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

Abstract

The embodiment of the invention relates to the field of high-precision positioning, and provides a multi-rotor unmanned aerial vehicle multi-source positioning method and device, a terminal and a medium, and the method comprises the steps: obtaining k pieces of positioning information of a to-be-positioned multi-rotor unmanned aerial vehicle at a positioning moment, the types of the k pieces of positioning information are different, and obtaining the current environment information of the to-be-positioned multi-rotor unmanned aerial vehicle; error factors corresponding to the k pieces of positioning information are obtained, and k first target error factors are obtained; and according to the k first target error factors and the current environment information, carrying out positioning information fusion on the k positioning information to obtain target positioning information when high-precision positioning is carried out on the to-be-positioned multi-rotor unmanned aerial vehicle, and finally fusing the multiple positioning information, the corresponding error factors and the environment information to obtain the target positioning information. And the accuracy of determining the target positioning information is improved.

Description

Technical Field

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

[0002] The control system of the multi-rotor UAV to be positioned needs to provide high-precision and real-time position, speed, attitude and other information.

[0003] Existing solutions combine multiple positioning information sources to obtain the position information of the multi-rotor drone to be located. However, these solutions typically use fixed weights to fuse the position information. This results in the fused position information being unable to accurately position the multi-purpose drone in various scenarios, leading to reduced positioning accuracy. Summary of the Invention

[0004] The embodiments of the present application provide a multi-source positioning method, device, terminal, and medium for a multi-rotor drone, which can improve the accuracy of drone positioning.

[0005] A first aspect of an embodiment of the present application provides a multi-source positioning method for a multi-rotor UAV, the method comprising:

[0006] Obtain k pieces of positioning information of the multi-rotor UAV to be positioned at the positioning moment, where the k pieces of positioning information are of different types, and obtain the current environment information of the multi-rotor UAV to be positioned;

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

[0008] The k positioning information are fused according to the k first target error factors and the current environment information to obtain the 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 pieces of positioning information to obtain k first target error factors includes:

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

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

[0012] In one possible implementation, performing error factor evaluation based on k pieces of device attribute information to obtain k first target error factors includes:

[0013] Extracting an inherent error factor according to a device identifier in target device attribute information, where the target device attribute information is any one of k device attribute information;

[0014] Determining a first error factor correction parameter according to the deployment information in the target device attribute information;

[0015] determining a second error factor correction parameter according to usage attribute information in the target device attribute information;

[0016] Correcting the inherent error factor using a first error factor correction parameter and a second error factor correction parameter to obtain a first target error factor;

[0017] Repeat the above method of extracting the inherent error factor according to the device identifier in the target device attribute information and correcting 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, until k device attribute information is obtained for error factor evaluation and k first target error factors are obtained.

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

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

[0020] Perform vector construction on the normalized environmental parameters to obtain a normalized environmental parameter vector;

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

[0022] Extract the offset volatility and offset mean vector based on k offset vectors;

[0023] Determine error factor fluctuation information based on the offset fluctuation and the offset mean vector;

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

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

[0026] Receiving first positioning 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 a verification processing result.

[0028] A second aspect of the embodiments of the present application provides a multi-rotor UAV multi-source positioning device, the device comprising:

[0029] The first acquisition unit is used to obtain k positioning information of the multi-rotor UAV to be located at the positioning moment, where the k positioning information are of different types, and to obtain current environment information of the multi-rotor UAV to be located;

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

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

[0032] In a possible implementation, the second acquiring unit is specifically configured to:

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

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

[0035] In one possible implementation, in performing error factor evaluation based on k pieces of device attribute information to obtain k first target error factors, the second acquisition unit is specifically configured to:

[0036] Extracting an inherent error factor according to a device identifier in target device attribute information, where the target device attribute information is any one of k device attribute information;

[0037] Determining a first error factor correction parameter according to the deployment information in the target device attribute information;

[0038] determining a second error factor correction parameter according to usage attribute information in the target device attribute information;

[0039] Correcting the inherent error factor using a first error factor correction parameter and a second error factor correction parameter to obtain a first target error factor;

[0040] Repeat the above method of extracting the inherent error factor according to the device identifier in the target device attribute information and correcting 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, until k device attribute information is obtained for error factor evaluation and k first target error factors are obtained.

