阅读说明：本技术 无人机姿态测量方法、装置、无人机及存储介质 (Unmanned aerial vehicle attitude measurement method and device, unmanned aerial vehicle and storage medium ) 是由 敬劼 于 2020-04-10 设计创作，主要内容包括：本申请公开了无人机姿态测量方法、装置、无人机及存储介质,该方法通过获取无人机的多个测量单元的测量数据；对获取的多组测量数据进行滤波处理,得到滤波结果；根据滤波结果筛选出若干组初步姿态数据；对筛选出的初步姿态数据进行融合,将融合结果作为无人机的姿态数据。本申请的有益效果在于：在无需对无人机进行硬件改造的情况下,利用飞控系统硬件上的冗余,能够快速、准确测量出无人机的姿态,显著地提升了无人机飞控系统在导航方面的稳定性和精确性,尤其是在单个测量单元发生故障的时候,能够及时切换数据源,避免无人机坠机事故；且计算量小,代码实现方法简便,实用性更强。(The application discloses an unmanned aerial vehicle attitude measurement method, an unmanned aerial vehicle attitude measurement device, an unmanned aerial vehicle and a storage medium, wherein the method comprises the steps of obtaining measurement data of a plurality of measurement units of the unmanned aerial vehicle; filtering the obtained multiple groups of measurement data to obtain a filtering result; screening a plurality of groups of preliminary attitude data according to the filtering result; and fusing the screened initial attitude data, and taking a fusion result as the attitude data of the unmanned aerial vehicle. The beneficial effect of this application lies in: under the condition that the hardware of the unmanned aerial vehicle is not required to be modified, the posture of the unmanned aerial vehicle can be quickly and accurately measured by utilizing the redundancy on the hardware of the flight control system, the stability and the accuracy of the unmanned aerial vehicle flight control system in the aspect of navigation are obviously improved, especially when a single measuring unit breaks down, a data source can be timely switched, and the crash accident of the unmanned aerial vehicle is avoided; and the calculation amount is small, the code implementation method is simple and convenient, and the practicability is stronger.)

1. An unmanned aerial vehicle attitude measurement method is characterized by comprising the following steps:

acquiring measurement data of a plurality of measurement units of the unmanned aerial vehicle;

filtering the obtained multiple groups of measurement data to obtain a filtering result, wherein the filtering result comprises preliminary attitude data corresponding to each group of measurement data and the data health degree of each group of measurement data;

screening a plurality of groups of preliminary attitude data according to the filtering result;

and fusing the screened initial attitude data, and taking a fusion result as the attitude data of the unmanned aerial vehicle.

2. The method of claim 1, wherein the filtering the acquired sets of measurement data to obtain a filtering result comprises:

respectively carrying out complementary filtering on each group of measurement data to obtain the angular velocity drift amount of each group of measurement data and the primary attitude data corresponding to each group of measurement data;

and determining the data health degree of each group of measurement data according to the angular speed drift amount.

3. The method of claim 1, wherein the filtering the acquired sets of measurement data to obtain a filtering result comprises:

preprocessing each set of measurement data, the preprocessing including timestamp alignment and/or low pass filtering;

and comparing the preprocessed groups of measurement data, and determining the data health degree of each group of measurement data according to the comparison result.

4. A method according to claim 3, wherein the measurement data comprises angular velocity and/or acceleration, and the comparison comprises:

determining the difference value of the modes of every two angular velocities, and determining the wild value and the normal value in each angular velocity according to the comparison result of each obtained difference value and a preset angular velocity threshold value;

and/or the presence of a gas in the gas,

and determining the difference value of the modes of every two accelerations, and determining the wild value and the normal value in each acceleration according to the comparison result of each obtained difference value and a preset acceleration threshold value.

5. The method of claim 1, wherein the filtering out sets of preliminary pose data according to the filtering result comprises:

and determining a preliminary attitude data set according to the data health degree, and screening two groups of preliminary attitude data with the shortest Euclidean distance from the preliminary attitude data set.

6. The method of claim 1, wherein fusing the filtered preliminary pose data comprises:

determining the fusion weight of each screened primary attitude data according to the data health degree of the measurement data corresponding to the primary attitude data;

and fusing the screened preliminary attitude data according to a weighted least square method based on the fusion weight.

