Multi-machine surveying and mapping management method and device, electronic equipment and storage medium

Abstract

The invention provides a multi-machine surveying and mapping management method and device, electronic equipment and a storage medium, and relates to the technical field of surveying and mapping. The method comprises the following steps: receiving a task instruction comprising a target ground resolution, a minimum overlapping degree, a task priority and a landform situation; according to the target ground resolution and a preset algorithm, calculating to obtain a navigation height, generating a plurality of navigation routes in combination with the landform situation, the lowest overlapping degree and the preset algorithm, and then generating control instructions corresponding to the unmanned aerial vehicles in combination with task priorities to control the unmanned aerial vehicles to perform surveying and mapping; and during surveying and mapping, surveying and mapping data returned by the unmanned aerial vehicles are spliced in real time. According to the method, the navigation height of the unmanned aerial vehicle is determined through the target ground resolution, and the navigation route is correspondingly generated according to the landform condition by combining the lowest overlapping degree, so that parallel generation of the multi-vehicle navigation route is realized, and surveying and mapping data returned by each unmanned aerial vehicle are spliced in real time; therefore, the reasonability of route planning and task allocation during unmanned aerial vehicle surveying and mapping is improved, and the surveying and mapping accuracy and efficiency are improved.

Description

Technical Field

[0001] This application relates to the field of surveying and mapping technology, and in particular to a multi-machine surveying and mapping management method, device, electronic equipment and storage medium. Background Technology

[0002] Unmanned aerial vehicle (UAV) mapping is a technology that uses UAVs equipped with sensors (cameras, lidar, etc.) to collect images or 3D data of ground features by planning flight routes, thereby realizing topographic mapping and 3D modeling. It is widely used in fields such as natural resource surveys, emergency rescue, and urban planning.

[0003] Current UAV mapping solutions are gradually replacing the initial single-drone operation with a simple multi-drone parallel mode. In the multi-drone route planning and multi-drone task allocation before the operation, the area is usually manually divided and then statically scheduled for each UAV based on preset fixed flight altitude, overlap and route pattern through ground station software.

[0004] However, because multi-drone task allocation relies on human experience and lacks dynamic adaptation to drone performance, battery power, and environmental factors, the current drone mapping scheme suffers from insufficient rationality in route planning and task allocation, poor mapping accuracy, and low efficiency. Summary of the Invention

[0005] The main purpose of this application is to propose a multi-drone mapping management method, device, electronic equipment and storage medium, which aims to improve the rationality of flight path planning and task allocation during UAV mapping, and improve the accuracy and efficiency of mapping.

[0006] In a first aspect, the present invention provides a multi-drone mapping management method, applied to ground control equipment, wherein the ground control equipment is communicatively connected to multiple unmanned aerial vehicles (UAVs), and the method includes: Receive task instructions, wherein the task instructions include: target ground resolution, minimum overlap, task priority, and terrain conditions; The flight altitude is calculated and obtained based on the target ground resolution and the preset flight altitude algorithm; Based on the flight altitude, the terrain conditions, the minimum overlap, and the preset route planning algorithm, multiple flight routes are generated, wherein the number of flight routes is less than or equal to the number of UAVs to be controlled; Based on the flight altitude, the flight route, and the task priority, control commands are generated for each of the UAVs, and corresponding control commands are sent to each of the UAVs to control each of the UAVs to perform mapping. The system receives the mapping data returned by each of the aforementioned UAVs and uses a stitching algorithm to stitch the data in real time to obtain the stitched mapping data.

[0007] In an optional implementation, calculating the flight altitude based on the target ground resolution and a preset flight altitude algorithm includes: The shooting parameters of the drone are determined, wherein the shooting parameters include: camera focal length, pixel size, and camera tilt angle; The theoretical flight altitude is calculated based on the camera focal length, the pixel size, the camera tilt angle, the target ground resolution, and a preset flight altitude algorithm. The flight altitude is determined based on the theoretical flight altitude and the preset maximum safe altitude.

[0008] In an optional implementation, the terrain features include: continuous planar terrain; the minimum overlap includes: minimum forward overlap and minimum lateral overlap. The process of generating multiple flight routes based on the flight altitude, terrain conditions, minimum overlap, and a preset route planning algorithm includes: Based on the number of drones, the area to be mapped is divided into multiple sub-strips, the number of which is less than or equal to the number of drones; The heading spacing and lateral spacing of each sub-band are determined based on the sailing altitude, minimum heading overlap, minimum lateral overlap, and the preset route planning algorithm corresponding to each sub-band. The navigation route corresponding to each sub-band is determined based on the heading spacing and the lateral spacing.

[0009] In an optional implementation, the terrain features further include: dispersed planar terrain; After dividing the area to be mapped into multiple sub-bands based on the number of drones, the method further includes: Calculate the sub-zone distances between multiple sub-zones under the aforementioned dispersed planar landform; Based on the sub-band distance and the preset sub-band merging rules, the sub-bands are merged to obtain at least one merged sub-band. Based on the navigation altitude, minimum heading overlap, minimum lateral overlap, and the preset route planning algorithm corresponding to each merged sub-band, the heading spacing and lateral spacing corresponding to each merged sub-band are determined respectively. Based on the heading spacing and the lateral spacing, the navigation route corresponding to each merged sub-band is determined.

[0010] In an optional implementation, the terrain features further include: a mixed planar and three-dimensional terrain. After determining the navigation route corresponding to each sub-band based on the heading spacing and the lateral spacing, the method further includes: Identify at least one three-dimensional target within a mixed planar and three-dimensional terrain. Determine the safe orbital radius and the required number of orbits for each of the three-dimensional targets; Based on the safe surrounding radius and the required number of surrounding circles, generate a three-dimensional surrounding route for each three-dimensional target; By connecting the navigation route and the three-dimensional circumferential route using a preset spline transition curve, a planar-three-dimensional hybrid route is obtained.

[0011] In an optional implementation, after sending corresponding control commands to each of the drones to control each of the drones to perform mapping based on the flight altitude and the flight route, the method further includes: Real-time navigation information for each of the aforementioned drones is acquired, including drone position, drone attitude, and ambient wind speed. The real-time minimum overlap is calculated based on the drone's position, drone attitude, ambient wind speed, and a preset overlap adjustment algorithm.

[0012] In an optional implementation, the method further includes: The state of each drone is determined based on its location and attitude. If any of the drones is in an abnormal state, then the remaining mission area of ​​the drone in the abnormal state is determined. Obtain the real-time battery levels of other drones in the vicinity of the drone in the abnormal state; Based on the real-time battery level, identify at least one target drone among other drones in the vicinity of the drone in the abnormal state. The target UAV is instructed to map the remaining mission area of ​​the UAV in the abnormal state.

