimensional coordinates corresponding to each image control point device based on the coordinate measurement method. The device recovery module is used to control the UAV to recover each image control point device based on the precise three-dimensional coordinates corresponding to each image control point device.

[0016] In one possible design, the device deployment module is specifically used to extract the flight path corresponding to the UAV from the flight path data, control the UAV to fly to the control point deployment area according to the flight path, determine the terrain data corresponding to the control point deployment area, determine the preset deployment height and preset deployment angle corresponding to each control point device based on the terrain data and the physical parameters corresponding to each control point device, and control the UAV to deploy each control point device based on the preset deployment height and preset deployment angle corresponding to each control point device.

[0017] In one possible design, the coordinate determination module is specifically used to generate the precise three-dimensional coordinates corresponding to each image control point device based on the satellite signals received by each image control point device if the network status of each image control point device is network connected; and to generate the precise three-dimensional coordinates corresponding to each image control point device based on the optical signals emitted by the UAV if the network status of each image control point device is network disconnected and in an optically responsive state.

[0018] In one possible design, the coordinate determination module is further configured to receive a wake-up confirmation signal corresponding to each image control point device, send a position acquisition command to each image control point device in parallel, so that each image control point device acquires satellite signals based on the timestamp in the corresponding position acquisition command, and when it receives a confirmation signal that all image control point devices have completed acquisition, it receives the precise three-dimensional coordinates calculated based on the satellite signals sent by each image control point device.

[0019] In one possible design, the coordinate determination module is further configured to control the UAV to transmit a first optical signal of a specific wavelength to each image control point device, control the UAV to receive a second optical signal reflected by each image control point device, determine the propagation time between the transmission of the first optical signal and the reception of the second optical signal, determine the incident angle of the first optical signal, determine the UAV position information, and determine the precise three-dimensional coordinates corresponding to each image control point device based on the propagation time, the incident angle, and the UAV position information.

[0020] In one possible design, the device recovery module is specifically used to control the UAV to recover each image control point device based on the positioning signal sent by each image control point device if the network status of each image control point device is network connected, and to control the UAV to collect environmental images of the image control point device and recover the image control point device based on the environmental images of the image control point device if the network status of each image control point device is network disconnected.

[0021] In one possible design, the device recovery module is also used to control the UAV to receive the positioning signal sent by each image control point device, parse the device identifier and current real-time position corresponding to each image control point device from the positioning signal, determine the initial deployment position of the corresponding image control point device based on the device identifier, calculate the deviation value between the initial deployment position and the current real-time position, and control the UAV to recover each image control point device based on the deviation value.

[0022] In one possible design, the device recovery module is further configured to control the UAV to fly to the current real-time location to recover the image control point device if the deviation value is less than a preset deviation value, and to control the UAV to collect environmental images of the image control point device and recover the image control point device based on the environmental images of the image control point device if the deviation value is not less than the preset deviation value.

[0023] According to a third aspect of the present disclosure, a computer device is provided, comprising: A memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method of the first or second aspect described above.

[0024] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, the method of the first aspect described above is implemented.

[0025] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: In this embodiment of the disclosure, the UAV system can deploy the control point device, determine the network status, generate accurate three-dimensional coordinates, and automatically retrieve the control point device based on flight route data and the deployment coordinates of the control point device. This effectively solves the problems of low efficiency and safety hazards in the manual deployment and retrieval of control point devices in related technologies, and improves the efficiency and safety of integrated control point measurement.

