Multi-unmanned aerial vehicle cooperative positioning method and system

Abstract

The invention discloses a multi-unmanned aerial vehicle cooperative positioning method and system, and belongs to the technical field of unmanned aerial vehicle positioning, a global coordinate system is defined firstly, a task starting point is taken as an original point, a Z axis is vertically upward, and unmanned aerial vehicle coordinate systems take respective inertial measurement units as centers; the unmanned aerial vehicle A is controlled to perform hovering motion at the indoor closed space laser radar feature point; controlling the unmanned aerial vehicle B to receive the pose covariance matrix and the local feature points of the unmanned aerial vehicle A in real time; laser SLAM output of the unmanned aerial vehicle B and the reference pose of the unmanned aerial vehicle A are fused through tight coupling Kalman filtering, accumulative errors are dynamically corrected, and an optimized pose and a covariance matrix of the unmanned aerial vehicle B under a global coordinate system are output; and unifying observation of the unmanned aerial vehicle A and observation of the unmanned aerial vehicle B to the same coordinate system by sharing pose constraints, and dynamically aligning local and global features to realize multi-unmanned aerial vehicle cooperative positioning. The method and the device are used for solving the technical problems of accumulated drifting and the like caused by long-time operation of laser SLAM in perception weakening scenes such as galleries and pipe galleries.

Description

Background Art

[0002] In a weak perception scenario such as an indoor enclosed space, due to factors such as sparse environmental features and poor lighting conditions, visual Simultaneous Localization and Mapping (SLAM) usually cannot be used, and laser SLAM has become the mainstream solution. However, laser SLAM is prone to cumulative errors during long-term operation, resulting in positioning drift, which affects the positioning accuracy and operation effect of the drone.

[0003] The limitations of multi-drone collaborative SLAM are specifically as follows: The difference in the external parameters of lidars between different models reaches ±3°, resulting in a 200% increase in the point cloud matching error; the multi-drone point cloud transmission requires a bandwidth of 20 - 50 Mbps, but the measured effective bandwidth of industrial WiFi in a closed environment is less than 10 Mbps; when 5 drones collaborate, the dimension of the state vector exceeds 10 4 , resulting in a three-order-of-magnitude increase in the time-consuming of backend optimization.

[0004] The prior art discloses a method, system and server for the step-by-step collaborative navigation of two robots in an underground pipeline, which can solve the problems of rapid accumulation of pure inertial navigation errors and the need to deploy base stations in advance for Ultra Wide Band (UWB) positioning, and has the advantages of low error, low cost, low power consumption and high degree of freedom; the disadvantages of this method are: the tight coupling optimization of multi-source heterogeneous data has not been realized. The Visual-Inertial Odometry (VIO) system adopts a factor graph optimization framework based on a sliding window, unifies the modeling of visual reprojection error, IMU pre-integration constraint (Inertial Measurement Unit) and UWB / laser ranging observations between robots, and synchronously solves the pose, velocity and sensor bias through non-linear optimization, which has a stronger error suppression ability than the strategy of stage-by-stage correction.

[0005] The prior art also discloses a positioning method for an Automated Guided Vehicle (AGV) in a factory based on single-base-station UWB and vision inertia, which is used to improve the positioning accuracy and robustness of the system. The disadvantages of this method are: the ranging error of single-anchor UWB (usually ±10 cm) will be directly transmitted to the AGV pose correction amount through the Jacobian matrix of the observation model. Especially when the AGV is far from the anchor point, the incident angle of the UWB signal approaches the normal direction, resulting in an aggravation of the geometric dilution of precision (GDOP) of the distance-position conversion.

[0006] In the prior art, the positioning accuracy of a single UAV system in a weak perception scenario is limited, and there is a lack of an effective error correction mechanism. Although multi-UAV collaborative schemes have been studied, most of them rely on global positioning (such as GPS) or pre-set beacons, and are not applicable to GPS-free closed environments.

[0007] Single-UAV laser SLAM: It relies on its own sensors, and the error accumulates linearly with the growth of the path.

[0008] Multi-UAV collaborative SLAM: Existing schemes are mostly based on joint mapping of vision or laser, with high computational complexity, and no dynamic correction mechanism is designed for the accumulated error.

[0009] UWB-assisted positioning: Existing research uses UWB ranging to assist SLAM, but it requires fixed base stations or pre-deployed anchors, lacking flexibility. Summary of the Invention

[0010] The technical problem to be solved by the present invention is to provide a multi-UAV collaborative positioning method and system in view of the deficiencies in the above-mentioned prior art. By using the first UAV as a dynamic reference node to provide real-time error correction for the second UAV, it is used to solve the technical problem of the accumulated error caused by the long-term operation of laser SLAM in weak perception scenarios such as corridors and pipe galleries.

