Unmanned aerial vehicle position information calibration method, system, equipment and medium

Abstract

The invention relates to an unmanned aerial vehicle position information calibration method, system and device and a medium, and belongs to the technical field of unmanned aerial vehicle positioning. The calibration method comprises the following steps: calling a device initialization module to load a master device and a slave device; receiving first unmanned aerial vehicle position information acquired by the master device and second unmanned aerial vehicle position information acquired by the slave device; performing timestamp alignment and preprocessing on the first unmanned aerial vehicle position information and the second unmanned aerial vehicle position information; respectively extracting a first dynamic feature of the first unmanned aerial vehicle position information and a second dynamic feature of the second unmanned aerial vehicle position information; step-by-step matching is carried out according to the angle range and the distance range based on a feature matching algorithm, a dynamic feature matching result is obtained, and a preliminary calibration parameter set is generated; performing optimization processing on the initial calibration parameter set, complementing missing data, and generating a calibration parameter dictionary; and generating unmanned aerial vehicle calibration position information according to the calibration parameter dictionary. The precision and reliability of the position information of the unmanned aerial vehicle can be improved.

Description

Technical Field

[0001] The present application relates to the field of drone positioning technology, and in particular to a method, system, device and medium for calibrating drone position information. Background Art

[0002] With the rapid development of drone technology, its application areas and scope are rapidly expanding, showing important practical value and technical potential in many fields such as military, logistics, agriculture, and environmental monitoring. In these application scenarios, accurate location information acquisition is the basis for the success of drone missions. However, due to the limitations of a single device (such as accuracy, coverage, and real-time), achieving high-precision calibration of drone location information still faces many challenges.

[0003] At present, drone positioning technology usually relies on data collection from a single device. For example, optoelectronic devices can provide relatively accurate location information due to their high-precision characteristics, but their working range is relatively limited; radar devices have a larger coverage range, but the positioning accuracy is relatively low. The limitations of this single device make it difficult to meet the requirements of accuracy and coverage at the same time, especially in complex dynamic scenes. The coordinated collection of multiple devices has become an effective means to improve the accuracy and reliability of drone location information.

[0004] In multi-device collaborative data collection, master devices and slave devices are often deployed separately to give full play to their respective advantages. For example, optoelectronic devices can be used as master devices to provide high-precision data support, while radar devices can be used as slave devices to expand the data collection range. The collaborative work of master and slave devices can effectively make up for the shortcomings of a single device.

[0005] However, drones are usually in high-speed motion, and their location information has strong temporal dynamics. In addition, due to the differences in working characteristics, sampling frequency and accuracy between the master and slave devices, directly fusing their collected data is prone to time asynchrony and spatial inconsistency. Therefore, how to balance the advantages of multiple devices and improve the accuracy and reliability of drone location information is a technical problem that needs to be solved urgently. Summary of the invention

[0006] In order to improve the accuracy and reliability of drone location information, the present application provides a drone location information calibration method, system, device and medium.

[0007] In the first aspect, the present application provides a method for calibrating the position information of a drone, which adopts the following technical solution: A method for calibrating position information of an unmanned aerial vehicle, the method comprising: Calling the device initialization module to load the master device and the slave device, and configuring the types and parameters of the master device and the slave device; Receiving first drone position information acquired by the master device and second drone position information acquired by the slave device, wherein the drone position information includes an angle range and a distance range of the position information; Aligning the timestamps of the first UAV position information and the second UAV position information and performing preprocessing; Respectively extracting a first dynamic feature of the first drone position information and a second dynamic feature of the second drone position information; Based on a feature matching algorithm, gradually matching the first dynamic feature with the second dynamic feature according to the angle range and the distance range to obtain a dynamic feature matching result; generating a preliminary calibration parameter set according to the dynamic feature matching result; Optimizing the preliminary calibration parameter set and completing missing data in the preliminary calibration parameter set to generate a calibration parameter dictionary; Generate drone calibration position information based on the calibration parameter dictionary.

[0008] By adopting the above technical solution, based on the collaborative collection of master and slave devices, the system can not only obtain high-precision positioning information, but also cover a larger range. Through real-time fusion and compensation of master and slave device data, the consistency problem in multi-source data fusion is solved, thereby ensuring that the final generated drone position information is comprehensive and accurate, and improving the accuracy and reliability of drone positioning.

