A method, system, device and medium for calibrating position information of a UAV

Abstract

The application relates to a UAV position information calibration method, system, device and medium, and belongs to the technical field of UAV positioning. The calibration method comprises the following steps: calling a device initialization module to load a master device and a slave device; receiving first UAV position information obtained by the master device and second UAV position information obtained by the slave device; aligning the first UAV position information and the second UAV position information by time stamp and pre-processing; extracting a first dynamic feature of the first UAV position information and a second dynamic feature of the second UAV position information respectively; based on a feature matching algorithm, gradually matching according to an angle range and a distance range, obtaining a dynamic feature matching result, and generating a preliminary calibration parameter set; optimizing the preliminary calibration parameter set, and completing missing data to generate a calibration parameter dictionary; and generating UAV calibration position information according to the calibration parameter dictionary. The application can improve the precision and reliability of UAV position information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle position information calibration method, system, device and medium. BACKGROUND

[0002] With the rapid development of unmanned aerial vehicle technology, its application fields and ranges are rapidly expanding, and it has shown important practical value and technical potential in many fields such as military, logistics, agriculture, and environmental monitoring. In these application scenarios, accurate position information acquisition is the basis for the success of unmanned aerial vehicle tasks. However, due to the limitations of single devices (such as precision, coverage range, and real-time performance), there are still many challenges in achieving high-precision calibration of unmanned aerial vehicle position information.

[0003] Currently, unmanned aerial vehicle positioning technology usually relies on data collection by single devices. For example, photoelectric devices can provide relatively accurate position 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 such single devices cannot meet the requirements of precision and coverage range at the same time, especially in complex dynamic scenarios. Multi-device collaborative collection has become an effective means to improve the precision and reliability of unmanned aerial vehicle position information.

[0004] In multi-device collaborative collection, master devices and slave devices are often deployed separately to take advantage of their respective advantages. For example, photoelectric 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 collection range. Through the collaborative work of master and slave devices, the shortcomings of single devices can be effectively compensated.

[0005] However, unmanned aerial vehicles are usually in a high-speed motion state, and their position information has strong time dynamics. In addition, due to the differences in working characteristics, sampling frequency, and precision between master devices and slave devices, directly fusing the data collected by them can easily cause problems such as time asynchronization and spatial inconsistency. Therefore, how to balance the advantages of multiple devices and improve the precision and reliability of unmanned aerial vehicle position information is a technical problem that needs to be solved at present. SUMMARY

[0006] In order to improve the precision and reliability of unmanned aerial vehicle position information, the present application provides an unmanned aerial vehicle position information calibration method, system, device and medium.

[0007] In a first aspect, the present application provides an unmanned aerial vehicle position information calibration method, which adopts the following technical solution:

[0008] An unmanned aerial vehicle position information calibration method, the calibration method comprising:

[0009] calling a device initialization module to load master devices and slave devices, and configuring the types and parameters of the master devices and the slave devices;

[0010] receive the first UAV position information acquired by the master device and the second UAV position information acquired by the slave device, the UAV position information including an angle range and a distance range of position information;

[0011] timestamp align and preprocess the first UAV position information and the second UAV position information;

[0012] extract a first dynamic feature of the first UAV position information and a second dynamic feature of the second UAV position information, respectively;

[0013] based on a feature matching algorithm, gradually match 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;

[0014] generate a preliminary calibration parameter set according to the dynamic feature matching result;

[0015] optimize the preliminary calibration parameter set and complete missing data in the preliminary calibration parameter set, to generate a calibration parameter dictionary;

[0016] generate UAV calibration position information according to the calibration parameter dictionary.

[0017] By adopting the above technical solutions, based on the cooperative collection of the master and slave devices, the system can obtain high-precision positioning information and cover a larger range, and through real-time fusion and compensation of the master and slave device data, the consistency problem in multi-source data fusion is solved, so that the finally generated UAV position information is comprehensive and accurate, and the precision and reliability of UAV positioning are improved.

[0018] Optionally, the step of gradually matching the first dynamic feature and the second dynamic feature according to the angle range and the distance range based on the feature matching algorithm to obtain a dynamic feature matching result includes:

[0019] preprocess the first dynamic feature and the second dynamic feature;

[0020] initialize an angle matching window and a distance matching window;

[0021] match the angle range in the first dynamic feature and the second dynamic feature one by one, and obtain an angle range matching result based on the angle matching window;

[0022] compare the corresponding distance ranges according to the angle range matching result one by one, and determine the corresponding distance range matching result based on the distance matching window;

[0023] 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;

[0024] Each matching pair in the dynamic feature matching result set is verified based on a matching offset threshold value;

[0025] Matching pairs exceeding the matching offset threshold value are eliminated to obtain a dynamic feature matching result that passes verification.