[0011] The present invention adopts the following technical solutions: A multi-UAV collaborative positioning method includes the following steps: Define a global coordinate system with the task starting point as the origin, the Z-axis perpendicular upward, and the UAV coordinate systems centered on their respective inertial measurement units; Control UAV A to hover at the lidar feature points in the indoor closed space; Control UAV B to receive the pose covariance matrix and local feature points of UAV A in real time; Fuse the lidar SLAM output of UAV B and the reference pose of UAV A through tightly coupled Kalman filtering to dynamically correct the accumulated error, and output the optimized pose and covariance matrix of UAV B in the global coordinate system; Through shared pose constraints, unify the observations of UAV A and UAV B to the same coordinate system, dynamically align local and global features, and achieve multi-UAV collaborative positioning.

[0012] Preferably, in the global coordinate system, take UAV A as the dynamic reference node, and respectively obtain the IMU angular velocity , acceleration , lidar point cloud , take UAV B as the operation main body, and obtain the lidar point cloud , and UAV A and UAV B are respectively centered on their respective inertial measurement units.

[0013] Preferably, the pose state of UAV A is as follows:

[0014] The pose state of UAV B is as follows:

[0015] Wherein, is the pose information, , is the quaternion of UAV A, is the set of x, y, z in the rectangular coordinate system of the UAV and the quaternion of the UAV orientation, is the pose of UAV B, , is the quaternion of UAV B.

[0016] Preferably, the dynamic correction of the cumulative error is specifically: UAV A moves periodically along the corridor axis, and the IMU pre-integration calculates the pose increment through the angular velocity and the acceleration , extracts the edge and plane features from the laser point cloud based on the LOAM algorithm, constructs the local map , optimizes the pose through the ICP algorithm, and obtains the optimized pose information , and generates the dynamic reference node trajectory; Uses the local matching of the binocular point cloud to construct the relative pose constraint, extracts the common area feature point set , and solves the relative pose; Optimizes the tightly coupled factor graph and dynamically corrects the cumulative error.

[0017] Preferably, the pose increment:

[0018] Wherein, is the zero bias of the accelerometer / gyroscope, is the conversion from quaternion to rotation matrix, is the initial velocity of UAV A, is the IMU acceleration measurement value of UAV A at time t, is the quaternion attitude of UAV A at time t.

[0019] Preferably, the optimized pose information is:

[0020] Wherein, is the rotation matrix, is the translation vector, and is the coordinate of the nearest neighbor point in the local map that matches

[0021] Preferably, the relative pose

[0022]

[0023] wherein, is the pose information of UAV B after optimization, is the rotation matrix, is the translation vector.

[0024] Preferably, optimize the objective function of the tightly coupled factor graph:

[0025] wherein, is the set of pose nodes of UAV B, is the laser odometry factor, is the IMU pre-integration factor, is the point cloud matching factor.

[0026] Preferably, assume that the front-end error of the laser SLAM of UAV B is , after the dual-UAV collaborative correction, the corrected global positioning error variance is obtained. When , , the error is effectively suppressed, and the corrected global positioning error variance is:

[0027] wherein, is the front-end positioning error variance of the laser SLAM.

[0028] In a second aspect, an embodiment of the present invention provides a multi-UAV collaborative positioning system, including: An initialization module that defines a global coordinate system with the task starting point as the origin, the Z-axis perpendicular upward, and the UAV coordinate system centered on their respective inertial measurement units; A first control module that controls UAV A to hover at the lidar feature points in the indoor enclosed space; A second control module that controls UAV B to receive the pose covariance matrix and local feature points of UAV A in real time; An error module that fuses the laser SLAM output of UAV B and the reference pose of UAV A through tightly coupled Kalman filtering, dynamically corrects the cumulative error, and outputs the optimized pose and covariance matrix of UAV B in the global coordinate system; A positioning module, through shared pose constraints, unifies the observations of UAV A and UAV B into the same coordinate system, dynamically aligns local and global features, and realizes cooperative positioning of multiple UAVs.

[0029] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned cooperative positioning method for multiple UAVs are realized.

[0030] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned cooperative positioning method for multiple UAVs are realized.

[0031] In a fifth aspect, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned cooperative positioning method for multiple UAVs are realized.

[0032] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, and when the computer program is executed by the electronic device, the steps of the above-mentioned cooperative positioning method for multiple UAVs are realized.

[0033] Compared with the prior art, the present invention has at least the following beneficial effects: A cooperative positioning method for multiple UAVs uses UAV A as a dynamic reference node to provide external observation constraints for UAV B in real time, significantly suppressing cumulative errors, providing global pose constraints in real time, effectively offsetting the long-term cumulative errors of SLAM, and avoiding the limitations of relying on fixed anchor points; combining IMU and laser point cloud local matching to construct cross-modal constraints, improving the positioning robustness in complex scenarios (low texture, dynamic obstacles); without GPS signals or preset beacons, forming a self-organizing positioning network through cooperation between UAVs, which is especially suitable for GPS-denied environments that require rapid deployment such as disaster rescue. The framework supports multi-UAV cooperation, and further improves the system fault tolerance and coverage through distributed information sharing.