[0013] Secondly, the present invention provides a multi-machine surveying and mapping management device, comprising: The receiving module is used to receive task instructions, wherein the task instructions include: target ground resolution, minimum overlap, task priority, and terrain conditions. The calculation module is used to calculate and obtain the flight altitude based on the target ground resolution and a preset flight altitude algorithm; The generation module is used to generate multiple flight routes based on the terrain conditions, the minimum overlap, and a preset flight route planning algorithm, wherein the number of flight routes is less than or equal to the number of UAVs to be controlled. The control module is used to generate control commands corresponding to each of the UAVs based on the flight altitude, the flight route, and the task priority, and send the corresponding control commands to each of the UAVs to control each of the UAVs to perform surveying and mapping. The stitching module is used to receive the mapping data returned by each of the UAVs and to stitch them together in real time using a stitching algorithm to obtain the stitched mapping data.

[0014] Thirdly, the present invention provides an electronic device, comprising: a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of any of the methods described in the foregoing embodiments.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the method as described in any of the foregoing embodiments.

[0016] The beneficial effects of this application are: The multi-drone mapping management method provided in this application includes: receiving a task instruction for a target building that includes the target ground resolution, minimum overlap, task priority, and terrain conditions; calculating the flight altitude corresponding to the target ground resolution based on a preset flight altitude algorithm and the target ground resolution in the task instruction; then determining a corresponding preset route planning algorithm based on the flight altitude, minimum overlap, and terrain conditions to generate multiple flight routes, the number of flight routes not exceeding the number of controlled drones; finally generating control instructions for each drone based on the flight routes and task priorities to control each drone to perform mapping according to the corresponding flight routes; receiving mapping data returned by each drone during mapping, and using a stitching algorithm to stitch the mapping data in real time to obtain the stitched mapping data. In this embodiment, the flight altitude of the corresponding UAV is determined by the preset target ground resolution, and the flight route is generated according to the terrain based on the minimum overlap. This enables the parallel generation of multiple flight routes before UAV mapping, and at least one UAV is assigned to each flight route so that each UAV can simultaneously complete the mapping of the target area according to the flight route. During the UAV mapping process, the mapping data returned by each UAV is stitched together in real time based on the stitching algorithm to ensure the real-time performance of work such as modeling based on the mapping data. This improves the rationality of flight route planning and task allocation during UAV mapping, and enhances the accuracy and efficiency of mapping. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of a multi-machine surveying and mapping management method provided in one embodiment of this application; Figure 2 This is a schematic diagram of a multi-machine surveying and mapping management method provided in another embodiment of this application; Figure 3 A schematic diagram of a multi-machine surveying and mapping management method provided in another embodiment of this application; Figure 4 A schematic diagram of a multi-machine surveying and mapping management method provided in another embodiment of this application; Figure 5 A schematic diagram of a multi-machine surveying and mapping management method provided in another embodiment of this application; Figure 6 A schematic diagram of a multi-machine surveying and mapping management method provided in another embodiment of this application; Figure 7 This is a schematic diagram of the structure of a multi-machine surveying and mapping management device provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0022] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0023] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0024] Current UAV mapping solutions are gradually replacing the initial single-drone operation with a simple multi-drone parallel mode. Before the operation, multi-drone flight path planning and task allocation typically involve manually dividing the area and then statically arranging tasks for each UAV based on preset fixed flight altitudes, overlaps, and flight path patterns using ground station software. However, because multi-drone task allocation relies on manual experience and lacks dynamic adaptation to UAV performance, battery power, and environmental factors, if UAV malfunctions or extreme weather occurs during the mapping process, the mapping work in the corresponding area cannot be completed smoothly. Furthermore, due to the lack of mapping data in the uncompleted areas, the accuracy of data stitching in other areas may decrease or even fail. Therefore, current UAV mapping solutions suffer from insufficient rationality in flight path planning and task allocation, resulting in poor mapping accuracy and low efficiency.

[0025] To address the aforementioned issues, the main objective of this application is to propose a multi-drone mapping management method, which aims to improve the rationality of flight path planning and task allocation during UAV mapping, and enhance mapping accuracy and efficiency.

[0026] Figure 1 This application provides a schematic flowchart of a multi-drone mapping management method, applied to ground control equipment. This ground control equipment can be, for example, a portable device with computing capabilities such as a computer, tablet, smartphone, or drone remote controller, but is not limited thereto. The ground control equipment is communicatively connected to multiple drones, such as... Figure 1 As shown, the method includes: S101. Receive mission instructions, including: target ground resolution, minimum overlap, mission priority, and terrain conditions.

[0027] For example, the aforementioned task instructions may be input by relevant personnel through the interactive device of the aforementioned ground control equipment, such as a computer keyboard, tablet computer, or smartphone touchscreen, but the specific source of the task instructions is not limited to the above content.

[0028] The aforementioned target ground sampling distance (GSD) can refer to, for example, a preset ground resolution. The specific value can be adjusted and determined according to the actual situation. It is used to represent the ground size corresponding to a single pixel in a digital image. It describes the distance between the center points of two consecutive pixels. GSD is proportional to the flight altitude. That is, the higher the flight altitude, the larger the GSD and the lower the ground resolution of the image; conversely, the lower the flight altitude, the smaller the GSD and the higher the ground resolution of the image.

[0029] The aforementioned minimum overlap is used to limit the minimum overlap during surveying. Overlap refers to the fact that adjacent images share the same area during the surveying process. It is usually expressed as a percentage, such as 60%, but it is not a limit. The specific value can be adjusted and determined according to actual needs. Overlap is a necessary condition for stereoscopic measurement and image stitching.

[0030] The aforementioned task priorities can be used to indicate whether efficiency or coverage quality should be prioritized when conducting UAV mapping, but are not limited to these two priorities. Each of the aforementioned priorities can correspond to triggering conditions. When a certain triggering condition is met, the UAV will be controlled to conduct mapping according to the corresponding priority. The specific number of priorities and the content of the triggering conditions can be adjusted and determined according to the actual situation. It can be understood that each priority can correspond to one triggering condition or multiple triggering conditions. When one priority corresponds to multiple triggering conditions, the UAV can be controlled to conduct mapping according to the corresponding priority after any one of the triggering conditions is met, partially met, or simultaneously met.

[0031] The aforementioned landform conditions can be divided according to the topography of the area to be surveyed. For example, various different landform conditions can be determined based on the flatness and distribution of the area to be surveyed. The number and division method of specific landform conditions can be selected and determined according to the actual situation, and there are no restrictions here.

