Tunnel UAV positioning method, device and non-volatile storage medium

Abstract

This invention discloses a method, apparatus, and non-volatile storage medium for locating unmanned aerial vehicles (UAVs) in tunnels. The method includes: acquiring magnetic field vector data, radar point cloud data, angular velocity, and acceleration of the target UAV at its current position within the target tunnel; calculating the relative displacement observation of the target UAV relative to the previous moment based on the radar point cloud data and a preset reference geometric contour map; and determining the real-time pose of the target UAV at the current moment using an extended Kalman filter algorithm based on the magnetic field vector data, relative displacement observation, angular velocity, acceleration, and a preset geomagnetic reference map. This invention solves the technical problems of low accuracy and easy failure of traditional positioning methods in complex tunnel environments caused by the lack of satellite signals, dust interference, and magnetic field fluctuations.

Description

Technical Field

[0001] This invention relates to the field of autonomous navigation technology for unmanned aerial vehicles (UAVs), and more specifically, to a method, apparatus, and non-volatile storage medium for positioning UAVs in tunnels. Background Technology

[0002] As a critical infrastructure of urban power grids, power cable tunnels have long faced severe challenges in their inspection, including complex environments, signal loss, and severe interference. Because GPS signals are completely blocked inside tunnels, drones cannot rely on satellite navigation for positioning. Current autonomous positioning technologies have significant limitations in this scenario: laser SLAM is sensitive to dust and smoke, easily failing in tunnel environments with low visibility or high dust concentrations, and consumes significant computational resources; while UWB positioning offers high accuracy, it requires the pre-deployment of numerous base station facilities, resulting in high costs and poor flexibility, making it difficult to meet emergency inspection needs under sudden faults; visual and inertial navigation fusion technologies are prone to feature loss and lock-off in low-light and low-texture environments; and pure geomagnetic navigation is heavily affected by time-varying magnetic fields generated by changes in cable load current within the tunnel, leading to large fluctuations in positioning accuracy and insufficient stability. Furthermore, traditional solutions struggle to simultaneously address the cumulative drift problem during long-distance flight and the absolute positioning challenge in complex electromagnetic environments, limiting the autonomous operation capabilities of drones in all tunnel scenarios.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, and non-volatile storage medium for unmanned aerial vehicle (UAV) positioning in tunnels, at least to solve the technical problems of low accuracy and easy failure of traditional positioning methods caused by the lack of satellite signals, dust interference, and magnetic field fluctuations in complex tunnel environments.

[0005] According to one aspect of the present invention, a method for locating a UAV in a tunnel is provided, comprising: acquiring magnetic field vector data, radar point cloud data, angular velocity, and acceleration of a target UAV at its current position in a target tunnel; calculating the relative displacement observation of the target UAV relative to the previous moment relative to the current moment based on the radar point cloud data and a preset reference geometric contour map; and determining the real-time pose of the target UAV at the current moment by using an extended Kalman filter algorithm based on the magnetic field vector data, the relative displacement observation, the angular velocity, the acceleration, and a preset geomagnetic reference map.

[0006] Optionally, before acquiring the magnetic field vector data and radar point cloud data corresponding to the current position of the target UAV in the target tunnel, the method further includes: collecting geomagnetic data of the target tunnel through an inspection device, wherein the inspection device moves along the path of the target tunnel; performing spatial coordinate mapping and interpolation processing on the geomagnetic data to generate a geomagnetic reference map; and detecting the physical structural features inside the target tunnel through lidar or millimeter-wave radar to construct a reference geometric contour map.

[0007] Optionally, acquiring the magnetic field vector data and radar point cloud data corresponding to the current position of the target UAV in the target tunnel includes: acquiring the magnetic field vector data by using a quantum magnetometer array set at the bottom of the target UAV; and acquiring the radar point cloud data by scanning the inner wall of the target tunnel using millimeter-wave radars set at the front and sides of the target UAV.

[0008] Optionally, magnetic field vector data can be acquired by using a quantum magnetometer array mounted on the bottom of the target UAV, including: acquiring raw magnetic field data using the quantum magnetometer array; and performing multiple preprocessing operations on the raw magnetic field data to obtain magnetic field vector data, wherein the multiple preprocessing operations include temperature compensation, wavelet transform, and differential compensation.

[0009] Optionally, based on radar point cloud data and a preset reference geometric contour map, the relative displacement observation value of the target UAV relative to the previous moment relative to the current moment is calculated, including: extracting geometric feature points from the radar point cloud data; matching the geometric feature points with the reference geometric contour map through a point cloud registration algorithm to determine the position change of the target UAV as the relative displacement observation value.