[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0027] The accompanying drawings, which are incorporated in and form part of this disclosure, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0028] Figure 1 This disclosure is a flowchart illustrating an integrated image control point measurement method based on an unmanned aerial vehicle (UAV) according to an exemplary embodiment. Figure 2 This is a front view schematic diagram of the image control point device according to an exemplary embodiment of the present disclosure; Figure 3 This is a top view schematic diagram of the image control point device according to an exemplary embodiment of the present disclosure; Figure 4 This is a three-dimensional structural schematic diagram of an image control point device according to an exemplary embodiment of the present disclosure; Figure 5 This is a top view schematic diagram of an image control point device with three supports according to an exemplary embodiment of the present disclosure; Figure 6 This disclosure is a schematic diagram of the structure of an integrated image control point measurement system based on an unmanned aerial vehicle (UAV) according to an exemplary embodiment. Figure 7 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0029] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0030] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0031] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0032] In related technologies, integrated image control point measurement requires operators to travel back and forth in complex environments multiple times, resulting in low efficiency and high costs. Setting up and measuring points in dangerous environments such as cliffs and swamps poses a safety threat to workers. Furthermore, in order to avoid danger or reduce labor intensity, workers tend to choose easily accessible locations rather than the theoretically best locations with the best aerial view and the most favorable conditions for uniform accuracy distribution. At the same time, errors are introduced in the centering, measurement antenna height, and manual data recording processes. Therefore, how to solve the problems of low efficiency, high danger, and limited accuracy in integrated image control point measurement has become the main problem to be solved.

[0033] The integrated image control point measurement method based on unmanned aerial vehicles (UAVs) in the embodiments of this disclosure will now be described in detail.

[0034] like Figure 1 As shown, Figure 1 This disclosure is a flowchart illustrating an integrated image control point measurement method based on an unmanned aerial vehicle (UAV) according to an exemplary embodiment, comprising the following steps: In step S110, flight route data and control point deployment parameters are acquired.

[0035] Because manual deployment of ground control points is inefficient and difficult to standardize in traditional surveying operations, and poses safety risks in areas with complex terrain and inaccessible to personnel, this embodiment first uses an unmanned aerial vehicle (UAV) system to acquire flight path data and ground control point deployment parameters.

[0036] Since the location of control points directly affects the accuracy of integrated control point measurement, in this embodiment of the disclosure, the UAV system sets corresponding layout coordinates for multiple control point devices based on the actual needs of the control point layout area. These layout coordinates are determined based on the topographic map or remote sensing image of the survey area to obtain control point layout parameters, so as to ensure the rationality of control point distribution in subsequent surveying operations.

[0037] The aforementioned flight path data includes the drone's flight path, altitude, speed, and waypoint information, ensuring coverage of all image control point deployment areas.

[0038] The aforementioned unmanned aerial vehicle (UAV) system includes: a UAV, a camera control point (ADC), and a terminal. The terminal is used to control the UAV and can transmit and communicate data with the UAV and the ADC.

[0039] In this embodiment, the image control point device is an integrated measurement device specifically designed for the deployment and recovery of unmanned aerial vehicles (UAVs). A front view schematic diagram of the image control point device is shown below. Figure 2 As shown, in Figure 2 The image controller integrates a signal transmission device and a lithium battery on its front. The signal transmission device has a built-in 4G / 5G module or LoRa wireless module. This module transmits the precise coordinates obtained by the Real-Time Kinematic (RTK) receiver back to the UAV system or a terminal within the UAV system in real time. The terminal can be a ground control center for communication with the UAV system. The 4G / 5G module can also send status information about the image controller, including battery level, location, and operating status. The signal transmission device is approximately 5-7 cm long and wide, and 1-3 cm high.

[0040] The aforementioned lithium battery is used to provide power to the image control point device. The battery capacity is set based on the actual situation of integrated image control point measurement. The length and width of the lithium battery are between 5 and 7 cm, and the height is between 1 and 3 cm.

[0041] The control point device uses a magnetically conductive material on its surface. To maximize the coefficient of friction while ensuring structural and functional integrity, a rubber-based functional coating is applied to the magnetically conductive material surface, and a texture processing process is simultaneously implemented. The top RTK receiver housing is made of thermoplastic polyurethane (TPU). The RTK receiver's length and width are between 4 and 6 cm, and its height is between 3 and 5 cm. Figure 2The transparent part can be a 360° surround prism, which can be made of tempered glass and can be 3 to 5 centimeters high. The textured part surrounding this area is made of polycarbonate and includes the image control point device's logo. Lead pellets can be placed at the bottom of the image control point device to lower its center of gravity and ensure its stability.