[0032] S102. Calculate and obtain the navigation altitude based on the target ground resolution and the preset navigation altitude algorithm.

[0033] For example, since the target ground resolution is proportional to the flight altitude, the corresponding flight altitude, i.e., the flight altitude, can be calculated using the target ground resolution.

[0034] S103. Based on the above-mentioned navigation altitude, the above-mentioned terrain conditions, the above-mentioned minimum overlap, and the preset route planning algorithm, generate multiple navigation routes.

[0035] The number of the aforementioned flight routes is less than or equal to the number of drones to be controlled.

[0036] For example, the aforementioned preset route planning algorithm may include multiple different algorithms, each corresponding to a specific terrain condition. Therefore, the aforementioned flight altitude, terrain condition, minimum overlap, and preset route planning algorithm generate multiple flight routes. For instance, it may refer to determining the corresponding preset route planning algorithm based on the terrain condition, and calculating the relevant data required to generate the flight route based on the aforementioned flight altitude and minimum overlap, thereby realizing the generation of the flight route. Of course, the specific types and quantities of the relevant data required to generate the flight route, the types and quantities of the aforementioned terrain conditions, and the types and quantities of algorithms included in the aforementioned preset route planning algorithm are not limited here and can be adjusted and determined according to specific circumstances.

[0037] Each of the aforementioned flight paths can correspond to a sub-strip (or sub-region) within the area to be mapped. In other words, a UAV completing a flight path can map the corresponding sub-strip. It is understood that at least one UAV should be assigned to map each flight path or sub-strip; therefore, the number of flight paths is less than or equal to the number of UAVs to be controlled.

[0038] S104. Based on the above-mentioned navigation route and the above-mentioned task priority, generate control commands corresponding to each of the above-mentioned UAVs, and send the corresponding control commands to each of the above-mentioned UAVs to control each of the above-mentioned UAVs to perform surveying and mapping.

[0039] For example, the aforementioned control commands may refer to instructions for the corresponding UAV to select which priority, which flight path, and which sub-band to map. It is understood that the aforementioned flight path may include the corresponding flight altitude.

[0040] The aforementioned drone mapping can refer to mapping the area to be mapped by using sensors such as cameras and lidar installed on the drone.

[0041] S105. Receive the mapping data returned by each of the above-mentioned UAVs, and use a stitching algorithm to stitch the data in real time to obtain the stitched mapping data.

[0042] For example, the mapping data returned by each of the aforementioned UAVs may refer to the UAVs returning the mapping data in real time through the communication connection between the UAVs and the ground control equipment when they are mapping through sensors. After receiving the mapping data returned in real time by each UAV, the ground control equipment uses a stitching algorithm to stitch the data in real time to obtain the stitched mapping data. The stitched mapping data can be used for 3D modeling, for example, to reflect the geographic information of the area to be mapped.

[0043] For example, the above-mentioned method of using a stitching algorithm to perform real-time stitching, obtaining stitched surveying data, and performing 3D modeling based on the stitched surveying data can specifically include the following steps: (1) Preprocessing of survey data.

[0044] For example, the aforementioned surveying data may be images, videos, etc., that include depth information. Taking the surveying data as an image and the sensor set on the drone as a camera as an example, the aforementioned preprocessing of the surveying data may include image denoising and image standardization. Image denoising can improve the accuracy and stability of subsequent processing, while image standardization may refer to adjusting the brightness, contrast, and color of the image to ensure the consistency between data. This can ensure the accuracy of stitching the surveying data and using it for 3D modeling.

[0045] (2) Feature extraction and matching.

[0046] For example, the feature extraction and matching of the surveying data described above can first be performed using robust feature extraction algorithms such as SIFT (Scale Invariant Feature Transform), SURF (Speeded Up Robust Features), and ORB (Oriented FAST and Rotated BRIEF) to extract key feature points in each image, ensuring the stability of feature points under different viewpoints.

[0047] Then, feature point matching is performed between different images using a matching algorithm to calculate the similarity between images. Efficient registration is performed under the premise of ensuring error-free matching. For specific matching algorithm types and diagrams, please refer to the similarity calculation method, which is not limited here.

[0048] (3) Image registration based on geometric constraints.

[0049] For example, the above-mentioned geometrically constrained image registration may include performing geometric correction on the image before image registration. The above-mentioned geometric correction on the image may refer to correcting the image using geometric transformation based on the camera's intrinsic and extrinsic parameters. During geometric correction, the viewpoint, depth, and camera position need to be considered to ensure consistency between different images.

[0050] The aforementioned image registration can refer to registering multiple real-world images, achieving high-precision alignment by calculating the geometric relationship between matching points, and ensuring that the data from different images can be accurately fused.

[0051] (4) Image stitching and fast stitching optimization.

[0052] For example, the aforementioned image stitching and fast stitching optimization may include local stitching optimization and weighted fusion and depth information processing. The aforementioned local stitching optimization may refer to optimizing the overlapping parts of adjacent images through a local stitching optimization algorithm to eliminate seam errors and geometric inconsistencies. The aforementioned weighted fusion and depth information processing may refer to combining data from different UAVs and using a weighted average method to optimize the image fusion effect, especially in depth information and stereo construction, to ensure seamless image connection.

[0053] (5) Three-dimensional modeling.

[0054] For example, the aforementioned 3D modeling may include point cloud generation and stereo reconstruction. Point cloud generation may refer to extracting feature points from an image and generating a 3D point cloud using Simultaneous Localization and Mapping (SLAM) or Structure from Motion (SfM) techniques. For real-world 3D data, depth information needs to be incorporated to ensure high accuracy and wide coverage of the generated point cloud. Stereo reconstruction may refer to performing stereo reconstruction using multi-view geometric information to generate a high-precision 3D model. This is particularly important in modeling complex structures such as buildings, roads, and terrain, where the spatial distribution of each point cloud needs to be refined.

[0055] (6) Optimization and global adjustment of splicing error.

[0056] For example, the aforementioned stitching error optimization and global adjustment may include local optimization and global optimization. Local optimization may refer to optimizing the local error of the stitching region, using the Iterative Closest Point (ICP) algorithm to adjust the accuracy of the stitching region and reduce stitching errors. Global optimization may refer to using global optimization algorithms such as Bundle Adjustment (BA) to adjust the spatial position of the overall 3D model and optimize the distribution of the point cloud, ensuring the spatial consistency of the entire model.

[0057] (7) Model output and visualization After the above steps, a three-dimensional model corresponding to the stitched survey data can be obtained. This three-dimensional model may include point cloud, mesh or other three-dimensional data formats, and can be displayed on three-dimensional visualization devices such as holographic projection.