[0010] Optionally, based on magnetic field vector data, relative displacement observations, angular velocity, and acceleration, the real-time pose of the target UAV at the current moment is determined using an extended Kalman filter algorithm. This includes: determining the predicted pose state and prediction covariance matrix at the current moment based on the pose state estimate, acceleration, and angular velocity at the previous moment; generating magnetic field observations and a magnetic field observation matrix based on the magnetic field vector data and a geomagnetic reference map; generating radar observations and a radar observation matrix based on the relative displacement observations; determining the observation noise covariance matrix according to the environmental quality in the target tunnel; calculating the Kalman gain based on the magnetic field observations, the magnetic field observation matrix, the radar observations, the radar observation matrix, and the observation noise covariance matrix; and updating the predicted pose state based on the Kalman gain to obtain the real-time pose.

[0011] According to another aspect of the present invention, a tunnel unmanned aerial vehicle (UAV) positioning device is also provided, comprising: an acquisition module, configured to acquire magnetic field vector data, radar point cloud data, angular velocity, and acceleration of a target UAV at its current position in a target tunnel; a calculation module, configured to calculate the relative displacement observation value of the target UAV relative to the previous moment relative to the current moment based on the radar point cloud data and a preset reference geometric contour map; and a determination module, configured to determine the real-time pose of the target UAV at the current moment based on the magnetic field vector data, the relative displacement observation value, the angular velocity, the acceleration, and a preset geomagnetic reference map, using an extended Kalman filter algorithm.

[0012] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, the device where the non-volatile storage medium is located is controlled to execute any of the above-described tunnel unmanned aerial vehicle positioning methods.

[0013] According to another aspect of the present invention, a computer device is also provided, the computer device including a processor, the processor being configured to run a program, wherein the program executes any of the tunnel unmanned aerial vehicle (UAV) positioning methods described above.

[0014] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements any of the above-described tunnel unmanned aerial vehicle (UAV) positioning methods.

[0015] In this embodiment of the invention, a tunnel UAV positioning method is adopted. This method acquires the magnetic field vector data, radar point cloud data, angular velocity, and acceleration of the target UAV at its current position within the target tunnel. Based on the radar point cloud data and a preset reference geometric contour map, the relative displacement observation value of the target UAV relative to the previous moment is calculated. Based on the magnetic field vector data, relative displacement observation value, angular velocity, acceleration, and a preset geomagnetic reference map, an extended Kalman filter algorithm is used to determine the real-time pose of the target UAV at the current moment. This achieves high-precision autonomous positioning within the tunnel, thus realizing robust, low-drift navigation without GPS dependence. Furthermore, it solves the technical problems of low accuracy and easy failure of traditional positioning methods caused by the lack of satellite signals, dust interference, and magnetic field fluctuations in complex tunnel environments. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 A hardware structure block diagram of a computer terminal for implementing a tunnel UAV positioning method is shown.

[0018] Figure 2 This is a flowchart illustrating the tunnel UAV positioning method provided according to an embodiment of the present invention;

[0019] Figure 3 This is an overall architecture diagram of an autonomous positioning system for a tunnel unmanned aerial vehicle (UAV) provided according to an optional embodiment of the present invention;

[0020] Figure 4 This is a structural block diagram of a tunnel unmanned aerial vehicle (UAV) positioning device provided according to an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] According to an embodiment of the present invention, a method for locating a tunnel unmanned aerial vehicle is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0024] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a tunnel UAV positioning method is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0025] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0026] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the tunnel UAV positioning method in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the tunnel UAV positioning method of the aforementioned application. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0027] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0028] Figure 2 This is a flowchart illustrating the tunnel UAV positioning method provided by an embodiment of the present invention, as shown below. Figure 2As shown, the method includes the following steps:

[0029] Step S201: Obtain the magnetic field vector data, radar point cloud data, angular velocity, and acceleration of the target UAV at its current position in the target tunnel.

[0030] In this step, the quantum magnetometer array located at the bottom of the UAV is responsible for capturing the three-dimensional magnetic field vector data of the current position in real time. Based on the diamond NV color center principle, it has high accuracy better than 1nT and strong anti-electromagnetic interference capability. It can effectively sense the subtle magnetic field changes generated by cable current in the tunnel and perform preprocessing such as temperature compensation, wavelet transform, and differential compensation to eliminate noise and drift. At the same time, 77GHz millimeter-wave radar deployed in the forward and lateral directions of the UAV scans the inner wall of the tunnel, penetrating dust and smoke to obtain high-precision radar point cloud data. This data is used to extract geometric features and register with a preset reference geometric contour map to calculate the relative displacement observation value of the UAV relative to the previous moment. In addition, the inertial measurement unit synchronously outputs the angular velocity and acceleration data of the current moment as an important input for the kinematic state. These four types of data—magnetic field vector, radar point cloud, angular velocity, and acceleration—together constitute the multi-source heterogeneous data foundation for the subsequent extended Kalman filter algorithm for state prediction, observation update, and real-time pose calculation, ensuring high-precision and robust autonomous positioning in the highly dynamic, weakly textured, and strongly interfered tunnel environment.