[0026] By using the above technical solution, the dynamic feature matching algorithm realizes efficient matching and verification of master and slave device data, gradually improves matching accuracy and eliminates invalid data, can generate a highly reliable dynamic feature matching result set, ensures the accuracy of the calibration process, optimizes the calculation efficiency, and provides solid data support for unmanned aerial vehicle positioning and calibration.

[0027] 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.

[0028] 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:

[0029] Iterating through the preliminary calibration parameter set, the preliminary calibration parameter set is subjected to redundancy elimination;

[0030] Detecting missing calibration parameters in the preliminary calibration parameter set and performing interpolation completion;

[0031] Initializing a calibration parameter dictionary; wherein the key of the calibration parameter dictionary is an angle range and a distance range, and the value is a corresponding calibration parameter set;

[0032] The preliminary calibration parameter set after interpolation completion is filled into the calibration parameter dictionary;

[0033] The calibration parameter dictionary is subjected to consistency verification to obtain a calibration parameter dictionary that passes verification.

[0034] By using the above technical solution, the preliminary calibration parameter set is optimized, redundant data is removed, random errors are reduced, missing angle and distance ranges are effectively completed by interpolation, accurate and systematic data support is provided for unmanned aerial vehicle calibration, and calibration efficiency and effect are significantly improved.

[0035] Optionally, the step of generating unmanned aerial vehicle calibration position information according to the calibration parameter dictionary includes:

[0036] Loading the calibration parameter set in the calibration parameter dictionary;

[0037] correct the first unmanned aerial vehicle position information and the second unmanned aerial vehicle position information respectively according to the calibration parameter set; and calculate a three-dimensional coordinate set corresponding to the master device based on the corrected first unmanned aerial vehicle position information;

[0038] calculate a three-dimensional coordinate set corresponding to the slave device based on the corrected second unmanned aerial vehicle position information;

[0039] fuse the three-dimensional coordinate sets corresponding to the master device and the slave device based on the timestamp to obtain fused unmanned aerial vehicle calibration position information.

[0040] By using the above technical solutions, the data of the master and slave devices are corrected and 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.

[0041] Optionally, the step of generating unmanned aerial vehicle calibration position information according to the calibration parameter dictionary further comprises:

[0042] validity verification is performed on the unmanned aerial vehicle calibration position information to detect the deviation of the unmanned aerial vehicle calibration position information from the first unmanned aerial vehicle position information;

[0043] If the deviation exceeds the preset threshold, a calibration error state is returned and the preliminary calibration parameter set is re-optimized.

[0044] If not, it is determined that the verification is passed, and the unmanned aerial vehicle calibration position information is formatted and output.

[0045] By using the above technical solutions, the new validity verification and deviation judgment steps are added, and the reliability and accuracy of the calibration result are further guaranteed. When the calibration result has a problem, the system can automatically return to an error state and re-optimize the preliminary calibration parameter set, thereby avoiding the influence of the error result on the downstream operation.

[0046] In a second aspect, the present application provides a unmanned aerial vehicle position information calibration system, which adopts the following technical solutions:

[0047] A unmanned aerial vehicle position information calibration system, the calibration system comprises:

[0048] An initialization calling module is configured 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;

[0049] A position information receiving module is configured to receive first unmanned aerial vehicle position information obtained by the master device and second unmanned aerial vehicle position information obtained by the slave device, wherein the unmanned aerial vehicle position information comprises an angle range and a distance range of position information;

[0050] a position information processing module, configured to timestamp-align and preprocess the first unmanned aerial vehicle position information and the second unmanned aerial vehicle position information;

[0051] a dynamic feature extraction module, configured to extract 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 respectively;

[0052] a feature matching module, configured 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;

[0053] a calibration parameter set generation module, configured to generate a preliminary calibration parameter set according to the dynamic feature matching result;

[0054] a calibration parameter dictionary generation module, configured to optimize the preliminary calibration parameter set and complete missing data in the preliminary calibration parameter set, to generate a calibration parameter dictionary;

[0055] a position information calibration module, configured to generate unmanned aerial vehicle calibration position information according to the calibration parameter dictionary.

[0056] Optionally, the calibration system further comprises:

[0057] a verification module, configured to verify validity of the unmanned aerial vehicle calibration position information, and detect deviation of the unmanned aerial vehicle calibration position information from the first unmanned aerial vehicle position information;

[0058] a judgment module, configured to judge whether the deviation exceeds a preset threshold, and output a first judgment result if the deviation exceeds the preset threshold, or output a second judgment result if the deviation does not exceed the preset threshold;

[0059] 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;

[0060] a calibration position information output module, configured to determine that the verification is passed in response to the second judgment result, and output the unmanned aerial vehicle calibration position information after format processing.

