Multi-rotor unmanned aerial vehicle multi-source positioning method and device, terminal and medium

Abstract

This application relates to the field of high-precision positioning, and provides a multi-source positioning method, device, terminal, and medium for multi-rotor UAVs. The method includes: acquiring k positioning information points of the multi-rotor UAV to be positioned at the positioning time, wherein the k positioning information points are of different types, and acquiring the current environmental information of the multi-rotor UAV to be positioned; acquiring error factors corresponding to the k positioning information points respectively, to obtain k first target error factors; and fusing the k positioning information points according to the k first target error factors and the current environmental information to obtain target positioning information for high-precision positioning of the multi-rotor UAV to be positioned. This method can combine multiple positioning information points, corresponding error factors, and environmental information to finally obtain target positioning information, thereby improving the accuracy of target positioning information determination.

Description

Technical Field

[0001] This application relates to the field of high-precision positioning technology, specifically to a multi-source positioning method, device, terminal, and medium for multi-rotor unmanned aerial vehicles. Background Technology

[0002] The control system of a multi-rotor UAV to be positioned needs to provide high-precision and real-time information such as position, velocity and attitude estimation.

[0003] Existing solutions combine multiple positioning information sources to obtain the location information of the multirotor UAV to be located. However, these solutions typically use fixed weight values ​​for location information fusion, which can lead to the fused location information being unsuitable for precise positioning in multi-purpose scenarios, resulting in reduced positioning accuracy. Summary of the Invention

[0004] This application provides a multi-source positioning method, device, terminal, and medium for multi-rotor unmanned aerial vehicles (UAVs), which can improve the accuracy of UAV positioning.

[0005] A first aspect of this application provides a multi-source positioning method for a multi-rotor unmanned aerial vehicle, the method comprising:

[0006] Obtain k positioning information points of the multi-rotor UAV to be positioned at the positioning time, where the k positioning information points are of different types, and obtain the current environmental information of the multi-rotor UAV to be positioned.

[0007] Obtain the error factors corresponding to k positioning information points to obtain k first target error factors;

[0008] Based on k first target error factors and current environmental information, k positioning information is fused to obtain target positioning information for high-precision positioning of the multi-rotor UAV to be positioned.

[0009] In one possible implementation, obtaining the error factors corresponding to the k positioning information to obtain the k first target error factors includes:

[0010] Obtain the attribute information of the positioning sensors corresponding to k positioning information points to obtain k device attribute information points;

[0011] Error factors are evaluated based on k device attribute information to obtain k first target error factors.

[0012] In one possible implementation, the step of evaluating error factors based on k device attribute information to obtain k first target error factors includes:

[0013] The inherent error factor is extracted based on the device identifier in the target device attribute information, where the target device attribute information is any one of k device attribute information;

[0014] Based on the deployment information in the target device attribute information, determine the first error factor correction parameter;

[0015] The second error factor correction parameter is determined based on the usage attribute information in the target device attribute information;

[0016] The inherent error factor is corrected using the first error factor correction parameter and the second error factor correction parameter to obtain the first target error factor;

[0017] Repeat the above method of extracting inherent error factors from the device identifier in the target device attribute information and then correcting the inherent error factors using the first error factor correction parameter and the second error factor correction parameter to obtain the first target error factor, until k device attribute information is obtained for error factor evaluation to obtain k first target error factors.

[0018] In one possible implementation, the step of fusing k positioning information based on k first target error factors and current environmental information to obtain target positioning information for high-precision positioning of the multi-rotor UAV to be positioned includes:

[0019] The environmental parameters in the environmental information are normalized to obtain normalized environmental parameters;

[0020] The normalized environment parameters are vectorized to obtain the normalized environment parameter vector;

[0021] Calculate the offset between the normalized environmental parameter vector and the standard environmental parameter vectors corresponding to the k first target error factors to obtain the k offset vectors;

[0022] Extract the offset volatility and offset mean vectors from the k offset vectors;

[0023] Error factor fluctuation information is determined based on offset volatility and offset mean vector;

[0024] By using error factor fluctuation information to fuse k first target error factors and k positioning information, the target positioning information for high-precision positioning of the multi-rotor UAV to be positioned is obtained.