[0031] Step S202: Based on radar point cloud data and a preset baseline geometric contour map, calculate the relative displacement observation value of the target UAV relative to the previous moment relative to the current moment.

[0032] In this step, firstly, identifiable geometric feature points are extracted from the real-time acquired radar point cloud data. Then, a point cloud registration algorithm is used to spatially align and match these real-time feature points with the baseline geometric contour map constructed in the offline stage based on LiDAR or millimeter-wave radar. By calculating the optimal four-way transformation matrix between the real-time point cloud and the map, the change in position of the target UAV relative to the previous moment can be accurately determined; this change is defined as the relative displacement observation. This process not only leverages the advantage of millimeter-wave radar in penetrating dust and smoke to overcome visual failure issues but also constrains the cumulative errors that may arise from inertial navigation through map matching. Under the extended Kalman filter framework, this relative displacement observation serves as a key observation input, working together with magnetic field vector data to correct the state prediction value, thereby achieving high-precision and robust autonomous positioning of the UAV in harsh tunnel scenarios where GPS is denied.

[0033] Step S203: Based on magnetic field vector data, relative displacement observations, angular velocity, acceleration, and a preset geomagnetic reference map, the real-time pose of the target UAV at the current moment is determined using an extended Kalman filter algorithm.

[0034] In this step, firstly, the pose state estimate from the previous moment is combined with the angular velocity and acceleration collected at the current moment to predict the pose state and its predicted covariance matrix. Secondly, an observation model is constructed. The magnetic field vector data acquired by the quantum magnetometer array and preprocessed with temperature compensation, wavelet transform, and differential compensation is matched with a preset geomagnetic reference map to generate magnetic field observation values ​​and corresponding magnetic field observation matrices. Simultaneously, geometric feature points in the millimeter-wave radar point cloud are extracted and registered with a reference geometric contour map to calculate relative displacement observation values ​​and their observation matrices. Subsequently, the sensor confidence level is dynamically assessed based on the environmental quality within the tunnel to determine the observation noise covariance matrix, and then the Kalman gain is calculated by combining the magnetic field and radar observation matrices. Finally, the Kalman gain is used to correct and update the pose state prediction values, thereby outputting a high-precision real-time pose. This process, by adaptively adjusting the observation noise covariance matrix, effectively suppresses the impact of electromagnetic interference and dust within the tunnel on positioning accuracy, achieving highly robust autonomous positioning without dependence on external facilities.

[0035] Through the above steps, the goal of high-precision autonomous positioning within the tunnel was achieved, thus realizing the technical effect of robust and low-drift navigation without GPS dependence. This solves the technical problems of low accuracy and easy failure of traditional positioning methods caused by the lack of satellite signals, dust interference, and magnetic field fluctuations in complex tunnel environments.

[0036] As an optional embodiment, before acquiring the magnetic field vector data and radar point cloud data corresponding to the current position of the target UAV in the target tunnel, the method further includes: collecting geomagnetic data of the target tunnel through an inspection device, wherein the inspection device moves along the path of the target tunnel; performing spatial coordinate mapping and interpolation processing on the geomagnetic data to generate a geomagnetic reference map; and detecting the physical structural features inside the target tunnel through lidar or millimeter-wave radar to construct a reference geometric contour map.

[0037] Optionally, an inspection device equipped with high-precision sensors moves along the target tunnel path to collect geomagnetic data along the way. The collected geomagnetic data is standardized using spatial coordinate mapping and interpolation algorithms to eliminate coordinate discrepancies and fill in sparse areas, thereby generating a geomagnetic reference map containing the three-dimensional magnetic field vector distribution at each location. This provides a magnetic field reference for subsequent real-time positioning. Simultaneously, lidar or millimeter-wave radar scans the tunnel walls to detect internal physical structural features such as sidewall contours and support structures. A high-precision reference geometric contour map is constructed using point cloud processing technology, serving as a priori environmental model for the UAV's visual or radar odometry calculations. This offline mapping process does not rely on external facilities. By pre-storing the geomagnetic reference map and geometric contour map, the UAV can achieve high-precision autonomous positioning during subsequent inspections by combining real-time sensor data, effectively solving the positioning challenges caused by the lack of GPS signals and complex environments within tunnels.

[0038] As an optional embodiment, acquiring the magnetic field vector data and radar point cloud data corresponding to the current position of the target UAV in the target tunnel includes: acquiring the magnetic field vector data by using a quantum magnetometer array set at the bottom of the target UAV; and acquiring the radar point cloud data by scanning the inner wall of the target tunnel using millimeter-wave radars set at the front and sides of the target UAV.