[0058] Of course, the above steps (1)-(7) are only one possible example. The actual process of using the splicing algorithm to splice in real time, obtain the spliced ​​survey data, and perform three-dimensional modeling based on the spliced ​​survey data can be the same as or different from the above steps (1)-(7), and is not limited to the above (1)-(7).

[0059] The multi-drone mapping management method provided in this application includes: receiving a task instruction for a target building that includes the target ground resolution, minimum overlap, task priority, and terrain conditions; calculating the flight altitude corresponding to the target ground resolution based on a preset flight altitude algorithm and the target ground resolution in the task instruction; then determining a corresponding preset route planning algorithm based on the flight altitude, minimum overlap, and terrain conditions to generate multiple flight routes, the number of flight routes not exceeding the number of controlled drones; finally generating control instructions for each drone based on the flight routes and task priorities to control each drone to perform mapping according to the corresponding flight routes; receiving mapping data returned by each drone during mapping, and using a stitching algorithm to stitch the mapping data in real time to obtain the stitched mapping data. In this embodiment, the flight altitude of the corresponding UAV is determined by the preset target ground resolution, and the flight route is generated according to the terrain based on the minimum overlap. This enables the parallel generation of multiple flight routes before UAV mapping, and at least one UAV is assigned to each flight route so that each UAV can simultaneously complete the mapping of the target area according to the flight route. During the UAV mapping process, the mapping data returned by each UAV is stitched together in real time based on the stitching algorithm to ensure the real-time performance of work such as modeling based on the mapping data. This improves the rationality of flight route planning and task allocation during UAV mapping, and enhances the accuracy and efficiency of mapping.

[0060] Figure 2 A flowchart illustrating a multi-machine surveying and mapping management method according to another embodiment of this application is provided below. Figure 2 Based on the above Figure 1 In this embodiment, the above-mentioned target ground resolution and preset navigation altitude algorithm are used to calculate and obtain the navigation altitude, including: S201. Determine the shooting parameters of the above-mentioned UAV, wherein the shooting parameters include: camera focal length, pixel size, and camera tilt angle.

[0061] For example, determining the shooting parameters of the aforementioned drone can be achieved by querying drone parameters, querying camera parameters, and combining the drone's position and attitude. The drone's position and attitude can be obtained, for example, through a positioning device and attitude sensor installed on the drone. Of course, the above are only possible methods for obtaining shooting parameters, and the actual methods for obtaining drone shooting parameters may be the same as or different from the examples above.

[0062] S202. Calculate and obtain the theoretical flight altitude based on the above-mentioned camera focal length, pixel size, camera tilt angle, target ground resolution, and preset flight altitude algorithm.

[0063] For example, the above-mentioned preset navigation altitude algorithm can be expressed as the following formula:

[0064] Among them, the above This refers to the theoretical flight altitude mentioned above, and the GSD mentioned above refers to the ground resolution of the target mentioned above. For the above camera focal length, the above For the above-mentioned cell size, the above-mentioned The above refers to the camera tilt angle. When shooting directly, the camera tilt angle can be 0°.

[0065] The theoretical flight altitude can be calculated using the above formula based on the camera focal length, pixel size, camera tilt angle, target ground resolution, and preset flight altitude algorithm.

[0066] S203. Determine the above-mentioned navigation altitude based on the theoretical navigation altitude and the preset maximum safe altitude.

[0067] For example, the aforementioned preset maximum safe altitude can be obtained directly by querying or calculating based on the performance parameters of the drone. This preset maximum safe altitude is the maximum altitude at which the drone can operate normally. For drones of different models, types, and performance, the preset maximum safe altitude may be different. Even for the same drone, the preset maximum safe altitude may be different in different locations and environments. The specific altitude can be adjusted and determined according to the actual situation, and no restrictions are imposed here.

[0068] The above-mentioned navigation altitude is determined based on the theoretical navigation altitude and the preset maximum safe altitude, and can be expressed as follows: H 实际 =min(H 理论 H max_safe ), Among them, the above H 实际 This refers to the aforementioned navigation altitude, H. max_safe For the aforementioned preset maximum safe height, the aforementioned min() is the minimum value function, when the aforementioned H 理论 <H above max_safe When, min() = H 理论 Conversely, min() = H max_safe This is to ensure that the drone's flight altitude does not exceed the preset maximum safe altitude.

[0069] It is understandable that the target ground resolution (GSD) may differ for different locations within the same area to be mapped. For example, the target ground resolution for key mapping areas may be 2 cm, while the target ground resolution for other areas may be 5 cm, but this is not a limitation.

[0070] Taking the target ground resolution of the key surveying area as 2cm and the target ground resolution of the remaining areas as 5cm as an example, since there are multiple target ground resolutions within the surveying area, these multiple target ground resolutions can be represented as ordered sets, such as G={GSD1,GSD2,...,GSDk}, where G is an ordered set of multiple target ground resolutions, and GSD1,GSD2,...,GSDk are multiple target ground resolutions within the same surveying area. For the case where the target ground resolution of the key surveying area is 2cm and the target ground resolution of the remaining areas is 5cm, we can have G={2cm, 5cm}. Correspondingly, for the ordered set of multiple target ground resolutions, the navigation altitude can also be an ordered set, for example, represented as H={H1,H2,...,Hk}, where H is an ordered set of multiple navigation altitudes corresponding to G, and H1,H2,...,Hk correspond to GSD1,GSD2,...,GSDk respectively.

[0071] Figure 3 A flowchart illustrating a multi-machine surveying and mapping management method is provided in another embodiment of this application, as shown below. Figure 3 As shown, optionally, in the above Figure 1 Based on the embodiments, the above-mentioned terrain features include: continuous planar terrain. The above-mentioned minimum overlap includes: minimum forward overlap and minimum lateral overlap.

[0072] The above-mentioned navigation altitude, terrain conditions, minimum overlap, and preset route planning algorithm generate multiple navigation routes, including: S301. Based on the number of UAVs mentioned above, the area to be surveyed is divided into multiple sub-strips, the number of which is less than or equal to the number of UAVs mentioned above.