[0025] In one possible implementation, the method further includes:

[0026] Receive the first location information sent by the associated drone;

[0027] The first positioning information is used to perform position verification processing on the target positioning information to obtain the verification processing result.

[0028] A second aspect of this application provides a multi-rotor unmanned aerial vehicle (UAV) multi-source positioning device, the device comprising:

[0029] The first acquisition unit is used to acquire k positioning information of the multi-rotor UAV to be positioned at the positioning time, wherein the k positioning information are of different types, and to acquire the current environmental information of the multi-rotor UAV to be positioned.

[0030] The second acquisition unit is used to acquire the error factors corresponding to k positioning information respectively, and obtain k first target error factors;

[0031] The fusion unit is used to fuse k positioning information based on k first target error factors and current environmental information to obtain target positioning information for high-precision positioning of the multi-rotor UAV to be positioned.

[0032] In one possible implementation, the second acquisition unit is specifically used for:

[0033] Obtain the attribute information of the positioning sensors corresponding to k positioning information points to obtain k device attribute information points;

[0034] Error factors are evaluated based on k device attribute information to obtain k first target error factors.

[0035] In one possible implementation, regarding the step of evaluating error factors based on k device attribute information to obtain k first target error factors, the second acquisition unit is specifically used for:

[0036] The inherent error factor is extracted based on the device identifier in the target device attribute information, where the target device attribute information is any one of k device attribute information;

[0037] Based on the deployment information in the target device attribute information, determine the first error factor correction parameter;

[0038] The second error factor correction parameter is determined based on the usage attribute information in the target device attribute information;

[0039] The inherent error factor is corrected using the first error factor correction parameter and the second error factor correction parameter to obtain the first target error factor;

[0040] Repeat the above method of extracting inherent error factors from the device identifier in the target device attribute information and then correcting the inherent error factors using the first error factor correction parameter and the second error factor correction parameter to obtain the first target error factor, until k device attribute information is obtained for error factor evaluation to obtain k first target error factors.

[0041] In one possible implementation, the fusion unit is specifically used for:

[0042] The environmental parameters in the environmental information are normalized to obtain normalized environmental parameters;

[0043] The normalized environment parameters are vectorized to obtain the normalized environment parameter vector;

[0044] Calculate the offset between the normalized environmental parameter vector and the standard environmental parameter vectors corresponding to the k first target error factors to obtain the k offset vectors;

[0045] Extract the offset volatility and offset mean vectors from the k offset vectors;

[0046] Error factor fluctuation information is determined based on offset volatility and offset mean vector;

[0047] By using error factor fluctuation information to fuse k first target error factors and k positioning information, the target positioning information for high-precision positioning of the multi-rotor UAV to be positioned is obtained.

[0048] In one possible implementation, the device is further used for:

[0049] Receive the first location information sent by the associated drone;

[0050] The first positioning information is used to perform position verification processing on the target positioning information to obtain the verification processing result.

[0051] A third aspect of this application provides a terminal including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the first aspect of this application.

[0052] A fourth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of this application.

[0053] A fifth aspect of this application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of this application. The computer program product may be a software installation package.

[0054] Implementing the embodiments of this application has the following beneficial effects:

[0055] By acquiring k positioning information points of the multi-rotor UAV to be positioned at the positioning time, where the k positioning information points are of different types, and by acquiring the current environmental information of the multi-rotor UAV to be positioned, and obtaining the error factors corresponding to the k positioning information points respectively, k first target error factors are obtained. Based on the k first target error factors and the current environmental information, the k positioning information points are fused to obtain the target positioning information for high-precision positioning of the multi-rotor UAV to be positioned. Therefore, it is possible to combine multiple positioning information points, corresponding error factors, and environmental information to finally obtain the target positioning information, thereby improving the accuracy of target positioning information determination. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This application provides a flowchart illustrating a multi-source positioning method for a multi-rotor unmanned aerial vehicle (UAV).