[0039] Optionally, firstly, in terms of hardware layout, the quantum magnetometer array is fixedly installed on the bottom of the target drone. This arrangement aims to ensure that the sensor can stably detect the magnetic field of the tunnel environment directly below the drone, thereby acquiring high-precision three-dimensional magnetic field vector data, providing a core reference benchmark for subsequent geomagnetic positioning. Secondly, millimeter-wave radar modules are deployed at the forward and lateral positions of the target drone. The forward radar monitors the environment in the direction the drone is moving, while the lateral radar monitors the environment to the side. The two work together to achieve a comprehensive, blind-spot-free scan of the tunnel wall. By emitting high-frequency electromagnetic waves and receiving reflected signals, the millimeter-wave radar can penetrate harsh media such as dust and water mist to directly acquire high-precision point cloud data of the tunnel wall. This specific sensor installation position combined with the scanning method not only ensures the continuity and integrity of data sampling but also effectively overcomes the blind spot problem caused by a single viewpoint, providing high-quality, multi-dimensional raw input data for subsequent calculation of relative displacement observations based on point cloud registration and multi-source data fusion through extended Kalman filtering.

[0040] As an optional embodiment, magnetic field vector data is acquired by using a quantum magnetometer array installed at the bottom of the target drone, including: acquiring raw magnetic field data through the quantum magnetometer array; performing multiple preprocessing operations on the raw magnetic field data to obtain magnetic field vector data, wherein the multiple preprocessing operations include temperature compensation, wavelet transform and differential compensation.

[0041] Optionally, firstly, a quantum magnetometer array deployed on the bottom of the UAV is used to capture raw magnetic field data in real time. This array, based on the diamond nitrogen-vacancy color center principle, possesses high sensitivity and resistance to electromagnetic interference. Subsequently, the acquired raw data undergoes a series of key preprocessing steps to eliminate noise interference and extract effective features. Temperature compensation is used to eliminate sensor drift caused by changes in ambient temperature, ensuring the stability of the measurement benchmark. Wavelet transform is used to filter out high-frequency random noise, retaining geomagnetic signals reflecting the tunnel's structural characteristics. Differential compensation addresses the time-varying background magnetic field generated by changes in cable current within the tunnel, dynamically canceling it by establishing a background magnetic field model, thereby separating the static geomagnetic features used for positioning. The raw data after these three processes is finally transformed into high-precision magnetic field vector data, providing reliable observation input for the subsequent extended Kalman filter algorithm.

[0042] In addition, a differential compensation algorithm can be used to address magnetic field fluctuations caused by changes in cable current within the tunnel.

[0043] Establish background magnetic field model Includes the time-varying component of the cable current, real-time measured values. By differencing the background model, the fixed geomagnetic component unaffected by the cable current is extracted.

[0044]

[0045] The cable current variation pattern is estimated using a Kalman filter to further eliminate residual interference.

[0046] As an optional embodiment, based on radar point cloud data and a preset reference geometric contour map, the relative displacement observation value of the target UAV relative to the previous moment is calculated, including: extracting geometric feature points from the radar point cloud data; matching the geometric feature points with the reference geometric contour map through a point cloud registration algorithm to determine the position change of the target UAV as the relative displacement observation value.

[0047] Optionally, the system first identifies and extracts representative geometric feature points from real-time acquired radar point cloud data. These feature points reflect the structural morphology of the tunnel wall. Then, a point cloud registration algorithm is used to spatially align and match the extracted feature points with a baseline geometric contour map constructed in the offline phase. The baseline geometric contour map is a high-precision map generated by processing physical structural features collected within the tunnel by inspection equipment, serving as a reference for positioning. The registration algorithm calculates the optimal spatial transformation relationship between the current feature point cloud and the baseline map, thereby determining the change in the target UAV's position in two-dimensional space from the previous moment to the current moment.

[0048] For example, matching real-time radar point clouds with geometric contour maps, or using inter-frame matching to obtain displacement increments. The observation equation is:

[0049]

[0050] Where k is the discrete-time index (current time); This is the radar observation vector (i.e., the relative displacement measurement value obtained through point cloud matching). To obtain the relative displacement observations (i.e., position changes) calculated through point cloud registration, and... (same value) and These are the pose state vectors (containing three-dimensional position and attitude angle information) of the target UAV at the current time and the previous time, respectively. This is the radar observation matrix (the Jacobian matrix used to map the state vector to the radar observation space). This is radar observation noise (usually assumed to be zero-mean Gaussian white noise).