[0073] For example, assuming the number of drones is n and the number of sub-bands is m, then m ≤ n. When dividing the area to be mapped into multiple sub-bands, in addition to considering the number of drones, the drones' endurance should also be taken into account. That is, it should be ensured that a drone can complete the mapping of one sub-band in one takeoff. If the drone's endurance is exhausted and it still cannot complete the mapping of the corresponding sub-band, then the sub-band division is too large. When the area to be mapped must be divided into more sub-bands than the current number of drones due to drone endurance limitations, the area to be mapped can be divided into multiple sub-bands. Each divided area can then be divided into multiple sub-bands, and each divided area can be mapped sequentially to reduce the number of sub-bands in a single multi-drone mapping. When the area to be mapped must be divided into more sub-bands than the current number of drones due to drone endurance limitations, the number of drones can also be increased to ensure that the number of drones is not less than the number of sub-bands.

[0074] S302. Based on the above-mentioned sailing altitude, the above-mentioned minimum heading overlap, the above-mentioned minimum lateral overlap and the above-mentioned preset route planning algorithm, determine the heading spacing and lateral spacing corresponding to each of the above-mentioned sub-bands.

[0075] For example, the heading spacing and lateral spacing corresponding to each of the sub-bands are determined based on the navigation altitude, the minimum heading overlap, the minimum lateral overlap, and the preset route planning algorithm. This can be achieved, for example, by the following formula: Sx=Lx(H)×(1 Of), Sy=Ly(H)×(1 Os), Wherein, Sx is the heading spacing, Sy is the lateral spacing, Lx(H) is the heading ground coverage of a single image determined based on the above flight altitude, Ly(H) is the lateral ground coverage of a single image determined based on the above flight altitude, Of is the minimum heading overlap, and Os is the minimum lateral overlap.

[0076] S303. Based on the above-mentioned heading spacing and the above-mentioned lateral spacing, determine the above-mentioned navigation route corresponding to each of the above-mentioned sub-bands.

[0077] For example, in addition to the heading spacing and the lateral spacing, the navigation route corresponding to each of the sub-bands may also include the navigation altitude used when calculating the heading spacing and the lateral spacing for each of the sub-bands. That is, in practice, the navigation route corresponding to each of the sub-bands can be determined based on the heading spacing, the lateral spacing and the corresponding navigation altitude.

[0078] Figure 4 This is a schematic flowchart of a multi-machine surveying and mapping management method provided in another embodiment of this application. The above-mentioned terrain situation also includes: dispersed planar terrain. The dispersed planar terrain refers to the area to be surveyed comprising multiple planar areas that are dispersed from each other, that is, the multiple sub-zones under the dispersed planar terrain are not directly adjacent or connected.

[0079] Please refer to Figure 4 In the above Figure 3 Based on the embodiments, after dividing the area to be mapped into multiple sub-zones using the aforementioned number of drones, the method further includes: S401. Calculate the sub-zone distances between multiple sub-zones under the above-mentioned dispersed planar landforms.

[0080] For example, the multiple sub-zones under the above-mentioned dispersed planar landform can be multiple sub-zones that are not connected to each other or are not directly adjacent. The sub-zone distance between the above-mentioned sub-zones can refer to the distance between the edges of two sub-zones or the distance between the center points of two sub-zones. The specific method for determining the center point of the sub-zone and the method for determining the self-zone distance between sub-zones can be determined according to the actual situation, and there is no specific limitation here. However, the determination method used to calculate the sub-zone distance between the multiple sub-zones under the above-mentioned dispersed planar landform should be consistent.

[0081] S402. Based on the above sub-band distance and the preset sub-band merging rules, merge the above sub-bands to obtain at least one merged sub-band.

[0082] For example, the aforementioned preset sub-band merging rule could refer to merging sub-bands whose distance is less than a preset sub-band distance threshold. Suppose there are three sub-bands, Z1, Z2, and Z3, where the sub-band distance between Z1 and Z2 is 20m, the sub-band distance between Z1 and Z3 is 100m, and the sub-band distance between Z2 and Z3 is 90m. The preset sub-band distance threshold is 30m. Then, Z1 and Z2 meet the preset sub-band merging rule and can be merged into Z1. 12 It is understood that the merged subband mentioned above refers to the subband that has been compared according to the preset subband merging rules, and is not limited to a subband formed by merging at least two subbands. That is, for the example above, the merged subband obtained refers to Z. 12 Z3 and Z4 are two merged sub-bands. Even though Z3 is not a sub-band formed by merging at least two sub-bands, it is still a merged sub-band because it has been compared according to the preset sub-band merging rules.

[0083] S403. Based on the above-mentioned navigation altitude, the above-mentioned minimum heading overlap, the above-mentioned minimum lateral overlap and the above-mentioned preset route planning algorithm, determine the heading spacing and lateral spacing corresponding to each of the above-mentioned merged sub-bands.

[0084] For example, the heading spacing and lateral spacing of each of the above-mentioned merged sub-bands are determined based on the above-mentioned navigation altitude, the above-mentioned minimum heading overlap, the minimum lateral overlap and the above-mentioned preset route planning algorithm. The calculation method can be the same as the calculation method in step S302 above, and will not be elaborated here.

[0085] S404. Based on the above-mentioned heading spacing and the above-mentioned lateral spacing, determine the above-mentioned navigation route corresponding to each of the above-mentioned merged sub-bands.

[0086] and Figure 3 Similarly, in the embodiment, step S303, in addition to the heading spacing and the lateral spacing, the navigation route corresponding to each of the above-mentioned merged sub-bands may also include the navigation altitude used when calculating the heading spacing and the lateral spacing corresponding to each of the above-mentioned merged sub-bands. That is, in practice, the navigation route corresponding to each of the above-mentioned merged sub-bands can be determined based on the heading spacing, the lateral spacing and the corresponding navigation altitude.

[0087] Figure 5 A flowchart illustrating a multi-machine surveying and mapping management method is provided in another embodiment of this application, as shown below. Figure 5 As shown, optionally, in the foregoing Figure 3 Based on the embodiments, the above-mentioned terrain features also include: planar-three-dimensional mixed terrain. This planar-three-dimensional mixed terrain refers to a terrain in which at least one three-dimensional object exists within a continuous planar terrain.

[0088] After determining the navigation route corresponding to each of the above sub-bands using the aforementioned heading spacing and lateral spacing, the method further includes: S501. Identify at least one three-dimensional target in a mixed planar and three-dimensional terrain.

[0089] For example, at least one three-dimensional target in the above-mentioned planar-three-dimensional mixed terrain can refer to buildings, etc., but is not specifically limited to this.

[0090] S502. Determine the safe orbital radius and the required number of orbits for each of the above-mentioned three-dimensional targets.

[0091] For example, the aforementioned safe orbiting radius and the required number of orbits can be determined based on the parameters of the aforementioned three-dimensional target. The specific determination method is not limited here. The aforementioned safe orbiting radius refers to the orbiting path radius that ensures no collision or other accidents occur when the UAV orbits the three-dimensional target. The safe orbiting radius can be related to the length and width of the three-dimensional target, for example.