[0009] Optionally, based on a feature matching algorithm, the step of gradually matching the first dynamic feature and the second dynamic feature according to the angle range and the distance range to obtain a dynamic feature matching result includes: Preprocessing the first dynamic feature and the second dynamic feature; Initialize the angle matching window and the distance matching window; Matching the angle ranges in the first dynamic feature and the second dynamic feature one by one, and obtaining an angle range matching result based on the angle matching window; Compare the corresponding distance ranges one by one according to the angle range matching results, and determine the corresponding distance range matching results based on the distance matching window; According to the distance range matching result, a horizontal angle offset value, a distance offset value and a speed offset value are calculated to obtain a dynamic feature matching result set; Verifying each matching pair in the dynamic feature matching result set based on a matching offset threshold; The matching pairs that exceed the matching offset threshold are eliminated to obtain the verified dynamic feature matching results.

[0010] By adopting the above technical solutions, the dynamic feature matching algorithm realizes efficient matching and verification of master and slave device data, gradually improves matching accuracy and eliminates invalid data, and can generate a highly reliable set of dynamic feature matching results, which not only ensures the accuracy of the calibration process, but also optimizes the computing efficiency, providing solid data support for UAV positioning and calibration.

[0011] Optionally, the preliminary calibration parameter set includes a horizontal angle compensation value, a pitch angle compensation value, a distance compensation value and a speed compensation value.

[0012] Optionally, the step of optimizing the preliminary calibration parameter set and completing missing data in the preliminary calibration parameter set to generate a calibration parameter dictionary includes: Traversing the preliminary calibration parameter set, and performing redundancy elimination on the preliminary calibration parameter set; Detecting missing calibration parameters in the preliminary calibration parameter set and performing interpolation to complete the missing parameters; Initialize a calibration parameter dictionary; wherein the keys of the calibration parameter dictionary are angle ranges and distance ranges, and the values ​​are corresponding calibration parameter sets; Filling the interpolated and completed preliminary calibration parameter set into the calibration parameter dictionary; A consistency check is performed on the calibration parameter dictionary to obtain a calibration parameter dictionary that passes the check.

[0013] By adopting the above technical solution, the preliminary calibration parameter set is optimized, redundant data is removed, random errors are reduced, and missing angles and distance ranges are effectively supplemented through interpolation, providing accurate and systematic data support for UAV calibration, significantly improving the efficiency and effectiveness of calibration.

[0014] Optionally, the step of generating the drone calibration position information according to the calibration parameter dictionary includes: Loading a calibration parameter set in the calibration parameter dictionary; Correcting the first UAV position information and the second UAV position information respectively according to the calibration parameter set; and calculating a three-dimensional coordinate set corresponding to the main device based on the corrected first UAV position information; Calculate a three-dimensional coordinate set corresponding to the slave device based on the corrected position information of the second drone; Based on the timestamp, the three-dimensional coordinate sets corresponding to the master device and the slave device are fused to obtain fused UAV calibration position information.

[0015] By adopting the above technical solution, the data of the master and slave devices are corrected and the three-dimensional coordinates are calculated respectively. Finally, the position information of the master and slave devices is unified into a high-precision result through timestamp alignment and fusion algorithm.

[0016] Optionally, after the step of generating the drone calibration position information according to the calibration parameter dictionary, the step further includes: Verifying the validity of the UAV calibration position information and detecting the deviation between the UAV calibration position information and the first UAV position information; Determine whether the deviation exceeds a preset threshold, and if so, return to a calibration error state and re-optimize the preliminary calibration parameter set; If not, it is determined that the verification is passed, and the calibration position information of the drone is formatted and then output.

[0017] By adopting the above technical solution and adding validity verification and deviation judgment steps, the reliability and accuracy of the calibration results are further guaranteed. When there is a problem with the calibration result, the system can automatically return to the error state and re-optimize the preliminary calibration parameter set to avoid the erroneous results affecting downstream operations.

[0018] In the second aspect, the present application provides a drone position information calibration system, which adopts the following technical solution: A UAV position information calibration system, the calibration system comprising: An initialization calling module, used to call a device initialization module to load a master device and a slave device, and configure the types and parameters of the master device and the slave device; A position information receiving module, used to receive the first drone position information acquired by the master device and the second drone position information acquired by the slave device, wherein the drone position information includes an angle range and a distance range of the position information; A position information processing module, used for aligning the timestamps of the first UAV position information and the second UAV position information and performing preprocessing; A dynamic feature extraction module, used to extract a first dynamic feature of the first drone position information and a second dynamic feature of the second drone position information respectively; A feature matching module, configured to gradually match the first dynamic feature with the second dynamic feature according to the angle range and the distance range based on a feature matching algorithm to obtain a dynamic feature matching result; A calibration parameter set generation module, used to generate a preliminary calibration parameter set according to the dynamic feature matching result; A calibration parameter dictionary generation module, used to optimize the preliminary calibration parameter set and complete the missing data in the preliminary calibration parameter set to generate a calibration parameter dictionary; The position information calibration module is used to generate the UAV calibration position information according to the calibration parameter dictionary.