[0041] In one possible implementation, the fusion unit is specifically configured to:

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

[0043] Perform vector construction on the normalized environmental parameters to obtain a normalized environmental parameter vector;

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

[0045] Extract the offset volatility and offset mean vector based on k offset vectors;

[0046] Determine error factor fluctuation information based on the offset fluctuation and the offset mean vector;

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

[0048] In one possible implementation, the device is further configured to:

[0049] Receiving first positioning 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 a verification processing result.

[0051] A third aspect of an embodiment of the present application provides a terminal, comprising 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 includes program instructions, and the processor is configured to call the program instructions and execute the step instructions in the first aspect of the embodiment of the present application.

[0052] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application.

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

[0054] The implementation of the embodiments of the present application has the following beneficial effects:

[0055] By obtaining k positioning information of the multi-rotor UAV to be positioned at the positioning moment, the k positioning information are of different types, and the current environmental information of the multi-rotor UAV to be positioned is obtained, the error factors corresponding to the k positioning information are obtained, and k first target error factors are obtained. Positioning information fusion is performed on the k positioning information according to the k first target error factors and the current environmental information to obtain target positioning information when the multi-rotor UAV to be positioned is performed with high precision. Therefore, the target positioning information can be finally obtained by combining multiple positioning information and the corresponding error factors and environmental information, thereby improving the accuracy of the target positioning information when determining. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0057] Figure 1 A flowchart of a multi-source positioning method for a multi-rotor drone is provided for an embodiment of the present application;

[0058] Figure 2 A schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0059] Figure 3 The present invention provides a schematic structural diagram of a multi-rotor UAV multi-source positioning device. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0061] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

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

[0063] In order to better understand the multi-source positioning method of a multi-rotor drone provided in an embodiment of the present application, the following first briefly introduces the drone positioning method in the existing scheme. In the existing scheme, there is a method of combining multiple positioning information for fusion to obtain the position information of the multi-rotor drone to be positioned, but when performing the fusion, it usually adopts a fixed weight value to fuse the position information. For example, by collecting multiple different types of positioning data, and then assigning a fixed fusion weight to each positioning data, and finally using the fusion weight to fuse, the fused position information is obtained. This will cause the fused position information to be unable to adapt to the precise positioning in multi-purpose scenarios, resulting in reduced accuracy during positioning.

[0064] In order to solve the above problems, the embodiment of the present application provides a multi-source positioning method for a multi-rotor drone, which can combine multiple positioning information with corresponding error factors and environmental information to finally obtain target positioning information, thereby improving the accuracy of determining the target positioning information.

[0065] See also Figure 1 , Figure 1 The present invention provides a flowchart of a multi-source positioning method for a multi-rotor drone. Figure 1 As shown, the method includes:

[0066] 101. Obtain k pieces of positioning information of the multi-rotor UAV to be positioned at the positioning moment, where the k pieces of positioning information are of different types, and obtain current environment information of the multi-rotor UAV to be positioned.

[0067] The multi-rotor UAV to be positioned is equipped with a high-precision time differential positioning measurement unit RTK, GPS, IMU posture measurement unit and altimeter, among which the altimeter includes a barometric altimeter, an ultrasonic ranging sensor and a radar altimeter.

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

[0069] Environmental information may include air pressure, wind speed, temperature, humidity, weather conditions, etc. Therefore, the above environmental information can be vectorized to obtain environmental parameters. The measurement accuracy of the device may fluctuate under different environmental factors. For example, the measurement accuracy of the barometric altimeter in the temperature range of a windless and normal working environment will be higher than the measurement accuracy in the temperature range of a windy and non-normal working environment. Although the impact of the environment on the measurement accuracy will not be too high, the flight time of the drone is relatively long during the flight phase. If the error accumulates over a long period of time, it may cause the overall measurement accuracy to drop significantly, or even lead to large deviations. Therefore, the positioning information can be fused in combination with environmental factors to improve accuracy.