[0092] The number of orbits required refers to the fact that when the UAV orbits the aforementioned 3D target for mapping, it may not be able to complete the mapping of the 3D target in just one orbit. Therefore, it may be necessary to orbit multiple times, with each orbit at a fixed interval at a different height or in a spiral ascent to complete the mapping of the 3D target. The number of orbits required may be related to the height of the 3D target, for example.

[0093] S503. Based on the above-mentioned safe surrounding radius and the above-mentioned required number of surrounding circles, generate the three-dimensional surrounding route corresponding to each of the above-mentioned three-dimensional targets.

[0094] For example, the aforementioned three-dimensional circular route may refer to a circular route with equal height layers or a spiral route, that is, a route that circles at different heights at fixed intervals or circles in a spiral ascent.

[0095] S504. Connect the above navigation route and the above three-dimensional circumnavigation route through a preset spline transition curve to obtain a planar-three-dimensional hybrid route.

[0096] For example, the above-mentioned planar-to-three-dimensional hybrid route obtained by connecting the above-mentioned navigation route and the above-mentioned three-dimensional circumferential route through a preset spline transition curve should be understood as, through... Figure 3In this embodiment, steps S301-S303 generate a flight path for the planar portion of the mixed planar and three-dimensional terrain, and steps S501-S504 generate a three-dimensional loop route for the three-dimensional portion of the mixed planar and three-dimensional terrain. Finally, the flight path and the three-dimensional loop route are connected by a preset spline transition curve to obtain the mixed planar and three-dimensional route. The preset spline transition curve can be, for example, a three-dimensional B-spline transition curve. This curve should meet the dynamic constraints of the UAV, such as the maximum climb angle and maximum curvature, to ensure that the UAV is stable and that the cameras and other sensors are always facing the target to be mapped.

[0097] In addition, in the above Figures 1-5 Based on any embodiment, after sending corresponding control commands to each of the aforementioned drones to control each of the aforementioned drones to perform surveying and mapping, the above method may further include: Real-time navigation information for each of the aforementioned drones is acquired. This real-time navigation information includes: drone position, drone attitude, and ambient wind speed.

[0098] Based on the aforementioned drone position, drone attitude, ambient wind speed, and preset overlap adjustment algorithm, the real-time minimum overlap is calculated and obtained.

[0099] For example, the drone's location, attitude, and ambient wind speed can be obtained through a positioning device, attitude sensor, and wind speed sensor installed on the drone.

[0100] The above-mentioned drone position, drone attitude, ambient wind speed, and preset overlap adjustment algorithm are used to calculate the real-time minimum overlap, which can be achieved, for example, through the following formula:

[0101]

[0102] Among them, the above This refers to the lowest heading overlap in the real-time minimum overlap, which is used to replace the lowest heading overlap in the aforementioned minimum overlap. The above This refers to the lowest lateral overlap in the real-time lowest overlap, which is used to replace the lowest lateral overlap in the aforementioned lowest overlap. The above For ambient wind speed, The upper limit of wind speed for safe operation of drones. This is a compensation coefficient, which can be adjusted and determined according to the actual situation. For example, it can be set to 0.05, but it is not limited to this. Based on the above-mentioned UAV position, UAV attitude, environmental wind speed, and preset overlap adjustment algorithm, the real-time minimum overlap is calculated and obtained, aiming to increase overlap in strong winds and ensure the quality of mapping data.

[0103] Based on the aforementioned real-time minimum overlap, the flight path (including flight altitude) or the mixed planar and three-dimensional flight path of the UAV can be dynamically adjusted in real time.

[0104] Regarding the above Figures 3-5 In an example, after determining the flight path or hybrid planar-three-dimensional route of the UAVs, the matching of each UAV with each flight path or hybrid planar-three-dimensional route can be achieved, for example, through a capacity-constrained multiple traveling salesman problem (mTSP), i.e., the allocation of mapping tasks for each UAV. The objective function of this capacity-constrained multiple traveling salesman problem is to minimize the total task time, and the constraint condition is that the total range of each UAV does not exceed its endurance limit. For example, it can be expressed as:

[0105] Among them, the above For the mission time of the i-th drone, the above For the i-th UAV, the flight path or the length of the mixed planar and three-dimensional flight path is given above. Let the above be the flight time of the i-th drone. for The corresponding drone flight speed.

[0106] After the tasks were assigned, each drone took off and carried out the surveying and mapping tasks in parallel.

[0107] Of course, the above is just one possible example. The allocation of each UAV mapping task is not limited to the multi-traveling salesman problem with capacity constraints. Even if the allocation of each UAV mapping task is achieved through the multi-traveling salesman problem with capacity constraints, the objective function and constraints of the multi-traveling salesman problem with capacity constraints are not limited to the examples above.

[0108] Figure 6 A flowchart illustrating a multi-machine surveying and mapping management method is provided in another embodiment of this application. Please refer to... Figure 6 In the above Figure 5 Based on the embodiments, the above method may further include: S601. Determine the status of each of the above-mentioned drones based on their positions and attitudes.

[0109] For example, the above-mentioned determination of the state of each of the above-mentioned drones based on the drone's position and the drone's attitude can refer to determining whether the drone is traveling according to the corresponding flight path or the mixed planar and three-dimensional flight path based on the drone's position, and determining whether the drone is traveling smoothly and normally based on the drone's attitude. The state of the drone can include, for example, a normal state and an abnormal state. When the drone deviates from the corresponding flight path or the mixed planar and three-dimensional flight path, and / or the drone is not traveling smoothly and normally, the corresponding drone is determined to be in an abnormal state.

[0110] S602. If any of the above-mentioned drones are in an abnormal state, then determine the remaining mission area of ​​the drones in the abnormal state.

[0111] For example, the remaining task area mentioned above refers to the area in the sub-band corresponding to the drone in an abnormal state that has not been mapped.

[0112] S603. Obtain the real-time battery levels of other drones in the vicinity of the drone in the above-mentioned abnormal state.

[0113] For example, other drones around the drone in the above-mentioned abnormal state may refer to drones in sub-strips adjacent to or close to the sub-strip of the drone in the abnormal state. The real-time battery level of other drones around the drone in the above-mentioned abnormal state can be obtained directly through the communication connection between the ground control equipment and the drone.

[0114] S604. Based on the real-time battery level, identify at least one target drone among the other drones in the vicinity of the drone in the above-mentioned abnormal state.