7. The method according to any one of claims 1-6, further comprising:

and navigating the unmanned aerial vehicle according to the attitude data of the unmanned aerial vehicle.

8. An unmanned aerial vehicle attitude measurement device, its characterized in that, the device includes:

the acquisition unit is used for acquiring the measurement data of a plurality of measurement units of the unmanned aerial vehicle;

the data processing unit is used for carrying out filtering processing on the obtained multiple groups of measurement data to obtain a filtering result, and the filtering result comprises preliminary attitude data corresponding to each group of measurement data and the data health degree of each group of measurement data; the system is used for screening a plurality of groups of preliminary attitude data according to the filtering result;

and the execution unit is used for fusing the screened initial attitude data, and taking a fusion result as the attitude data of the unmanned aerial vehicle.

9. A drone, wherein the drone includes: a processor; and a memory arranged to store computer-executable instructions that, when executed, cause the processor to perform the method of any one of claims 1-7.

10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-7.

Technical Field

The application relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle attitude measurement method and device, an unmanned aerial vehicle and a storage medium.

Background

With the rapid development of scientific technology, unmanned aerial vehicles are widely applied to various life scenes, for example, the unmanned aerial vehicles are adopted for delivery of take-out or express delivery, so that a large amount of labor can be saved, and the delivery efficiency is obviously improved; if adopt unmanned aerial vehicle to carry out disaster rescue again, can reach the place that people can not reach fast. In order to realize safe autonomous flight in various application scenes, an unmanned aerial vehicle system needs extremely high stability and reliability, the unmanned aerial vehicle system is mainly divided into three parts of control, guidance and navigation, and generally software and hardware redundancy is performed on each part of the unmanned aerial vehicle in the prior art to improve the reliability of the system.

Disclosure of Invention

In view of the above, the present application is proposed in order to provide a method, an apparatus, a drone and a storage medium for measuring the attitude of a drone that overcome or at least partially solve the above problems.

According to an aspect of the application, an unmanned aerial vehicle attitude measurement method is provided, and the method comprises the following steps:

acquiring measurement data of a plurality of measurement units of the unmanned aerial vehicle;

filtering the obtained multiple groups of measurement data to obtain a filtering result, wherein the filtering result comprises preliminary attitude data corresponding to each group of measurement data and the data health degree of each group of measurement data;

screening a plurality of groups of preliminary attitude data according to the filtering result;

and fusing the screened initial attitude data, and taking a fusion result as the attitude data of the unmanned aerial vehicle.

Optionally, in the method, performing filtering processing on the obtained multiple sets of measurement data, and obtaining a filtering result includes:

respectively carrying out complementary filtering on each group of measurement data to obtain the angular velocity drift amount of each group of measurement data and the primary attitude data corresponding to each group of measurement data;

and determining the data health degree of each group of measurement data according to the angular speed drift amount.

Optionally, in the method, performing filtering processing on the obtained multiple sets of measurement data, and obtaining a filtering result includes:

preprocessing each set of measurement data, the preprocessing including timestamp alignment and/or low pass filtering;

and comparing the preprocessed groups of measurement data, and determining the data health degree of each group of measurement data according to the comparison result.

Optionally, in the above method, the measurement data includes angular velocity and/or acceleration, and the comparing includes:

determining the difference value of the modes of every two angular velocities, and determining the wild value and the normal value in each angular velocity according to the comparison result of each obtained difference value and a preset angular velocity threshold value;

and/or the presence of a gas in the gas,

and determining the difference value of the modes of every two accelerations, and determining the wild value and the normal value in each acceleration according to the comparison result of each obtained difference value and a preset acceleration threshold value.

Optionally, in the above method, screening out a plurality of sets of preliminary posture data according to the filtering result includes:

and determining a preliminary attitude data set according to the data health degree, and screening two groups of preliminary attitude data with the shortest Euclidean distance from the preliminary attitude data set.

Optionally, in the above method, fusing the screened preliminary posture data includes:

determining the fusion weight of each screened primary attitude data according to the data health degree of the measurement data corresponding to the primary attitude data;

and fusing the screened preliminary attitude data according to a weighted least square method based on the fusion weight.

Optionally, in the above method, the method further includes:

and navigating the unmanned aerial vehicle according to the attitude data of the unmanned aerial vehicle.