[0034] Furthermore, by introducing the role division of the dynamic reference node (UAV A) and the operation entity (UAV B), real-time error correction is realized without external infrastructure. The two UAVs are used as benchmarks for each other. In GPS-denied or dynamic obstacle scenarios, the positioning continuity is maintained through cross-modal (IMU + laser) data closed-loop.

[0035] Furthermore, the dynamic trajectory generated by the periodic movement of UAV A provides real-time global constraints for UAV B. Through ICP optimization and LOAM feature matching, it effectively suppresses IMU drift and SLAM cumulative errors, and improves the positioning stability during long-duration flights. IMU pre-integration provides high-frequency pose increments. Edge / plane features are extracted from the lidar point cloud to construct a tightly coupled factor graph to fuse multi-source data, complementing the short-term accuracy of the IMU and the advantages of lidar environmental perception, and enhancing the robustness in complex corridor scenarios. The relative pose is solved based on the common feature point set, and cross-aircraft constraints are constructed by combining the dynamic node trajectories to achieve local map alignment and dynamic error correction, avoiding the problem of single-aircraft positioning divergence and improving the system fault tolerance. The tightly coupled optimization architecture reduces the computational complexity and is applicable to dynamic environments. Without pre-deployed anchors, relying only on inter-aircraft relative observations, it still maintains high accuracy in GPS-denied or structured repetitive scenarios.

[0036] Furthermore, the IMU pre-integration, lidar matching residuals, and inter-aircraft relative pose constraints are uniformly modeled as weighted residual terms. The sensor confidence is dynamically allocated through the covariance matrix to achieve efficient fusion of multi-modal data and improve global consistency. The dynamic trajectory of UAV A is introduced as a prior constraint in the objective function to correct the cumulative errors of UAV B in real time (such as IMU zero bias and lidar matching drift), especially avoiding positioning divergence in repetitive structure scenarios such as long corridors. The tightly coupled architecture reduces the computational amount through sparsity optimization and supports real-time operation. At the same time, the factor graph has fault tolerance for abnormal data (such as dynamic obstacle interference point clouds), and outliers are removed through the Mahalanobis distance threshold to ensure system stability. The objective function can flexibly integrate new sensors (such as vision, UWB) or more UAV nodes, and the system is extended by augmenting the state variables and constraint terms to adapt to complex task requirements.

[0037] Furthermore, the error suppression effect is accurately identified through the error propagation model.

[0038] It can be understood that the beneficial effects of the second to sixth aspects above can be referred to the relevant descriptions in the first aspect above, and will not be elaborated here.

[0039] In summary, through the dynamic reference node and tightly coupled factor graph optimization, the present invention fuses IMU / lidar data to suppress the cumulative drift of the lidar. Cross-aircraft point cloud matching constructs global constraints to eliminate the cumulative errors of single aircraft. Without external anchors or GPS, relying on the complementarity of multi-source data, it achieves high-precision positioning in complex scenarios, with autonomy, robustness, and real-time performance, and is applicable to GPS-denied environments such as emergency rescue and warehousing logistics.

[0040] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0041] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the accompanying drawings required for use in the embodiments of the present application. Obviously, the accompanying drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other accompanying drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 It is a schematic flowchart of the present invention; Figure 2 It is a data acquisition diagram; Figure 3 It is a schematic diagram of a computer device provided by an embodiment of the present invention; Figure 4 It is a block diagram of an electronic device provided by an embodiment of the present invention.

[0043] Among them, 60. Computer device; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access storage unit; 6202. Cache storage unit; 6203. Read-only storage unit; 6204. Program / utilities; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed implementation manners

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0045] In the description of the present invention, it should be understood that the terms "include" and "comprise" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0046] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0047] It should be further understood that the term "and / or" used in the specification and appended claims of the present invention refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. For example, A and / or B can represent three cases: A exists alone, A and B exist simultaneously, and B exists alone. Additionally, in the present invention, the character " / " generally indicates an "or" relationship between the associated objects before and after.

[0048] It should be understood that although terms such as first, second, third, etc. may be used in the embodiments of the present invention to describe preset ranges, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0049] Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determining" or "when detected (stated condition or event)" or "in response to detecting (stated condition or event)".

[0050] Various structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the drawings. These figures are not drawn to scale, where for the purpose of clear expression, some details are enlarged and some details may be omitted. The shapes of various regions and layers shown in the figures and their relative sizes and positional relationships are only exemplary, and in practice, there may be deviations due to manufacturing tolerances or technical limitations. And those skilled in the art can design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0051] The present invention provides a multi-UAV collaborative positioning method. By introducing a dual-UAV collaborative system and using the first UAV as a dynamic reference node to provide real-time error correction for the second UAV, the positioning accuracy of laser SLAM in a perception-weakened scenario is improved; while ensuring accuracy, it avoids the dependence on fixed infrastructure and complex communication requirements, and is particularly suitable for strongly constrained scenarios such as emergency inspection and underground pipe corridors.