[0115] For example, based on the real-time battery level, at least one target drone can be identified among other drones in the vicinity of the drone in the abnormal state. Specifically, at least one target drone can be identified within a preset range in the vicinity of the drone in the abnormal state.

[0116] For example, the target drone could be at least one of the drones with the highest real-time battery level among the surrounding drones in an abnormal state, and the target drone's real-time battery level should be at least greater than the estimated battery level required to complete the mapping of its corresponding sub-band. Alternatively, a preset number of drones with real-time battery levels greater than a preset threshold could be selected from a preset range around the aforementioned drone in an abnormal state as target drones, without specific limitations.

[0117] S605, instruct the aforementioned target UAV to map the remaining mission area of ​​the aforementioned UAV in abnormal condition.

[0118] Figure 7This is a schematic diagram of a multi-machine surveying and mapping management device according to an embodiment of this application. This device can execute the aforementioned multi-machine surveying and mapping management method. The device can be integrated into portable devices with computing and processing capabilities, such as computers, tablets, smartphones, and drone remote controllers. Figure 7 As shown, the device may include: The receiving module 710 is used to receive task instructions, which include: target ground resolution, minimum overlap, task priority, and terrain conditions.

[0119] The calculation module 720 is used to calculate and obtain the navigation altitude based on the target ground resolution and the preset navigation altitude algorithm.

[0120] The generation module 730 is used to generate multiple flight routes based on the above-mentioned terrain conditions, the above-mentioned minimum overlap, and the preset flight route planning algorithm, wherein the number of the above-mentioned flight routes is less than or equal to the number of UAVs to be controlled.

[0121] The control module 740 is used to generate control commands corresponding to each of the above-mentioned UAVs based on the above-mentioned flight altitude, the above-mentioned flight route, and the above-mentioned task priority, and send the corresponding control commands to each of the above-mentioned UAVs to control each of the above-mentioned UAVs to perform surveying and mapping.

[0122] The stitching module 750 is used to receive the mapping data returned by each of the above-mentioned UAVs and to stitch them together in real time using a stitching algorithm to obtain the stitched mapping data.

[0123] The multi-drone mapping management method provided in this application includes: receiving a task instruction for a target building that includes the target ground resolution, minimum overlap, task priority, and terrain conditions; calculating the flight altitude corresponding to the target ground resolution based on a preset flight altitude algorithm and the target ground resolution in the task instruction; then determining a corresponding preset route planning algorithm based on the flight altitude, minimum overlap, and terrain conditions to generate multiple flight routes, the number of flight routes not exceeding the number of controlled drones; finally generating control instructions for each drone based on the flight routes and task priorities to control each drone to perform mapping according to the corresponding flight routes; receiving mapping data returned by each drone during mapping, and using a stitching algorithm to stitch the mapping data in real time to obtain the stitched mapping data. In this embodiment, the flight altitude of the corresponding UAV is determined by the preset target ground resolution, and the flight route is generated according to the terrain based on the minimum overlap. This enables the parallel generation of multiple flight routes before UAV mapping, and at least one UAV is assigned to each flight route so that each UAV can simultaneously complete the mapping of the target area according to the flight route. During the UAV mapping process, the mapping data returned by each UAV is stitched together in real time based on the stitching algorithm to ensure the real-time performance of work such as modeling based on the mapping data. This improves the rationality of flight route planning and task allocation during UAV mapping, and enhances the accuracy and efficiency of mapping.

[0124] Optionally, the aforementioned calculation module 720 is specifically used to determine the shooting parameters of the aforementioned UAV, wherein the shooting parameters include: camera focal length, pixel size, and camera tilt angle. Based on the aforementioned camera focal length, pixel size, camera tilt angle, target ground resolution, and a preset flight altitude algorithm, the theoretical flight altitude is calculated. Based on the theoretical flight altitude and a preset maximum safe altitude, the aforementioned flight altitude is determined.

[0125] Optionally, the aforementioned terrain features include: continuous planar terrain. The aforementioned minimum overlap includes: minimum forward overlap and minimum lateral overlap.

[0126] The aforementioned generation module 730 is specifically used to divide the area to be mapped into multiple sub-strips based on the number of UAVs, wherein the number of sub-strips is less than or equal to the number of UAVs. Based on the flight altitude, minimum forward overlap, minimum lateral overlap, and a preset route planning algorithm corresponding to each sub-strip, the forward spacing and lateral spacing are determined for each sub-strip. Based on the forward spacing and lateral spacing, the corresponding flight path for each sub-strip is determined.

[0127] Optionally, the above-mentioned landforms also include: dispersed planar landforms.

[0128] The aforementioned generation module 730 can also be used to calculate the sub-band distances between multiple sub-bands under the aforementioned dispersed planar terrain. Based on the sub-band distances and preset sub-band merging rules, the sub-bands are merged to obtain at least one merged sub-band. The heading spacing and lateral spacing corresponding to each merged sub-band are determined based on the aforementioned navigation altitude, the aforementioned minimum forward overlap, the aforementioned minimum lateral overlap, and the aforementioned preset route planning algorithm. The navigation route corresponding to each merged sub-band is determined based on the aforementioned heading spacing and the aforementioned lateral spacing.

[0129] Optionally, the above-mentioned landforms also include: mixed planar and three-dimensional landforms.

[0130] The aforementioned generation module 730 can also be used to determine at least one three-dimensional target in a mixed planar and three-dimensional terrain. It determines the safe orbital radius and the required number of orbits for each of the three-dimensional targets. Based on the safe orbital radius and the required number of orbits, it generates a three-dimensional orbital route for each of the three-dimensional targets. By connecting the navigation route and the three-dimensional orbital route using a preset spline transition curve, a mixed planar and three-dimensional route is obtained.

[0131] Optionally, the aforementioned calculation module 720 can also be used to acquire real-time navigation information for each of the aforementioned UAVs, including: UAV position, UAV attitude, and ambient wind speed. Based on the aforementioned UAV position, UAV attitude, ambient wind speed, and a preset overlap adjustment algorithm, the real-time minimum overlap is calculated and obtained.

[0132] Optionally, the control module 740 can also be used to determine the state of each UAV based on its location and attitude. If any UAV is in an abnormal state, the remaining task area of ​​the UAV in the abnormal state is determined. The real-time battery levels of other UAVs in the vicinity of the UAV in the abnormal state are obtained. Based on the real-time battery levels, at least one target UAV is identified among the other UAVs in the vicinity of the UAV in the abnormal state. The target UAV is instructed to map the remaining task area of ​​the UAV in the abnormal state.

[0133] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0134] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device can be a portable device with computing and processing functions, such as a computer, tablet computer, smartphone, or drone remote controller. Figure 8 As shown, the device 800 includes: The processor 810, storage medium 820, and bus 830 are connected via bus 830.