According to another aspect of this application, an unmanned aerial vehicle attitude measurement device is provided, the device includes:

the acquisition unit is used for acquiring the measurement data of a plurality of measurement units of the unmanned aerial vehicle;

the data processing unit is used for carrying out filtering processing on the obtained multiple groups of measurement data to obtain a filtering result, and the filtering result comprises preliminary attitude data corresponding to each group of measurement data and the data health degree of each group of measurement data; the system is used for screening a plurality of groups of preliminary attitude data according to the filtering result;

and the execution unit is used for fusing the screened initial attitude data, and taking a fusion result as the attitude data of the unmanned aerial vehicle.

Optionally, in the apparatus, the data processing unit is configured to perform complementary filtering on each set of measurement data, respectively, to obtain an angular velocity drift amount of each set of measurement data and preliminary attitude data corresponding to each set of measurement data; and determining the data health degree of each group of measurement data according to the angular speed drift amount.

Optionally, in the above apparatus, the data processing unit is configured to perform preprocessing on each set of measurement data, where the preprocessing includes timestamp alignment and/or low-pass filtering; and the system is used for comparing the preprocessed groups of measurement data and determining the data health degree of each group of measurement data according to the comparison result.

Optionally, in the apparatus, the measurement data includes angular velocity and/or acceleration, and the data processing unit is configured to determine a difference between two moduli of angular velocity, and determine a outlier and a normal value in each angular velocity according to a comparison result between each obtained difference and a preset angular velocity threshold; and/or determining the difference value of the modes of every two accelerations, and determining the wild value and the normal value in each acceleration according to the comparison result of each obtained difference value and a preset acceleration threshold value.

Optionally, in the above apparatus, the data processing unit is configured to determine a preliminary posture data set according to the data health degree, and screen two sets of preliminary posture data with the shortest euclidean distance from the preliminary posture data set.

Optionally, in the apparatus, the execution unit is configured to determine a fusion weight of each screened primary attitude data according to a data health degree of measurement data corresponding to the primary attitude data; and fusing the screened preliminary attitude data according to a weighted least square method based on the fusion weight.

Optionally, in the above apparatus, the execution unit is further configured to perform navigation of the drone according to the attitude data of the drone.

According to yet another aspect of the present application, there is provided a drone, wherein the drone comprises: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform a method as any one of above.

According to yet another aspect of the application, a computer readable storage medium is provided, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method as any of the above.

According to the technical scheme, the measurement data of the plurality of measurement units of the unmanned aerial vehicle are acquired; filtering the obtained multiple groups of measurement data to obtain a filtering result, wherein the filtering result comprises preliminary attitude data corresponding to each group of measurement data and the data health degree of each group of measurement data; screening a plurality of groups of preliminary attitude data according to the filtering result; and fusing the screened initial attitude data, and taking a fusion result as the attitude data of the unmanned aerial vehicle. The beneficial effect of this application lies in: under the condition that the hardware of the unmanned aerial vehicle is not required to be modified, the posture of the unmanned aerial vehicle can be quickly and accurately measured by utilizing the redundancy on the hardware of the flight control system, the stability and the accuracy of the unmanned aerial vehicle flight control system in the aspect of navigation are obviously improved, especially when a single measuring unit breaks down, a data source can be timely switched, and the crash accident of the unmanned aerial vehicle is avoided; and the calculation amount is small, the code implementation method is simple and convenient, and the practicability is stronger.

The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:

fig. 1 shows a schematic flow diagram of a method for measuring the attitude of an unmanned aerial vehicle according to an embodiment of the present application;

fig. 2 shows a schematic flow diagram of a method of drone attitude measurement according to another embodiment of the present application;

fig. 3 shows a schematic structural diagram of an unmanned aerial vehicle attitude measurement apparatus according to an embodiment of the present application;

figure 4 shows a schematic structural diagram of a drone according to one embodiment of the present application;

FIG. 5 shows a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present application.

Detailed Description

Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

Fig. 1 shows a schematic flow chart of a method for measuring an attitude of an unmanned aerial vehicle according to an embodiment of the present application, the method including:

step S110, measurement data of a plurality of measurement units of the unmanned aerial vehicle are acquired.