[0042] The above-mentioned dimensions are set to meet the payload capacity of the drone while ensuring the stable deployment and reliable recovery of the image control point device.

[0043] Optionally, the prism can be mounted via a control thread, which can be used to fine-tune the mounting angle of the prism to ensure optical alignment accuracy. To ensure the practicality of the control point device, an indicator light can also be mounted on the top of the control point device. The indicator light can be a light-emitting diode (LED) for nighttime measurement.

[0044] To improve the efficiency of drone deployment and recovery, this embodiment of the present disclosure installs a high-power conductive magnet at the bottom of the image control point device. When recovering the image control point device, the drone connects to the conductive magnet at the bottom of the image control point device via an electromagnet on its bottom, thereby realizing the recovery of the image control point device. The outer shell of the magnetic conductive part is made of aluminum alloy.

[0045] See the top view diagram of the control point device. Figure 3 As shown, in Figure 3 In the diagram, 6 represents the image control point identifier of the image control point device, the area where the screw is located is the center of the image control point device, and the white grid is the texture of the image control point device, which is only for illustrative purposes.

[0046] It should be noted that a preset number of supports, such as three supports, can be installed at the bottom of the image control device to ensure its stability. Figure 2 Taking a single support frame as an example, each frame joint has a screw and a small motor. The motor is connected to the control module of the image control point device (ADC), which is located inside the ADC and controls the motor's movement. When the ADC's angle needs to be adjusted, the UAV system or its terminal can send a command to the ADC. This command causes the control module in the ADC to send an angle adjustment signal to the motor. The motor then rotates the screw, and the screw turning in or out changes the angle of the support frame joint, allowing the ADC to rotate to a preset angle. By coordinating the adjustment of a preset number of supports, the attitude angle of the ADC can be adjusted.

[0047] A three-dimensional structural schematic diagram of the image control point device in the embodiments of this disclosure is referenced. Figure 4 As shown, in Figure 4The image control point device is equipped with three supports to ensure its stability upon landing. A top-view diagram of the image control point device with three supports is provided for reference. Figure 5 As shown, in Figure 5 In this system, the angle between adjacent supports of the image control point device is 120°, and each support can be controlled individually, thereby ensuring the stability of the image control point device on the slope.

[0048] Using the methods described above, the UAV system obtained the basic parameters required for the deployment and retrieval of ground control points, providing a basis for subsequent control of the UAV to perform deployment operations.

[0049] In step S120, based on the flight path data and each deployment coordinate, the UAV is controlled to deploy each image control point device.

[0050] After acquiring the aforementioned basic parameters, the traditional deployment of ground control points (GCPs), from placing markers such as ground markings and paint to using RTK receivers for measurement, relies heavily on skilled technicians and has a very low degree of automation. Therefore, to avoid the problems of low efficiency and safety risks associated with manually deploying GCPs, this embodiment of the present disclosure requires the UAV system to extract the UAV's flight path from the flight path data after acquiring the flight path data and deployment coordinates, and then control the UAV to fly to the GCP deployment area according to the flight path, and determine the terrain data corresponding to the GCP deployment area.

[0051] Furthermore, to ensure the accurate attitude of the image control point (ADC) devices after landing and to avoid malfunctions caused by external forces, this embodiment of the present disclosure determines a preset deployment height and angle for each ADC device based on terrain data and the corresponding physical parameters, including the weight and drop resistance of the ADC device (a higher drop resistance indicates better drop resistance). The UAV system controls the UAV's flight altitude and attitude according to the preset deployment height and angle for each deployment coordinate, and finally, controls the UAV to deploy the corresponding ADC device at the deployment coordinate. During the deployment of the ADC device, image feature data of the corresponding environment is recorded.

[0052] The above-mentioned determination of the preset deployment height and preset deployment angle for each image control point device includes: using the drop resistance height as the preset safety upper limit parameter, adjusting the drop resistance height proportionally based on the weight of the image control point device to obtain the preset deployment height, and different terrain data corresponding to different preset deployment angles. In order to ensure the accuracy of the preset deployment angle, it is also necessary to adjust the preset deployment angle based on the drop resistance performance of the image control point device to obtain the final preset deployment angle.