[0051] Change in position Defined as relative displacement observations, these provide high-precision constraint information for the short-term motion of the UAV, supplementing the accumulated errors of the inertial navigation system. This process effectively utilizes the ability of millimeter-wave radar to penetrate dust and smoke, enabling stable extraction of structural features even in environments with weak textures or without GPS. This provides reliable observation input for subsequent extended Kalman filter fusion algorithms, ensuring the robustness and accuracy of the positioning system.

[0052] As an optional implementation, based on magnetic field vector data, relative displacement observations, angular velocity, and acceleration, the real-time pose of the target UAV at the current moment is determined using an extended Kalman filter algorithm. This includes: determining the predicted pose state and prediction covariance matrix at the current moment based on the pose state estimate, acceleration, and angular velocity at the previous moment; generating magnetic field observations and a magnetic field observation matrix based on the magnetic field vector data and a geomagnetic reference map; generating radar observations and a radar observation matrix based on the relative displacement observations; determining the observation noise covariance matrix according to the environmental quality in the target tunnel; calculating the Kalman gain based on the magnetic field observations, magnetic field observation matrix, radar observations, radar observation matrix, and observation noise covariance matrix; and updating the predicted pose state based on the Kalman gain to obtain the real-time pose.

[0053] Optionally, firstly, the algorithm calculates the predicted pose state and prediction covariance matrix at the current moment based on the pose state estimate from the previous moment combined with the angular velocity and acceleration at the current moment through a kinematic model, using this as a priori estimate. Secondly, two observation models are constructed: magnetic field vector data obtained by a quantum magnetometer is compared with a pre-constructed geomagnetic reference map to generate magnetic field observation values ​​and corresponding magnetic field observation matrices; simultaneously, radar observation values ​​and radar observation matrices are generated based on relative displacement observation values ​​calculated from radar point cloud data to provide relative position constraints. Considering the impact of environmental factors such as dust concentration and electromagnetic interference in the tunnel on sensor accuracy, the algorithm dynamically determines the observation noise covariance matrix based on real-time environmental quality, thereby adaptively adjusting the confidence levels of each sensor. Subsequently, the magnetic field and radar observation values, observation matrices, and noise covariance matrices are substituted into the extended Kalman filter formula to calculate the Kalman gain. Finally, this gain is used to correct and update the predicted pose state, thereby outputting a high-precision, highly robust real-time pose, effectively suppressing inertial drift and overcoming the limitations of a single sensor in complex tunnel environments.

[0054] State prediction (based on IMU):

[0055]

[0056]

[0057] in, For discrete-time index (current time); The pose state prediction value is obtained by predicting the pose state at the current time based on the pose state estimation value at the previous time. This is the state transition function (based on the inertial kinematics model, the pose change is deduced from angular velocity and acceleration); This is the estimated pose state value from the previous moment; The current control input vector (containing angular velocity and acceleration data); To predict the covariance matrix (characterizing the uncertainty of the predicted state values); State transition matrix (state transition function) (The Jacobian matrix of the state vector). Let be the covariance matrix of the previous time step; This is the process noise covariance matrix (characterizing the uncertainty of the motion model itself).

[0058] Real-time measurement of the three-dimensional magnetic field vector Location observations are obtained by matching with geomagnetic reference maps and using particle filtering or nearest neighbor algorithms. The observation equation is:

[0059]

[0060] in, For real-time measurement of three-dimensional magnetic field vectors (including Magnetic field strength components in three directions); The magnetic field observation vector (the estimated position obtained by matching the geomagnetic reference map) ); This is the magnetic field observation matrix (the Jacobian matrix used to map the state vector to the geomagnetic observation space). This refers to magnetic field observation noise (reflecting the measurement uncertainty caused by magnetic field fluctuations during geomagnetic matching).

[0061] Status Update:

[0062]

[0063]

[0064]

[0065] in, Kalman gain (used to balance the reliability of state predictions versus observations); The joint observation matrix (maps the state vector to the joint observation space, derived from the magnetic field observation matrix) and radar observation matrix (Stacked structure) The noise covariance matrix is ​​observed (dynamically adjusted according to the environmental quality in the target tunnel, used to reflect the confidence level of each sensor at the current moment). The joint observation vector (composed of magnetic field observations) and radar observations Composed of various elements) It is the identity matrix; This is the updated estimated pose state at the current moment; This is the updated covariance matrix.

[0066] The confidence levels of each sensor can be assessed based on the current environmental quality, and the fusion weights can be dynamically adjusted: when there is strong magnetic field interference (such as sudden changes in cable current), the observation noise can be increased. Reduce magnetic navigation weight; when dust concentration is high, appropriately increase... (But the radar is still usable); when there are many tunnel feature points, reduce Increase the weight of radar odometers.

