Low-altitude unmanned aerial vehicle dynamic visual positioning method and device under ultra wide band

Abstract

The invention discloses a low-altitude unmanned aerial vehicle dynamic visual positioning method and device under an ultra wide band, and relates to the technical field of unmanned aerial vehicle positioning and navigation.The method comprises the steps that UWB anchor points are arranged in an operation area, a low-altitude unmanned aerial vehicle calibration operation track is obtained, and an anchor point set is configured through signal fitting; when the unmanned aerial vehicle works along the track, the anchor point group is activated according to the front authentication track point; receiving a UWB signal and carrying out abnormity identification; if the threshold value is triggered, activating a visual acquisition device, and obtaining IMU data; visual key feature points are extracted, the dynamic fusion network is initialized, the pose is corrected based on multi-source data linkage, and a linkage pose correction result is generated. The technical problems that in non-electrical variable control and adjustment of the low-altitude unmanned aerial vehicle pose in the prior art, due to the complex environment, positioning deviation and failure are prone to occurring, and the requirement for precise operation is difficult to meet are solved, precise positioning of the low-altitude unmanned aerial vehicle pose is achieved, precise positioning in the complex environment is achieved, the failure is avoided, and the working efficiency is improved. And the technical effect of precise operation requirements is met.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of unmanned aerial vehicle positioning and navigation, in particular to a low-altitude unmanned aerial vehicle dynamic visual positioning method and device under ultra-wide band. BACKGROUND

[0002] In low-altitude unmanned aerial vehicle operation, accurate control and adjustment of non-electric variables such as pose are crucial to flight safety and task execution effect. Existing technologies mostly rely on single or conventional fusion positioning methods. In complex environments, it is difficult to ensure positioning accuracy and stability in the face of signal interference and scene dynamic changes. When the traditional scheme is used for low-altitude unmanned aerial vehicle positioning, positioning deviation and failure are prone to occur due to complex environments such as signal shielding and variable motion states, which cannot meet the requirements of accurate operation. SUMMARY

[0003] The application provides a low-altitude unmanned aerial vehicle dynamic visual positioning method and device under ultra-wide band, which is used to solve the technical problem that existing technologies are prone to positioning deviation and failure in the control and adjustment of non-electric variables of low-altitude unmanned aerial vehicle pose due to complex environments, and cannot meet the requirements of accurate operation.

[0004] In a first aspect, the application provides a low-altitude unmanned aerial vehicle dynamic visual positioning method under ultra-wide band, which comprises the following steps: arranging UWB anchor points in an operation area, and obtaining a calibration operation trajectory of a low-altitude unmanned aerial vehicle; performing signal fitting according to the calibration operation trajectory and the distribution of the UWB anchor points, and configuring an anchor point group mapped with the calibration operation trajectory; when the low-altitude unmanned aerial vehicle executes an operation task along the calibration operation trajectory, performing anchor point group activation configuration according to a pre-authorization trajectory point of the low-altitude unmanned aerial vehicle; receiving UWB signals emitted by the low-altitude unmanned aerial vehicle by using the activated anchor point group, and performing abnormal identification of the UWB signals in the anchor point group; if the abnormal identification result triggers a preset abnormal threshold, activating a visual acquisition device of the low-altitude unmanned aerial vehicle to capture environmental features, and obtaining a motion data set of an IMU sensor; after extracting key feature points from the visual acquisition result, initializing a dynamic fusion network by using the pre-authorization trajectory point, performing linkage pose correction based on the key feature points, the motion data set and the UWB signals, and generating a linkage pose correction result.

[0005] In a second aspect of the present application, a low-altitude unmanned aerial vehicle dynamic visual positioning device under ultra-wideband is provided, the device comprising: an anchor point group configuration module, configured to lay UWB anchor points in a work area, and to obtain a calibration work trajectory of a low-altitude unmanned aerial vehicle, to perform signal fitting according to the calibration work trajectory and UWB anchor point distribution, and to configure an anchor point group mapped with the calibration work trajectory; an anchor point group activation module, configured to perform anchor point group activation configuration according to a pre-authorization trajectory point of the low-altitude unmanned aerial vehicle when the low-altitude unmanned aerial vehicle performs a work task along the calibration work trajectory; an anchor point group anomaly identification module, configured to receive UWB signals transmitted by the low-altitude unmanned aerial vehicle by using the activated anchor point group, and to perform anomaly identification of the UWB signals in the anchor point group; a motion data set acquisition module, configured to activate a visual acquisition device of the low-altitude unmanned aerial vehicle to capture environmental features and to acquire a motion data set of an IMU sensor if the anomaly identification result triggers a preset anomaly threshold; and a linkage pose correction result acquisition module, configured to initialize a dynamic fusion network by using the pre-authorization trajectory point after extracting key feature points from the visual acquisition result, to perform linkage pose correction based on the key feature points, the motion data set, and the UWB signals, and to generate a linkage pose correction result.

[0006] The one or more technical solutions provided in the present application have at least the following technical effects or advantages: The present application realizes precise control and adjustment of the unmanned aerial vehicle pose, a non-electric variable, by laying UWB anchor points in a low-altitude work scene, using a dynamic fusion network, performing trajectory fitting, feature extraction, multi-source data linkage pose correction, and the like, in combination with anchor point group dynamic management and anomaly compensation mechanisms, so that the pose positioning of the low-altitude unmanned aerial vehicle is more precise and reliable in a complex environment, the demand for non-electric variable control and adjustment in low-altitude work is met, precise positioning of the low-altitude unmanned aerial vehicle pose is achieved, precise positioning in a complex environment is realized and failure is avoided, and the technical effect of meeting the demand for precise work is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0008] Figure 1 is a flowchart of a low-altitude unmanned aerial vehicle dynamic visual positioning method under ultra-wideband provided by the embodiments of the present application.

[0009] Figure 2 is a structural schematic diagram of a low-altitude unmanned aerial vehicle dynamic visual positioning device under ultra-wideband provided by the embodiments of the present application.

[0010] The reference signs are explained as follows: anchor group configuration module 1, anchor group activation module 2, anchor group anomaly identification module 3, motion data set acquisition module 4, and linkage pose correction result acquisition module 5. DETAILED DESCRIPTION

[0011] The application provides a low-altitude unmanned aerial vehicle dynamic visual positioning method and device under an ultra-wideband, and aims to solve the technical problem that in the non-electric variable control adjustment of the low-altitude unmanned aerial vehicle pose, positioning deviation and failure are prone to occur due to complex environment, and it is difficult to meet the precise operation demand.

[0012] The technical solutions in the embodiments of the application will be clearly and completely described in connection with the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0013] It should be noted that the terms "first", "second", and the like in the specification and the above drawings of the application are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules not clearly listed or inherent to these processes, methods, products or devices.

[0014] Embodiment one, as shown in the low-altitude unmanned aerial vehicle dynamic visual positioning method under the ultra-wideband, wherein the method comprises: Figure 1 Step A100: arranging UWB anchors in the operation area, and acquiring a calibration operation trajectory of the low-altitude unmanned aerial vehicle, fitting signals according to the calibration operation trajectory and the UWB anchor distribution, and configuring anchor groups mapped with the calibration operation trajectory. Step A100: arranging UWB anchors in the operation area, and acquiring a calibration operation trajectory of the low-altitude unmanned aerial vehicle, fitting signals according to the calibration operation trajectory and the UWB anchor distribution, and configuring anchor groups mapped with the calibration operation trajectory.