[0061] In a third aspect, the present application provides a computer device, which adopts the technical scheme as follows:

[0062] A computer device comprises a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to implement the steps of the method according to the first aspect.

[0063] In a fourth aspect, the present application provides a computer readable storage medium, which adopts the technical scheme as follows:

[0064] A computer-readable storage medium storing a computer program capable of being loaded and executed by a processor to perform any one of the methods of the first aspect.

[0065] 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 final generated unmanned aerial vehicle position information is comprehensive and accurate, improving the accuracy and reliability of unmanned aerial vehicle positioning. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 is a first flowchart of an unmanned aerial vehicle position information calibration method according to an embodiment of the present application.

[0067] Figure 2 is a second flowchart of an unmanned aerial vehicle position information calibration method according to an embodiment of the present application.

[0068] Figure 3 is a third flowchart of an unmanned aerial vehicle position information calibration method according to an embodiment of the present application.

[0069] Figure 4 is a fourth flowchart of an unmanned aerial vehicle position information calibration method according to an embodiment of the present application.

[0070] Figure 5 is a fifth flowchart of an unmanned aerial vehicle position information calibration method according to an embodiment of the present application. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical scheme and advantages of the present application clearer, the following will combine the drawings of the present application with the specific embodiments of the present application to further describe the present application in detail. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. Figures 1-5 The present application will be described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0072] The embodiments of the present application disclose an unmanned aerial vehicle position information calibration method.

[0073] Referring to Figure 1 , an unmanned aerial vehicle position information calibration method, the calibration method comprising:

[0074] Step S101, call the device initialization module to load the master device and the slave device, configure the type and parameters of the master device and the slave device; wherein the master device (such as an optical-electric device) and the slave device (such as a radar device) have different precision and characteristics, and by calling the initialization module, the following functions can be realized: system call to the underlying driver to identify and access the device, to ensure that the 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 state and running state of the device to ensure that the device is fault-free.

[0075] Exemplarily, the master device can be set as an optical-electric device through an interface function, and its monitored angle range is configured; similarly, the slave device can be set as a radar device, and its distance monitoring range is configured.

[0076] Step S102, receive the first unmanned aerial vehicle position information obtained by the master device and the second unmanned aerial vehicle position information obtained by the slave device, the unmanned aerial vehicle position information including the angle range and distance range of the position information;

[0077] Specifically, the master device has high precision and is responsible for providing accurate position information; the slave device has a larger coverage range and provides rough position information, and this master-slave device cooperative collection mechanism helps to balance the precision and coverage range. Exemplarily, the master device and the slave device structure the output position information as {angle range, distance range, timestamp}.

[0078] Step S103, time stamp alignment and preprocessing of the first unmanned aerial vehicle position information and the second unmanned aerial vehicle position information;

[0079] Wherein, the data collected by the master-slave device usually has different timestamps and precision, so the data needs to be aligned and preprocessed to ensure the consistency of the subsequent processing logic, which can be standardized by calling the time alignment algorithm. In addition, the data preprocessing step includes 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 conform to physical constraints as invalid frames).

[0080] Step S104, respectively extract the first dynamic feature of the first unmanned aerial vehicle position information and the second dynamic feature of the second unmanned aerial vehicle position information;

[0081] Wherein, the dynamic feature extraction is to extract key features from the data of the master-slave device, so as to facilitate subsequent matching and calibration, and the dynamic feature specifically includes important change information in the unmanned aerial vehicle motion trajectory, such as angle offset, distance change trend, etc.

[0082] Specifically, the high-precision data provided by the master device can directly extract the instantaneous angle and position of the UAV, such as {angle offset: 1°, distance change: 10 meters}; the slave device data needs to be extracted by interpolation and fitting methods, such as calculating the trend of distance change based on the time series of multiple frames of data: {trend slope: -0.5 meters / second}.

[0083] Step S105, based on the feature matching algorithm, the first dynamic feature and the second dynamic feature are matched step by step according to the angle range and the distance range, and a dynamic feature matching result is obtained;

[0084] Among them, the feature matching is the core step of the UAV calibration, through the comparison of the dynamic features of the master and slave devices, the corresponding relationship between the two is found out, and then the parameters required for calibration are generated.

[0085] Exemplarily, a multi-level feature matching method can be adopted, first coarse matching is performed in the angle range, and then fine matching is performed according to the distance range in the matched angle 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}.