[0067] As an optional embodiment, an autonomous positioning system for tunnel unmanned aerial vehicles based on the fusion of quantum magnetic navigation and millimeter-wave radar is also provided. Figure 3This is an overall architecture diagram of an autonomous positioning system for a tunnel unmanned aerial vehicle (UAV) according to an optional embodiment of the present invention, such as... Figure 3 As shown, it includes:

[0068] The drone flight platform, equipped with various sensors and computing units, is used to perform tunnel inspection tasks.

[0069] The quantum magnetic navigation module, installed on the bottom of the drone, includes: a quantum magnetometer array (based on the NV color center principle) that measures the three-dimensional magnetic field vector in the tunnel in real time with a measurement accuracy better than 1nT; and a magnetic field preprocessing unit that filters, denoises, and compensates for the temperature of the raw magnetic field data.

[0070] The millimeter-wave radar module, installed in the forward and lateral directions of the UAV, operates at a frequency of 77GHz, has a ranging accuracy of ±2cm, and can penetrate dust and water mist to scan the inner wall of tunnels to obtain point cloud data.

[0071] Inertial measurement unit (IMU) measures the angular velocity and acceleration of the UAV.

[0072] The fusion positioning processor receives the aforementioned multi-source data, runs a hierarchical fusion positioning algorithm, and outputs the real-time pose of the UAV.

[0073] The storage and communication unit stores geomagnetic reference maps and inspection data, and can communicate with ground stations.

[0074] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0075] Through the above description of the embodiments, those skilled in the art can clearly understand that the tunnel UAV positioning method according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0076] According to embodiments of the present invention, an apparatus for implementing the above-described tunnel UAV positioning method is also provided. Figure 4This is a structural block diagram of a tunnel unmanned aerial vehicle (UAV) positioning device provided according to an embodiment of the present invention, such as... Figure 4 As shown, the device includes an acquisition module 41, a calculation module 42, and a determination module 43. The device will be described below.

[0077] The acquisition module 41 is used to acquire the magnetic field vector data, radar point cloud data, angular velocity, and acceleration of the target UAV at the current position in the target tunnel.

[0078] The calculation module 42, connected to the acquisition module 41, is used to calculate the relative displacement observation value of the target UAV relative to the previous moment relative to the current moment, based on radar point cloud data and a preset benchmark geometric contour map.

[0079] The determination module 43, connected to the calculation module 42, is used to determine the real-time pose of the target UAV at the current moment based on magnetic field vector data, relative displacement observations, angular velocity, acceleration, and a preset geomagnetic reference map, using an extended Kalman filter algorithm.

[0080] It should be noted that the acquisition module 41, calculation module 42, and determination module 43 mentioned above correspond to steps S201 to S203 in the embodiments. Multiple modules implement the same instances and application scenarios as their corresponding steps, but are not limited to the content disclosed in the above embodiments. It should also be noted that the above modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0081] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0082] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the tunnel UAV positioning method and device in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned tunnel UAV positioning method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0083] The processor can access the information and application programs stored in the memory via the transmission device to perform the following steps: acquire the magnetic field vector data, radar point cloud data, angular velocity, and acceleration of the target UAV at its current position in the target tunnel; calculate the relative displacement observation value of the target UAV relative to the previous moment relative to the current moment based on the radar point cloud data and a preset reference geometric contour map; and determine the real-time pose of the target UAV at the current moment using an extended Kalman filter algorithm based on the magnetic field vector data, relative displacement observation value, angular velocity, acceleration, and a preset geomagnetic reference map.

[0084] Optionally, the processor may also execute program code for the following steps: before acquiring the magnetic field vector data and radar point cloud data corresponding to the target UAV at its current position in the target tunnel, the processor further includes: collecting geomagnetic data of the target tunnel through an inspection device, wherein the inspection device moves along the path of the target tunnel; performing spatial coordinate mapping and interpolation processing on the geomagnetic data to generate a geomagnetic reference map; and detecting the physical structural features inside the target tunnel through lidar or millimeter-wave radar to construct a reference geometric contour map.

[0085] Optionally, the processor may also execute program code for the following steps: acquiring magnetic field vector data and radar point cloud data corresponding to the current position of the target UAV in the target tunnel, including: acquiring magnetic field vector data by means of a quantum magnetometer array set at the bottom of the target UAV; and acquiring radar point cloud data by means of millimeter-wave radars set at the front and sides of the target UAV to scan the inner wall of the target tunnel.

[0086] Optionally, the processor may also execute program code for the following steps: acquiring magnetic field vector data through a quantum magnetometer array located at the bottom of the target UAV, including: acquiring raw magnetic field data through the quantum magnetometer array; performing multiple preprocessing operations on the raw magnetic field data to obtain magnetic field vector data, wherein the multiple preprocessing operations include temperature compensation, wavelet transform, and differential compensation.