[0015] In the embodiments of the application, the UWB anchor is a fixed reference point arranged in the operation area, which is used for UWB signal interaction with the low-altitude unmanned aerial vehicle. The calibration operation trajectory is a low-altitude unmanned aerial vehicle operation trajectory preset by those skilled in the art.

[0016] Specifically, when deploying UWB anchors in the work area, those skilled in the art need to determine the number and position of anchors according to the geometric shape and spatial scale of the area. For example, 5 anchors are deployed at the corners and center of a rectangular work area with a length of 50 meters and a width of 30 meters to form a distributed positioning network. After the anchors are deployed, the low-altitude unmanned aerial vehicle is controlled to fly along the preset calibration work trajectory. Assuming that the trajectory contains 100 equidistant trajectory points, the unmanned aerial vehicle flies at a speed of 2 meters / second and synchronously records the UWB signal time of arrival (ToA), signal strength (RSSI) and other data of each trajectory point. The average value is taken for 3 repeated flights to reduce random errors.

[0017] Based on the calibration work trajectory and the distribution of UWB anchors, the least squares method is used to fit the signal data. First, the UWB anchor signal time difference (TDoA) data corresponding to each trajectory point of the unmanned aerial vehicle flying along the calibration work trajectory is obtained. The spatial coordinates (x, y, z) of each trajectory point are taken as the independent variable, and the TDoA values corresponding to each anchor are taken as the dependent variable. By minimizing the sum of squares of errors between the actual measured TDoA values and the theoretical TDoA values calculated based on the coordinates, the coefficients of the signal propagation model are solved, thereby establishing the mapping relationship between the TDoA of each anchor signal and the spatial distance of the trajectory point, and forming a mathematical model describing the propagation law of the UWB signal in the work area. Provide accurate signal fitting basis for subsequent anchor group configuration. Exemplarily, for the trajectory point P(x, y, z), the function relationship between the signal propagation time and the coordinates of anchors A, B and C obtained by fitting is: =0.3x+0.2y+0.5z+0.1, =0.2x+0.4y+0.3z+0.2, =0.4x+0.1y+0.4z+0.15, the root mean square error (RMSE) is less than 0.05 seconds.

[0018] According to the signal fitting result, the anchor group is configured according to the trajectory. The calibration trajectory is divided into 20 continuous segments, each segment contains 5 trajectory points, the signal quality score of each anchor is calculated for each segment of the trajectory, the ToA accuracy, RSSI value and multipath effect parameters are integrated, and the top 3 anchors are selected to form the mapping anchor group. For example, the anchor group corresponding to the trajectory segment S1 is {A, B, C}, and the signal quality scores are 92, 88 and 85, respectively, all of which are higher than the threshold value 80, ensuring the positioning accuracy.

[0019] Through dynamic signal fitting and anchor group configuration, the positioning error can be controlled within 0.2 meters under the same shielding scene, which provides accurate basic anchor mapping relationship for subsequent dynamic activation and abnormal compensation of anchor groups when the unmanned aerial vehicle works along the trajectory, and effectively improves the robustness of the positioning device in complex environments.

[0020] Step A200: When the low-altitude unmanned aerial vehicle performs a task along the calibration operation trajectory, the anchor group activation configuration is performed according to the pre-authentication trajectory point of the low-altitude unmanned aerial vehicle.

[0021] In the embodiment of the application, the pre-authentication trajectory point is used to identify trajectory drift, establish a pre-drift amount, perform time sequence flight fitting, match the calibration operation trajectory, and activate the mapping anchor group when the low-altitude unmanned aerial vehicle performs a task along the calibration operation trajectory.

[0022] Optionally, when the low-altitude unmanned aerial vehicle performs a task along the calibration operation trajectory, the trajectory drift is identified according to the pre-authentication trajectory point of the low-altitude unmanned aerial vehicle to establish a pre-drift amount with a direction identifier, a tolerance zone map containing a drift threshold, available anchor mapping, and position shielding is established by combining the operation area and the UWB anchor distribution, the pre-drift amount is subjected to regional tolerance analysis, if it passes, the time sequence flight fitting is performed based on the pre-authentication trajectory point, and the anchor group is activated after the fitting result is matched with the calibration operation trajectory. The specific steps are described in detail in A210-A230.

[0023] Step A300: The activated anchor group receives the UWB signal transmitted by the low-altitude unmanned aerial vehicle, and performs abnormal identification of the UWB signal in the anchor group.

[0024] In the embodiment of the application, the UWB signal is an ultra-wideband wireless signal, which transmits data by using a nanosecond-level non-sine wave narrow pulse, measures the propagation time of the pulse signal between the transmitting end and the receiving end, and determines the distance between the two points to realize the positioning function.

[0025] In an embodiment of the application, when the activated anchor group receives the UWB signal transmitted by the low-altitude unmanned aerial vehicle, first, the signal parameter is collected in real time by the signal receiving module of each anchor point. The module has the ability to collect UWB signal parameters such as signal strength RSSI and signal arrival time ToA in real time, which can support the subsequent abnormal identification and pose correction process. The collected UWB signal parameters include signal strength (RSSI), signal arrival time (ToA), and time difference (TDoA). Taking a typical operation scenario as an example, each anchor point in the anchor group receives signals at a frequency of 10 Hz, compares the collected RSSI data with a preset threshold (such as -70 dBm), and calculates the error between ToA and the theoretical propagation time, such as setting the threshold to 0.1 seconds. If the RSSI of a certain anchor point is lower than -70 dBm for three consecutive times and the ToA error exceeds 0.1 seconds, it is determined that the signal is abnormal.

[0026] In the anomaly identification process, a sliding window algorithm is used to analyze the signals in the anchor group in batches. For example, a 5-second sliding window is set, and the proportion of abnormal signals in the window is counted. When the proportion of abnormal signals exceeds 30%, the preset abnormal threshold is triggered. Through Kalman filtering, the signal parameters are denoised, the measurement error of RSSI is reduced, the ToA error is optimized and reduced, and the accuracy of anomaly identification is improved.

[0027] Through multi-parameter joint analysis (RSSI, ToA, TDoA) and sliding window statistics, the false positive rate of anomaly identification is reduced, and stable identification accuracy can be maintained in complex environments with multipath interference. It provides reliable signal anomaly trigger conditions for subsequent visual acquisition and pose correction, effectively improving the environmental adaptability and robustness of low-altitude unmanned aerial vehicle positioning devices.

[0028] Step A400: If the anomaly identification result triggers the preset abnormal threshold, activate the visual acquisition device of the low-altitude unmanned aerial vehicle to capture environmental features and obtain the motion data set of the IMU sensor.

[0029] In the embodiments of the present application, the IMU sensor is generally composed of accelerometers, gyroscopes and other sensors. The accelerometer can measure the acceleration of the object in three axial directions, i.e. X, Y and Z axes, and the gyroscope can measure the angular velocity of the object around these three axes. By integrating these parameters, the attitude, position and motion trajectory of the object can be obtained.

[0030] Specifically, when the UWB signal anomaly identification result triggers the preset abnormal threshold, such as when the RSSI of the anchor group is lower than -70dBm for 5 consecutive times and the ToA error exceeds 0.1 seconds, the visual acquisition device carried by the low-altitude unmanned aerial vehicle is automatically activated, such as a RGB camera with a resolution of 1280x720, to capture environmental feature images at a frequency of 30 frames / second. At the same time, the motion data set of the IMU sensor with a sampling rate of 100Hz is obtained, including three-axis acceleration obtained by an accelerometer with a range of ±5g and three-axis angular velocity obtained by a gyroscope with a range of ±2000° / s.