[0086] Step S106, generating a preliminary calibration parameter set according to the dynamic feature matching result;

[0087] 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 on the basis of dynamic feature matching, the compensation values required for calibration are further calculated, including angle, distance and speed compensation. For example, according to the matching result, the horizontal angle compensation value (such as 0.2°), the pitch angle compensation value, the distance compensation value (such as 20 meters) and the speed compensation value are calculated, and these compensation values are stored in the preliminary calibration parameter set.

[0088] 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;

[0089] Among them, the goal of calibration parameter optimization is to eliminate redundancy and inconsistency in the data, and to complete the missing parameters by interpolation. Specifically, parameter optimization, for example, weighted average of high-frequency features (such as angle change), reduces random error; data completion, for example, completes some un-covered angle range (such as missing data of 4°-5°) by interpolation.

[0090] It can be understood that the optimized and completed calibration parameter dictionary has higher precision and integrity, and can be directly used for calibration calculation.

[0091] Step S108, generating the UAV calibration position information according to the calibration parameter dictionary.

[0092] Wherein, 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, so as to align with the master device data, and the unified format UAV position information is output, such as {coordinate: (x, y, z), timestamp: 10:00:00}.

[0093] In the above embodiment, based on the cooperative collection of the master and slave devices, the system can obtain high-precision positioning information and cover a larger range. Through real-time fusion and compensation of the master and slave device data, the consistency problem in multi-source data fusion is solved, so as to ensure that the finally generated UAV position information is comprehensive and accurate, and the precision and reliability of UAV positioning are improved.

[0094] Reference Figure 2 As an embodiment of step S105, 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 the dynamic feature matching result includes:

[0095] Step S201, preprocessing the first dynamic feature and the second dynamic feature;

[0096] Wherein, the dynamic feature original data may contain noise or abnormal values, and direct use may cause matching error. The purpose of preprocessing is to improve data quality through normalization, denoising and abnormal value filtering.

[0097] Exemplarily, the angle range is standardized to [0, 1] to facilitate subsequent calculation and comparison; the distance range is standardized according to the maximum monitoring distance of the device to eliminate the scale difference between different devices; a filter (such as a low-pass filter) can be used to smooth the high-frequency noise in the dynamic feature; data beyond the range is removed according to the physical limit (such as the minimum and maximum working range of the device).

[0098] Step S202, initializing the angle matching window and the distance matching window;

[0099] Wherein, the initialization of the matching window is to set the tolerance range of matching, so as to control the precision and efficiency of matching. The setting of the angle matching window and the distance matching window should be based on the accuracy of the device and the actual task demand.

[0100] For example, if the accuracy of the device angle measurement is 0.1°, the angle matching window W A =[-0.1, 0.1] can be set; if the distance measurement error is 10 meters, the distance matching window W D =[-10 meters, 10 meters] can be set.

[0101] It can be understood that by reasonably setting the matching window, the accuracy of feature matching is improved, and irrelevant data is avoided.

[0102] In step S203, the angle range in the first dynamic feature and the second dynamic feature is matched one by one, and the angle range matching result is obtained based on the angle matching window.

[0103] The angle range matching is the first step of feature matching, and the tolerance range of the angle offset value can be used to quickly screen potential matching features.

[0104] Specifically, the angle matching process is as follows: each first dynamic feature T i of the master device and each second dynamic feature T j of the slave device are traversed. i j If ΔA∈W A , the matching result M A (i,j) is recorded.

[0105] In step S204, the corresponding distance range is compared one by one according to the angle range matching result, and the corresponding distance range matching result is determined based on the distance matching window.

[0106] The corresponding distance range is further compared based on the angle range matching, and the accuracy of matching is improved through the double constraints of angle and distance, and false matching is avoided.

[0107] Specifically, the distance matching process is as follows: for each angle matching result M A (i,j), the distance offset value ΔD=D i -D j is calculated, and if ΔD∈W D , the matching pair M D (i,j) is recorded. It should be noted that for the feature pair that matches the angle and the distance at the same time, different weights can be given according to the importance of the device.

[0108] In step S205, the horizontal angle offset value, the distance offset value and the speed offset value are calculated according to the distance range matching result, and a dynamic feature matching result set is obtained.

[0109] The calculation of the dynamic feature matching result is the core step of the calibration parameter generation, and the difference between the master device data and the slave device data is accurately quantified by calculating the offset value.

[0110] Specifically, the calculation formula of the horizontal angle offset value is ΔA=A i -A j The calculation formula of the distance offset value is ΔD=D i -D j ​, the calculation formula of the speed offset value is: AV = V i -V j Finally, the matching result can be stored in structured data, for example: {i, j, AA, AD, AV}.