[0087] Optionally, the processor may also execute program code for the following steps: based on radar point cloud data and a preset reference geometric contour map, calculate the relative displacement observation value of the target UAV relative to the previous moment relative to the current moment, including: extracting geometric feature points from the radar point cloud data; matching the geometric feature points with the reference geometric contour map through a point cloud registration algorithm to determine the position change of the target UAV as the relative displacement observation value.

[0088] Optionally, the processor may also execute program code for the following steps: Based on magnetic field vector data, relative displacement observations, angular velocity, and acceleration, determine the real-time pose of the target UAV at the current moment using an extended Kalman filter algorithm, including: determining the predicted pose state and prediction covariance matrix at the current moment based on the pose state estimate, acceleration, and angular velocity at the previous moment; generating magnetic field observations and a magnetic field observation matrix based on magnetic field vector data and a geomagnetic reference map; generating radar observations and a radar observation matrix based on relative displacement observations; determining the observation noise covariance matrix according to the environmental quality in the target tunnel; calculating the Kalman gain based on the magnetic field observations, magnetic field observation matrix, radar observations, radar observation matrix, and observation noise covariance matrix; and updating the predicted pose state based on the Kalman gain to obtain the real-time pose.

[0089] This invention provides a method for locating unmanned aerial vehicles (UAVs) in tunnels. By acquiring the magnetic field vector data, radar point cloud data, angular velocity, and acceleration of the UAV at its current position within the target tunnel, and based on the radar point cloud data and a pre-set reference geometric contour map, the relative displacement observation of the UAV relative to the previous moment is calculated. Using the magnetic field vector data, relative displacement observation, angular velocity, acceleration, and a pre-set geomagnetic reference map, an extended Kalman filter algorithm is employed to determine the real-time pose of the UAV at the current moment. This achieves high-precision autonomous positioning within the tunnel, realizing robust, low-drift navigation without GPS dependence. Furthermore, it solves the technical problems of low accuracy and easy failure of traditional positioning methods in complex tunnel environments caused by the lack of satellite signals, dust interference, and magnetic field fluctuations.

[0090] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0091] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the tunnel UAV positioning method provided in the above embodiments.

[0092] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0093] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring magnetic field vector data, radar point cloud data, angular velocity, and acceleration of the target UAV at its current position in the target tunnel; calculating the relative displacement observation value of the target UAV relative to the previous moment relative to the current moment based on the radar point cloud data and a preset reference geometric contour map; and determining the real-time pose of the target UAV at the current moment using an extended Kalman filter algorithm based on the magnetic field vector data, relative displacement observation value, angular velocity, acceleration, and a preset geomagnetic reference map.

[0094] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: before acquiring the magnetic field vector data and radar point cloud data corresponding to the current position of the target UAV in the target tunnel, the method further includes: collecting geomagnetic data of the target tunnel through an inspection device, wherein the inspection device moves along the path of the target tunnel; performing spatial coordinate mapping and interpolation processing on the geomagnetic data to generate a geomagnetic reference map; and detecting the physical structural features inside the target tunnel through lidar or millimeter-wave radar to construct a reference geometric contour map.

[0095] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring magnetic field vector data and radar point cloud data corresponding to the current position of the target UAV in the target tunnel, including: acquiring magnetic field vector data by means of a quantum magnetometer array set at the bottom of the target UAV; and acquiring radar point cloud data by means of millimeter-wave radars set at the front and sides of the target UAV to scan the inner wall of the target tunnel.

[0096] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring magnetic field vector data through a quantum magnetometer array located at the bottom of the target drone, including: acquiring raw magnetic field data through the quantum magnetometer array; performing multiple preprocessing operations on the raw magnetic field data to obtain magnetic field vector data, wherein the multiple preprocessing operations include temperature compensation, wavelet transform, and differential compensation.

[0097] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: calculating the relative displacement observation value of the target UAV relative to the previous moment relative to the current moment based on radar point cloud data and a preset reference geometric contour map, including: extracting geometric feature points in the radar point cloud data; matching the geometric feature points with the reference geometric contour map through a point cloud registration algorithm to determine the position change of the target UAV as the relative displacement observation value.

[0098] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining the real-time pose of the target UAV at the current moment based on magnetic field vector data, relative displacement observations, angular velocity, and acceleration using an extended Kalman filter algorithm, including: determining the predicted pose state and prediction covariance matrix at the current moment based on the pose state estimate, acceleration, and angular velocity at the previous moment; generating magnetic field observations and a magnetic field observation matrix based on magnetic field vector data and a geomagnetic reference map; generating radar observations and a radar observation matrix based on relative displacement observations; determining the observation noise covariance matrix according to the environmental quality in the target tunnel; calculating the Kalman gain based on the magnetic field observations, the magnetic field observation matrix, the radar observations, the radar observation matrix, and the observation noise covariance matrix; and updating the predicted pose state based on the Kalman gain to obtain the real-time pose.