[0070] 102. Obtain error factors corresponding to k pieces of positioning information, and obtain k first target error factors.

[0071] The device attribute information of the positioning sensor corresponding to the positioning information can be obtained separately, and then the corresponding first target error factor is determined based on the device attribute information. This error factor can be understood as the combination of the inherent error factor of the positioning sensor itself after manufacture, and the error information caused by the aging of the device and circuit, and the adhesion of particles in the environment, which causes errors in the measured information after use.

[0072] 103. Perform positioning information fusion on the k positioning information according to the k first target error factors and the current environment information to obtain target positioning information for high-precision positioning of the multi-rotor UAV to be positioned.

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

[0074] In this example, by obtaining k positioning information of the multi-rotor UAV to be positioned at the positioning moment, the k positioning information are of different types, and the current environmental information of the multi-rotor UAV to be positioned is obtained, the error factors corresponding to the k positioning information are obtained, and k first target error factors are obtained. The k positioning information are fused according to the k first target error factors and the current environmental information to obtain the target positioning information for high-precision positioning of the multi-rotor UAV to be positioned. Therefore, the target positioning information can be finally fused by combining multiple positioning information and the corresponding error factors and environmental information, thereby improving the accuracy of determining the target positioning information.

[0075] In one possible implementation, a method for obtaining error factors corresponding to k pieces of positioning information 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. Evaluate the error factors based on the k device attribute information to obtain k first target error factors.

[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 during positioning after leaving the factory; deployment information can be understood as the area where the multi-rotor drone to be located is deployed, for example, in what type of environment it is deployed to perform the relevant flight mission; and usage attribute information can be understood as the usage duration, frequency, and maintenance information of the multi-rotor drone to be located.

[0079] Therefore, the loss of the multi-rotor UAV to be positioned is different when it is deployed in different scenarios and performs different flight missions. Therefore, the error factor can be evaluated in combination with the attribute information to obtain the first target error factor.

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

[0081] In this example, since the multi-rotor drone to be positioned is deployed in different scenarios and performs different flight missions, the loss of the multi-rotor drone to be positioned is different. Therefore, the deployment information and usage attribute information can be used to correct the inherent error factor to obtain the first target error factor, so that a more accurate error factor can be determined by adapting to the usage environment, thereby 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. Extracting an inherent error factor 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;

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

[0085] B3. Determine a second error factor correction parameter based on the usage attribute information in the target device attribute information;

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

[0087] B5. Repeat the above method of extracting the inherent error factor based on the device identifier in the target device attribute information and correcting 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, until k device attribute information is obtained for error factor evaluation and k first target error factors are obtained.

[0088] Among them, the inherent error factor is the error factor corresponding to the inherent positioning error of the device when it is positioned after leaving the factory. It is an error caused by the device properties and its error is inevitable.

[0089] After a drone is deployed, it typically performs flight missions within the deployment area until it is retired. Therefore, it will remain within the deployment area for a long period of time, that is, in an outdoor environment performing environmental monitoring and early warning tasks. It will be exposed to adverse conditions within the deployment area for a long time. For example, when performing tasks such as landslide monitoring, landslides typically occur after heavy rain or prolonged rainy weather, and the environment it faces is typically high humidity and strong winds. Long-term exposure to such conditions will reduce the accuracy of the device's operation and positioning. Therefore, by combining factors such as air pressure, wind speed, temperature, humidity, and weather conditions in the deployment environment to correct the error factor, we can truly consider the actual deployment environment and improve the accuracy of the final first target error factor.

[0090] Specifically, the impact of each environmental parameter on the device, as well as information on environmental parameter fluctuations, can be obtained. This can be determined by optimizing a large number of samples. For example, a sub-correction factor is set for each environmental parameter under simulation conditions specified in the deployment information. This factor is then combined with an optimization algorithm to determine the maximum contribution of the environmental parameter to the error under various environmental parameters. The correction factor corresponding to this maximum contribution is then used as the correction factor for the corresponding 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 the 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 to predict aging, thereby obtaining 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 drone to be located, and the aging curve can be used to predict the aging degree corresponding to the current usage attribute information.