[0111] Step S206, verifying each matching pair in the dynamic feature matching result set based on the matching offset threshold; the purpose of the verification step is to filter out reliable matching features and eliminate invalid data with too large deviations, and if any offset value in the matching pair exceeds the threshold range, the matching pair can be determined as invalid.

[0112] Step S207, eliminating the matching pairs exceeding the matching offset threshold to obtain the dynamic feature matching results that pass the verification.

[0113] Wherein, the dynamic feature matching result set is traversed, all matching pairs that do not pass the verification are removed, and the matching pairs that pass the verification are output in a unified format, for example: {i, j, AA, AD, AV}.

[0114] In the above embodiments, the dynamic feature matching algorithm realizes efficient matching and verification of master and slave device data, gradually improves matching accuracy and eliminates invalid data, can generate a highly reliable dynamic feature matching result set, ensures the accuracy of the calibration process, optimizes the calculation efficiency, and provides solid data support for unmanned aerial vehicle positioning and calibration.

[0115] Referring to Figure 3 As an embodiment of step S107, the step of optimizing the preliminary calibration parameter set and completing the missing data in the preliminary calibration parameter set to generate the calibration parameter dictionary includes:

[0116] Step S301, traversing the preliminary calibration parameter set to eliminate redundancy of the preliminary calibration parameter set;

[0117] Wherein, the preliminary calibration parameter set may contain duplicate data or redundant records, such as data points with similar angle ranges and distance ranges, and the purpose of redundancy elimination is to reduce data redundancy, improve calibration efficiency, and ensure the simplicity and consistency of the calibration parameter set.

[0118] Step S302, detecting missing calibration parameters in the preliminary calibration parameter set and performing interpolation completion;

[0119] Wherein, due to the limitations of the device or the collection process, the preliminary calibration parameter set may have missing data in the angle range or distance range, and through interpolation completion, a complete calibration parameter coverage range can be generated to provide complete data support for subsequent tasks.

[0120] Specifically, by traversing all possible combinations of the angle range and the distance range (all A, D values within the required range of the task), combinations not present in the preliminary calibration parameter set are marked. For missing angle range and distance range combinations, linear interpolation can be performed based on adjacent data points.

[0121] Step S303, initialize the calibration parameter dictionary;

[0122] Wherein, the key of the calibration parameter dictionary is the angle range and the distance range, and the value is the corresponding calibration parameter set;

[0123] 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.

[0124] Exemplarily, the key of the calibration parameter dictionary is (A, D), representing the combination of the angle range and the distance range, and the value is the calibration parameter set {C A ,C θ ,C D ,C V}.

[0125] Step S304, fill the preliminary calibration parameter set after interpolation completion to the calibration parameter dictionary;

[0126] Wherein, the filling operation is to store the completed calibration parameter set in the dictionary, so that the correspondence of the key-value pair is clear, and after the filling is completed, the calibration parameter dictionary can be used as an efficient query tool.

[0127] Step S305, perform consistency check on the calibration parameter dictionary, and obtain the calibration parameter dictionary that passes the check.

[0128] Wherein, 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 discontinuity), and through the check, the reliability of the calibration parameter dictionary can be further improved.

[0129] In the above embodiments, the preliminary calibration parameter set is optimized, redundant data is removed, random errors are reduced, and missing angles and distance ranges are effectively completed by interpolation, providing accurate and systematic data support for unmanned aerial vehicle calibration, and significantly improving the calibration efficiency and effect.

[0130] Referring to Figure 4 , as an embodiment of step S108, the step of generating unmanned aerial vehicle calibration position information according to the calibration parameter dictionary comprises:

[0131] Step S401, load the calibration parameter set in the calibration parameter dictionary;

[0132] The calibration parameter dictionary stores a calibration parameter set generated according to dynamic feature matching, including compensation values (such as horizontal angle compensation values, pitch angle compensation values, distance compensation values, and speed compensation values) for different angle ranges and distance ranges, and the purpose of loading the calibration parameter set is to provide accurate basis for subsequent position information correction.

[0133] At step S402, the first unmanned aerial vehicle position information and the second unmanned aerial vehicle position information are respectively corrected according to the calibration parameter set.

[0134] The first unmanned aerial vehicle position information obtained by the master device and the second unmanned aerial vehicle position information obtained by the slave device respectively describe the angle range, distance range, and timestamp of the unmanned aerial vehicle, and the purpose of correction is to compensate for errors of both through the calibration parameters, so that the data of both are consistent and aligned as much as possible.

[0135] It should be noted that the first unmanned aerial vehicle position information is usually high-precision data obtained by the master 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 unmanned aerial vehicle position information is usually low-precision data, and the correction of the calibration parameter set will adjust the second unmanned aerial vehicle position information more, so as to make it as close as possible to the data of the master device.