[0052] Adopt a dual-UAV dynamic collaborative correction mechanism: Role division: One UAV serves as a dynamic reference node (carrying a high-precision IMU and lidar), and the other UAV serves as the operation main body; Error suppression: By relative ranging between two UAVs (such as UWB or lidar ranging) and local point cloud matching, relative pose constraints are constructed in real time and embedded in the simultaneous localization and mapping (SLAM) backend optimization; Resource optimization: Only intermittent communication is required to exchange key state variables (such as pose covariance), reducing the bandwidth requirement to the 10 kbps level.

[0053] Embodiment 1 Please refer to Figure 1 , a multi-UAV collaborative positioning method of the present invention includes the following steps: S1. In the initialization stage, a global coordinate system is defined with the task starting point as the origin and the Z-axis perpendicular upward. The UAV coordinate system is centered on its respective inertial measurement unit (IMU).

[0054] Hardware preparation: Prepare a multi-UAV system with hovering ability and communication module, which can hover at a suitable position in a weak perception scenario and obtain its own accurate position information through laser SLAM.

[0055] UAV A (dynamic reference node): Pose state:

[0056] Among them, is the pose information, , is the quaternion of UAV A. The main function of the quaternion is to accurately estimate its own attitude (i.e., direction) in real time in UAV equipment. The quaternion can simply represent the attitude of the robot. is the set of x, y, z in the UAV rectangular coordinate system and the quaternion of the UAV orientation.

[0057] Sensor: IMU angular velocity , acceleration , lidar point cloud .

[0058] UAV B (operation main body): Pose state:

[0059] Among them, is the pose of UAV B, , is the quaternion of UAV B; Sensor: lidar point cloud .

[0060] Define the coordinate system: Global coordinate system : With the task starting point as the origin and the Z-axis perpendicular upward.

[0061] UAV A / B Body Coordinate System Respectively centered on their own IMUs.

[0062] S2. Generate the trajectory of UAV A. Perform hovering motion at a location with rich lidar feature points in an indoor enclosed space to provide precise positioning information for UAV B; S3. UAV B operates and collects data, and receives the pose covariance matrix and local feature points of UAV A in real time, such as Figure 2 as shown.

[0063] S4. Construct an optimization problem and perform cooperative error correction for the two UAVs. Fusion of the lidar SLAM output of UAV B and the reference pose of UAV A through tightly coupled Kalman filtering (TCKF) to dynamically correct the accumulated error.

[0064] S401. Generate the dynamic reference node trajectory (UAV A); UAV A moves periodically along the corridor axis, and realizes short-term high-precision positioning through a high-precision IMU (1) IMU pre-integration calculates the pose increment through the angular velocity and acceleration :

[0065] where, is the bias of the accelerometer / gyroscope, is the conversion from quaternion to rotation matrix, is the initial velocity of UAV A, is the IMU acceleration measurement value of UAV A at time t, is the quaternion attitude of UAV A at time t.

[0066] (2) Local point cloud matching Extract edge and plane features from the lidar point cloud based on the LOAM algorithm, construct a local map , optimize the pose through the ICP algorithm, and obtain the optimized pose information ; The optimized pose information is:

[0067] where, is the rotation matrix, is the translation vector, is the coordinate of the nearest neighbor point in the local map that matches .

[0068] S402. Construct the relative pose constraint between the two UAVs; Laser point cloud local matching: Utilize the local matching of dual-camera point clouds to construct relative pose constraints: Extract the feature point set of the common area and solve the relative pose

[0069]

[0070] Among them, is the pose information of UAV B after optimization, is the rotation matrix, is the translation vector.

[0071] S403. Optimize the tightly coupled factor graph.

[0072] Define the tightly coupled factor graph: Laser odometry factor , IMU pre-integration factor , point cloud matching factor ; Objective function:

[0073] Among them, is the set of pose nodes of UAV B.

[0074] S5. Error suppression performance analysis.

[0075] Assume that the front-end error of laser SLAM of UAV B is . After dual-camera collaborative correction, the global error variance is reduced to:

[0076] Among them, is the global positioning error variance after correction, is the front-end positioning error variance of laser SLAM. When , the error is effectively suppressed.

[0077] Those skilled in the art of the present technology can understand that various aspects of the present invention can be implemented as a system, method, or program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuitry", "module", or "platform" here.

[0078] Example 2 The present invention provides a multi-UAV collaborative positioning system, which can be used to implement the above-mentioned multi-UAV collaborative positioning method. Specifically, the multi-UAV collaborative positioning system includes an initialization module, a first control module, a second control module, an error module, and a positioning module.