[0058] Figure 2 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application;

[0059] Figure 3 This application provides a schematic diagram of the structure of a multi-rotor unmanned aerial vehicle (UAV) multi-source positioning device. Detailed Implementation

[0060] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0061] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0062] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0063] To better understand the multi-source positioning method for multi-rotor UAVs provided in this application, a brief introduction to existing UAV positioning methods is given below. Existing methods fuse multiple positioning information sources to obtain the location information of the multi-rotor UAV to be located. However, these fusion methods typically use fixed weight values ​​for location information fusion. For example, by collecting various types of positioning data, assigning a fixed fusion weight to each type of data, and finally using these weights to fuse the data, the fused location information is obtained. This results in the fused location information being unsuitable for precise positioning in multi-purpose scenarios, leading to reduced positioning accuracy.

[0064] To address the aforementioned issues, this application provides a multi-source positioning method for multi-rotor unmanned aerial vehicles (UAVs), which combines multiple positioning information, corresponding error factors, and environmental information to ultimately obtain target positioning information, thereby improving the accuracy of target positioning information determination.

[0065] Please see Figure 1 , Figure 1 This application provides a flowchart illustrating a multi-source localization method for a multi-rotor unmanned aerial vehicle (UAV). Figure 1 As shown, the method includes:

[0066] 101. Obtain k positioning information points of the multi-rotor UAV to be positioned at the positioning time. The k positioning information points are of different types. Also, obtain the current environmental information of the multi-rotor UAV to be positioned.

[0067] The multi-rotor UAV to be positioned is equipped with a high-precision time-difference positioning measurement unit (RTK), GPS, IMU attitude measurement unit, and altimeter, which includes a barometric altimeter, ultrasonic ranging sensor, and radar altimeter.

[0068] A simple distinction can be made based on horizontal and vertical directions. Horizontal information includes RTK high-precision measurement data and single-point GPS data, while vertical information includes RTK high-precision measurement data, single-point GPS data, the first altitude measured by a barometric altimeter, the second altitude measured by an ultrasonic distance sensor, and the third altitude measured by a radar altimeter. Therefore, positioning information can be fused separately for the horizontal and vertical directions.

[0069] Environmental information can include air pressure, wind speed, temperature, humidity, and weather conditions. Therefore, this environmental information can be vectorized to obtain environmental parameters. The measurement accuracy of the device may fluctuate under different environmental factors. For example, a barometric altimeter's measurement accuracy is higher in windless, normal operating temperature ranges than in windy, non-normal operating temperature ranges. Although the environment's impact on measurement accuracy is not significant, the flight time of a drone is relatively long. Accumulated errors over a long period can lead to a significant overall decrease in measurement accuracy, or even large deviations. Therefore, combining environmental factors with positioning information through fusion processing can improve accuracy.

[0070] 102. Obtain the error factors corresponding to each of the k positioning information to obtain the k first target error factors.

[0071] This can be achieved by acquiring the device attribute information of the positioning sensor corresponding to the positioning information, and then determining the corresponding first target error factor based on the device attribute information. This type of error factor can be understood as the inherent error factor that exists in the positioning sensor itself after it is manufactured, and the error information caused by the aging of the device and circuit, and the adhesion of particles in the environment after use. The error factor is obtained by combining the two.

[0072] 103. Based on the k first target error factors and the current environmental information, perform positioning information fusion on the k positioning information to obtain the target positioning information when performing high-precision positioning of the multi-rotor UAV to be positioned.

[0073] The offset vector can be obtained by combining the normalized environmental parameter vector constructed based on the current environmental information with the optimal environment that the sensor is adapted to. The offset fluctuation and offset mean vectors are then extracted. Finally, the offset fluctuation and offset mean vectors are combined to fuse k positioning information to obtain the target positioning information.