[0136] At step S403, a three-dimensional coordinate set corresponding to the master device is calculated based on the corrected first unmanned aerial vehicle position information.

[0137] The three-dimensional coordinate calculation of the master device is to convert the corrected angle and distance information into x, y, and z coordinates in space, and since the data of the master device usually has high precision, the three-dimensional coordinate set {(x1, y1, z1, T1)} generated by the master device can be used as a reference for fusion.

[0138] At step S404, a three-dimensional coordinate set corresponding to the slave device is calculated based on the corrected second unmanned aerial vehicle position information.

[0139] 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.

[0140] At step S405, the three-dimensional coordinate sets corresponding to the master device and the slave device are fused based on the timestamp to obtain fused unmanned aerial vehicle calibration position information.

[0141] The three-dimensional coordinate fusion of the master and slave devices is to unify the position information of both into a high-precision result, and the fusion can be realized through timestamp alignment and weighted average. The fused three-dimensional coordinate set {(x final , y final , z finalThe UAV position information with high precision is provided, and the advantages of the master device and the slave device are integrated.

[0142] Exemplarily, for the master device coordinate set (x1, y1, z1, T1) and the slave device coordinate set (x2, y2, z2, T2), a record pair (T1≈T2) with the closest timestamp is found, and then a weighted average coordinate is calculated for the record pair aligned in time:

[0143] x final =w1+x1+w2+x2

[0144] y final =w1+y1+w2+y2;

[0145] z final =w1+z1+w2+z2

[0146] wherein the weight w1 corresponding to the master device and the weight w2 corresponding to the slave device can be set according to the device precision, and generally w1>w2.

[0147] In the above embodiment, the data of the master device and the slave device are respectively corrected and three-dimensional coordinates are calculated, and finally the position information of the master device and the slave device is unified into a high-precision result through a timestamp alignment and fusion algorithm.

[0148] With reference to Figure 5 , as a further embodiment of the calibration method, after the step of generating the UAV calibration position information according to the calibration parameter dictionary, the method further comprises:

[0149] In step S501, the validity of the UAV calibration position information is verified, and the deviation of the UAV calibration position information from the first UAV position information is detected.

[0150] The core of the validity verification is to check whether the calibrated UAV calibration position information is consistent with the high-precision master device (the first UAV position information). By comparing the deviation of the two, the accuracy of the calibration can be judged, and it is ensured that the output result meets the expectation.

[0151] In step S502, it is judged whether the deviation exceeds a preset threshold. If yes, the process jumps to step S503; if no, the process jumps to step S504.

[0152] In step S503, a calibration error state is returned, and the preliminary calibration parameter set is re-optimized.

[0153] Specifically, the calculated deviation is compared with the preset threshold to judge whether the calibration result meets the precision requirement. If the deviation exceeds the threshold, it means that the calibration result may have a problem, and the preliminary calibration parameter set needs to be re-optimized to improve the calibration effect. An error state is returned to prompt the operation failure, and an opportunity is provided for subsequent optimization.

[0154] In step S504, it is determined that the verification is passed, and the UAV calibration position information is formatted and outputted.

[0155] Wherein, when the deviation is within the threshold range, the calibration result is considered valid. The formatting processing is to arrange the calibration position information into a unified structure and output, facilitating subsequent system calling and analysis.

[0156] In the above embodiment, the effectiveness verification and deviation judgment steps are added, and the reliability and accuracy of the calibration result are further guaranteed. When the calibration result has a problem, the system can automatically return an error state and re-optimize the preliminary calibration parameter set, thereby avoiding the influence of the error result on the downstream operation.

[0157] The application also discloses a UAV position information calibration system.

[0158] A UAV position information calibration system, the calibration system comprising:

[0159] An initialization calling module is configured 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;

[0160] A position information receiving module is configured to receive first UAV position information acquired by the master device and second UAV position information acquired by the slave device, wherein the UAV position information comprises an angle range and a distance range of position information;

[0161] A position information processing module is configured to align the first UAV position information and the second UAV position information in time stamp and pre-process the first UAV position information and the second UAV position information;

[0162] A dynamic feature extraction module is configured to extract a first dynamic feature of the first UAV position information and a second dynamic feature of the second UAV position information, respectively;

[0163] A feature matching module is configured 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;

[0164] A calibration parameter set generation module is configured to generate a preliminary calibration parameter set according to the dynamic feature matching result;

[0165] A calibration parameter dictionary generation module is configured to optimize the preliminary calibration parameter set, and complete missing data in the preliminary calibration parameter set, to generate a calibration parameter dictionary;

[0166] A position information calibration module is configured to generate UAV calibration position information according to the calibration parameter dictionary.