[0079] Among them, the initialization module defines a global coordinate system with the task starting point as the origin and the Z-axis perpendicular upward, and the UAV coordinate system is centered on its respective inertial measurement unit; The first control module controls UAV A to hover at the lidar feature points in the indoor enclosed space; The second control module controls UAV B to receive the pose covariance matrix and local feature points of UAV A in real time; Taking UAV A as a dynamic reference node, the IMU angular velocity , acceleration , lidar point cloud are respectively obtained. Taking UAV B as the operation main body, the lidar point cloud is obtained. UAV A and UAV B are respectively centered on their respective inertial measurement units. The pose state of UAV A is as follows:

[0080] The pose state of UAV B is as follows:

[0081] Among them, is the pose information, , is the quaternion of UAV A, is the set of x, y, z in the UAV rectangular coordinate system and the quaternion of the UAV orientation, is the pose of UAV B, , is the quaternion of UAV B.

[0082] The error module fuses the laser SLAM output of UAV B and the reference pose of UAV A through tightly coupled Kalman filtering, dynamically corrects the cumulative error, and outputs the optimized pose and covariance matrix of UAV B in the global coordinate system; Specifically, dynamically correcting the cumulative error is as follows: UAV A moves periodically along the corridor axis. The IMU pre-integration calculates the pose increment through the angular velocity and acceleration . Based on the LOAM algorithm, the edge and plane features are extracted from the lidar point cloud , and a local map is constructed. The pose is optimized through the ICP algorithm to obtain the optimized pose information , and the dynamic reference node trajectory is generated; Using dual - machine point clouds to construct relative pose constraints through local matching and extract the common - area feature point set and solve the relative pose; Dynamically correct the cumulative error through tightly - coupled factor graph optimization.

[0083] Pose increment:

[0084] where, is the zero - bias of the accelerometer / gyroscope, is the conversion from quaternion to rotation matrix, is the initial velocity of UAV A, is the IMU acceleration measurement value of UAV A at time t, is the quaternion attitude of UAV A at time t.

[0085] The optimized pose information is:

[0086] where, is the rotation matrix, is the translation vector, is the coordinate of the nearest neighbor point in the local map that matches .

[0087] Solving the relative pose

[0088]

[0089] where, is the pose information of UAV B after optimization, is the rotation matrix, is the translation vector.

[0090] The objective function for optimizing the tightly - coupled factor graph:

[0091] where, is the set of pose nodes of UAV B, is the laser odometry factor, is the IMU pre - integration factor, is the point cloud matching factor.

[0092] Assume that the front - end error of the laser SLAM of UAV B is , after dual - machine collaborative correction, the corrected global positioning error variance is obtained. When When the error is effectively suppressed, and the corrected global positioning error variance is:

[0093] wherein, is the variance of the front-end positioning error of the laser SLAM.

[0094] The positioning module unifies the observations of UAV A and UAV B into the same coordinate system by sharing pose constraints, dynamically aligns local and global features, and realizes cooperative positioning of multiple UAVs.

[0095] Embodiment 3 The present invention provides a terminal device, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Graphics Processing Unit (GPU), Tensor Processing Unit (TPU), Digital Signal Processor (DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the multi-UAV cooperative positioning method, including: Define a global coordinate system with the task starting point as the origin and the Z-axis perpendicular upward, and the UAV coordinate system centered on their respective inertial measurement units; control UAV A to hover at the lidar feature points in the indoor enclosed space; control UAV B to receive the pose covariance matrix and local feature points of UAV A in real time; fuse the laser SLAM output of UAV B and the reference pose of UAV A through tightly coupled Kalman filtering, dynamically correct the cumulative error, and output the optimized pose and covariance matrix of UAV B in the global coordinate system; unify the observations of UAV A and UAV B into the same coordinate system by sharing pose constraints, dynamically align local and global features, and realize cooperative positioning of multiple UAVs.

[0096] Please refer toFigure 3 , the terminal device is a computer device. The computer device 60 in this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, it implements the multi-UAV collaborative positioning method in the embodiment. To avoid repetition, it will not be elaborated here one by one. Alternatively, when the computer program 63 is executed by the processor 61, it implements the functions of each model / unit in the multi-UAV collaborative positioning system in the embodiment. To avoid repetition, it will not be elaborated here one by one.

[0097] The computer device 60 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art can understand that Figure 3 merely examples of the computer device 60, which do not constitute a limitation on the computer device 60, may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0098] The so-called processor 61 may be a central processing unit (CPU), or may also be other general-purpose processors, a graphics processing unit (GPU), a tensor processing unit (TPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0099] The memory 62 may be an internal storage unit of the computer device 60, such as the hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk equipped on the computer device 60, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.

[0100] Further, the memory 62 may also include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0101] Please refer to Figure 4 , the terminal device is the electronic device 600, and the electronic device 600 is presented in the form of a general computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0102] Among them, the storage unit stores program codes, and the program codes can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the method part of this specification. For example, the processing unit 610 can execute steps as shown in Figure 1 .

[0103] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only storage unit (ROM) 6203.

[0104] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205. Such program modules 6205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. The implementation of a network environment may be included in each or some combination of these examples.