[0074] In this example, by acquiring k positioning information points of the multi-rotor UAV to be positioned at the positioning time, where the k positioning information points are of different types, and acquiring the current environmental information of the multi-rotor UAV to be positioned, the error factors corresponding to the k positioning information points are obtained, resulting in k first target error factors. Based on the k first target error factors and the current environmental information, the k positioning information points are fused to obtain the target positioning information for high-precision positioning of the multi-rotor UAV to be positioned. Therefore, it is possible to combine multiple positioning information points, corresponding error factors, and environmental information to finally obtain the target positioning information, thereby improving the accuracy of target positioning information determination.

[0075] In one possible implementation, a method for obtaining k error factors corresponding to k positioning information points to obtain k first target error factors includes:

[0076] A1. Obtain the attribute information of the positioning sensors corresponding to k positioning information to obtain k device attribute information;

[0077] A2. Based on the error factor evaluation of k device attribute information, k first target error factors are obtained.

[0078] The device attribute information includes inherent error factors, deployment information, and usage attribute information. Specifically, the inherent error factor can be understood as the error factor corresponding to the inherent positioning error of the device after it leaves the factory; deployment information can be understood as the area where the multi-rotor drone to be positioned is deployed, for example, the type of environment in which it performs the relevant flight mission; usage attribute information can be understood as the usage duration, frequency of use, maintenance information, etc., of the multi-rotor drone to be positioned.

[0079] Therefore, the wear and tear on the multi-rotor UAV to be located varies depending on the deployment scenario and the flight mission it performs. Thus, the error factor can be evaluated by combining attribute information to obtain the first target error factor.

[0080] For example, the inherent error factor can be corrected using deployment information and attribute information to obtain the first target error factor.

[0081] In this example, since the wear and tear on the multi-rotor UAV to be located varies depending on the deployment scenario and the flight mission it performs, deployment information and usage attribute information can be used to correct the inherent error factor, thereby obtaining the first target error factor. This allows for the determination of a more accurate error factor that is adapted to the usage environment, thus improving the accuracy of subsequent positioning.

[0082] In one possible implementation, a method for evaluating error factors based on k device attribute information to obtain k first target error factors includes:

[0083] B1. Extract the inherent error factor based on the device identifier in the target device attribute information. The target device attribute information is any one of the k device attribute information.

[0084] B2. Determine the first error factor correction parameter based on the deployment information in the target device attribute information;

[0085] B3. Determine the second error factor correction parameters based on the usage attribute information in the target equipment attribute information;

[0086] B4. Correct the inherent error factor using the first error factor correction parameter and the second error factor correction parameter to obtain the first target error factor;

[0087] B5. Repeat the above method of extracting inherent error factors from the device identifier in the target device attribute information and then using the first error factor correction parameter and the second error factor correction parameter to correct the inherent error factors to obtain the first target error factor, until k device attribute information is obtained for error factor evaluation to obtain k first target error factors.

[0088] The inherent error factor is the error factor corresponding to the inherent positioning error of the equipment when it is positioned after leaving the factory. It is an error caused by the equipment's attributes and is unavoidable.

[0089] After deployment, drones typically operate within their designated area until retirement. Therefore, they remain in this area for extended periods, performing environmental monitoring and early warning tasks outdoors. They face unfavorable environmental conditions within the deployment area. For example, when monitoring landslides, which often occur after heavy rain or prolonged periods of overcast weather, the drones typically encounter high humidity and strong winds. Prolonged exposure to such environments reduces the accuracy of their equipment's operation and positioning. Therefore, by incorporating factors such as air pressure, wind speed, temperature, humidity, and weather conditions within the deployment environment to correct for error factors, a more accurate assessment of the actual deployment environment can be achieved, ultimately improving the accuracy of the initial target error factor.

[0090] Specifically, the impact of each environmental parameter on the equipment, as well as the fluctuation information of these parameters, can be obtained. This impact can be derived by optimizing a large number of samples. For example, simulating multiple environmental parameters in the deployment information, a sub-correction factor is set for each parameter. Using this factor and an optimization algorithm, the maximum contribution of each environmental parameter to the error under various conditions is obtained. The correction factor corresponding to the maximum contribution is then used as the correction factor for that environmental parameter. Finally, error fusion is performed to obtain the corresponding first error factor correction parameter.