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

[0093] Based on the duration of each flight mission, the frequency of use within a certain time period, and maintenance information (e.g., a certain time period can be one month or three months, i.e., the frequency of use of the multi-rotor drone to be located within one month), the frequency of use can be quantified into an aging value. Higher frequency of use results in a larger aging value, while lower frequency of use results in a smaller aging value. A fixed mapping relationship between maintenance information and aging values ​​can be employed: a greater maintenance frequency in the maintenance information results in a smaller aging value, and a lower maintenance frequency results in a larger aging value. Longer flight missions result in a larger aging value, while shorter flight missions result in a smaller aging value. Therefore, quantization can be performed based on the corresponding parameters within a certain time period to obtain a quantized aging value. The aging values ​​from multiple time periods can then be combined 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. The aging values ​​from each time period are then connected using a smooth curve to form the aging curve. This aging curve can then be used to predict the degree of aging, obtaining an aging value corresponding to the usage attribute information. The second error factor correction parameter can then be determined based on this aging value. The second error factor correction parameter corresponding to the aging value may be determined according to a preset mapping relationship between the aging value and the error factor correction parameter.

[0094] The product of the first error factor correction parameter, the second error factor correction parameter, and the inherent error factor can be determined as the first target error factor, thereby achieving correction processing for the inherent error factor and improving the accuracy of subsequent processing.

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

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

[0097] C2. construct a vector of the normalized environmental parameters to obtain a normalized environmental 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 k offset vectors;

[0099] C4. Extract the offset volatility and offset mean vector based on the k offset vectors;

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

[0101] C6. Use the error factor fluctuation information to fuse the k first target error factors and the k positioning information to obtain the target positioning information for high-precision positioning of the multi-rotor UAV to be positioned.

[0102] The environmental parameters can be normalized using a common normalization method to obtain normalized environmental parameters. Normalization is performed to keep the parameters within a fixed parameter range, thereby improving the representation of fluctuation information during subsequent offset fluctuation extraction, concentrating the fluctuation values ​​within a small range, and improving the accuracy of the fluctuation display.

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

[0104] The standard environmental parameters corresponding to each first target error factor can be extracted. Since different sensors have different operating environment parameters with the lowest error, the parameters of the operating environment with the lowest error can be used as standard environmental parameters to quantify and offset the error, more accurately reflecting the impact of the actual environment on the error. The difference between the corresponding items of 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. The volatility can be represented using the mean square error (MSE). Specifically, the mean square error corresponding to each vector value is extracted to obtain multiple sub-offset volatility values. The mean of these sub-offset volatility values ​​is then determined as the offset volatility. The mean of the corresponding values ​​in the k offset vectors can be directly calculated to obtain the offset mean vector.

[0106] After the offset fluctuation is determined, the error factor fluctuation information may be calculated in combination with the offset mean vector. Specifically, the product of the offset fluctuation and the offset mean vector may be determined as the error factor fluctuation information.

[0107] Finally, the error factor fluctuation information is superimposed on the k first target error factors, and the actual second target error factor is obtained. The weight calculation is performed according to the k second target error factors to obtain the fusion weights corresponding to the k positioning information. Finally, the fusion weights are used for weight calculation to obtain the target positioning information.

[0108] When the error factor fluctuation information is superimposed on the k first target error factors, the error factor fluctuation information can be first superimposed and quantized to obtain a quantized value. Finally, the sum of the quantized value and the k first target error factors is determined as the actual second target error factor. Finally, based on the mapping relationship between the second target error factor and the corresponding fusion weight, the fusion weight corresponding to each second target error factor is determined. Finally, the fusion weight is used to perform weight calculation to obtain the target positioning information. Therefore, the fluctuation caused by environmental factors is superimposed on the target error factor, and the superposition processing is performed in combination with the sensitivity of multiple sensors to the environment (multiple sensors actually perform collaborative determination when determining position information), thereby achieving smooth and stable data and further improving the accuracy of target positioning information determination.

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

[0110] D1, receiving the first positioning 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 a 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 positioned, so that it can receive the first positioning information sent by it.

[0113] The first positioning information may be position information determined using the multi-source positioning method for the multi-rotor drone in the aforementioned embodiment.