[0031] During the visual acquisition process, the original image is first preprocessed to remove distortion and enhance contrast, ensuring that the environmental features are clear and distinguishable. IMU data is denoised through Kalman filtering algorithm, reducing the acceleration noise density to 0.005g / √Hz and optimizing the angular velocity random walk to 0.05° / √h, providing high-quality motion data for subsequent pose correction.

[0032] In addition, when the UWB signal anomaly identification result does not trigger the preset anomaly threshold, the visual acquisition device and the IMU data acquisition process are not activated, but return to the initial monitoring state of receiving the UWB signal and performing anomaly identification, and continuously monitoring the UWB signal in the anchor group in real time to ensure timely capture of subsequent possible signal anomaly conditions.

[0033] Through the synchronous acquisition and preprocessing of vision and IMU data, in the UWB signal failure scenario, the robustness problem of single UWB positioning in a complex environment is effectively solved, multi-source heterogeneous data support is provided for the dynamic fusion network, and adaptive switching and precision guarantee of low-altitude unmanned aerial vehicle pose correction are realized.

[0034] Step A500: After extracting key feature points from the visual acquisition result, the dynamic fusion network is initialized using the pre-authentication trajectory point, the linkage pose correction based on the key feature points, the motion data set and the UWB signal is performed, and the linkage pose correction result is generated.

[0035] In the embodiment of the application, the visual acquisition result is the environmental feature image captured by the visual acquisition device of the low-altitude unmanned aerial vehicle, and the original and preprocessed image data after image preprocessing are used for subsequent feature extraction. The key feature point is a representative image feature point established by constructing a multi-scale extraction resolution for multi-scale feature extraction on the preprocessed visual acquisition image, and selecting spatially uniformly distributed features according to the extraction result. The dynamic fusion network is a network initialized by configuring the joint authentication trust degree of the UWB signal after the trajectory deviation of the pre-authentication trajectory point. It is used to receive the backtracking image, the key feature point, the motion data set and the UWB signal.

[0036] Specifically, first, the key feature points are extracted from the visual acquisition result, including obtaining the original image and performing preprocessing such as distortion removal and enhancement, constructing a multi-scale extraction resolution for multi-scale feature extraction, and then selecting spatially uniformly distributed features according to the extraction result to establish key feature points. The specific steps are described in detail in A510-A520.

[0037] Next, the dynamic fusion network is initialized using the pre-authentication trajectory point, including obtaining its trajectory deviation, configuring the joint authentication trust degree of the UWB signal based on the trajectory deviation, and then initializing and managing the dynamic fusion network based on the trust degree. The specific steps are described in detail in A541-A543.

[0038] Finally, the pre-authentication trajectory point is used to initialize the dynamic fusion network and perform joint pose correction, including performing low-frequency sliding window image backtracking positioning to determine whether it meets the preset interval threshold, if it meets, calling the preset period backtracking image synchronous input dynamic fusion network containing the image, activating the first image processing layer to perform key point matching with the nearest neighbor sliding window image to generate the first pose compensation, and simultaneously activating the IMU processing layer and the UWB processing layer to establish the second and third pose compensations based on the motion data set and the UWB signal respectively, and finally outputting the joint pose correction result according to the three outputs, and the specific steps are described in detail in A531-A537.

[0039] Further, the step A200 in the method provided by the embodiment of the application comprises: A210: trajectory drift identification is performed using the pre-authentication trajectory point to establish a pre-drift amount, wherein the pre-drift amount is provided with a drift direction identifier.

[0040] A220: a tolerance zone map is established according to the work area and the UWB anchor point distribution, wherein each area in the tolerance zone map is provided with a drift threshold, a coverage available anchor point mapping, and a position anchor point occlusion.

[0041] A230: regional tolerance analysis of the pre-drift amount is performed using the tolerance zone map, if the regional tolerance analysis result is a pass result, then time sequence flight fitting is performed according to the pre-authentication trajectory point, and after the time sequence flight fitting result is matched with the calibration work trajectory, the mapped anchor point group is activated.

[0042] In the embodiment of the application, the trajectory drift is the spatial coordinate deviation of the pre-authentication trajectory point and the corresponding point of the calibration trajectory when the low-altitude unmanned aerial vehicle flies along the calibration work trajectory.

[0043] Specifically, when the low-altitude unmanned aerial vehicle works along the calibration trajectory, first, trajectory drift identification is performed based on the pre-authentication trajectory point. The pre-drift amount is established by calculating the spatial coordinate deviation, such as the difference of three-dimensional coordinates (x, y, z), between the current trajectory point and the corresponding point of the calibration trajectory. For example, if the calibration trajectory point coordinate is (10, 5, 3) and the current pre-authentication trajectory point coordinate is (10.3, 5, 3.1), the horizontal drift amount is 0.3 meters and the vertical drift amount is 0.1 meters, and the drift direction identifier is determined to be 15° east in the north direction. This step provides a quantitative basis for dynamic adjustment of the anchor point group through real-time deviation calculation.

[0044] Then, when constructing the tolerance zone map according to the work area layout and the UWB anchor point distribution, first, the work area is divided into a plurality of regular areas according to a 5*5 meter specification by using a grid division method, for example, a 20*16 grid matrix is formed in a rectangular area of 100*80 meters, and each grid corresponds to a unique coordinate identifier, such as (row, column). Then, for each grid area, a drift threshold is preset in combination with the positioning accuracy requirement of the unmanned aerial vehicle, for example, the maximum drift allowed in the horizontal direction is 0.5 meters, and the maximum drift allowed in the vertical direction is 0.3 meters. The threshold is determined by analyzing the deviation range of the 95% confidence interval in the historical trajectory data.

[0045] Next, based on the physical layout position and signal coverage range of the UWB anchor points, an available anchor point mapping is associated with each grid area: by measuring the signal strength (RSSI) and time of arrival (ToA) of each anchor point in different grids, the top 3 anchor points with the highest signal quality score (comprehensive RSSI≥-70dBm and ToA error≤0.05 seconds) are selected to form an anchor group, for example, the grid area (2, 3) is associated with the anchor group {A2, A3, A5} because the signal scores of the anchor points A2, A3, and A5 are 90, 88, and 85 respectively. At the same time, based on the anchor signal failure records in the past 72 hours, RSSI<-70dBm and ToA error>0.1 seconds are defined as failure, and the shielding probability of the anchor points in each grid area is calculated, for example, the anchor point A1 in the area (1, 1) has appeared 18 times in the historical data, and the total monitoring time is 72 times, so the shielding probability is calculated as 18 / 72=25%, and finally a three-dimensional tolerance zone map containing the drift threshold, anchor group mapping, and shielding probability is formed, which provides accurate spatial data support for subsequent regional tolerance analysis.

[0046] Finally, when using the tolerance zone map to perform regional tolerance analysis on the pre-shift amount, if the horizontal drift threshold of a certain area is 0.5 meters and the current drift amount is 0.3 meters, which is less than the threshold, it is determined to pass. After passing, a three-order spline interpolation algorithm is used to fit the pre-authentication trajectory points in time sequence, and the coordinates of the next 5 trajectory points are predicted. Then, a dynamic time warping (DTW) algorithm is used to match the predicted trajectory points with the calibration trajectory, and when the matching error is less than 0.2 meters, the mapped anchor group is activated.