[0135] The storage medium 820 stores machine-readable instructions that can be executed by the processor 810. When the electronic device is running, the processor 810 executes the aforementioned machine-readable instructions to perform the multi-machine mapping management method.

[0136] It should be understood that, Figure 8 The structure shown is only a schematic diagram of an electronic device; the electronic device may also include components that are larger than those shown. Figure 8 The more or fewer components shown, or having the same Figure 8 The different configurations shown. Figure 8 The components shown can be implemented using hardware, software, or a combination thereof.

[0137] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the multi-machine mapping management method described in the above method embodiments.

[0138] Computer-readable storage media can be electronic storage devices such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, computer-readable storage media includes non-transitory computer-readable storage medium. The computer-readable storage medium has storage space for program code that performs any of the method steps described above. This program code can be read from or written to one or more computer program exhibits. The program code can be compressed, for example, in a suitable form.

[0139] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program exhibits according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0140] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0141] If the functionality is implemented as a software module and sold or used as an independent exhibit, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software exhibit. This computer software exhibit is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0142] The above description is merely a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural transformations made based on the inventive concept of this application and the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the patent protection scope of this application.

Claims

1. A multi-machine surveying and mapping management method, characterized in that, The method, applied to ground control equipment that is communicatively connected to multiple unmanned aerial vehicles (UAVs), includes: Receive task instructions, wherein the task instructions include: target ground resolution, minimum overlap, task priority, and terrain conditions; The flight altitude is calculated and obtained based on the target ground resolution and the preset flight altitude algorithm; Based on the flight altitude, the terrain conditions, the minimum overlap, and the preset route planning algorithm, multiple flight routes are generated, wherein the number of flight routes is less than or equal to the number of UAVs to be controlled. Based on the flight route and the task priority, control commands are generated for each UAV, and corresponding control commands are sent to each UAV to control each UAV to perform mapping. The system receives the mapping data returned by each of the aforementioned UAVs and uses a stitching algorithm to stitch the data in real time to obtain the stitched mapping data.

2. The multi-machine surveying and mapping management method according to claim 1, characterized in that, The step of calculating and obtaining the flight altitude based on the target ground resolution and a preset flight altitude algorithm includes: The shooting parameters of the drone are determined, wherein the shooting parameters include: camera focal length, pixel size, and camera tilt angle; The theoretical flight altitude is calculated based on the camera focal length, the pixel size, the camera tilt angle, the target ground resolution, and a preset flight altitude algorithm. The flight altitude is determined based on the theoretical flight altitude and the preset maximum safe altitude.

3. The multi-machine surveying and mapping management method according to claim 1, characterized in that, The terrain features include: continuous planar terrain; the minimum overlap includes: minimum forward overlap and minimum lateral overlap; The process of generating multiple flight routes based on the flight altitude, terrain conditions, minimum overlap, and a preset route planning algorithm includes: Based on the number of drones, the area to be mapped is divided into multiple sub-strips, the number of which is less than or equal to the number of drones; The heading spacing and lateral spacing of each sub-band are determined based on the sailing altitude, minimum heading overlap, minimum lateral overlap, and the preset route planning algorithm corresponding to each sub-band. The navigation route corresponding to each sub-band is determined based on the heading spacing and the lateral spacing.

4. The multi-machine surveying and mapping management method according to claim 3, characterized in that, The aforementioned landform types also include: dispersed planar landforms; After dividing the area to be mapped into multiple sub-bands based on the number of drones, the method further includes: Calculate the sub-zone distances between multiple sub-zones under the aforementioned dispersed planar landform; Based on the sub-band distance and the preset sub-band merging rules, the sub-bands are merged to obtain at least one merged sub-band. Based on the navigation altitude, minimum heading overlap, minimum lateral overlap, and the preset route planning algorithm corresponding to each merged sub-band, the heading spacing and lateral spacing corresponding to each merged sub-band are determined respectively. Based on the heading spacing and the lateral spacing, the navigation route corresponding to each merged sub-band is determined.

5. The multi-machine surveying and mapping management method according to claim 3, characterized in that, The aforementioned landforms also include: mixed planar and three-dimensional landforms; After determining the navigation route corresponding to each sub-band based on the heading spacing and the lateral spacing, the method further includes: Identify at least one three-dimensional target within a mixed planar and three-dimensional terrain. Determine the safe orbital radius and the required number of orbits for each of the three-dimensional targets; Based on the safe surrounding radius and the required number of surrounding circles, generate a three-dimensional surrounding route for each three-dimensional target; By connecting the navigation route and the three-dimensional circumferential route using a preset spline transition curve, a planar-three-dimensional hybrid route is obtained.

6. The multi-machine surveying and mapping management method according to any one of claims 1-5, characterized in that, After sending corresponding control commands to each of the drones to control them to perform mapping, the method further includes: Real-time navigation information for each of the aforementioned drones is acquired, including drone position, drone attitude, and ambient wind speed. The real-time minimum overlap is calculated based on the drone's position, drone attitude, ambient wind speed, and a preset overlap adjustment algorithm.

7. The multi-machine surveying and mapping management method according to claim 6, characterized in that, The method further includes: The state of each drone is determined based on its location and attitude. If any of the drones is in an abnormal state, then the remaining mission area of ​​the drone in the abnormal state is determined. Obtain the real-time battery levels of other drones in the vicinity of the drone in the abnormal state; Based on the real-time battery level, identify at least one target drone among other drones in the vicinity of the drone in the abnormal state. The target UAV is instructed to map the remaining mission area of ​​the UAV in the abnormal state.

8. A multi-machine surveying and mapping management device, characterized in that, include: The receiving module is used to receive task instructions, wherein the task instructions include: target ground resolution, minimum overlap, task priority, and terrain conditions. The calculation module is used to calculate and obtain the flight altitude based on the target ground resolution and a preset flight altitude algorithm; The generation module is used to generate multiple flight routes based on the terrain conditions, the minimum overlap, and a preset flight route planning algorithm, wherein the number of flight routes is less than or equal to the number of UAVs to be controlled. The control module is used to generate control commands corresponding to each of the UAVs based on the flight altitude, the flight route, and the task priority, and send the corresponding control commands to each of the UAVs to control each of the UAVs to perform surveying and mapping. The stitching module is used to receive the mapping data returned by each of the UAVs and to stitch them together in real time using a stitching algorithm to obtain the stitched mapping data.

9. An electronic device, characterized in that, include: The device includes a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is in operation, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method as described in any one of claims 1-7.

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