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

[0115] For the same example as above, please refer to Figure 2 , Figure 2 A schematic diagram of the structure of a terminal provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the system comprises a processor, an input device, an output device and a memory, which are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions, and the program includes instructions for executing the following steps;

[0116] Obtain k pieces of positioning information of the multi-rotor UAV to be positioned at the positioning moment, where the k pieces of positioning information are of different types, and obtain the current environment information of the multi-rotor UAV to be positioned;

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

[0118] The k positioning information are fused according to the k first target error factors and the current environment information to obtain the target positioning information for high-precision positioning of the multi-rotor UAV to be positioned.

[0119] In this example, by obtaining k positioning information of the multi-rotor UAV to be positioned at the positioning moment, the k positioning information are of different types, and the current environmental information of the multi-rotor UAV to be positioned is obtained, the error factors corresponding to the k positioning information are obtained, and k first target error factors are obtained. The k positioning information are fused according to the k first target error factors and the current environmental information to obtain the target positioning information for high-precision positioning of the multi-rotor UAV to be positioned. Therefore, the target positioning information can be finally fused by combining multiple positioning information and the corresponding error factors and environmental information, thereby improving the accuracy of determining the target positioning information.

[0120] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to implement the above functions, the terminal includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of the various examples described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

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

[0122] In line with the above, please see Figure 3 , Figure 3 The present invention provides a schematic diagram of a multi-rotor UAV multi-source positioning device. Figure 3 As shown, the device includes:

[0123] The first acquisition unit 301 is used to obtain k pieces of positioning information of the multi-rotor UAV to be located at the positioning moment, where the k pieces of positioning information are of different types, and to obtain current environment information of the multi-rotor UAV to be located;

[0124] The second acquisition unit 302 is configured to acquire error factors corresponding to k pieces of positioning information, and obtain k first target error factors;

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

[0126] In a possible implementation, the second acquiring unit 302 is specifically configured to:

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

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

[0129] In one possible implementation, in terms of evaluating the error factors according to the k pieces of device attribute information to obtain the k first target error factors, the second acquisition unit 302 is specifically configured to:

[0130] Extracting an inherent error factor according to a device identifier in target device attribute information, where the target device attribute information is any one of k device attribute information;

[0131] Determining a first error factor correction parameter according to the deployment information in the target device attribute information;

[0132] determining a second error factor correction parameter according to usage attribute information in the target device attribute information;

[0133] Correcting the inherent error factor using a first error factor correction parameter and a second error factor correction parameter to obtain a first target error factor;

[0134] Repeat the above method of extracting the inherent error factor according to the device identifier in the target device attribute information and correcting 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, until k device attribute information is obtained for error factor evaluation and k first target error factors are obtained.

[0135] In a possible implementation, the fusion unit 303 is specifically configured to:

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

[0137] Perform vector construction on the normalized environmental parameters to obtain a normalized environmental parameter vector;

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

[0139] Extract the offset volatility and offset mean vector based on k offset vectors;

[0140] Determine error factor fluctuation information based on the offset fluctuation and the offset mean vector;

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

[0142] In one possible implementation, the device is further configured to:

[0143] Receiving first positioning 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 a verification processing result.

[0145] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any multi-rotor drone multi-source positioning method described in the above method embodiments.

[0146] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program enables a computer to execute part or all of the steps of any multi-rotor drone multi-source positioning method described in the above method embodiments.

[0147] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0148] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

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

[0150] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0151] In addition, the functional units in the various embodiments of the application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software program modules.

[0152] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially 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, and the computer software product is stored in a memory, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[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 completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0154] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A multi-source positioning method for a multi-rotor UAV, characterized in that: The method comprises: Obtain k pieces of positioning information of the multi-rotor UAV to be positioned at the positioning moment, where the k pieces of positioning information are of different types, and obtain the current environment information of the multi-rotor UAV to be positioned; Obtain the error factors corresponding to k positioning information respectively, and obtain k first target error factors; The k positioning information are fused according to the k first target error factors and the current environment information to obtain the target positioning information for high-precision positioning of the multi-rotor UAV to be positioned.