[0167] In the above embodiments, through dynamic feature matching and calibration parameter optimization of the master device (such as an optical-electric device) and the slave device (such as a radar device), high-precision calibration of the unmanned aerial vehicle position information is achieved, which is suitable for various application scenarios such as unmanned aerial vehicle navigation, positioning and target tracking.

[0168] As a further implementation of the calibration system, the calibration system further comprises:

[0169] The verification module is configured to verify the validity of the unmanned aerial vehicle calibration position information, and detect the deviation of the unmanned aerial vehicle calibration position information from the first unmanned aerial vehicle position information.

[0170] The judgment module is configured to judge whether the deviation exceeds a preset threshold, and if so, output a first judgment result; if not, output a second judgment result.

[0171] The calibration error processing module is configured to, in response to the first judgment result, return a calibration error state and re-optimize the preliminary calibration parameter set.

[0172] The calibration position information output module is configured to, in response to the second judgment result determining that the verification is passed, format and output the unmanned aerial vehicle calibration position information.

[0173] The unmanned aerial vehicle position information calibration system of the embodiments of the present application can implement any of the above-mentioned unmanned aerial vehicle position information calibration methods, and the specific working processes of each module in the unmanned aerial vehicle position information calibration system can refer to the corresponding processes in the above-mentioned method embodiments.

[0174] In the several embodiments provided in the present application, it should be understood that the provided methods and systems can be implemented in other manners. For example, the above-mentioned system embodiments are merely schematic; for example, the division of a certain module is merely a logical function division, and there can be another division manner in actual implementation; for example, a plurality of modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0175] The embodiments of the present application also disclose a computer device.

[0176] The computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the above-mentioned unmanned aerial vehicle position information calibration method when executing the computer program.

[0177] The embodiments of the present application also disclose a computer readable storage medium.

[0178] The computer readable storage medium stores a computer program capable of being loaded and executed by a processor to implement any of the above-mentioned unmanned aerial vehicle position information calibration methods.

[0179] Computer readable storage media can include any tangible media that stores programs (e.g., physical media), which can be used by or in connection with an instruction execution system, apparatus, or device. The system, apparatus, or device reads the program from the computer readable storage media and executes the program by using at least one processing unit. In the hybrid embodiment, the program can be executed in the same device or another device that is different from the device that executes the program. The computer readable media can also be, and include any suitable media that stores instructions, including a hard disk, a floppy disk, a CD-ROM, a DVD, a Blu-ray Disc, a flash memory, a DRAM, a SRAM, a cache, a cache memory, a ROM, an EEPROM, or any suitable memory. The program can be transmitted from the computer readable storage media to a computer memory (e.g., a RAM or a cache), where the program executed by the instruction execution system, apparatus, or device. As long as the program is transmitted, the computer readable storage media is part of the general scope of the computer readable media. The program code contained on the computer readable media can be transmitted using any suitable medium, including but not limited to wireless, wire line, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0180] It should be noted that the above-described embodiments are merely intended to illustrate the present application, and the description of each embodiment focuses on different aspects. The parts not described in detail in a certain embodiment can be understood with reference to the relevant description of other embodiments.

[0181] 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 the specification (including the abstract and the drawings) can be replaced by other equivalent or similar features unless specifically stated. That is, each feature is only an example of a series of equivalent or similar features unless specifically stated.

Claims

1. A method for calibrating position information of a UAV, characterized in that, The calibration method comprises: The device initialization module loads the master device and the slave device, and configures the types and parameters of the master device and the slave device; Receiving first unmanned aerial vehicle position information obtained by the master device and second unmanned aerial vehicle position information obtained by the slave device, wherein the unmanned aerial vehicle position information comprises an angle range and a distance range of position information; Timestamp alignment and preprocessing are performed on the first unmanned aerial vehicle position information and the second unmanned aerial vehicle position information; First dynamic features of the first unmanned aerial vehicle position information and second dynamic features of the second unmanned aerial vehicle position information are extracted respectively; The first dynamic features and the second dynamic features are preprocessed; An angle matching window and a distance matching window are initialized; The angle ranges in the first dynamic features and the second dynamic features are matched one by one, and an angle range matching result is obtained based on the angle matching window; According to the angle range matching result, the corresponding distance ranges are compared one by one, and a corresponding distance range matching result is determined 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, and a dynamic feature matching result set is obtained; Each matching pair in the dynamic feature matching result set is verified based on a matching offset threshold; Matching pairs exceeding the matching offset threshold are removed, and a dynamic feature matching result that passes verification is obtained; A preliminary calibration parameter set is generated according to the dynamic feature matching result; The preliminary calibration parameter set is optimized and missing data in the preliminary calibration parameter set is completed to generate a calibration parameter dictionary; Unmanned aerial vehicle calibration position information is generated according to the calibration parameter dictionary.