[0019] Optionally, the calibration system further includes: A verification module, used to verify the validity of the UAV calibration position information and detect the deviation between the UAV calibration position information and the first UAV position information; A judgment module, used to judge whether the deviation exceeds a preset threshold, and if so, output a first judgment result; if not, output a second judgment result; a calibration error processing module, configured to return a calibration error state and re-optimize the preliminary calibration parameter set in response to the first judgment result; The calibration position information output module is used to determine that the verification is passed in response to the second judgment result, and output the calibration position information of the drone after formatting.

[0020] In a third aspect, the present application provides a computer device, which adopts the following technical solution: A computer device comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the methods in the first aspect.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: the above technical solution balances the advantages of multiple devices while ensuring that the ultimately generated drone location information is comprehensive and accurate, thereby improving the accuracy and reliability of drone positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a first flow chart of a method for calibrating the position information of a drone according to one of the embodiments of the present application.

[0024] Figure 2 This is a second flow chart of a method for calibrating the position information of a drone according to one of the embodiments of the present application.

[0025] Figure 3 This is a third flow chart of a method for calibrating a drone's position information according to one of the embodiments of the present application.

[0026] Figure 4 This is a fourth flow chart of a method for calibrating a drone's position information according to one of the embodiments of the present application.

[0027] Figure 5This is a fifth flow chart of a method for calibrating a drone's position information according to one of the embodiments of the present application. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-5 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0029] The embodiment of the present application discloses a method for calibrating the position information of a drone.

[0030] Reference Figure 1 , a method for calibrating the position information of a UAV, the calibration method comprising: Step S101, call the device initialization module to load the master device and the slave device, and configure the types and parameters of the master device and the slave device; where the master device (such as an optoelectronic device) and the slave device (such as a radar device) have different precision and characteristics, by calling the initialization module, the following functions can be achieved: the system calls the underlying driver to identify and access the device to ensure that hardware resources are available; parameter configuration includes the type of device (high precision or low precision), working range (angle range, distance range) and sampling frequency, etc. In addition, the device initialization module also needs to check the connection status and operating status of the device to ensure that the device is fault-free.

[0031] Exemplarily, the master device can be set as an optoelectronic device through an interface function and configure its monitoring angle range; similarly, the slave device can be set as a radar device and configure its distance monitoring range.

[0032] Step S102, receiving the first drone position information acquired by the master device and the second drone position information acquired by the slave device, where the drone position information includes an angle range and a distance range of the position information; Specifically, the master device has high precision and is responsible for providing accurate location information; the slave device has a wider coverage and provides rough location information. This mechanism of master-slave device collaborative collection helps balance accuracy and coverage. Exemplarily, the master device and the slave device structure the output location information data, such as {angle range, distance range, timestamp}.

[0033] Step S103, aligning the timestamps of the first UAV position information and the second UAV position information and performing preprocessing; The data collected by the master and slave devices usually have different timestamps and precisions, so the data needs to be aligned and preprocessed to ensure the consistency of the subsequent processing logic. The timestamps of the master and slave devices can be standardized by calling the time alignment algorithm. In addition, the data preprocessing steps include denoising (filtering out random noise in the signal, such as removing high-frequency interference through a low-pass filter) and anomaly detection (marking data that exceeds the device range or does not meet physical constraints as invalid frames).

[0034] Step S104, extracting a first dynamic feature of the first drone position information and a second dynamic feature of the second drone position information respectively; Among them, dynamic feature extraction is to extract key features from the data of the master and slave devices to facilitate subsequent matching and calibration. The dynamic features specifically include important change information in the UAV's motion trajectory, such as angle offset, distance change trend, etc.

[0035] Specifically, the high-precision data provided by the master device can directly extract the instantaneous angle and position of the drone, such as {angle offset: 1°, distance change: 10 meters}; the slave device data needs to extract dynamic features through interpolation and fitting methods, for example, based on the time series of multiple frames of data, calculate the trend of distance change: {trend slope: -0.5 meters / second}.