2. The multi-source positioning method for a multi-rotor UAV according to claim 1, characterized in that: The step of obtaining the error factors corresponding to the k pieces of positioning information to obtain k first target error factors includes: Obtain the attribute information of the positioning sensors corresponding to the k positioning information, and obtain k device attribute information; The error factors are evaluated based on the k device attribute information to obtain k first target error factors.

3. The multi-source positioning method for a multi-rotor UAV according to claim 2, characterized in that: The step of evaluating the error factors based on the k device attribute information to obtain k first target error factors includes: Extracting an inherent error factor according to a device identifier in target device attribute information, where the target device attribute information is any one of k device attribute information; Determining a first error factor correction parameter according to the deployment information in the target device attribute information; determining a second error factor correction parameter according to usage attribute information in the target device attribute information; Correcting the inherent error factor using a first error factor correction parameter and a second error factor correction parameter to obtain a first target error factor; Repeat the above method of extracting the inherent error factor according to the device identifier in the target device attribute information and correcting 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, until k device attribute information is obtained for error factor evaluation and k first target error factors are obtained.

4. The multi-source positioning method for a multi-rotor UAV according to claim 2 or 3, characterized in that: The method of performing positioning information fusion on the k positioning information according to the k first target error factors and the current environment information to obtain target positioning information for high-precision positioning of the multi-rotor UAV to be positioned includes: Normalizing the environmental parameters in the environmental information to obtain normalized environmental parameters; Perform vector construction on the normalized environmental parameters to obtain a normalized environmental parameter vector; Calculating the offset between the normalized environmental parameter vector and the standard environmental parameter vectors corresponding to the k first target error factors to obtain k offset vectors; Extract the offset volatility and offset mean vector based on k offset vectors; Determine error factor fluctuation information based on the offset fluctuation and the offset mean vector; The error factor fluctuation information is used to fuse the k first target error factors and k positioning information to obtain the target positioning information for high-precision positioning of the multi-rotor UAV to be positioned.

5. The multi-source positioning method for a multi-rotor UAV according to claim 4, characterized in that: The method further comprises: Receiving first positioning information sent by the associated drone; The first positioning information is used to perform position verification processing on the target positioning information to obtain a verification processing result.

6. A multi-source positioning device for a multi-rotor UAV, characterized in that: The device comprises: The first acquisition unit is used to obtain k positioning information of the multi-rotor UAV to be located at the positioning moment, where the k positioning information are of different types, and to obtain current environment information of the multi-rotor UAV to be located; A second acquisition unit is used to acquire error factors corresponding to k positioning information respectively, and obtain k first target error factors; The fusion unit is used to fuse the k positioning information according to the k first target error factors and the current environment information to obtain the target positioning information for high-precision positioning of the multi-rotor UAV to be positioned.

7. The multi-source positioning device for a multi-rotor UAV according to claim 6, characterized in that: The second acquiring unit is specifically configured to: Obtain the attribute information of the positioning sensors corresponding to the k positioning information, and obtain k device attribute information; The error factors are evaluated based on the k device attribute information to obtain k first target error factors.

8. The multi-source positioning device for a multi-rotor UAV according to claim 7, characterized in that: In the aspect of evaluating the error factors according to the k pieces of device attribute information to obtain the k first target error factors, the second acquisition unit is specifically configured to: Extracting an inherent error factor according to a device identifier in target device attribute information, where the target device attribute information is any one of k device attribute information; Determining a first error factor correction parameter according to the deployment information in the target device attribute information; determining a second error factor correction parameter according to usage attribute information in the target device attribute information; Correcting the inherent error factor using a first error factor correction parameter and a second error factor correction parameter to obtain a first target error factor; Repeat the above method of extracting the inherent error factor according to the device identifier in the target device attribute information and correcting 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, until k device attribute information is obtained for error factor evaluation and k first target error factors are obtained.

9. A terminal, characterized in that: The method comprises a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the multi-source positioning method of the multi-rotor unmanned aerial vehicle according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the processor executes the multi-rotor drone multi-source positioning method according to any one of claims 1 to 5.

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