Unmanned aerial vehicles are widely applied in various scenes, and particularly quad-rotor unmanned aerial vehicles show unique advantages in many application fields, such as express throwing, takeaway delivery, security enforcement and the like, and even in some terrain complex scenes, such as disaster relief, military reconnaissance and the like. This just requires unmanned aerial vehicle to have very high stability and accuracy nature, and prior art often promotes unmanned aerial vehicle's stability through carrying out a large amount of redundancies of software and hardware to unmanned aerial vehicle, and this has increased unmanned aerial vehicle's manufacturing cost undoubtedly, has increased unmanned aerial vehicle's weight, and then has influenced unmanned aerial vehicle's flight performance. The unmanned aerial vehicle attitude measurement method is characterized in that the data sources can be switched in time when a single data measurement unit breaks down or data is abnormal, so that the accuracy and reliability of unmanned aerial vehicle attitude measurement are ensured, and the stability and reliability of navigation estimation are further ensured.

First, measurement data of a plurality of measurement units of the drone are acquired. The flight control system of the unmanned aerial vehicle is a core system of the whole flight process of the unmanned aerial vehicle, such as finishing takeoff, air flight, task execution, return recovery and the like, is equivalent to the effect of a driver on human-computer for the unmanned aerial vehicle, and is one of the most core technologies of the unmanned aerial vehicle. Flight control system generally includes sensor, airborne computer and three major parts of servo actuation equipment, and the function of realizing mainly has three main types of unmanned aerial vehicle gesture stability and control, unmanned aerial vehicle task equipment management and emergency control. In this application, a plurality of measuring units of unmanned aerial vehicle can be for but not limiting to Inertial Measurement Unit (IMU), and measuring unit's measured data derives from its sensor, including redundant sensor, for example: gyroscopes, accelerators, geomagnetic sensors, Global Positioning Systems (GPS), air pressure sensors, ultrasonic sensors, optical flow sensors, and the like. The acquired measurement data includes, but is not limited to: angular velocity, acceleration, yaw angle, fly height, etc. If one sensor has a plurality of redundancies, the measurement data of each sensor is acquired, and if the number of the gyroscopes is 5, 5 groups of angular velocity data are acquired simultaneously.

And step S120, filtering the obtained multiple groups of measurement data to obtain a filtering result, wherein the filtering result comprises the preliminary attitude data corresponding to each group of measurement data and the data health degree of each group of measurement data.

The obtained multiple groups of measurement data are filtered, and the filtering is an operation of filtering out specific band frequencies in the signals and is an important measure for inhibiting and preventing interference. In this embodiment, the filtering algorithm may adopt one or a combination of several in the prior art according to the requirement for data, including but not limited to amplitude limiting filtering, median filtering, arithmetic mean filtering, recursive mean filtering, median mean filtering, amplitude limiting mean filtering, first-order lag filtering, weighted recursive mean filtering, debounce filtering, amplitude limiting debounce filtering, and the like.

And filtering the obtained multiple groups of measurement data to obtain a filtering result, wherein the filtering result comprises preliminary attitude data corresponding to each group of measurement data and the data health degree of each group of measurement data.

And carrying out filtering processing on a group of measurement data to obtain a group of filtering results, wherein the group of filtering results comprise but are not limited to acceleration, angular velocity, Euler angles (pitch angle, roll angle and yaw angle), horizontal height and the like, the parameters determine the flight attitude of the unmanned aerial vehicle, and the flight attitude information is the primary attitude data corresponding to the group of filtering results. Because the hardware of the unmanned aerial vehicle has redundancy, if the same 5 groups of measurement units exist in parallel and run simultaneously, 5 groups of preliminary attitude data can be obtained after filtering processing, and under the condition that no sensor is damaged, the parameters in each group of preliminary attitude data are the same and all comprise angular velocity, acceleration, yaw angle, horizontal height and the like.

Above-mentioned multiunit measuring unit that parallels, because sensor installation angle, operating condition or certain single sensor have factors such as trouble, the measured data of gathering are not totally reliable, in order to ensure unmanned aerial vehicle flight attitude measurement's accuracy, this application is in the in-process of carrying out filtering process to multiunit measured data, the data health degree to multiunit measured data is makeed statistics of, the data health degree can be regarded as the measured data quality of measuring unmanned aerial vehicle's multiunit measuring unit, can be used for one of the standard of follow-up unmanned aerial vehicle attitude estimation even, if can keep the measured data that data health degree is high, can reject some measured data that data health degree is very low, with the fail safe nature of ensureing follow-up unmanned aerial vehicle flight attitude estimation.