[0036] Step S105, based on the feature matching algorithm, gradually matching the first dynamic feature and the second dynamic feature according to the angle range and the distance range to obtain a dynamic feature matching result; Among them, feature matching is the core step of UAV calibration. By comparing the dynamic features of the master and slave devices, the corresponding relationship between the two is found, and then the parameters required for calibration are generated.

[0037] For example, a multi-level feature matching method may be used, firstly a rough match is performed within the angle range, and then a fine match is performed within the matched angle range according to the distance range. For example, for the master device feature {angle: 5°, distance: 500 meters} and the slave device feature {angle: 4.8°~5.2°, distance: 480~520 meters}, the matching result is {horizontal offset: 0.2°, distance offset: 20 meters}.

[0038] Step S106, generating a preliminary calibration parameter set according to the dynamic feature matching result; Among them, the preliminary calibration parameter set includes horizontal angle compensation value, pitch angle compensation value, distance compensation value and speed compensation value; it can be understood that, based on the dynamic feature matching, the compensation values ​​required for calibration are further calculated, including compensation of angle, distance and speed. For example, the horizontal angle compensation value (such as 0.2°), pitch angle compensation value, distance compensation value (such as 20 meters) and speed compensation value are calculated according to the matching results, and these compensation values ​​are stored in the preliminary calibration parameter set.

[0039] Step S107, optimizing the preliminary calibration parameter set, and completing the missing data in the preliminary calibration parameter set to generate a calibration parameter dictionary; The goal of calibration parameter optimization is to eliminate redundancy and inconsistency in the data and to fill in missing parameters through interpolation. Specifically, parameter optimization can be done by weighted averaging high-frequency features (such as angle changes) to reduce random errors, while data filling can be done by interpolating to fill in some uncovered angle ranges (such as missing data of 4° to 5°).

[0040] It can be understood that the optimized and completed calibration parameter dictionary has higher accuracy and completeness and can be directly used for calibration calculations.

[0041] Step S108, generating drone calibration position information according to the calibration parameter dictionary.

[0042] Among them, based on the calibration parameter dictionary, the position information of the master and slave devices is converted into unified high-precision coordinate information. Specifically, the slave device position information is corrected according to the calibration parameter dictionary to align it with the master device data, and the drone position information in a unified format is output, such as {coordinates: (x, y, z), timestamp: 10:00:00}.

[0043] In the above implementation mode, based on the collaborative collection of master and slave devices, the system can not only obtain high-precision positioning information, but also cover a larger range. Through real-time fusion and compensation of master and slave device data, the consistency problem in multi-source data fusion is solved, thereby ensuring that the final generated UAV position information is comprehensive and accurate, and improving the accuracy and reliability of UAV positioning.

[0044] Reference Figure 2 As an implementation of step S105, based on a feature matching algorithm, the first dynamic feature and the second dynamic feature are gradually matched according to the angle range and the distance range to obtain a dynamic feature matching result, including: Step S201, preprocessing the first dynamic feature and the second dynamic feature; Among them, the dynamic feature raw data may contain noise or outliers, and direct use may lead to matching errors. The purpose of preprocessing is to improve data quality through normalization, denoising and outlier filtering.

[0045] Exemplarily, the angle range is standardized to [0, 1] to facilitate subsequent calculations and comparisons; the distance range is standardized according to the maximum monitoring distance of the device to eliminate scale differences between different devices; a filter (such as a low-pass filter) can be used to smooth high-frequency noise in dynamic features; and out-of-range data is eliminated based on physical limitations (such as the minimum and maximum operating ranges of the device).

[0046] Step S202, initializing the angle matching window and the distance matching window; Among them, the initialization of the matching window is to set the matching tolerance range, so as to control the matching accuracy and efficiency. The setting of the angle matching window and the distance matching window should be based on the accuracy of the equipment and the actual task requirements.

[0047] For example, if the device's angle measurement accuracy is 0.1°, the angle matching window W can be set A =[-0.1, 0.1]; if the distance measurement error is 10 meters, you can set W D =[-10 meters, 10 meters].

[0048] It can be understood that by reasonably setting the matching window, the accuracy of feature matching is improved while avoiding the involvement of irrelevant data.

[0049] Step S203, matching the angle ranges in the first dynamic feature and the second dynamic feature one by one, and obtaining an angle range matching result based on the angle matching window; Among them, angle range matching is the first step of feature matching, and the tolerance range of angle offset values ​​can be used to quickly screen potential matching features.

[0050] Specifically, the angle matching process is as follows: traverse each first dynamic feature T of the master device i and each second dynamic characteristic T of the slave device j , calculate the angle offset value ΔA=A i -A j , if ΔA∈W A , then record the matching result M A (i,j).