2. The unmanned aerial vehicle position information calibration method of claim 1, wherein: The preliminary calibration parameter set comprises a horizontal angle compensation value, a pitch angle compensation value, a distance compensation value and a speed compensation value.

3. The unmanned aerial vehicle position information calibration method of claim 2, wherein, 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 comprises: Iterate through the preliminary calibration parameter set to eliminate redundancies in the preliminary calibration parameter set; Detect missing calibration parameters in the preliminary calibration parameter set and perform interpolation completion; 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; The interpolation-completed preliminary calibration parameter set is filled into the calibration parameter dictionary; The calibration parameter dictionary is subjected to consistency verification to obtain a calibration parameter dictionary that passes verification.

4. The unmanned aerial vehicle position information calibration method according to any one of claims 1 to 3, characterized in that, The step of generating unmanned aerial vehicle calibration position information according to the calibration parameter dictionary comprises: Loading a calibration parameter set in the calibration parameter dictionary; According to the calibration parameter set, the first unmanned aerial vehicle position information and the second unmanned aerial vehicle position information are respectively corrected; Based on the corrected first unmanned aerial vehicle position information, a three-dimensional coordinate set corresponding to the master device is calculated; Based on the corrected second unmanned aerial vehicle position information, a three-dimensional coordinate set corresponding to the slave device is calculated; Based on the timestamps, the three-dimensional coordinate sets corresponding to the master device and the slave device are fused to obtain fused unmanned aerial vehicle calibration position information.

5. The unmanned aerial vehicle position information calibration method of claim 4, wherein, The method further comprises, after the step of generating the UAV calibration position information according to the calibration parameter dictionary: validating the UAV calibration position information, detecting deviation of the UAV calibration position information from the first UAV position information; determining whether the deviation exceeds a preset threshold, and if so, returning a calibration error state and re-optimizing the preliminary calibration parameter set; if not, determining that the validation is passed, and outputting the UAV calibration position information after format processing.

6. A UAV position information calibration system, characterized in that, The calibration system comprises: an initialization calling module configured to call a device initialization module to load a master device and a slave device, and configure types and parameters of the master device and the slave device; a position information receiving module configured to receive first UAV position information acquired by the master device and second UAV position information acquired by the slave device, the UAV position information comprising an angle range and a distance range of position information; a position information processing module configured to align time stamps of the first UAV position information and the second UAV position information and pre-process the first UAV position information and the second UAV position information; a dynamic feature extraction module configured to extract first dynamic features of the first UAV position information and second dynamic features of the second UAV position information respectively; a feature matching module configured to pre-process the first dynamic features and the second dynamic features, initialize an angle matching window and a distance matching window, match the angle ranges in the first dynamic features and the second dynamic features one by one, obtain 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 result, determine a corresponding distance range matching result based on the distance matching window, calculate a horizontal angle offset value, a distance offset value and a speed offset value according to the distance range matching result, and obtain a dynamic feature matching result set, verify each matching pair in the dynamic feature matching result set based on a matching offset threshold, and eliminate matching pairs exceeding the matching offset threshold to obtain a dynamic feature matching result passing the verification; a calibration parameter set generation module configured to generate a preliminary calibration parameter set according to the dynamic feature matching result; a calibration parameter dictionary generation module configured to optimize the preliminary calibration parameter set, and complete missing data in the preliminary calibration parameter set to generate a calibration parameter dictionary; a position information calibration module configured to generate UAV calibration position information according to the calibration parameter dictionary.

7. The unmanned aerial vehicle position information calibration system of claim 6, wherein, The calibration system further comprises: a validation module configured to validate the UAV calibration position information, and detect deviation of the UAV calibration position information from the first UAV position information; a determination module configured to determine whether the deviation exceeds a preset threshold, and if so, output a first determination result; if not, output a second determination result; a calibration error processing module configured to, in response to the first determination result, return a calibration error state and re-optimize the preliminary calibration parameter set; a calibration position information output module configured to, in response to the second determination result, determine that the validation is passed, and output the UAV calibration position information after format processing.

8. A computer device, comprising: A computer program product comprising a storage medium to store the program which, when executed by a processor, implements the method as claimed in any of claims 1 to 5.

9. A computer-readable storage medium, characterized in that: A computer program product comprising a storage medium to store the program which, when executed by a processor, implements the method as claimed in any of claims 1 to 5.

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