[0091] When determining the second error factor correction parameter based on usage attribute information, the corresponding aging degree can be determined based on the usage attribute information, and the second error factor correction parameter can be determined based on the aging degree. A general aging model can be used for aging prediction to obtain the aging degree corresponding to the usage attribute information. Alternatively, an aging curve can be generated based on the historical usage parameters of the multi-rotor UAV to be located, and the aging degree corresponding to the current usage attribute information can be predicted based on the aging curve.

[0092] Specifically, a method for constructing an aging curve based on historical usage parameters can be:

[0093] Based on the usage duration of each flight mission, the usage frequency within a certain time period, and maintenance information (e.g., a month or three months, i.e., the usage frequency of the multi-rotor UAV to be located within one month), the usage frequency can be quantified into an aging value. A higher usage frequency results in a larger aging value, and a lower usage frequency results in a smaller aging value. A fixed mapping relationship between maintenance information and aging values ​​can be used: a higher maintenance frequency in the maintenance information results in a smaller aging value, and vice versa. Similarly, a longer usage duration during a flight mission results in a larger aging value, and a shorter usage duration results in a smaller aging value. Therefore, the corresponding parameters within a certain time period can be quantified to obtain the quantified aging value. Then, the aging values ​​from multiple time periods are merged to construct an aging curve. The horizontal axis of the aging curve can be the time axis, and the vertical axis can be the aging value axis. Connecting the aging values ​​of each time period with a smooth curve yields the aging curve. This aging curve can then be used to predict the degree of aging, obtaining the aging value corresponding to the usage attribute information. Based on this aging value, the second error factor correction parameter is determined. The second error factor correction parameter corresponding to the aging value can be determined based on the preset mapping relationship between the aging value and the error factor correction parameter.

[0094] The first target error factor can be determined by multiplying the first error factor correction parameter, the second error factor correction parameter, and the inherent error factor. This allows for the correction of the inherent error factor, improving the accuracy of subsequent processing.

[0095] In one possible implementation, the step of fusing k positioning information based on k first target error factors and current environmental information to obtain target positioning information for high-precision positioning of the multi-rotor UAV to be positioned includes:

[0096] C1. Normalize the environmental parameters in the environmental information to obtain normalized environmental parameters;

[0097] C2. Construct a vector from the normalized environment parameters to obtain the normalized environment parameter vector;

[0098] C3. Calculate the offset between the normalized environmental parameter vector and the standard environmental parameter vectors corresponding to the k first target error factors to obtain the k offset vectors.

[0099] C4. Extract the offset volatility and offset mean vectors from the k offset vectors;

[0100] C5. Determine the error factor fluctuation information based on the offset volatility and the offset mean vector;

[0101] C6. Using error factor fluctuation information, the k first target error factors and k positioning information are fused to obtain the target positioning information when performing high-precision positioning of the multi-rotor UAV to be positioned.

[0102] The method for normalizing environmental parameters can be a general normalization approach to obtain normalized environmental parameters. Normalization aims to keep the parameters within a fixed range, thereby improving the representation of fluctuation information during subsequent offset fluctuation extraction. It concentrates fluctuation values ​​within a small range, improving the accuracy of fluctuation display.

[0103] A general vector construction method can be used to construct the normalized environment parameter vector. Each environment parameter in the normalized environment parameter vector corresponds to a parameter in the vector.

[0104] The standard environmental parameters corresponding to each first target error factor can be extracted separately. Since different sensors have different parameters for the operating environment with the lowest error during operation, the parameters of the operating environment with the lowest error can be used as standard environmental parameters for error quantification, offsetting, etc., which can more realistically reflect the impact of the actual environment on the error. The difference between corresponding terms between the normalized environmental parameter vector and the corresponding standard environmental parameter vector can be used as the offset to construct the corresponding offset vector.