[0053] When the weight is not higher than the set weight, it means that the image control device is relatively light, has high air resistance, and low impact force upon landing. Therefore, the preset deployment height can be close to the drop resistance height. When the weight is higher than the set weight, it means that the image control device is relatively heavy, has high impact force upon landing, and the preset deployment height needs to be lower than the drop resistance height by a set percentage. The set percentage can be adjusted according to the actual application scenario, for example, 20% to 30%, which will not be explained in detail here.

[0054] Based on terrain data, determine the corresponding preset deployment angle range, and arbitrarily select a preset deployment angle from this range. When the drop resistance height is not higher than the set drop resistance height, it indicates that the image control device has low drop resistance and weak impact resistance, and the preset deployment angle needs to be reduced. The reduction angle can be 2°. When the drop resistance height is higher than the set drop resistance height, it indicates that the image control device has high drop resistance and the preset deployment angle can be maintained.

[0055] Furthermore, the slope value corresponding to the terrain in the terrain data is determined. When the slope value is greater than the slope value threshold (which can be 15°), it is determined to be sloping terrain; otherwise, it is determined to be flat terrain. The slope value can be calculated based on the elevation value in the terrain data. Since the slope formula is a technique known to those skilled in the art, it will not be described in detail here.

[0056] For example, the preset deployment angle range for flat terrain is (0, 10°), and the preset deployment angle is 8°. At the same time, when the drop height is not higher than the set drop height, the preset deployment angle needs to be reduced by 5° so that the image control device is closer to a vertical landing and the impact force is evenly distributed at the bottom of the image control device. The preset deployment angle is determined to be 3°. When the drop height is higher than the set drop height, the preset deployment angle is determined to be 8°.

[0057] The preset projection angle, preset projection angle range, and reduced angle value mentioned above can all be adjusted based on the actual integrated image control point measurement scenario, which will not be explained in detail here.

[0058] The aforementioned terrain data can be determined based on lidar scanning or ground images captured by high-resolution cameras. LiDAR scanning involves the UAV emitting a laser beam towards the ground and calculating the distance based on the reflection time, thereby constructing three-dimensional terrain data. High-resolution cameras capture ground images and then process these images using stereo vision algorithms to reconstruct the terrain's undulations, thus obtaining terrain data.

[0059] The aforementioned stereo vision algorithm can be a semi-global matching algorithm (SGM). Since determining terrain data based on LiDAR scanning and determining terrain data based on the SGM algorithm are technologies known to those skilled in the art, they will not be explained in detail here.

[0060] Based on the above method, each image control point is an independent, high-precision measurement point with its own RTK receiver. Theoretically, the image control points can be deployed in the optimal position for uniform precision distribution, thereby reducing the errors introduced by manual centering and antenna height measurement.

[0061] The above method enables the automatic deployment of image control point devices one by one, reducing manual intervention and thus improving the efficiency of image control point deployment, thereby enhancing the safety and efficiency of integrated image control point measurement.

[0062] In step S130, the network state corresponding to each image control point device is determined, the corresponding coordinate measurement method is selected based on the network state, and the precise three-dimensional coordinates corresponding to each image control point device are determined based on the coordinate measurement method.

[0063] Because integrated image control point measurement suffers from unstable communication conditions and limited positioning signals, this embodiment of the present disclosure requires determining the network status of the image control point device after deployment to generate the corresponding precise three-dimensional coordinates. The network status includes the wireless communication status between the image control point device and the UAV system. The specific process is as follows: If the network status of each control point device is network connected, then the precise three-dimensional coordinates corresponding to each control point device are generated based on the satellite signals received by each control point device.

[0064] Specifically, after the UAV system deploys the image control point (ARP) devices, the ARP devices are in standby mode. The UAV system sends a wake-up command to each ARP device, instructing it to transition from standby to operational mode. To ensure that each ARP device has reached its corresponding deployment coordinates and successfully started, the UAV system receives a wake-up confirmation signal for each ARP device. This signal indicates that the ARP device has successfully received the wake-up command from the UAV system, and that its power supply, sensors, communication modules, and other components are functioning normally. The UAV system then sends position acquisition commands to each ARP device in parallel, enabling each ARP device to acquire satellite signals based on the timestamp in its corresponding position acquisition command. The timestamps in the position acquisition commands for each ARP device are identical.