[0105] The bus 630 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any one of the multiple bus structures.

[0106] The electronic device 600 can also communicate with one or more external devices 700 (such as a keyboard, a pointing device, a Bluetooth device, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (such as a router, a modem). Such communication can be carried out through the input / output interface 650. Moreover, the electronic device 600 can also communicate with one or more networks (such as a local area network, a wide area network, and / or a public network, such as the Internet) through the network adapter 660. The network adapter 660 can communicate with other modules of the electronic device 600 through the bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0107] Embodiment 4 The present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. It can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, one or more instructions suitable for being loaded and executed by a processor are stored in this storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples of the computer-readable storage medium here include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disc read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0108] The computer-readable storage medium also includes a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, and this readable medium can send, propagate, or transmit a program used by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, radio frequency, etc., or any suitable combination of the above.

[0109] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as an independent software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network or a wide area network, or can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0110] One or more instructions stored in the computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the multi-UAV collaborative positioning method in the above embodiments; the one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: Define a global coordinate system with the task starting point as the origin and the Z-axis perpendicular upward, and the UAV coordinate systems centered on their respective inertial measurement units; control UAV A to hover at the lidar feature points in the indoor enclosed space; control UAV B to receive the pose covariance matrix and local feature points of UAV A in real time; fuse the laser SLAM output of UAV B and the reference pose of UAV A through tightly coupled Kalman filtering, dynamically correct the cumulative error, and output the optimized pose and covariance matrix of UAV B in the global coordinate system; through shared pose constraints, unify the observations of UAV A and UAV B to the same coordinate system, dynamically align the local and global features, and achieve multi-UAV collaborative positioning.

[0111] In each of the embodiments provided in the present application, the database involved may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., without limitation. In each of the embodiments provided in the present application, the processor involved may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without limitation.

[0112] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0113] Embodiment 5 Experimental setup Simulation environment: Scenario 1: A 100-meter straight corridor (3 meters wide and 4 meters high), simulating the structure of an underground utility tunnel.

[0114] Scenario 2: A complex elbow pipe (80 meters in total length, including 3 90° elbows and 2 branch pipes), simulating a mine environment.

[0115] Simulation tools: Gazebo + ROS (Robot Operating System), with the lidar noise model set to ±2 cm and the IMU zero-offset error set to 0.01 m / s² (accelerometer) and 0.1° / s (gyroscope).

[0116] Hardware prototype: Drone A: Equipped with a Velodyne VLP-16 lidar, an ADI Tactical-grade IMU, and a Decawave UWB module.

[0117] Drone B: Equipped with a Hesai PandarXT-32 lidar and an NVIDIA Jetson Xavier computing unit.

[0118] Communication module: LoRa (Semtech SX1276) + WiFi (ESP32), with a bandwidth upper limit of 100 kbps.

[0119] Comparison method: Single UAV laser SLAM (LeGO-LOAM algorithm).

[0120] Conclusion: The present invention reduces the absolute trajectory error (ATE) to 14% - 21% of the traditional method, verifying the error suppression effect.

[0121] Preferably, use UAVs and ground robots Implementation method: Deploy multiple ground robots in the task area, such as ground robots with strong passability like quadruped robots, and provide calibration information for UAV B through the ground robots.

[0122] It can reduce the dependence on UAV A and is applicable to scenarios with long-duration tasks and complex ground road conditions.

[0123] Example 6 Experimental design Experimental platform and toolchain Build a high-precision indoor simulation environment (warehouse and corridor composite structure) based on ROS (Robot Operating System) and Gazebo. The specific parameters are as follows: UAV models: UAVs A and B equipped with Velodyne VLP-16 lidar (16 beams, vertical field of view ±15°, horizontal field of view 360°) and BMI088 six-axis IMU (sampling frequency 200 Hz). UAV A hovers at the intersection (feature point density ≥ 50 points / m²), and UAV B executes tasks along a dynamic trajectory (obstacle avoidance, detour, sharp turn).

[0124] Dynamic obstacle model: Generate moving pedestrians and obstacles (speed 0.5 - 2 m / s, trajectory is rectangular or random path) through the Gazebo Actor plugin to simulate indoor dynamic interference.

[0125] SLAM algorithm: UAV B uses the LOAM (Lidar Odometry and Mapping) algorithm for local mapping, with a point cloud registration accuracy of ±0.1 m; UAV A realizes tightly coupled lidar-IMU fusion through the Fast-LIO2 algorithm and outputs the global pose covariance matrix.

[0126] Data fusion framework design Tightly Coupled Extended Kalman Filter (Tightly Coupled EKF) architecture: State vector: Includes position p ∈R 3, Speed v ∈R 3 , Attitude quaternion q ∈S 3 , Accelerometer bias ba ∈R 3 , Gyroscope bias bg ∈R 3 , a total of 16 dimensions.