The calculation of the data health degree can adopt any one or a combination of several in the prior art, for example, the data health degree can be limited to be a numerical value in the range of 0-100, the initial data health degree of the measurement data is set to be 100, the final data health degree of the measurement data is calculated on the basis of the initial data health degree according to a preset rule, if a wild value exists in the measurement data, the final data health degree is reduced by 5 on the basis of the initial data health degree, and if a certain item of data is missing, the final data health degree of each measurement unit of the unmanned aerial vehicle is reduced by 20 on the basis of the initial data health degree, so that the final data health degree of each measurement unit of the unmanned aerial vehicle is obtained.

And S130, screening a plurality of groups of preliminary attitude data according to the filtering result.

Screening a plurality of groups of preliminary attitude data according to the filtering result, wherein unhealthy and unreasonable data in the measurement data of the unmanned aerial vehicle measurement unit can be removed, and data with better quality can be further selected from all reasonable data to carry out subsequent steps so as to save the calculated amount.

The screening process can be performed according to the plurality of groups of preliminary posture data, for example, one or more data in the preliminary posture data are utilized, and if the number of the outliers in the angular velocity is judged to exceed the preset number, the group of preliminary posture data are removed.

The screening process can also be carried out according to the numerical health degree of the measurement data, for example, the measurement data with the preset data health degree of more than 60 and including 60 can be reserved, and the measurement data with the data health degree of less than 60 can be eliminated.

And S140, fusing the screened initial attitude data, and taking a fusion result as the attitude data of the unmanned aerial vehicle.

And 4, fusing the retained initial attitude data, and taking the fusion result as the attitude data of the unmanned aerial vehicle. Data fusion is an information processing technique that combines, correlates, and combines multiple sets or types of source data and information to obtain more accurate results.

The fusion method in the application can adopt one or more of the prior art, and can be direct fusion of data layers, for example, averaging all accelerations in each group is taken as the fusion result of the accelerations.

The fusion in the application can also be a fusion of feature layers, for example, the feature information such as angular velocity, acceleration, yaw angle, horizontal height and the like in each group of measurement data is comprehensively analyzed and processed, the fusion of the feature layers has the advantages of realizing considerable information compression and being beneficial to real-time processing, and the extracted features are directly related to decision analysis, so that the fusion result can give out feature information required by the decision analysis to the maximum extent.

The fusion in the application can also be a decision-making level fusion, namely, a preliminary conclusion on an observed target is established according to data of different types of sensors, and then decision-making level fusion judgment is carried out through association processing, and a joint inference result is finally obtained.

According to the method shown in the figure 1, under the condition that hardware transformation of the unmanned aerial vehicle is not needed, the posture of the unmanned aerial vehicle can be rapidly and accurately measured by utilizing redundancy on the flight control system hardware, the stability and the accuracy of the unmanned aerial vehicle flight control system in the navigation aspect are obviously improved, especially when a single measuring unit breaks down, a data source can be timely switched, and the unmanned aerial vehicle crash accident is avoided; and the calculation amount is small, the code implementation method is simple and convenient, and the practicability is stronger.

In an embodiment of the application, in the method, performing filtering processing on the acquired multiple sets of measurement data, and obtaining a filtering result includes: respectively carrying out complementary filtering on each group of measurement data to obtain the angular velocity drift amount of each group of measurement data and the primary attitude data corresponding to each group of measurement data; and determining the data health degree of each group of measurement data according to the angular speed drift amount.

The accelerometer is sensitive to the acceleration of the unmanned aerial vehicle, and the error of the calculated inclination angle by taking the instantaneous value is larger; the angle obtained by integrating the gyroscope is not influenced by the acceleration of the unmanned aerial vehicle, but the errors caused by integral drift and temperature drift are larger along with the increase of time. Therefore, the two sensors can just make up the mutual defects, and the complementary filtering processing is respectively carried out on each group of measured data, so that the purpose can be achieved. The complementary filtering is to use the angle obtained by the gyroscope as the optimum in a short time, and to average the angle sampled by the acceleration at a fixed time to correct the angle obtained by the gyroscope. Namely, the gyroscope is used more accurately in a short time, and mainly used; the accelerometer is more accurate for a long time, and the specific gravity of the accelerometer is increased at the time, so that the effect is complemented.