[0047] Through trajectory drift dynamic identification, accurate modeling of the tolerance zone map, and time sequence flight fitting and matching, the correct activation rate of the anchor group is improved, and the positioning error is reduced under the same shielding scenario, effectively solving the problems of anchor group activation lag and misplacement in complex environments, and realizing dynamic and accurate configuration of anchor group resources in the positioning process of low-altitude unmanned aerial vehicles.

[0048] Further, step A230 in the method provided by the embodiments of the present application comprises: A231: If the regional tolerance analysis result is a failure result, a backtracking window is established, the trajectory is backtracked using the backtracking window, and a trajectory backtracking result is established.

[0049] A232: Time series flight prediction is performed using the trajectory backtracking result, and a time series flight prediction result is established.

[0050] A233: Position anchor point shielding and position distance-based redistribution are performed in the coverage available anchor point mapping using the time series flight prediction result, and an anchor point group is reactivated according to a redistribution result.

[0051] Optionally, when the area tolerance analysis result fails, a backtracking window is first established to capture trajectory historical information. According to the flight speed of 2 m / s of the unmanned aerial vehicle, the backtracking window can be set to contain trajectory points in the past 5 seconds, that is, a range of 10 meters, for example, the backtracking window of the current time t covers 5 trajectory points at times t-5s to t, and one point is recorded every 1 second. By extracting the three-dimensional coordinates (x, y, z) and corresponding UWB signal quality parameters such as RSSI and ToA of these historical trajectory points, a sliding average filtering algorithm is used to denoise the trajectory, and a trajectory backtracking result is established, so that the coordinate error of the trajectory points is reduced.

[0052] Then, when performing time series flight prediction based on the trajectory backtracking result, a cubic spline interpolation algorithm is used to fit the backtracking trajectory, and the specific process is as follows: first, 5 trajectory points in the past 5 seconds are extracted from the backtracking window, each point containing a timestamp and three-dimensional coordinates , , , for example, the backtracking trajectory point set is {(t0, 10, 5, 3), (t1, 12, 5.2, 3.1), (t2, 14, 5.5, 3.2), (t3, 16, 5.8, 3.3), (t4, 18, 6.0, 3.4)}.

[0053] Secondly, the time axis is standardized as relative time with the current time t as the reference, such as t0=-5s, t1=-4s,..., t4=0s, and boundary conditions for cubic spline interpolation are constructed, usually setting the second derivative at both ends to 0, which is the natural boundary condition. Then, a cubic polynomial is constructed between each two adjacent trajectory points: where is the constant term of the piecewise polynomial at the node , corresponding to the coordinate value of the trajectory point at time; is the linear change rate of the polynomial near , which is related to the first derivative (speed) of the trajectory at the node; is the cubic term coefficient, which is used to ensure the second derivative continuity of adjacent piecewise polynomials at the connection point, ensuring smooth transition of the trajectory. By solving a system of linear equations, the function value, the first derivative and the second derivative of adjacent polynomials at the connection point are ensured to be continuous, for example at t1, , , After completing the construction of the interpolation model, the predicted coordinates are calculated by substituting the future 5 time points, t+1 to t+5 seconds, i.e. relative time 1s to 5s, into each piecewise polynomial, to obtain the time series flight prediction result, which can provide high-precision trajectory prediction data for anchor group reassignment.

[0054] Finally, the time series flight prediction result is used for reassignment in the coverage available anchor mapping of the tolerance zone map. The anchor reassignment rule is set: preferentially selecting anchors with an occlusion probability < 20% and a distance < 5 meters from the predicted trajectory point. For example, among the available anchors corresponding to the region of the predicted trajectory point (16, 5.8, 3.3) at time t3, anchor A has an occlusion probability of 15% and a distance of 4.2 meters, anchor B has an occlusion probability of 25% and a distance of 3.8 meters, and anchor C has an occlusion probability of 18% and a distance of 4.5 meters. Finally, anchors A and C are selected to form a new anchor group. In the reassignment process, the anchor signal quality score is calculated, formula: score = 100-50x occlusion probability-20x (distance / 5), to ensure that the scores of the newly activated anchor groups are all > 80 points.

[0055] By capturing historical trajectories through the backtracking window, optimizing future paths through time series prediction, and dynamically reassigning anchor groups based on occlusion probability and distance, the correct reassignment rate of anchor groups is improved, the positioning error is reduced under the same occlusion scenario, effectively solving the problem of adaptive adjustment after anchor group failure in traditional methods, and achieving dynamic fault tolerance and intelligent reconfiguration of anchor resources for low-altitude unmanned aerial vehicle positioning devices in complex environments.

[0056] Further, step A220 in the method provided by the embodiments of the present application comprises: A221: performing dynamic occlusion identification based on historical records on the work area to establish a failure frequency.

[0057] A222: configuring a dynamic update frequency of the tolerance zone map according to the failure frequency, and performing dynamic update management of the tolerance zone map using the dynamic update frequency.

[0058] In the embodiments of the present application, the failure frequency is statistical data established after dynamic occlusion identification based on historical records on the work area, and is used to reflect the signal failure of UWB anchors in the area.

[0059] Specifically, in the establishment of the tolerance zone map, first, the dynamic occlusion identification based on historical records is carried out in the working area. Through the collection of historical data such as signal strength (RSSI) and signal arrival time (ToA) of UWB anchor points in the past 72 hours, the failure times of each anchor point in different areas are counted. For example, in a 200 square meter working area, it is divided into 10x10 meter grid areas, and for the anchor points in each grid, when RSSI is lower than -70dBm and ToA error exceeds 0.1 seconds, it is determined as a failure, and thus the anchor point failure frequency of each area is established. For example, anchor point A in a certain area fails 12 times in the past 24 hours, and the failure frequency is calculated to be 0.5 times / hour, while anchor point B in another area has a failure frequency of 0.1 times / hour. Through statistical analysis of historical data, a quantitative basis is provided for dynamic updating of the tolerance zone map.

[0060] Then, the dynamic update frequency of the tolerance zone map is configured according to the failure frequency. The mapping relationship between failure frequency and update frequency is set as shown in Table 1. For example, the tolerance zone map update frequency of the corresponding grid in the area where anchor point A is located is configured to 5 minutes according to the failure frequency of 0.5 times / hour, while the update frequency of the area where anchor point B is located is configured to 15 minutes according to the failure frequency of 0.1 times / hour. By monitoring the change of failure frequency in real time, the update frequency is dynamically adjusted to realize dynamic updating management of the tolerance zone map, so that the map data continuously matches the occlusion state of the working area.

[0061] Through the establishment of failure frequency based on dynamic occlusion identification based on historical records, and the dynamic configuration of map update frequency according to the failure frequency, the real-time performance of the tolerance zone map is improved, the anchor point group activation error rate is reduced in the same working environment, and the problem that the map data is disconnected from the actual occlusion situation under the traditional fixed updating mechanism is effectively solved, and the adaptive matching of the tolerance zone map to the dynamic change of the working environment is realized.

[0062] Table 1: Mapping relationship table of failure and update frequency

[0063] Further, the step A500 in the method provided by the embodiment of the application comprises: A510: Obtain the original image of the visual acquisition result, and perform image preprocessing, which includes image distortion correction and image enhancement.