[0105] The offset volatility can be extracted from the vector values ​​in the offset vector. Volatility can be represented using the mean squared error method, which involves extracting the mean squared error corresponding to each vector value class, resulting in multiple sub-offset volatility values. Finally, the mean of these sub-offset volatility values ​​is determined as the offset volatility. Alternatively, the mean of the corresponding values ​​in k offset vectors can be calculated directly to obtain the offset mean vector.

[0106] After determining the offset volatility, the error factor volatility information can be calculated by combining it with the offset mean vector. Specifically, the product of the offset volatility and the offset mean vector can be used to determine the error factor volatility information.

[0107] Finally, the error factor fluctuation information is superimposed onto the k first target error factors to obtain the actual second target error factors. Weights are calculated based on the k second target error factors to obtain the fusion weights corresponding to the k positioning information. Finally, the fusion weights are used to calculate the target positioning information.

[0108] When superimposing error factor fluctuation information onto k first target error factors, the specific process involves first quantizing the error factor fluctuation information to obtain quantized values. Then, the sum of these quantized values ​​and the k first target error factors is used to determine the actual second target error factors. Finally, based on the mapping relationship between the second target error factors and their corresponding fusion weights, the fusion weights for each second target error factor are determined. Finally, the fusion weights are used for weight calculation to obtain the target positioning information. Therefore, superimposing environmental fluctuations onto the target error factors, while simultaneously combining the sensitivity of multiple sensors to the environment (the multiple sensors actually cooperate in determining the position information), achieves data smoothing and stability, further improving the accuracy of target positioning information determination.

[0109] In one possible implementation, the method further includes:

[0110] D1. Receive the first location information sent by the associated drone;

[0111] D2. Use the first positioning information to perform position verification processing on the target positioning information to obtain the verification processing result.

[0112] Among them, the associated drone can be understood as a drone that is at a preset spatial distance from the multi-rotor drone to be located, so that it can receive the first positioning information sent by the drone.

[0113] The first positioning information can be the location information determined using the multi-source positioning method for multi-rotor UAVs in the aforementioned embodiments.

[0114] Finally, a verification process is performed based on the two location information to obtain the verification result. The verification process can employ a general location verification method.

[0115] For examples consistent with the above embodiments, please refer to... Figure 2 , Figure 2 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application, such as... Figure 2 As shown, it includes a processor, an input device, an output device, and a memory, which are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The program includes instructions for performing the following steps.

[0116] Obtain k positioning information points of the multi-rotor UAV to be positioned at the positioning time, where the k positioning information points are of different types, and obtain the current environmental information of the multi-rotor UAV to be positioned.

[0117] Obtain the error factors corresponding to k positioning information points to obtain k first target error factors;

[0118] Based on k first target error factors and current environmental information, k positioning information is fused to obtain target positioning information for high-precision positioning of the multi-rotor UAV to be positioned.

[0119] In this example, by acquiring k positioning information points of the multi-rotor UAV to be positioned at the positioning time, where the k positioning information points are of different types, and acquiring the current environmental information of the multi-rotor UAV to be positioned, the error factors corresponding to the k positioning information points are obtained, resulting in k first target error factors. Based on the k first target error factors and the current environmental information, the k positioning information points are fused to obtain the target positioning information for high-precision positioning of the multi-rotor UAV to be positioned. Therefore, it is possible to combine multiple positioning information points, corresponding error factors, and environmental information to finally obtain the target positioning information, thereby improving the accuracy of target positioning information determination.

[0120] The above mainly describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the terminal includes the corresponding hardware structure and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0121] This application embodiment can divide the terminal into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0122] For those consistent with the above, please refer to Figure 3 , Figure 3 This application provides a schematic diagram of the structure of a multi-rotor unmanned aerial vehicle (UAV) multi-source positioning device. For example... Figure 3 As shown, the device includes:

[0123] The first acquisition unit 301 is used to acquire k positioning information of the multi-rotor UAV to be positioned at the positioning time, wherein the k positioning information are of different types, and to acquire the current environmental information of the multi-rotor UAV to be positioned.