The accelerometer needs to filter high-frequency signals, the gyroscope needs to filter low-frequency signals, the complementary filter is high-pass or low-pass or complementary through different filters according to different sensor characteristics, then signals of the whole frequency band are obtained through addition, for example, the accelerometer measures the inclination angle, the dynamic response is slow, the signals are unavailable at high frequency, and therefore the high frequency can be restrained through the low pass; the gyroscope has fast response and can measure the inclination angle after integration, but the signal is not good in a low-frequency band due to null shift and the like, and low-frequency noise can be inhibited through high-pass filtering. By combining the two, the advantages of the gyroscope and the accelerometer are combined to obtain a signal with good high frequency and low frequency, and complementary filtering needs to select switching frequency points, namely high-pass and low-pass frequencies.

The gyro angular velocity drift is a main error source or interference of an inertial navigation system working for a long time, and the existence of the gyro angular velocity drift seriously limits the improvement of the working time and the precision of the inertial navigation system, so that the angular velocity drift can be used as an important standard for measuring whether measured data are healthy or not. The calculation of the amount of angular velocity drift can be performed by any of the methods known in the art, such as mathematical modeling, which conventionally assumes that some particular model formula exists, and then determines the relevant parameters from experimental data that can be properly described by the model, but that do not truly describe the process of generating the data; the existing method tends to treat experimental data as a random sequence, does not need to assume the existence of a certain model a priori, but judges whether a determined phenomenon is consistent with the experimental data according to the analysis of the data, and the method for establishing the mathematical model comprises the steps of identifying the type of the model, estimating parameters and testing the result in an overlapping process of three stages.

Determining the data health degree of each group of measurement data according to the angular velocity drift amount, for example, dividing the data health degree into different grades according to the size of the angular velocity drift amount, for example, determining the data health degree as one grade when the angular velocity drift amount is less than or equal to a first threshold value, determining the data health degree as two grade when the angular velocity drift amount is greater than the first threshold value and less than or equal to a second threshold value; the data health may also be expressed as a specific number, such as a number in the range of 0-100.

The system error caused by the angular velocity drift is accumulated along with time, so that the system error is very necessary to be counted, the data health degree of the measured data is determined by the angular velocity drift amount, the measured data is selected to be reserved or rejected according to the data health degree, and the influence of the error caused by the angular velocity drift on the measurement accuracy of the flight attitude of the unmanned aerial vehicle can be greatly reduced.

In an embodiment of the application, in the method, performing filtering processing on the acquired multiple sets of measurement data, and obtaining a filtering result includes: preprocessing each set of measurement data, the preprocessing including timestamp alignment and/or low pass filtering; and comparing the preprocessed groups of measurement data, and determining the data health degree of each group of measurement data according to the comparison result.

The measurement data measured by the measurement unit of the drone often needs to be preprocessed for subsequent calculations, and in this embodiment, it is recommended to preprocess each set of measurement data by timestamp alignment and/or low-pass filtering.

The time stamp is aligned to ensure that data obtained at the same time are compared with each other when each group of data is compared subsequently. The method of aligning the time stamp can adopt any one among the prior art, and optional time stamp aligns completely, can choose for use the time stamp near, because unmanned aerial vehicle attitude probably takes place sharp change during the flight, therefore this embodiment recommends to use the time stamp align completely, and it is close more strictly for the time stamp.

The method includes the steps of preprocessing measured data, mainly providing data which is appropriate in volume and only contains required information for subsequent processing, wherein filtering methods are generally used. Low-pass filtering provides a smooth version of the signal, primarily by rejecting short-term fluctuations, preserving long-term trends.

And comparing the preprocessed groups of measurement data, and determining the data health degree of each group of measurement data according to the comparison result. The method can pre-estimate the flight attitude of the unmanned aerial vehicle according to each set of preprocessed measurement data, match the pre-estimated attitude with the attitude in the database, compare the matching result with the data corresponding to the flight attitude of the matched unmanned aerial vehicle, and determine the data health degree of each set of measurement data according to the comparison result.

In this embodiment, the preprocessed sets of measurement data may also be compared with each other, for example, when the measurement data includes angular velocity and/or acceleration, a difference between two moduli of angular velocity is determined, and a wild value and a normal value in each angular velocity are determined according to a comparison result between each obtained difference and a preset angular velocity threshold; and/or determining the difference value of the modes of every two accelerations, and determining the wild value and the normal value in each acceleration according to the comparison result of each obtained difference value and a preset acceleration threshold value.