[0064] A520: Construct a multi-scale extraction resolution, perform multi-scale feature extraction on the preprocessed image using the multi-scale extraction resolution, select spatially uniformly distributed features according to the multi-scale feature extraction result, and establish key feature points.

[0065] In the embodiments of the present application, the multi-scale extraction resolution refers to constructing a multi-layer image representation with different resolutions when extracting features from the preprocessed image, so as to capture feature information of different scales in the image.

[0066] Specifically, when extracting key feature points from the visual acquisition result, first, the original image output by the visual acquisition device is acquired, such as an RGB image with a resolution of 1280*720, and image preprocessing is performed. The original image is de-distorted by Zhang Zhengyou's calibration method, and the lens distortion coefficients k1 and k2 are respectively corrected to 0.05 and -0.03 to eliminate the image distortion caused by the physical characteristics of the lens; then histogram equalization and contrast limited adaptive histogram equalization (CLAHE) are used for image enhancement to improve the image gray entropy and significantly improve the recognition of environmental features.

[0067] Subsequently, a multi-scale extraction resolution is constructed, and a Gaussian pyramid model is usually used to generate 8 scale layers with a scale factor of 1.2 to perform multi-scale feature extraction on the preprocessed image from 0.5 times the original resolution to 2 times the original resolution. Taking the ORB algorithm as an example, features are extracted on each scale layer using a FAST corner detector and a BRIEF descriptor, and about 2000 feature points can be extracted from a single image, of which the feature points extracted at the 1.0 scale layer account for about 45%, and the feature points extracted at the 0.5 scale layer and the 2 scale layer account for about 25% and 30% respectively, forming an ORB feature point set covering different spatial scales.

[0068] Then, according to the multi-scale feature extraction result, spatially uniformly distributed feature selection is performed, the image is divided into 16*16 grid regions, the feature points in each grid are sorted according to the response intensity, the top 3 strongest feature points are retained, and the redundant features gathered in the local area are removed. After this processing, the spatial distribution variance of the feature points in the image is reduced to 0.3, ensuring that there are at least 3 feature points in each 100*100 pixel region, and a uniformly distributed key feature point set is constructed.

[0069] By eliminating distortion and enhancing contrast through image preprocessing, combining multi-scale feature extraction and spatially uniformly distributed selection, the repeated detection rate of feature points is improved, effectively solving the problems of insufficient number and uneven distribution of feature points in traditional methods, providing high-quality visual feature input for subsequent linkage pose correction, and significantly improving the precision and environmental adaptability of low-altitude unmanned aerial vehicle dynamic positioning.

[0070] Further, the step A500 in the method provided by the embodiments of the present application comprises: A531: Perform low-frequency sliding window image backtracking to locate the nearest neighbor sliding window image.

[0071] A532: Determine whether the nearest neighbor sliding window image meets a preset interval threshold.

[0072] A533: If the nearest neighbor sliding window image meets the preset interval threshold, call the backtracking image within the preset period, synchronize the backtracking image to the dynamic fusion network, and the backtracking image includes the nearest neighbor sliding window image.

[0073] A534: Activate the first image processing layer to perform key point matching using the key feature points and the nearest neighbor sliding window image, and generate the first pose compensation using the key point matching result and the inter-frame sliding window matching result in the backtracking image.

[0074] A535: Activate the IMU processing layer to perform motion trajectory fitting based on the motion data set, and establish the second pose compensation.

[0075] A536: Activate the UWB processing layer to perform position fitting based on the UWB signal, and establish the third pose compensation.

[0076] A537: Output the linkage pose correction result according to the first pose compensation, the second pose compensation, and the third pose compensation.

[0077] In the embodiments of the present application, the nearest neighbor sliding window image is the sliding window image closest to the current time located by performing low-frequency sliding window image backtracking, which is used for subsequent key point matching and pose compensation calculation. The IMU processing layer is a functional module for processing the motion data set collected by the IMU sensor, and the second pose compensation is established by motion trajectory fitting based on the motion data set. The UWB processing layer is a functional module for position fitting based on the UWB signal, and the third pose compensation is established by processing the UWB signal.

[0078] Specifically, first, low-frequency sliding window image backtracking is performed, such as setting the sliding window interval to 0.5 seconds, and locating the sliding window image closest to the current time by searching the image sequence. Taking the collection frequency of the unmanned aerial vehicle 30 frames / second as an example, if the current time is t, the nearest neighbor sliding window image is usually the image at t-0.5 seconds. This step reduces the calculation amount by using the sliding window mechanism, and reduces the calculation load compared with the traditional full-frame processing.

[0079] When it is judged that the nearest neighbor sliding window image meets the preset interval threshold (such as time interval ≤0.5 seconds and image feature change rate <30%), the backtracking image within the preset period (such as the past 5 seconds) is called, and it is synchronized with the nearest neighbor sliding window image to the dynamic fusion network. For example, when the unmanned aerial vehicle flies at a speed of 2 meters / second, the backtracking image within 5 seconds covers a trajectory range of about 10 meters, ensuring that the input image sequence can reflect the recent motion characteristics, and providing spatiotemporal continuous visual data for pose compensation.

[0080] After activating the first image processing layer, ORB feature points (approximately 1,000 per frame) are extracted from the preprocessed image. These key feature points are then used to perform key point matching with the nearest neighbor sliding window image. A Hamming distance matching algorithm is used to calculate the similarity of the feature point descriptors, with a Hamming distance threshold of 50 set to screen out valid matching point pairs. Next, the matching error between adjacent frames is verified using the inter-frame sliding window matching results within the retrospective image. The matching error is required to be less than 2 pixels to eliminate mismatched points. Based on these filtered matching results, the first pose compensation is generated by calculating the positional transformation relationship of the feature points between different frames. This includes translation compensation (e.g., 0.1 meter) and rotation compensation (e.g., 1°), providing visual compensation data support for subsequent linked pose correction.

[0081] After activating the IMU processing layer, a Kalman filter is applied to the triaxial acceleration (range ±5g) and triaxial angular velocity (range ±2000° / s) data collected by the IMU sensor at a 100Hz sampling rate. A state-space model is first constructed, using the drone's position, velocity, and attitude angle as state variables and acceleration and angular velocity as observation variables. The accelerometer noise density is 0.01g / √Hz. During the filtering process, the state prediction equation is used to estimate the motion state at the next moment. The actual acquired acceleration and angular velocity data are then compared with the predicted values ​​using the observation equation, and the noise covariance matrix is ​​calculated to optimize the state estimate. After Kalman filtering, a second pose compensation is generated, reducing the trajectory prediction error to 0.1m / s. This effectively suppresses high-frequency noise interference, provides high-precision trajectory constraints for pose correction, and achieves real-time and accurate fitting of the drone's motion state.

[0082] After activation, the UWB processing layer first receives UWB signals transmitted by low-altitude drones and returned via the activated anchor point group. Using a multilateration algorithm, it calculates the signal's time of arrival (ToA) or time difference of arrival (TDoA). This is then combined with the coordinates of the deployed UWB anchor points (known in 3D) to determine the drone's real-time position. The UWB signal itself has a positioning accuracy of 0.1 meters. The processing layer then performs denoising and filtering on the raw signal, such as using a median filter to eliminate burst noise. The processing layer also adjusts the position fitting weight based on the joint authentication confidence level (configured based on the trajectory deviation of the previously authenticated track points). When the confidence level reaches 90%, a weighted fusion algorithm is used to limit the position fitting error to within 0.15 meters. For example, when calculating the final position, the UWB signal is weighted 0.3, complementing the visual weight of 0.4 and the IMU weight of 0.3. This creates a third pose compensation, providing a high-precision position constraint for the coordinated pose correction.