[0124] The second acquisition unit 302 is used to acquire the error factors corresponding to k positioning information respectively, and obtain k first target error factors;

[0125] The fusion unit 303 is used to fuse k positioning information based on k first target error factors and current environmental information to obtain target positioning information for high-precision positioning of the multi-rotor UAV to be positioned.

[0126] In one possible implementation, the second acquisition unit 302 is specifically used for:

[0127] Obtain the attribute information of the positioning sensors corresponding to k positioning information points to obtain k device attribute information points;

[0128] Error factors are evaluated based on k device attribute information to obtain k first target error factors.

[0129] In one possible implementation, regarding the step of evaluating error factors based on k device attribute information to obtain k first target error factors, the second acquisition unit 302 is specifically used for:

[0130] The inherent error factor is extracted based on the device identifier in the target device attribute information, where the target device attribute information is any one of k device attribute information;

[0131] Based on the deployment information in the target device attribute information, determine the first error factor correction parameter;

[0132] The second error factor correction parameter is determined based on the usage attribute information in the target device attribute information;

[0133] The inherent error factor is corrected using the first error factor correction parameter and the second error factor correction parameter to obtain the first target error factor;

[0134] Repeat the above method of extracting inherent error factors from the device identifier in the target device attribute information and then correcting the inherent error factors using the first error factor correction parameter and the second error factor correction parameter to obtain the first target error factor, until k device attribute information is obtained for error factor evaluation to obtain k first target error factors.

[0135] In one possible implementation, the fusion unit 303 is specifically used for:

[0136] The environmental parameters in the environmental information are normalized to obtain normalized environmental parameters;

[0137] The normalized environment parameters are vectorized to obtain the normalized environment parameter vector;

[0138] Calculate the offset between the normalized environmental parameter vector and the standard environmental parameter vectors corresponding to the k first target error factors to obtain the k offset vectors;

[0139] Extract the offset volatility and offset mean vectors from the k offset vectors;

[0140] Error factor fluctuation information is determined based on offset volatility and offset mean vector;

[0141] By using error factor fluctuation information to fuse k first target error factors and k positioning information, the target positioning information for high-precision positioning of the multi-rotor UAV to be positioned is obtained.

[0142] In one possible implementation, the device is further used for:

[0143] Receive the first location information sent by the associated drone;

[0144] The first positioning information is used to perform position verification processing on the target positioning information to obtain the verification processing result.

[0145] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the multi-rotor UAV multi-source positioning methods described in the above method embodiments.

[0146] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the multi-rotor UAV multi-source positioning methods described in the above method embodiments.

[0147] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0148] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0149] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0150] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0151] Furthermore, the functional units in the various embodiments of the application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0152] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0153] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.

[0154] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A multi-source positioning method for a multi-rotor unmanned aerial vehicle, characterized in that, The method includes: Obtain k positioning information points of the multi-rotor UAV to be positioned at the positioning time, where the k positioning information points are of different types, and obtain the current environmental information of the multi-rotor UAV to be positioned. Obtain the error factors corresponding to k positioning information points to obtain k first target error factors; Based on k first target error factors and current environmental information, k positioning information is fused to obtain target positioning information for high-precision positioning of the multi-rotor UAV to be positioned. The step of obtaining the error factors corresponding to the k positioning information to obtain the k first target error factors includes: Obtain the attribute information of the positioning sensors corresponding to k positioning information points to obtain k device attribute information points; Based on the error factor evaluation of k device attribute information, k first target error factors are obtained; The step of fusing k positioning information based on k first target error factors and current environmental information to obtain target positioning information for high-precision positioning of the multi-rotor UAV to be positioned includes: The environmental parameters in the environmental information are normalized to obtain normalized environmental parameters; The normalized environment parameters are vectorized to obtain the normalized environment parameter vector; Calculate the offset between the normalized environmental parameter vector and the standard environmental parameter vectors corresponding to the k first target error factors to obtain the k offset vectors; Extract the offset volatility and offset mean vectors from the k offset vectors; Error factor fluctuation information is determined based on offset volatility and offset mean vector; By using error factor fluctuation information to fuse k first target error factors and k positioning information, the target positioning information for high-precision positioning of the multi-rotor UAV to be positioned is obtained.