[0065] The image control point device will respond to the position acquisition command to acquire coordinates. When it receives the acquisition completion confirmation signal from all image control point devices, it will receive the precise three-dimensional coordinates sent by each image control point device.

[0066] Optionally, the image control point can also be woken up based on the ground pressure value. When the ground pressure value is greater than the set pressure value, the image control point automatically switches from standby mode to working mode. After receiving the position acquisition command, the image control point receives satellite signals through its built-in RTK receiver, and then connects to the Continuous Operational Reference System (CORS) or a base station for differential positioning. When the RTK receiver enters the fixed solution state, which means that the RTK receiver achieves stable working state with centimeter-level positioning accuracy through communication with satellites and base stations, it means that the positioning accuracy has reached the preset positioning accuracy. At this time, the image control point automatically calculates and records the precise three-dimensional coordinates of the point, such as: horizontal accuracy ≤ 2cm, elevation accuracy ≤ 3cm. The precise three-dimensional coordinates can be automatically uploaded to the UAV system through 4G / 5G modules or wireless modules such as LoRa.

[0067] If the network state of each image control point device is disconnected and it is in an optically responsive state, then based on the optical signal emitted by the UAV, the precise three-dimensional coordinates corresponding to each image control point device are generated. The optically responsive state is when the prism can reflect the first optical signal emitted by the UAV.

[0068] Specifically, the system controls the UAV to emit a first optical signal of a specific wavelength, such as 850nm near-infrared wavelength, to each image control point. The prism on the image control point reflects the first optical signal. The UAV system then controls the UAV to receive the second optical signal reflected by each image control point. Based on this, the system determines the propagation time between the UAV emitting the first optical signal and receiving the second optical signal, as well as the incident angle of the first optical signal. Then, the UAV's position information is determined. Finally, based on the propagation time, incident angle, and UAV position information, the precise three-dimensional coordinates corresponding to each image control point are determined.

[0069] The aforementioned incident angle can be determined based on the visual sensor on the UAV, and the aforementioned precise three-dimensional coordinates can be determined based on triangulation. The two coordinate generation methods can be selected based on the network status. Determining the incident angle based on the visual sensor and the triangulation method are techniques well-known to those skilled in the art; therefore, they will not be elaborated upon here.

[0070] It should be noted that after collecting the precise three-dimensional coordinates of each ground control point device, the UAV system controls the UAV to fly above the ground control point deployment area to perform remote sensing photography and obtain images of the deployment area. At this time, in the images of the deployment area, the ground control point devices represent high-precision ground control points, which is beneficial for subsequent topographic mapping and regional monitoring, thereby ensuring that the efficiency of integrated ground control point measurement can be improved.

[0071] By using the above method and adopting different coordinate generation methods according to different network conditions, it is possible to obtain accurate three-dimensional coordinates of the image control point device even under network constraints, thereby ensuring the continuity and reliability of integrated image control point measurement.

[0072] In step S140, the UAV is controlled to retrieve each image control point device based on the precise three-dimensional coordinates corresponding to each image control point device.

[0073] To ensure the integrity of the integrated image control point measurement process and to prevent the loss of image control point devices after deployment, this embodiment of the present disclosure, after generating the precise three-dimensional coordinates corresponding to the image control point device, needs to control the UAV to retrieve the corresponding image control point device based on the precise three-dimensional coordinates. The specific retrieval process is as follows: Before the drone retrieves the control point device, the drone system determines the network status of the control point device. If the network status of each control point device is connected, the drone will retrieve each control point device based on the positioning signal sent by each control point device. If the network status of each control point device is disconnected, the drone will collect environmental images of the control point device and retrieve the control point device based on the environmental images, thereby ensuring that the control point device can be retrieved accurately.