[0083] Finally, according to the three types of pose compensation results, through weighted fusion, the visual weight is 0.4, the IMU weight is 0.3, and the UWB weight is 0.3, and the linkage pose correction result is output.

[0084] Through dynamic fusion of multiple source data, the problem of insufficient robustness of a single sensor is effectively solved, and high-precision pose correction and environmental adaptability of a low-altitude unmanned aerial vehicle in a dynamic scene are realized.

[0085] Further, the step A532 in the method provided in the embodiment of the application comprises: A532-1: If the nearest neighbor sliding window image cannot meet the preset interval threshold, a visual acquisition device is activated to perform continuous image frame acquisition, and a continuous frame set is established.

[0086] A532-2: A second image processing layer is activated to perform key point matching on the key feature points and the continuous frame set, and a fourth pose compensation is established.

[0087] A532-3: A linkage pose correction result is output according to the second pose compensation, the third pose compensation and the fourth pose compensation.

[0088] In one embodiment, when the nearest neighbor sliding window image does not meet the preset interval threshold, such as when the time interval exceeds 0.5 seconds or the feature change rate exceeds 50%, the visual acquisition device is automatically activated to perform continuous image frame acquisition at a frequency of 30 frames per second, and a continuous frame set containing 10-15 frames is established. For example, when the image interval becomes large due to high-speed flight of the unmanned aerial vehicle, the continuously acquired images can cover a motion trajectory of 0.3-0.5 seconds, providing dense visual data support for subsequent processing.

[0089] After the second image processing layer is activated, the FLANN (Fast Library for Approximate Nearest Neighbors) algorithm is used to perform feature matching on the key feature points (about 1000 ORB feature points per frame) and the continuous frame set. FLANN first constructs an index structure of a high-dimensional feature space, such as a KD tree or a hierarchical clustering tree, and quickly finds candidate matching points for each feature point through an approximate nearest neighbor search strategy. For the BRIEF binary descriptor of the ORB feature, the Hamming distance is used to calculate the similarity between feature points, and the Hamming distance represents the number of different bits between two binary strings. When the distance is less than 50, it is determined that the features are similar. After FLANN screening, about 800 pairs of valid matching points per frame can be retained. This process greatly reduces the candidate set of false matches through fast indexing and distance threshold filtering, and lays a foundation for subsequent accurate matching.

[0090] Then, on the basis of the FLANN preliminary matching, the RANSAC (random sample consensus) algorithm is used to remove the mis-matching points. The algorithm finds the optimal transformation model through iteration: first, randomly select 4 pairs of matching points as the minimum sample set, calculate the pose transformation matrix between adjacent frames, such as the homography matrix or the essential matrix, then test all matching points with the matrix, and determine the points that meet the preset error threshold (such as projection error < 2 pixels) as inliers. After multiple iterations, the model with the most inliers is selected as the final transformation matrix, at this time the mis-matching rate can be reduced from the initial 20% to less than 5%. Finally, based on all inliers, the accurate pose transformation is recalculated, and the fourth pose compensation is established, such as translation compensation 0.2 meters, rotation compensation 2°, effectively capturing the attitude changes of the unmanned aerial vehicle during rapid movement.

[0091] According to the second pose compensation output by the IMU processing layer, based on the 100Hz sampled motion trajectory fitting, the error is 0.1 meters / second; the third pose compensation of the UWB processing layer has a positioning accuracy of 0.15 meters, and the fourth pose compensation is output again through weighted fusion, with a vision weight of 0.5, an IMU weight of 0.3, and a UWB weight of 0.2, to output the linkage pose correction result.

[0092] Through continuous frame acquisition and multi-frame feature matching, high-precision pose correction can still be maintained when the visual information is sparse, effectively solving the problem of insufficient robustness of traditional methods in dynamic scenes. Through dense visual data supplement and multi-source data fusion, stable positioning of the low-altitude unmanned aerial vehicle in complex motion states is realized.

[0093] Further, the step A500 in the method provided by the embodiments of the present application comprises: A541: Obtain a trajectory deviation of the pre-authentication trajectory point.

[0094] A542: Configure a joint authentication trust degree of the UWB signal according to the trajectory deviation.

[0095] A543: Perform initialization management of the dynamic fusion network based on the joint authentication trust degree.

[0096] In the embodiments of the present application, the joint authentication trust degree is a weight parameter used to represent the reliability of the UWB signal, which is configured according to the trajectory deviation of the pre-authentication trajectory point.

[0097] Optionally, first, calculate the three-dimensional coordinate difference (such as x, y, z axis deviation) of the pre-authentication trajectory point and the corresponding point of the calibration operation trajectory to obtain the trajectory deviation. Taking a certain trajectory as an example, if the calibration trajectory point coordinate is (10, 5, 3) and the pre-authentication trajectory point is (10.3, 5, 3.1), the horizontal deviation is 0.3 meters and the vertical deviation is 0.1 meters, forming a trajectory deviation vector. This step provides a quantitative basis for subsequent trust degree configuration through real-time deviation calculation.

[0098] Then, the joint authentication confidence of the UWB signal is configured according to the trajectory deviation. A mapping relationship between the deviation and the confidence is set, as shown in Table 2. For example, if the trajectory deviation is 0.3 meters at a certain moment, the joint authentication confidence of the UWB signal is 90%, which is used to represent the reliability weight of the UWB signal. By dynamically adjusting the confidence, the weight of the UWB signal is reduced when the trajectory deviation is large, so as to avoid the influence of false signals on the positioning accuracy.

[0099] The initialization management of the dynamic fusion network is performed based on the joint authentication confidence. The confidence is used as the weight parameter of the network input layer. For example, when the confidence is 90% during initialization, the weight coefficient of the UWB signal processing branch is set to 0.9, and the weights of the visual and IMU branches are set to 0.5 and 0.6 respectively, so as to realize the dynamic fusion of multi-source data through weight distribution.

[0100] The UWB signal confidence is dynamically configured through the trajectory deviation, and the fusion network is initialized based on the UWB signal confidence, which effectively solves the problem of insufficient robustness caused by fixed signal confidence in traditional methods, realizes the adaptive initialization of the dynamic fusion network to the trajectory deviation of the unmanned aerial vehicle, and improves the environmental adaptability of the positioning device.

[0101] Table 2: Mapping relationship table of deviation and confidence

[0102] Further, the step A500 in the method provided by the embodiment of the application comprises: A551: Matching the warning level according to the linkage pose correction result to establish a warning level matching result.

[0103] A552: Reporting a warning signal by using the warning level matching result, and performing warning feedback.

[0104] In one embodiment, after the linkage pose correction result is generated, first, the warning level is matched according to the pose deviation amount in the correction result. Three-level warning thresholds are set: when the horizontal deviation is ≤0.5 meters and the vertical deviation is ≤0.3 meters, it is a safe level (no warning); when the horizontal deviation is 0.5-1 meter or the vertical deviation is 0.3-0.5 meters, it is a yellow warning; and when the horizontal deviation is >1 meter or the vertical deviation is >0.5 meters, it is a red warning. For example, if the correction result shows that the horizontal deviation of the current position of the unmanned aerial vehicle from the calibration trajectory is 0.8 meters and the vertical deviation is 0.4 meters, it is matched to the yellow warning level. The classification standard is obtained by statistical analysis of historical accident data, and a person skilled in the art can adjust the corresponding numerical value according to the actual situation.