[0051] Step S204, comparing the corresponding distance ranges one by one according to the angle range matching results, and determining the corresponding distance range matching results based on the distance matching window; Among them, on the basis of angle range matching, the corresponding distance range is further compared. Through the dual constraints of angle and distance, the matching accuracy is improved and mismatching is avoided.

[0052] Specifically, the distance matching process is as follows: for each angle matching result M A (i, j), calculate the distance offset value ΔD = Di -D j , if ΔD∈W D , then record the matching pair M D (i, j). It should be noted that for feature pairs that match both angle and distance, different weights can also be assigned according to the importance of the device.

[0053] Step S205, calculating the horizontal angle offset value, the distance offset value and the speed offset value according to the distance range matching result, and obtaining a dynamic feature matching result set; The calculation of dynamic feature matching results is the core step in generating calibration parameters, and the difference between the master and slave device data is accurately quantified by calculating the offset value.

[0054] Specifically, the calculation formula of the horizontal angle offset value is: ΔA = A i -A j , the distance offset value is calculated as: ΔD = D i -D j , the speed offset value is calculated as: ΔV = V i -V j ; Finally, the matching results can be stored in structured data, for example: {i, j, ΔA, ΔD, ΔV}.

[0055] Step S206, verify each matching pair in the dynamic feature matching result set based on the matching offset threshold; wherein, the purpose of the verification step is to screen out reliable matching features and eliminate invalid data with excessive deviations. If any offset value in the matching pair exceeds the threshold range, the matching pair can be determined to be marked as invalid.

[0056] Step S207, eliminating matching pairs that exceed the matching offset threshold, and obtaining a verified dynamic feature matching result.

[0057] Among them, the dynamic feature matching result set is traversed, all matching pairs that have not passed the verification are removed, and the matching pairs that have passed the verification are output in a unified format, for example: {i, j, ΔA, ΔD, ΔV}.

[0058] In the above implementation, the dynamic feature matching algorithm realizes efficient matching and verification of master and slave device data, gradually improves matching accuracy and eliminates invalid data, and can generate a highly reliable dynamic feature matching result set, which not only ensures the accuracy of the calibration process, but also optimizes the computing efficiency, providing solid data support for drone positioning and calibration.

[0059] Reference Figure 3 As an implementation of step S107, the steps of optimizing the preliminary calibration parameter set and completing the missing data in the preliminary calibration parameter set to generate a calibration parameter dictionary include: Step S301, traversing a preliminary calibration parameter set and performing redundancy elimination on the preliminary calibration parameter set; Among them, the preliminary calibration parameter set may contain duplicate data or redundant records, such as data points with similar angle ranges and distance ranges. The purpose of redundancy elimination is to reduce data redundancy, improve calibration efficiency, and ensure the simplicity and consistency of the calibration parameter set.

[0060] Step S302, detecting missing calibration parameters in the preliminary calibration parameter set and performing interpolation to complete them; Among them, due to the limitations of the equipment or the acquisition process, the preliminary calibration parameter set may have missing data in the angle range or distance range. Through interpolation and completion, a complete calibration parameter coverage range can be generated to provide complete data support for subsequent tasks.

[0061] Specifically, by traversing all possible combinations of angle ranges and distance ranges (all A, D values ​​within the range required by the task), the combinations that do not exist in the preliminary calibration parameter set are marked. For the missing angle range and distance range combinations, linear interpolation can be performed based on adjacent data points.

[0062] Step S303, initializing the calibration parameter dictionary; The keys of the calibration parameter dictionary are the angle range and distance range, and the values ​​are the corresponding calibration parameter sets; Specifically, the structure of the calibration parameter dictionary is designed to efficiently store and quickly retrieve calibration parameters, and the purpose of initializing the dictionary is to provide storage space for subsequent data filling.

[0063] For example, the key of the calibration parameter dictionary is (A, D), which represents a combination of an angle range and a distance range, and the value is a calibration parameter set {C A ,C θ ,C D ,C V}.

[0064] Step S304, filling the calibration parameter dictionary with the preliminary calibration parameter set after interpolation completion; Among them, the filling operation is to store the completed calibration parameter set into the dictionary so that the corresponding relationship between its key-value pairs is clear. After the filling is completed, the calibration parameter dictionary can be used as an efficient query tool.

[0065] Step S305 , performing consistency check on the calibration parameter dictionary to obtain a calibration parameter dictionary that passes the check.