[0074] Specifically, when the network status of the image control point devices is network connected, the control drone receives the positioning signal sent by each image control point device, and then parses the device identifier and current real-time position of each image control point device from the positioning signal. The current real-time position can be latitude and longitude. Since the position of the image control point devices may be offset by wind, terrain subsidence or animal contact, in order to ensure that the position accuracy of the image control point devices meets the measurement requirements, it is necessary to determine the initial deployment position of the corresponding image control point device based on the device identifier, and calculate the deviation value between the initial deployment position and the current real-time position. Based on the deviation value, the control drone is used to retrieve each image control point device.

[0075] Furthermore, if the deviation value is less than the preset deviation value, it means that the current real-time position of the image control point device meets the measurement requirements and the precise three-dimensional coordinates collected by the image control point device have high accuracy. In this case, the drone is controlled to fly to the current real-time position to retrieve the image control point device. If the deviation value is not less than the preset deviation value, it means that the current real-time position of the image control point device does not meet the measurement requirements and the retrieval path planned by the drone based on the deployment location has a large error. In this case, the drone is controlled to collect environmental images of the image control point device, locate the actual position of the image control point device through the environmental images, and then control the drone to retrieve the image control point device based on the actual position.

[0076] It should be noted that when the current real-time location does not meet the measurement requirements while the network is connected, a positioning method based on environmental images can also be used.

[0077] The actual location mentioned above can be determined based on the feature data of the environmental image and the image control point device recorded when it is deployed, using an image feature matching algorithm. The image feature matching algorithm can be the scale-invariant feature transform (SIFT) algorithm, the Oriented FAST and Rotated BRIEF (ORB) algorithm, etc., which will not be elaborated on here.

[0078] Based on the above method, different methods are used to recover the image control point devices according to their network status. The two methods can be switched seamlessly, which saves the recovery time of the image control point devices and improves the independent operation capability of the UAV system. By controlling the deviation value, the UAV is controlled to recover the image control point devices, which ensures the accuracy of the recovery and improves the reuse rate of the image control point devices.

[0079] Using the above methods, the UAV system can deploy, determine network status, generate accurate 3D coordinates, and automatically retrieve ground control points (GCPs) based on flight path data and the deployment coordinates of GCPs. This effectively solves the problems of low efficiency and safety hazards associated with manual deployment and retrieval of GCPs in related technologies, and improves the efficiency and safety of integrated ground control point measurement.

[0080] like Figure 6 The diagram shown is a schematic representation of an integrated image control point measurement system based on an unmanned aerial vehicle (UAV) according to an exemplary embodiment, comprising the following steps: The data acquisition module 601 is used to acquire flight path data and control point layout parameters; the control point layout parameters include: the layout coordinates corresponding to each control point device; The device deployment module 602 is used to control the UAV to deploy each image control point device based on the flight path data and each deployment coordinate; The coordinate determination module 603 is used to determine the network state corresponding to each image control point device, select the corresponding coordinate measurement method based on the network state, and determine the precise three-dimensional coordinates corresponding to each image control point device based on the coordinate measurement method. The device recovery module 604 is used to control the UAV to recover each image control point device based on the precise three-dimensional coordinates corresponding to each image control point device.

[0081] In one possible design, the device deployment module 602 is specifically used to extract the flight path corresponding to the UAV from the flight path data, control the UAV to fly to the control point deployment area according to the flight path, determine the terrain data corresponding to the control point deployment area, determine the preset deployment height and preset deployment angle corresponding to each control point device based on the terrain data and the physical parameters corresponding to each control point device, and control the UAV to deploy each control point device based on the preset deployment height and preset deployment angle corresponding to each control point device.

[0082] In one possible design, the coordinate determination module 603 is specifically used to generate the precise three-dimensional coordinates corresponding to each image control point device based on the satellite signals received by each image control point device if the network status of each image control point device is network connected; and to generate the precise three-dimensional coordinates corresponding to each image control point device based on the optical signals emitted by the UAV if the network status of each image control point device is network disconnected and in an optically responsive state.