[0099] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: acquire magnetic field vector data, radar point cloud data, angular velocity, and acceleration of a target UAV at its current position in a target tunnel; calculate the relative displacement observation value of the target UAV relative to the previous moment relative to the current moment based on the radar point cloud data and a preset reference geometric contour map; and determine the real-time pose of the target UAV at the current moment using an extended Kalman filter algorithm based on the magnetic field vector data, relative displacement observation value, angular velocity, acceleration, and a preset geomagnetic reference map.

[0100] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0101] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0102] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0103] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0104] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0105] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product 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 described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0106] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for locating unmanned aerial vehicles (UAVs) in tunnels, characterized in that, include: Acquire the magnetic field vector data of the target UAV at its current position in the target tunnel, the radar point cloud data at the current position, the angular velocity at the current moment, and the acceleration at the current moment; Based on the radar point cloud data and the preset benchmark geometric contour map, the relative displacement observation value of the target UAV relative to the previous moment relative to the current moment is calculated. Based on the magnetic field vector data, the relative displacement observations, the angular velocity, the acceleration, and the preset geomagnetic reference map, the real-time pose of the target UAV at the current moment is determined by the extended Kalman filter algorithm.

2. The method according to claim 1, characterized in that, Before acquiring the magnetic field vector data corresponding to the current position of the target UAV in the target tunnel and the radar point cloud data corresponding to the current position, the method further includes: The geomagnetic data of the target tunnel is collected by the inspection equipment, wherein the inspection equipment moves along the path of the target tunnel; The geomagnetic data is subjected to spatial coordinate mapping and interpolation to generate the geomagnetic reference map; The physical structural features inside the target tunnel are detected using lidar or millimeter-wave radar, and the baseline geometric contour map is constructed.

3. The method according to claim 1, characterized in that, The acquisition of the magnetic field vector data and radar point cloud data corresponding to the current position of the target UAV in the target tunnel includes: The magnetic field vector data is obtained by using a quantum magnetometer array installed on the bottom of the target drone; By using millimeter-wave radars positioned in front of and to the sides of the target UAV, the inner walls of the target tunnel are scanned to obtain radar point cloud data.

4. The method according to claim 3, characterized in that, The acquisition of the magnetic field vector data by means of a quantum magnetometer array installed on the bottom of the target drone includes: Raw magnetic field data is collected using the quantum magnetometer array. The original magnetic field data is subjected to multiple preprocessing operations to obtain the magnetic field vector data. The multiple preprocessing operations include temperature compensation, wavelet transform, and differential compensation.

5. The method according to claim 1, characterized in that, The calculation of the relative displacement observation value of the target UAV relative to the previous moment relative to the current moment, based on the radar point cloud data and the preset reference geometric contour map, includes: Extract geometric feature points from the radar point cloud data; By using a point cloud registration algorithm, the geometric feature points are matched with the reference geometric contour map to determine the position change of the target UAV, which is then used as the relative displacement observation value.

6. The method according to claim 1, characterized in that, The process of determining the real-time pose of the target UAV at the current moment using an extended Kalman filter algorithm based on the magnetic field vector data, the relative displacement observations, the angular velocity, and the acceleration includes: Based on the pose state estimate, acceleration, and angular velocity at the previous moment, determine the pose state prediction and prediction covariance matrix at the current moment. Based on the magnetic field vector data and the geomagnetic reference map, magnetic field observation values ​​and a magnetic field observation matrix are generated. Based on the relative displacement observations, radar observations and a radar observation matrix are generated; The observation noise covariance matrix is ​​determined based on the environmental quality in the target tunnel. The Kalman gain is calculated based on the magnetic field observations, the magnetic field observation matrix, the radar observations, the radar observation matrix, and the observation noise covariance matrix. Based on the Kalman gain, the predicted pose state is updated to obtain the real-time pose.

7. A tunnel unmanned aerial vehicle (UAV) positioning device, characterized in that, include: The acquisition module is used to acquire the magnetic field vector data of the target UAV at its current position in the target tunnel, the radar point cloud data at the current position, the angular velocity at the current moment, and the acceleration at the current moment. The calculation module is used to calculate the relative displacement observation value of the target UAV relative to the previous time relative to the current time, based on the radar point cloud data and the preset reference geometric contour map. The determination module is used to determine the real-time pose of the target UAV at the current moment based on the magnetic field vector data, the relative displacement observation value, the angular velocity, the acceleration, and the preset geomagnetic reference map, using an extended Kalman filter algorithm.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the tunnel UAV positioning method according to any one of claims 1 to 6.

9. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the tunnel UAV positioning method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the tunnel UAV positioning method according to any one of claims 1 to 6.

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