2. The multi-copter drone multi-source positioning method of claim 1, wherein, The step of evaluating error factors based on k device attribute information to obtain k first target error factors includes: The inherent error factor is extracted based on the device identifier in the target device attribute information, where the target device attribute information is any one of k device attribute information; Based on the deployment information in the target device attribute information, determine the first error factor correction parameter; The second error factor correction parameter is determined based on the usage attribute information in the target device attribute information; The inherent error factor is corrected using the first error factor correction parameter and the second error factor correction parameter to obtain the first target error factor; Repeat the above method of extracting inherent error factors from the device identifier in the target device attribute information and then correcting the inherent error factors using the first error factor correction parameter and the second error factor correction parameter to obtain the first target error factor, until k device attribute information is obtained for error factor evaluation to obtain k first target error factors.

3. The multi-copter drone multi-source positioning method of claim 1, wherein, The method further includes: Receive the first location information sent by the associated drone; The first positioning information is used to perform position verification processing on the target positioning information to obtain the verification processing result.

4. A multi-copter unmanned aerial vehicle multi-source positioning device, characterized in that, The device includes: The first acquisition unit is used to acquire k positioning information of the multi-rotor UAV to be positioned at the positioning time, wherein the k positioning information are of different types, and to acquire the current environmental information of the multi-rotor UAV to be positioned. The second acquisition unit is used to acquire the error factors corresponding to k positioning information respectively, and obtain k first target error factors; The fusion unit is used to fuse k positioning information based on k first target error factors and current environmental information to obtain target positioning information for high-precision positioning of the multi-rotor UAV to be positioned. The second acquisition unit is specifically used for: Obtain the attribute information of the positioning sensors corresponding to k positioning information points to obtain k device attribute information points; Based on the error factor evaluation of k device attribute information, k first target error factors are obtained; The step of fusing k positioning information based on k first target error factors and current environmental information to obtain target positioning information for high-precision positioning of the multi-rotor UAV to be positioned includes: The environmental parameters in the environmental information are normalized to obtain normalized environmental parameters; The normalized environment parameters are vectorized to obtain the normalized environment parameter vector; Calculate the offset between the normalized environmental parameter vector and the standard environmental parameter vectors corresponding to the k first target error factors to obtain the k offset vectors; Extract the offset volatility and offset mean vectors from the k offset vectors; Error factor fluctuation information is determined based on offset volatility and offset mean vector; By using error factor fluctuation information to fuse k first target error factors and k positioning information, the target positioning information for high-precision positioning of the multi-rotor UAV to be positioned is obtained.

5. The multi-copter drone multi-source positioning apparatus of claim 4, wherein, In the process of evaluating error factors based on k device attribute information to obtain k first target error factors, the second acquisition unit is specifically used for: The inherent error factor is extracted based on the device identifier in the target device attribute information, where the target device attribute information is any one of k device attribute information; Based on the deployment information in the target device attribute information, determine the first error factor correction parameter; The second error factor correction parameter is determined based on the usage attribute information in the target device attribute information; The inherent error factor is corrected using the first error factor correction parameter and the second error factor correction parameter to obtain the first target error factor; Repeat the above method of extracting inherent error factors from the device identifier in the target device attribute information and then correcting the inherent error factors using the first error factor correction parameter and the second error factor correction parameter to obtain the first target error factor, until k device attribute information is obtained for error factor evaluation to obtain k first target error factors.

6. A terminal, characterized by comprising: The system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the multi-rotor UAV multi-source positioning method as described in any one of claims 1-3.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the multi-source localization method for a multi-rotor unmanned aerial vehicle as described in any one of claims 1-3.

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