[0066] The purpose of consistency check is to ensure that the data in the calibration parameter dictionary has no logical errors (such as duplicate values, abnormal values ​​or discontinuities). Through the check, the reliability of the calibration parameter dictionary can be further improved.

[0067] In the above implementation, the preliminary calibration parameter set is optimized to remove redundant data, reduce random errors, and effectively supplement the missing angle and distance range through interpolation, providing accurate and systematic data support for UAV calibration, significantly improving the calibration efficiency and effect.

[0068] Reference Figure 4 As an implementation of step S108, the step of generating the calibration position information of the drone according to the calibration parameter dictionary includes: Step S401, loading a calibration parameter set in a calibration parameter dictionary; Among them, the calibration parameter dictionary stores the calibration parameter set generated according to dynamic feature matching, including compensation values ​​for different angle ranges and distance ranges (such as horizontal angle compensation value, pitch angle compensation value, distance compensation value and speed compensation value). The purpose of loading the calibration parameter set is to provide an accurate basis for subsequent position information correction.

[0069] Step S402, respectively correcting the first UAV position information and the second UAV position information according to the calibration parameter set; Among them, the first drone position information obtained by the master device and the second drone position information obtained by the slave device respectively describe the angle range, distance range and timestamp of the drone. The goal of correction is to compensate for the errors of the two through calibration parameters so that the data of the two are as consistent and aligned as possible.

[0070] It should be noted that the first UAV position information is usually high-precision data obtained by the main device, and the correction of the calibration parameter set is mainly used for fine-tuning (such as correcting a small amount of system deviation or measurement error), while the second UAV position information is usually low-precision data. The correction of the calibration parameter set will have a greater impact on the adjustment of the second UAV position information to make it as close as possible to the data of the main device.

[0071] Step S403, calculating a three-dimensional coordinate set corresponding to the main device based on the corrected first drone position information; Among them, the three-dimensional coordinate calculation of the main device is based on the corrected angle and distance information, which is converted into x, y, and z coordinates in space. Since the data of the main device usually has higher precision, the three-dimensional coordinate set {(x1, y1, z1, T1)} generated by the main device can be used as the basis for fusion.

[0072] Step S404, calculating a three-dimensional coordinate set corresponding to the slave device based on the corrected second drone position information; Among them, the generated three-dimensional coordinate set {(x2, y2, z2, T2)} can describe the position of the slave device in space and provide supplementary information for data fusion.

[0073] Step S405: Based on the timestamp, the three-dimensional coordinate sets corresponding to the master device and the slave device are fused to obtain the fused UAV calibration position information.

[0074] The 3D coordinate fusion of the master and slave devices is to unify the position information of the two devices into a high-precision result. The fusion can be achieved through timestamp alignment and weighted averaging. The fused 3D coordinate set {(x final ,y final ,z final ,T)} provides high-precision UAV location information and integrates the advantages of master and slave devices.

[0075] Exemplarily, for the master device coordinate set (x1, y1, z1, T1) and the slave device coordinate set (x2, y2, z2, T2), find the record pair with the closest timestamp (T1≈T2), and then calculate the weighted average coordinate for the time-aligned record pair: x final =w1+x1+w2+x2 y final =w1+y1+w2+y2; z final =w1+z1+w2+z2 Among them, the weight w1 corresponding to the master device and the weight w2 corresponding to the slave device can be set according to the device accuracy, usually w1>W2.

[0076] In the above implementation, the data of the master and slave devices are respectively corrected and the three-dimensional coordinates are calculated, and finally the position information of the master and slave devices is unified into a high-precision result through timestamp alignment and fusion algorithm.

[0077] Reference Figure 5 As a further implementation of the calibration method, after the step of generating the calibration position information of the drone according to the calibration parameter dictionary, the method further includes: Step S501, verifying the validity of the drone calibration position information, and detecting the deviation between the drone calibration position information and the first drone position information; Among them, the core of the validity verification is to check whether the calibration position information of the calibrated drone is consistent with the high-precision main device (the first drone position information). By comparing the deviation between the two, the accuracy of the calibration can be judged to ensure that the output results meet expectations.

[0078] Step S502, determine whether the deviation exceeds a preset threshold, if so, jump to step S503; if not, jump to step S504; Step S503, returning to the calibration error state and re-optimizing the preliminary calibration parameter set; Specifically, the calculated deviation is compared with the preset threshold to determine whether the calibration result meets the accuracy requirements. If the deviation exceeds the threshold, it means that there may be problems with the calibration result, and the preliminary calibration parameter set needs to be re-optimized to improve the calibration effect. An error status is returned to indicate that the operation failed, while providing an opportunity for subsequent optimization.