[0083] In one possible design, the coordinate determination module 603 is further configured to receive a wake-up confirmation signal corresponding to each image control point device, send a position acquisition command to each image control point device in parallel, so that each image control point device acquires satellite signals based on the timestamp in the corresponding position acquisition command, and when it receives a confirmation signal that all image control point devices have completed acquisition, it receives the precise three-dimensional coordinates calculated based on the satellite signals sent by each image control point device.

[0084] In one possible design, the coordinate determination module 603 is further configured to control the UAV to transmit a first optical signal of a specific wavelength to each image control point device, control the UAV to receive a second optical signal reflected by each image control point device, determine the propagation time between the transmission of the first optical signal and the reception of the second optical signal, determine the incident angle of the first optical signal, determine the UAV position information, and determine the precise three-dimensional coordinates corresponding to each image control point device based on the propagation time, the incident angle, and the UAV position information.

[0085] In one possible design, the device recovery module 604 is specifically used to control the UAV to recover each image control point device based on the positioning signal sent by each image control point device if the network status of each image control point device is network connected, and to control the UAV to collect environmental images of the image control point device and recover the image control point device based on the environmental images of the image control point device if the network status of each image control point device is network disconnected.

[0086] In one possible design, the device recovery module 604 is further configured to control the UAV to receive the positioning signal sent by each image control point device, parse the device identifier and current real-time position corresponding to each image control point device from the positioning signal, determine the initial deployment position of the corresponding image control point device based on the device identifier, calculate the deviation value between the initial deployment position and the current real-time position, and control the UAV to recover each image control point device based on the deviation value.

[0087] In one possible design, the device recovery module 604 is further configured to control the UAV to fly to the current real-time location to recover the image control point device if the deviation value is less than a preset deviation value, and to control the UAV to collect environmental images of the image control point device and recover the image control point device based on the environmental images of the image control point device if the deviation value is not less than the preset deviation value.

[0088] This disclosure provides a computer device, including: A memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method of any of the above embodiments.

[0089] This specification describes an embodiment of an integrated image control point measurement method based on an unmanned aerial vehicle (UAV), which can be applied to computer devices, such as servers or terminal devices. The device embodiment can be implemented through software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by the processor of the computer device loading the corresponding computer program instructions from non-volatile memory into memory for execution. From a hardware perspective, such as... Figure 7 The diagram shown is a structural schematic of a computer device used in an embodiment of this specification for an integrated image control point measurement method based on an unmanned aerial vehicle (UAV). Except for... Figure 7 In addition to the processor 710, memory 730, network interface 720, and non-volatile memory 740 shown, the server or electronic device where the UAV-based integrated image control point measurement device 731 is located may also include other hardware depending on the actual function, which will not be described in detail here.

[0090] This disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement the methods of any of the above embodiments. The execution method and beneficial effects are similar, and will not be described again here.

[0091] The aforementioned computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0092] The computer program described above can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this disclosure. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer device, partially on the user's device, as a standalone software package, partially on the user's computer device and partially on a remote computer device, or entirely on a remote computer device or server.

[0093] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0094] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0095] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An integrated image control point measurement method based on unmanned aerial vehicles (UAVs), characterized in that, Applied to unmanned aerial vehicle (UAV) systems, including: Acquire flight path data and control point deployment parameters; the control point deployment parameters include: the deployment coordinates corresponding to each control point device; Based on the flight path data and each deployment coordinate, the UAV is controlled to deploy each image control point device. The network state corresponding to each image control point device is determined, the corresponding coordinate measurement method is selected based on the network state, and the precise three-dimensional coordinates corresponding to each image control point device are determined based on the coordinate measurement method. Based on the precise three-dimensional coordinates corresponding to each image control point device, the UAV is controlled to recover each image control point device.

2. The method according to claim 1, characterized in that, The step of controlling the UAV to deploy each image control point device based on the flight path data and each deployment coordinate includes: Extract the flight path corresponding to the UAV from the flight path data; Control the UAV to fly to the area where the ground control points are deployed according to the flight path, and determine the terrain data corresponding to the area where the ground control points are deployed; Based on the terrain data and the physical parameters corresponding to each image control point device, the preset deployment height and preset deployment angle corresponding to each image control point device are determined; Based on the preset deployment height and preset deployment angle corresponding to each image control point device, the UAV is controlled to deploy each image control point device.