[0105] Next, the warning level matching results are used to execute warning signal reporting and feedback. A yellow warning triggers an audible and visual alarm, such as a buzzer beeping at two-second intervals and a flashing yellow LED on the aircraft. Simultaneously, a deceleration command is sent to the flight control system, reducing the flight speed from 2 meters per second to 1 meter per second. A red warning, in addition to a strong audible and visual alarm, a continuous buzzer sounding and a flashing red LED, automatically activates the return-to-home sequence, controlling the drone to return to its starting point via the shortest possible route.

[0106] Through multi-level threshold matching and graded feedback strategies, the warning accuracy is improved. Combined with the linkage posture correction results, closed-loop management from error detection to risk disposal is achieved, effectively reducing the collision risk in low-altitude UAV operations and improving the safety and reliability of the device.

[0107] In summary, the ultra-wideband low-altitude UAV dynamic visual positioning method provided by the embodiments of the present application has the following technical effects: This application collects environmental images in the calibrated trajectory area and tolerance zone map coverage area of ​​the operation area, obtains key feature point data through multi-scale feature extraction and spatial uniform distribution selection, calculates the spatial distribution and matching relationship of the feature points, and combines the drift threshold and anchor point mapping of the tolerance zone map to perform posture correction, thereby accurately realizing the dynamic visual positioning of low-altitude UAVs under ultra-wideband, making the posture correction results of low-altitude UAVs more accurate and reliable, achieving precise positioning of the low-altitude UAV posture, realizing precise positioning in complex environments and avoiding failure, and meeting the technical effects of precise operation requirements.

[0108] Example 2, as Figure 2 As shown, based on the same inventive concept as the aforementioned embodiment 1, the embodiment of the present application provides a low-altitude UAV dynamic visual positioning device under ultra-wideband, the device comprising: Anchor point group configuration module 1, the anchor point group configuration module 1 is used to deploy UWB anchor points in the operation area, and obtain the calibrated operation trajectory of the low-altitude drone, perform signal fitting according to the calibrated operation trajectory and the UWB anchor point distribution, and configure the anchor point group mapped to the calibrated operation trajectory.

[0109] Anchor point group activation module 2, the anchor point group activation module 2 is used to activate the anchor point group according to the pre-authenticated trajectory points of the low-altitude UAV when the low-altitude UAV performs the operation task along the calibrated operation trajectory.

[0110] The anchor point group anomaly recognition module 3 is used to use the activated anchor point group to receive the UWB signal transmitted by the low-altitude UAV and perform anomaly recognition of the UWB signal within the anchor point group.

[0111] The motion data set acquisition module 4 is configured to activate the visual acquisition device of the low-altitude unmanned aerial vehicle to capture environmental features and acquire a motion data set of the IMU sensor if the abnormality identification result triggers a preset abnormality threshold.

[0112] The linkage pose correction result acquisition module 5 is configured to extract key feature points from the visual acquisition result, initialize a dynamic fusion network by using the pre-authentication trajectory point, perform linkage pose correction based on the key feature points, the motion data set and the UWB signal, and generate a linkage pose correction result.

[0113] Further, the anchor group activation module 2 is configured to perform the following steps: The pre-authentication trajectory point is used to identify trajectory drift and establish a pre-drift amount, wherein the pre-drift amount is provided with a drift direction identifier; a tolerance zone map is established according to the work area and the UWB anchor point distribution, and each area in the tolerance zone map is provided with a drift threshold, a coverage available anchor point mapping and a position anchor point occlusion; the pre-drift amount is analyzed in the area by using the tolerance zone map, if the area tolerance analysis result is a pass result, the pre-authentication trajectory point is used to perform time sequence flight fitting, and after the time sequence flight fitting result is matched with the calibrated work trajectory, the mapped anchor group is activated.

[0114] Further, the anchor group activation module 2 is configured to perform the following steps: If the area tolerance analysis result is a fail result, a backtracking window is established, the trajectory is backtracked by using the backtracking window, and a trajectory backtracking result is established; the time sequence flight prediction is performed by using the trajectory backtracking result, and a time sequence flight prediction result is established; the time sequence flight prediction result is used to perform redistribution based on the position anchor point occlusion and the position distance in the coverage available anchor point mapping, and the anchor group is reactivated according to the redistribution result.

[0115] Further, the anchor group activation module 2 is configured to perform the following steps: The work area is subjected to dynamic occlusion identification based on historical records, and a failure frequency is established; the dynamic update frequency of the tolerance zone map is configured according to the failure frequency, and the dynamic update management of the tolerance zone map is performed by using the dynamic update frequency.

[0116] Further, the linkage pose correction result acquisition module 5 is configured to perform the following steps: The original image of the visual acquisition result is acquired, image preprocessing is performed, the image preprocessing includes image de-distortion and image enhancement; a multi-scale extraction resolution is constructed, the multi-scale feature extraction of the preprocessed image is performed by using the multi-scale extraction resolution, the spatial uniform distribution feature selection is performed according to the multi-scale feature extraction result, and the key feature points are established.

[0117] Further, the linkage pose correction result acquisition module 5 is used to execute the following steps: Perform low-frequency sliding window image backtracking to locate the nearest neighbor sliding window image; Determine whether the nearest neighbor sliding window image meets the preset interval threshold value; if the nearest neighbor sliding window image meets the preset interval threshold value, call the backtracking image within the preset period, synchronize the backtracking image to the dynamic fusion network, the backtracking image includes the nearest neighbor sliding window image; activate the first image processing layer to perform key point matching using the key feature points and the nearest neighbor sliding window image, generate a first pose compensation using the key point matching result and the inter-frame sliding window matching result in the backtracking image; activate the IMU processing layer to perform motion trajectory fitting based on the motion data set to establish a second pose compensation; activate the UWB processing layer to perform position fitting based on the UWB signal to establish a third pose compensation; and output the linkage pose correction result according to the first pose compensation, the second pose compensation and the third pose compensation.

[0118] Further, the linkage pose correction result acquisition module 5 is used to execute the following steps: If the nearest neighbor sliding window image cannot meet the preset interval threshold value, activate the visual acquisition device to perform continuous image frame acquisition to establish a continuous frame set; activate the second image processing layer to perform key point matching using the key feature points and the continuous frame set to establish a fourth pose compensation; and output the linkage pose correction result according to the second pose compensation, the third pose compensation and the fourth pose compensation.

[0119] Further, the linkage pose correction result acquisition module 5 is used to execute the following steps: Obtain the trajectory deviation of the front authentication trajectory point; configure the joint authentication trust degree of the UWB signal according to the trajectory deviation; and perform initialization management of the dynamic fusion network based on the joint authentication trust degree.

[0120] Further, the linkage pose correction result acquisition module 5 is used to execute the following steps: Match the warning level according to the linkage pose correction result to establish a warning level matching result; use the warning level matching result to report a warning signal and perform warning feedback.

[0121] The low-altitude unmanned aerial vehicle dynamic visual positioning device under the ultra-wideband provided by the embodiment of the application can execute the low-altitude unmanned aerial vehicle dynamic visual positioning method under the ultra-wideband provided by any embodiment of the application, and has the corresponding function modules and beneficial effects of the execution method.