[0127] Prediction stage: Based on the IMU angular velocity ω t and acceleration a t , update the state through the motion model: p t+1 =p t +v t Δt + 1 / 2(C ns (q t )(a t −b a ) + g)Δt 2 Among them, C ns (q t ) is the rotation matrix converted from the attitude quaternion, g is the gravitational acceleration.

[0128] Correction stage: Fuse the local feature points (plane and edge features) of laser SLAM with the covariance matrix of UAV A, and eliminate abnormal observations through the Mahalanobis distance.

[0129] Experimental group and control group Control group 1: Only use the LOAM algorithm of UAV B, without collaborative correction.

[0130] Control group 2: Loose coupling fusion (only fuse the pose results and ignore the covariance transfer).

[0131] Experimental group: The tightly coupled collaborative positioning method proposed in this paper, sharing the covariance matrix and feature constraints in real time Evaluation index

[0132] Experimental data and result analysis Cumulative error suppression effect

[0133] Long corridor scenario: In independent SLAM, the cumulative error grows exponentially due to feature repeatability. In the experimental group, through the fixed reference pose constraint of UAV A, the error is suppressed within 0.2 m (89% reduction).

[0134] Helical ascent scenario: The integration error of the IMU angular velocity accumulates significantly in the control group. In the experimental group, the gyroscope zero bias is dynamically corrected through tight-coupling filtering, and the error is reduced by 86%.

[0135] Dynamic characteristics of heading angle error

[0136] In the sharp turn scenario, the heading angle error of the loose-coupling group increases significantly due to the lack of fused angular velocity. In the experimental group, through the joint optimization of IMU and laser features, the heading accuracy is improved by 77%.

[0137] Covariance convergence process Convergence time: The average convergence time of the experimental group is 5.6 s, which is 54% shorter than that of the loose-coupling group (12.3 s).

[0138] Covariance propagation: The sharing of the covariance matrix of UAV A reduces the initial uncertainty of UAV B by 60%, accelerating the filtering convergence.

[0139] Typical scenario simulation verification Dynamic obstacle interference test Success rate: When the density of dynamic obstacles in the experimental group is 5 per 10 m², the success rate is 93%, while that of the control group 1 is only 62%.

[0140] Robustness mechanism: Through global-local feature dynamic alignment (such as door frames and columns), the positioning stability is maintained under 80% occlusion conditions.

[0141] Multi-UAV collaborative scalability verification Formation scale: The experiment is extended to 5 UAVs, with 1 as the reference node and the remaining 4 for collaborative positioning.

[0142] Performance decay: When the formation scale increases, the communication delay causes the positioning error to increase by about 15%, but it is still better than independent SLAM (error > 200%).

[0143] Error-state Kalman filter (ES-EKF): The state is divided into nominal state and error state, reducing the linearization error, especially suitable for large-angle motions.

[0144] Resource efficiency: Only 1 high-precision UAV is required as the reference node, reducing the hardware cost by 70%, suitable for large-scale formations.

[0145] Multi-source data fusion: The tight coupling of lidar and IMU compensates for the defects of single sensors, and the positioning error fluctuation is less than 5% in scenarios with light changes and electromagnetic interference.

[0146] Dynamic feature management: Real-time indexing of global-local features through KD-Tree, with a 40% improvement in feature matching speed.

[0147] In this embodiment, high-precision navigation of the drone in a complex indoor environment is achieved through tight-coupling collaborative positioning, with the cumulative error reduced by 74% and the covariance convergence speed increased by 54%. Future research directions include: Communication optimization: Research on low-latency distributed filtering architectures to reduce the communication overhead of multi-drone collaboration.

[0148] Semantic fusion: Introduce semantic labels of visual SLAM to enhance the ability to identify dynamic obstacles.

[0149] Hardware deployment: Transplant the algorithm to the NVIDIA Jetson platform to achieve embedded real-time processing.

[0150] In summary, a multi-drone collaborative positioning method and system according to the present invention has significant advantages in a closed environment, with high precision, strong anti-interference ability, and excellent cooperation efficiency, specifically as follows: First, by having drone A act as a "positioning beacon" at a fixed position to continuously provide stable environmental feature data, and drone B uses this reference information to calibrate its own position in real time, effectively suppressing the cumulative positioning drift problem caused by environmental repetition in traditional laser SLAM, and the positioning accuracy is significantly improved; Second, the dual-drone data sharing mechanism forms a redundant system with mutual verification. Even in areas with scarce features such as corridors and pipelines, it can still work stably and is more reliable than single-drone operation; Third, through coordinate system unification and dynamic feature matching, multiple drones can collaboratively construct a complete environmental map, which not only avoids repeated scanning but also improves the mapping efficiency, and is especially suitable for scenarios that require multi-drone cooperation such as disaster rescue and pipeline inspection.

[0151] In addition, the system has strong scalability. Newly added drones can quickly integrate into the collaboration system by simply accessing the data sharing network, greatly improving the execution flexibility of complex tasks. This method demonstrates significant technical advantages in indoor scenarios where GPS fails, providing a more reliable positioning solution for the application of drones in fields such as industrial inspection and underground exploration.