Specifically, if the model of the angular velocity in each set of measurement data is obtained by calculation, then the angular velocities in the plurality of measurement data are combined in pairs, for example, if 5 gyroscopes exist in the unmanned aerial vehicle, 5 sets of angular velocity data exist at the same time, and the 5 sets of angular velocity data are combined in pairs, that is, 10 sets of combinations in pairs exist, and the frequency of occurrence of each angular velocity is 4. Subtracting the obtained two module values to obtain a module difference value, comparing the difference value with a preset angular velocity threshold value, and determining that the two angular velocities are not wild values if the difference value is less than or equal to the preset angular velocity threshold value; if the difference is greater than the preset angular velocity threshold, the two angular velocities are possible to be outliers, the two angular velocities are marked, and when the number of times that a certain angular velocity is marked as possible outliers is greater than or equal to the preset number of times, such as 2 times, the angular velocity is judged to be an outlier.

Similarly, the method for determining the acceleration median value can be adopted but is not limited to the above method.

In an embodiment of the present application, in the method, screening out sets of preliminary pose data according to the filtering result includes: and determining a preliminary attitude data set according to the data health degree, and screening two groups of preliminary attitude data with the shortest Euclidean distance from the preliminary attitude data set.

After the data health degree of each group of measured data is determined, the quality of the measured data can be judged according to the data health degree, the data with good quality is reserved, and unreasonable data is eliminated. For example, 5 sets of measurement data are screened to reserve 3 sets of measurement data, and the 3 sets of measurement data are a preliminary attitude data set and can be used for subsequent calculation.

And screening two groups of initial attitude data with the shortest Euclidean distance from the initial attitude data set. Specifically, for example, the 3 sets of measurement data are pairwise combined into 3 pairs of data, the euclidean distance of each pair of data is calculated, and finally two sets of preliminary attitude data with the shortest euclidean distance are selected as the basis of subsequent calculation.

In the present embodiment, the distance metric is a euclidean distance, which is also called as an euclidean distance, and may be understood as a true distance between two points in an m-dimensional space, or a natural length of a vector, that is, a distance from the point to an origin, and the euclidean distance in a two-dimensional space and a three-dimensional space is an actual distance between the two points. The distance measurement commonly used in the art is also cosine distance, but cosine distance mainly represents relative difference in direction, and euclidean distance mainly represents absolute difference in value, so the present embodiment uses euclidean distance.

In an embodiment of the application, in the above method, fusing the screened-out preliminary pose data includes: determining the fusion weight of each screened primary attitude data according to the data health degree of the measurement data corresponding to the primary attitude data; and fusing the screened preliminary attitude data according to a weighted least square method based on the fusion weight.

According to the data health degree of the measurement data corresponding to the preliminary attitude data, the principle of determining the fusion weight of each screened preliminary attitude data is to assign a high fusion weight to the preliminary attitude data corresponding to the high data health degree, and assign a low fusion weight to the preliminary attitude data corresponding to the low data health degree, and the distribution method of the fusion weight may adopt any one of the prior art, for example, if the data health degree is a specific value, a reasonable fusion weight may be assigned to the preliminary attitude data according to the value size, for example, if 3 sets of measurement data have data health degrees of 50, 60 and 70, the fusion weights assigned to the preliminary attitude data corresponding to the 3 sets of measurement data are 0.5, 0.6 and 0.7.

And based on the fusion weight, fusing the screened preliminary attitude data according to a weighted least square method to obtain final attitude data of the unmanned aerial vehicle. The traditional general least square method is used when statistical information of measurement value errors does not exist, but the general least square method does not consider the characteristic that two measuring instruments are different in measurement accuracy, if the statistical information of the measurement value errors is known, a weighted least square method is suggested to be adopted, the state estimation accuracy is further improved, the minimum performance index is changed in the different place of the weighted least square method and the general least square method, a weight matrix is added, the embodiment selects to enable the fusion weight value with high data health degree to be larger, and the fusion weight value with low data health degree to be smaller, and the minimum performance index in the embodiment is formed.

The embodiment adopts a weighted least square method, considers the characteristics of different measuring instruments and measuring accuracy, considers the problem of data health degree, and further improves the accuracy of unmanned aerial vehicle attitude measurement.