[0079] Step S504: determine that the verification is passed, and format the drone calibration position information and then output it.

[0080] Among them, when the deviation is within the threshold range, the calibration result is considered valid. Formatting is to organize the calibration position information into a unified structure and output it to facilitate subsequent system calls and analysis.

[0081] In the above implementation, the validity verification and deviation judgment steps are added, and the reliability and accuracy of the calibration results are further guaranteed. When there is a problem with the calibration result, the system can automatically return to an error state and re-optimize the preliminary calibration parameter set, thereby preventing the erroneous result from affecting downstream operations.

[0082] The embodiment of the present application also discloses a drone position information calibration system.

[0083] A UAV position information calibration system, the calibration system comprising: The initialization calling module is used to call the device initialization module to load the master device and the slave device, and configure the types and parameters of the master device and the slave device; A position information receiving module, used to receive the first drone position information acquired by the master device and the second drone position information acquired by the slave device, the drone position information including the angle range and distance range of the position information; A position information processing module, used to align the timestamps of the first UAV position information and the second UAV position information and perform preprocessing; A dynamic feature extraction module, used to extract a first dynamic feature of the first drone position information and a second dynamic feature of the second drone position information respectively; A feature matching module, used to gradually match the first dynamic feature and the second dynamic feature according to the angle range and the distance range based on a feature matching algorithm to obtain a dynamic feature matching result; A calibration parameter set generation module, used to generate a preliminary calibration parameter set according to the dynamic feature matching result; A calibration parameter dictionary generation module is used to optimize the preliminary calibration parameter set and complete the missing data in the preliminary calibration parameter set to generate a calibration parameter dictionary; The position information calibration module is used to generate the UAV calibration position information according to the calibration parameter dictionary.

[0084] In the above implementation, high-precision calibration of the UAV position information is achieved through dynamic feature matching and calibration parameter optimization between the master device (such as optoelectronic equipment) and the slave device (such as radar equipment), which is suitable for various application scenarios such as UAV navigation, positioning and target tracking.

[0085] As a further implementation of the calibration system, the calibration system further includes: A verification module, used to verify the validity of the drone calibration position information and detect the deviation between the drone calibration position information and the first drone position information; A judgment module, used to judge whether the deviation exceeds a preset threshold, and if so, output a first judgment result; if not, output a second judgment result; A calibration error processing module, configured to return a calibration error state and re-optimize the preliminary calibration parameter set in response to the first judgment result; The calibration position information output module is used to determine that the verification is passed in response to the second judgment result, and to format the calibration position information of the drone and then output it.

[0086] A drone position information calibration system in an embodiment of the present application can implement any of the above-mentioned drone position information calibration methods, and the specific working process of each module in a drone position information calibration system can refer to the corresponding process in the above-mentioned method embodiment.

[0087] In the several embodiments provided in this application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are only illustrative; for example, the division of a certain module is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0088] The embodiment of the present application also discloses a computer device.

[0089] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for calibrating the position information of a drone as described above is implemented.

[0090] The embodiment of the present application also discloses a computer-readable storage medium.

[0091] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the above-mentioned methods for calibrating the position information of a drone.

[0092] Among them, computer-readable storage media can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus or device; the program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0093] It should be noted that in the above embodiments, the description of each embodiment has different emphases, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0094] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A method for calibrating the position information of an unmanned aerial vehicle, characterized in that: The calibration method comprises: Calling the device initialization module to load the master device and the slave device, and configuring the types and parameters of the master device and the slave device; Receiving first drone position information acquired by the master device and second drone position information acquired by the slave device, wherein the drone position information includes an angle range and a distance range of the position information; Aligning the timestamps of the first UAV position information and the second UAV position information and performing preprocessing; Respectively extracting a first dynamic feature of the first drone position information and a second dynamic feature of the second drone position information; Based on a feature matching algorithm, gradually matching the first dynamic feature with the second dynamic feature according to the angle range and the distance range to obtain a dynamic feature matching result; generating a preliminary calibration parameter set according to the dynamic feature matching result; Optimizing the preliminary calibration parameter set and completing missing data in the preliminary calibration parameter set to generate a calibration parameter dictionary; Generate drone calibration position information based on the calibration parameter dictionary.