[0152] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above-mentioned division of each functional unit and module is used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0153] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0154] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0155] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal and method can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For example, the division of the module or unit is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in electrical, mechanical or other forms.

[0156] The unit described as a separated component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0157] In addition, in each embodiment of the present invention, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0158] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0159] This application is described with reference to the flowcharts and / or block diagrams of methods, devices, and computer program products according to the embodiments of this application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0160] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the process in Figure 1One or more processes and / or blocks Figure 1 The functions specified in one or more blocks.

[0161] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 One or more processes and / or blocks Figure 1 The steps of the functions specified in one or more blocks.

[0162] The above content is only to illustrate the technical idea of the present invention, and the protection scope of the present invention cannot be limited thereby. Any modifications made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A multi-UAV collaborative positioning method, characterized in that: The following steps are involved: Define the global coordinate system, with the mission start point as the origin, the Z axis pointing vertically upward, and the drone coordinate system centered on their respective inertial measurement units; Control drone A to hover in an indoor closed space with abundant lidar feature points; Control UAV B to receive the pose covariance matrix and local feature points of UAV A in real time; The laser SLAM output of UAV B is fused with the reference pose of UAV A through tightly coupled Kalman filtering, the accumulated error is dynamically corrected, and the optimized pose and covariance matrix of UAV B in the global coordinate system are output; By sharing pose constraints, the pose information of UAV A and UAV B is unified into the same coordinate system, and local and global features are dynamically aligned to achieve multi-UAV collaborative positioning.

2. The multi-UAV collaborative positioning method according to claim 1, characterized in that: In the global coordinate system, drone A is used as the dynamic reference node to obtain the IMU angular velocity , acceleration , LiDAR point cloud , take drone B as the main body of the operation and obtain the lidar point cloud , UAV A and UAV B are centered on their respective inertial measurement units.

3. The multi-UAV collaborative positioning method according to claim 2, characterized in that: The posture status of drone A is as follows: The posture status of drone B is as follows: in, is the posture information, , is the quaternion of drone A, is the set of quaternions of x, y, z and the orientation of the drone in the rectangular coordinate system of the drone, is the position of drone B, , is the quaternion of drone B.

4. The multi-UAV collaborative positioning method according to claim 1, characterized in that: The dynamic correction cumulative error is specifically: Drone A moves periodically along the corridor axis, and the IMU pre-integration is performed through the angular velocity and acceleration Calculate the pose increment based on the LOAM algorithm for the laser point cloud Extract edge and plane features and build local maps , optimize the posture through the ICP algorithm to obtain the optimized posture information , generate dynamic reference node trajectory; Using dual-machine point cloud Local matching constructs relative pose constraints and extracts feature point sets in common areas , solve the relative pose; The accumulated error is dynamically corrected through tightly coupled factor graph optimization.

5. The multi-UAV collaborative positioning method according to claim 4, characterized in that: Pose increment: in, is the accelerometer / gyroscope bias, is the quaternion-to-rotation matrix, is the initial speed of UAV A, is the IMU acceleration measurement value of UAV A at time t, is the quaternion attitude of UAV A at time t.

6. The multi-UAV collaborative positioning method according to claim 4, characterized in that: Optimized pose information for: in, is the rotation matrix, is the translation vector, For local maps The coordinates of the nearest neighbor of the match.

7. The multi-UAV collaborative positioning method according to claim 4, characterized in that: Solving relative pose in, To optimize the B drone’s posture information, is the rotation matrix, is the translation vector.

8. The multi-UAV collaborative positioning method according to claim 4, characterized in that: The objective function for optimizing the tightly coupled factor graph is: in, is the pose node set of UAV B, is the laser odometry factor, is the IMU pre-integration factor, is the point cloud matching factor.

9. The multi-UAV collaborative positioning method according to claim 4, characterized in that: Assume that the laser SLAM front-end error of drone B is After the dual-machine collaborative correction, the corrected global positioning error variance is obtained ,when hour, , the error is effectively suppressed, and the global positioning error variance after correction is for: in, is the laser SLAM front-end positioning error variance.

10. A multi-UAV collaborative positioning system, characterized in that: include: Initialization module, defines the global coordinate system, with the mission starting point as the origin, the Z axis vertically upward, and the drone coordinate system centered on their respective inertial measurement units; The first control module controls the drone A to hover at the laser radar feature point in the indoor closed space; The second control module controls UAV B to receive the pose covariance matrix and local feature points of UAV A in real time; The error module fuses the laser SLAM output of drone B with the reference pose of drone A through a tightly coupled Kalman filter, dynamically corrects the accumulated error, and outputs the optimized pose and covariance matrix of drone B in the global coordinate system; The positioning module unifies the observations of UAV A and UAV B into the same coordinate system by sharing pose constraints, dynamically aligns local and global features, and realizes multi-UAV collaborative positioning.

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