[0122] Although the present application makes various references to certain modules in the apparatus according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server, the various units and modules are only divided according to the functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific name of each functional unit is only for the convenience of mutual differentiation, and does not serve to limit the protection scope of the present application.

[0123] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application. In some cases, the actions or steps described in the present application can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

Claims

1. The low-altitude UAV dynamic visual positioning method under ultra-wideband is characterized by: The method comprises: Deploy UWB anchor points in the operation area, obtain the calibrated operation trajectory of the low-altitude UAV, perform signal fitting based on the calibrated operation trajectory and the UWB anchor point distribution, and configure an anchor point group mapped to the calibrated operation trajectory; When the low-altitude UAV performs an operation task along the calibrated operation trajectory, the anchor point group activation configuration is performed according to the pre-certified trajectory points of the low-altitude UAV; Utilize the activated anchor point group to receive UWB signals transmitted by low-altitude drones and perform abnormal identification of UWB signals within the anchor point group; If the abnormal recognition result triggers the preset abnormal threshold, the visual acquisition device of the low-altitude UAV is activated to capture environmental features and obtain the motion data set of the IMU sensor; After extracting key feature points from the visual acquisition results, the dynamic fusion network is initialized using the pre-authenticated trajectory points, and the linked pose correction based on the key feature points, motion data set, and UWB signal is performed to generate the linked pose correction results.

2. The low-altitude UAV dynamic visual positioning method under ultra-wideband according to claim 1 is characterized in that: The anchor point group activation configuration based on the pre-authenticated trajectory points of the low-altitude UAV includes: Using the pre-authentication track point to perform track drift identification, and establish a pre-drift amount, wherein the pre-drift amount is provided with a drift direction identifier; Establishing a tolerance zone map according to the operation area and the UWB anchor point distribution, wherein each area in the tolerance zone map is set with a drift threshold, a coverage available anchor point mapping, and a position anchor point occlusion; The tolerance zone map is used to perform regional tolerance analysis of the front drift amount. If the regional tolerance analysis result is a passing result, a time sequence flight fitting is performed according to the front authentication trajectory point. After the time sequence flight fitting result is used to match the calibration operation trajectory, the mapped anchor point group is activated.

3. The low-altitude UAV dynamic visual positioning method under ultra-wideband as claimed in claim 2, characterized in that: The method of performing regional tolerance analysis of the leading drift amount by using the tolerance zone map includes: If the regional tolerance analysis result is a failed result, a backtracking window is established, and the trajectory is backtracked using the backtracking window to establish a trajectory backtracking result; Performing time sequence flight prediction using the trajectory backtracking result to establish a time sequence flight prediction result; The temporal flight prediction result is used to perform reallocation based on position anchor occlusion and position distance in the coverage available anchor point map, and the anchor point group is reactivated according to the reallocation result.

4. The method for dynamic visual positioning of low-altitude UAVs under ultra-wideband as claimed in claim 2, characterized in that: The establishing of a tolerance zone map according to the operating area and the distribution of the UWB anchor points includes: Perform dynamic occlusion identification based on historical records on the operating area and establish failure frequencies; The dynamic update frequency of the tolerance zone map is configured according to the failure frequency, and the dynamic update management of the tolerance zone map is performed using the dynamic update frequency.

5. The method for dynamic visual positioning of low-altitude UAVs under ultra-wideband according to claim 1, wherein: The step of extracting key feature points from the visual acquisition results includes: Acquire the original image of the visual acquisition result and perform image preprocessing, wherein the image preprocessing includes image dedistortion and image enhancement; A multi-scale extraction resolution is constructed, and multi-scale feature extraction is performed on the pre-processed image using the multi-scale extraction resolution. Spatially uniformly distributed features are selected based on the multi-scale feature extraction results to establish key feature points.

6. The method for dynamic visual positioning of low-altitude UAVs under ultra-wideband according to claim 5, characterized in that: The method of initializing the dynamic fusion network using the pre-authenticated trajectory points and performing the linkage posture correction based on the key feature points, the motion data set, and the UWB signal includes: Perform low-frequency sliding window image backtracking to locate the nearest neighbor sliding window image; Determining whether the nearest neighbor sliding window image meets a preset interval threshold; If the nearest neighbor sliding window image meets a preset interval threshold, calling a backtracking image within a preset period, and synchronously inputting the backtracking image into the dynamic fusion network, the backtracking image including the nearest neighbor sliding window image; Activate the first image processing layer to perform key point matching using the key feature points and the nearest neighbor sliding window image, and generate the first pose compensation using the key point matching result and the inter-frame sliding window matching result in the retrospective image; Activate the IMU processing layer to perform motion trajectory fitting based on the motion data set and establish the second pose compensation; Activate the UWB processing layer to perform position fitting based on the UWB signal and establish the third pose compensation; Output a linkage posture correction result according to the first posture compensation, the second posture compensation, and the third posture compensation.

7. The method for dynamic visual positioning of low-altitude UAVs under ultra-wideband according to claim 6, characterized in that: The determining whether the nearest neighbor sliding window image meets a preset interval threshold includes: If the nearest neighbor sliding window image cannot meet the preset interval threshold, activating the visual acquisition device to perform continuous image frame acquisition and establish a continuous frame set; activating the second image processing layer to perform key point matching using the key feature points and the continuous frame set to establish a fourth pose compensation; Output a linkage posture correction result according to the second posture compensation, the third posture compensation, and the fourth posture compensation.

8. The method for dynamic visual positioning of low-altitude UAVs under ultra-wideband as claimed in claim 1, characterized in that: The method of initializing the dynamic fusion network using the pre-authenticated trajectory points includes: Get the trajectory deviation of the pre-authenticated trajectory point; configuring a joint authentication trust level of the UWB signal according to the trajectory deviation; Initialization management of the dynamic fusion network is performed based on the joint authentication trust level.

9. The method for dynamic visual positioning of low-altitude UAVs under ultra-wideband according to claim 1, wherein: Generating the linkage posture correction result includes: Performing warning level matching of posture warning according to the linkage posture correction result, and establishing a warning level matching result; The warning level matching result is used to report a warning signal and perform warning feedback.

10. A low-altitude UAV dynamic visual positioning device under ultra-wideband, characterized in that: The device is used to implement the low-altitude UAV dynamic visual positioning method under ultra-wideband according to any one of claims 1 to 9, comprising: Anchor point group configuration module, used to deploy UWB anchor points in the operation area, obtain the calibration operation trajectory of the low-altitude UAV, perform signal fitting based on the calibration operation trajectory and the UWB anchor point distribution, and configure the anchor point group mapped to the calibration operation trajectory; An anchor point group activation module, used for activating the anchor point group according to the pre-certified trajectory points of the low-altitude UAV when the low-altitude UAV performs the operation task along the calibrated operation trajectory; Anchor group anomaly recognition module, used to use the activated anchor group to receive UWB signals transmitted by low-altitude drones and perform anomaly recognition of UWB signals within the anchor group; The motion data set acquisition module is used to activate the visual acquisition device of the low-altitude UAV to capture environmental features and obtain the motion data set of the IMU sensor if the abnormal recognition result triggers the preset abnormal threshold; The linked pose correction result acquisition module is used to extract key feature points from the visual acquisition results, initialize the dynamic fusion network using the pre-authenticated trajectory points, perform linked pose correction based on key feature points, motion data sets, and UWB signals, and generate linked pose correction results.

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