2. A method for calibrating the position information of a drone according to claim 1, characterized in that: Based on the feature matching algorithm, the step of gradually matching the first dynamic feature and the second dynamic feature according to the angle range and the distance range to obtain a dynamic feature matching result includes: Preprocessing the first dynamic feature and the second dynamic feature; Initialize the angle matching window and the distance matching window; Matching the angle ranges in the first dynamic feature and the second dynamic feature one by one, and obtaining an angle range matching result based on the angle matching window; Compare the corresponding distance ranges one by one according to the angle range matching results, and determine the corresponding distance range matching results based on the distance matching window; According to the distance range matching result, a horizontal angle offset value, a distance offset value and a speed offset value are calculated to obtain a dynamic feature matching result set; Verifying each matching pair in the dynamic feature matching result set based on a matching offset threshold; The matching pairs that exceed the matching offset threshold are eliminated to obtain the verified dynamic feature matching results.

3. A method for calibrating the position information of a drone according to claim 1, characterized in that: The preliminary calibration parameter set includes a horizontal angle compensation value, a pitch angle compensation value, a distance compensation value, and a speed compensation value.

4. A method for calibrating the position information of a UAV according to claim 3, characterized in that: The steps of optimizing the preliminary calibration parameter set and completing the missing data in the preliminary calibration parameter set to generate a calibration parameter dictionary include: Traversing the preliminary calibration parameter set, and performing redundancy elimination on the preliminary calibration parameter set; Detecting missing calibration parameters in the preliminary calibration parameter set and performing interpolation to complete the missing parameters; Initialize a calibration parameter dictionary; wherein the keys of the calibration parameter dictionary are angle ranges and distance ranges, and the values ​​are corresponding calibration parameter sets; Filling the interpolated and completed preliminary calibration parameter set into the calibration parameter dictionary; A consistency check is performed on the calibration parameter dictionary to obtain a calibration parameter dictionary that passes the check.

5. A method for calibrating the position information of a drone according to any one of claims 1 to 4, characterized in that: The step of generating the calibration position information of the drone according to the calibration parameter dictionary comprises: Loading a calibration parameter set in the calibration parameter dictionary; Correcting the first UAV position information and the second UAV position information respectively according to the calibration parameter set; Calculate a three-dimensional coordinate set corresponding to the main device based on the corrected position information of the first drone; Calculate a three-dimensional coordinate set corresponding to the slave device based on the corrected position information of the second drone; Based on the timestamp, the three-dimensional coordinate sets corresponding to the master device and the slave device are fused to obtain fused UAV calibration position information.

6. A method for calibrating the position information of a UAV according to claim 5, characterized in that: After the step of generating the drone calibration position information according to the calibration parameter dictionary, the method further includes: Verifying the validity of the UAV calibration position information and detecting the deviation between the UAV calibration position information and the first UAV position information; Determine whether the deviation exceeds a preset threshold, and if so, return to a calibration error state and re-optimize the preliminary calibration parameter set; If not, it is determined that the verification is passed, and the calibration position information of the drone is formatted and then output.

7. A UAV position information calibration system, characterized in that: The calibration system comprises: An initialization calling module, used to call a device initialization module to load a master device and a slave device, and configure the types and parameters of the master device and the slave device; A position information receiving module, used to receive the first drone position information acquired by the master device and the second drone position information acquired by the slave device, wherein the drone position information includes an angle range and a distance range of the position information; A position information processing module, used for aligning the timestamps of the first UAV position information and the second UAV position information and performing preprocessing; A dynamic feature extraction module, used to extract a first dynamic feature of the first drone position information and a second dynamic feature of the second drone position information respectively; A feature matching module, configured to gradually match the first dynamic feature with the second dynamic feature according to the angle range and the distance range based on a feature matching algorithm to obtain a dynamic feature matching result; A calibration parameter set generation module, used to generate a preliminary calibration parameter set according to the dynamic feature matching result; A calibration parameter dictionary generation module, used to optimize the preliminary calibration parameter set and complete the missing data in the preliminary calibration parameter set to generate a calibration parameter dictionary; The position information calibration module is used to generate the UAV calibration position information according to the calibration parameter dictionary.

8. The UAV position information calibration system according to claim 7, characterized in that: The calibration system further comprises: A verification module, used to verify the validity of the UAV calibration position information and detect the deviation between the UAV calibration position information and the first UAV position information; A judgment module, used to judge whether the deviation exceeds a preset threshold, and if so, output a first judgment result; if not, output a second judgment result; a calibration error processing module, configured to return a calibration error state and re-optimize the preliminary calibration parameter set in response to the first judgment result; The calibration position information output module is used to determine that the verification is passed in response to the second judgment result, and output the calibration position information of the drone after formatting.

9. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 6 when executing the program.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 6.

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