3. The method according to claim 1, characterized in that, The step of determining the network state corresponding to each image control point device, selecting the corresponding coordinate measurement method based on the network state, and determining the precise three-dimensional coordinates corresponding to each image control point device based on the coordinate measurement method includes: If the network status of each image control point device is network connected, then based on the satellite signals received by each image control point device, the precise three-dimensional coordinates corresponding to each image control point device are generated; If the network status of each image control point device is disconnected and it is in an optically responsive state, then the precise three-dimensional coordinates corresponding to each image control point device are generated based on the optical signals emitted by the UAV.

4. The method according to claim 3, characterized in that, The step of generating the precise three-dimensional coordinates corresponding to each image control point device based on the satellite signals received by each image control point device includes: Receive the wake-up confirmation signal corresponding to each image control point device, and send a position acquisition command to each image control point device in parallel, so that each image control point device acquires satellite signals based on the timestamp in the corresponding position acquisition command; When a confirmation signal confirming the completion of data acquisition from all image control point devices is received, the precise three-dimensional coordinates calculated based on the satellite signals are received from each image control point device.

5. The method according to claim 3, characterized in that, The process of generating the precise three-dimensional coordinates corresponding to each image control point device based on the optical signals emitted by the UAV includes: The UAV is controlled to transmit a first optical signal of a specific wavelength to each of the image control point devices; The drone is controlled to receive the second optical signal reflected by each image control point device; Determine the propagation time between the UAV transmitting the first optical signal and receiving the second optical signal, and determine the incident angle of the first optical signal; The drone's location information is determined, and based on the propagation duration, the incident angle, and the drone's location information, the precise three-dimensional coordinates corresponding to each image control point device are determined.

6. The method according to claim 1, characterized in that, The step of controlling the UAV to recover each image control point device based on the precise three-dimensional coordinates corresponding to each image control point device includes: If the network status of each image control point device is network connected, then based on the positioning signal sent by each image control point device, the UAV is controlled to retrieve each image control point device; If the network status of each image control point device is disconnected, the UAV is controlled to collect environmental images of the image control point device, and the image control point device is retrieved based on the environmental images.

7. The method according to claim 6, characterized in that, The step of controlling the UAV to retrieve each image control point device based on the positioning signal sent by each image control point device includes: The drone is controlled to receive positioning signals sent by each of the image control point devices; The device identifier and current real-time location of each image control point device are parsed from the positioning signal. Based on the device identifier, determine the initial deployment position of the corresponding image control point device, and calculate the deviation value between the initial deployment position and the current real-time position; Based on the deviation value, the UAV is controlled to recover each image control point device.

8. The method according to claim 7, characterized in that, The step of controlling the UAV to recover each image control point device based on the deviation value includes: If the deviation value is less than the preset deviation value, then control the UAV to fly to the current real-time position to retrieve the image control point device; If the deviation value is not less than the preset deviation value, the UAV is controlled to acquire environmental images of the image control point device, and the image control point device is retrieved based on the environmental images of the image control point device.

9. An integrated image control point measurement system based on an unmanned aerial vehicle (UAV), characterized in that, Applied to unmanned aerial vehicle (UAV) systems, the systems include: The data acquisition module is used to acquire flight path data and control point deployment parameters; the control point deployment parameters include: the deployment coordinates corresponding to each control point device; The device deployment module is used to control the UAV to deploy each image control point device based on the flight path data and each deployment coordinate; The coordinate determination module is used to determine the network state corresponding to each image control point device, select the corresponding coordinate measurement method based on the network state, and determine the precise three-dimensional coordinates corresponding to each image control point device based on the coordinate measurement method. The device recovery module is used to control the UAV to recover each image control point device based on the precise three-dimensional coordinates corresponding to each image control point device.

10. A computer device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the integrated image control point measurement method based on an unmanned aerial vehicle (UAV) as described in any one of claims 1-8.

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