In an embodiment of the application, in the above method, the method further comprises: and navigating the unmanned aerial vehicle according to the attitude data of the unmanned aerial vehicle.

When the unmanned aerial vehicle navigates, and carries out route planning or waypoint and confirms, all need go on according to unmanned aerial vehicle gesture information under most situations, therefore in this application, the attitude data of the unmanned aerial vehicle that adopts the measuring method of above-mentioned unmanned aerial vehicle gesture to obtain all can be used to unmanned aerial vehicle's navigation, and the method that mentions in the above-mentioned embodiment both can the exclusive use also can use in combination.

The above embodiments may be implemented individually or in combination, and specifically, fig. 2 shows a schematic flow diagram of an unmanned aerial vehicle attitude measurement method according to another embodiment of the present application.

Firstly, measurement data of a plurality of detection units of the unmanned aerial vehicle are obtained, then complementary filtering processing is carried out on each group of measurement data, and angular velocity drift amount of each group of data and preliminary attitude data of the unmanned aerial vehicle corresponding to each group of data are obtained through processing.

And comparing the angular velocity drift amount with a preset threshold value, and determining the data health degree of each group of measurement data according to the comparison result, wherein the data health degree can be recorded as the data health degree of the angular velocity drift amount.

Aligning the time stamps to the measurement data of each group of detection units, then performing low-pass processing, calculating the difference value of the modes of every two angular velocities and accelerations, determining the field value and the normal value in each angular velocity and acceleration according to the comparison result of each difference value and the corresponding preset threshold value, and determining the data health degree of each group of measurement data according to the number of the field values in the angular velocity, and recording the data health degree as the angular velocity data health degree; and determining the data health degree of each group of measurement data according to the number of the field values in the acceleration, and recording as the acceleration data health degree.

And screening out a preliminary attitude data set from preliminary attitude data corresponding to each group of measurement data by combining the angular velocity drift amount data health degree, the angular velocity data health degree and the acceleration data health degree.

And calculating the Euclidean distance between every two groups of data in the preliminary attitude data set, and selecting the two groups of data with the shortest Euclidean distance to perform the subsequent fusion step.

And determining fusion weights of the two sets of screened preliminary attitude data by combining the angular velocity drift amount data health degree, the angular velocity data health degree and the acceleration data health degree, and fusing the screened preliminary attitude data according to a weighted least square method based on the fusion weights to obtain final flight attitude information of the unmanned aerial vehicle.

Further, navigation can be performed according to the final flight attitude information of the unmanned aerial vehicle.

Fig. 3 shows a schematic structural diagram of an unmanned aerial vehicle attitude measurement device according to an embodiment of the present application, where the unmanned aerial vehicle attitude measurement device 300 includes:

an obtaining unit 310 is configured to obtain measurement data of a plurality of measurement units of the drone.

Unmanned aerial vehicles are widely applied in various scenes, and particularly quad-rotor unmanned aerial vehicles show unique advantages in many application fields, such as express throwing, takeaway delivery, security enforcement and the like, and even in some terrain complex scenes, such as disaster relief, military reconnaissance and the like. This just requires unmanned aerial vehicle to have very high stability and accuracy nature, and prior art often promotes unmanned aerial vehicle's stability through carrying out a large amount of redundancies of software and hardware to unmanned aerial vehicle, and this has increased unmanned aerial vehicle's manufacturing cost undoubtedly, has increased unmanned aerial vehicle's weight, and then has influenced unmanned aerial vehicle's flight performance. The unmanned aerial vehicle attitude measurement method is characterized in that the data sources can be switched in time when a single data measurement unit breaks down or data is abnormal, so that the accuracy and reliability of unmanned aerial vehicle attitude measurement are ensured, and the stability and reliability of navigation estimation are further ensured.

First, measurement data of a plurality of measurement units of the drone are acquired. The flight control system of the unmanned aerial vehicle is a core system of the whole flight process of the unmanned aerial vehicle, such as finishing takeoff, air flight, task execution, return recovery and the like, is equivalent to the effect of a driver on human-computer for the unmanned aerial vehicle, and is one of the most core technologies of the unmanned aerial vehicle. Flight control system generally includes sensor, airborne computer and three major parts of servo actuation equipment, and the function of realizing mainly has three main types of unmanned aerial vehicle gesture stability and control, unmanned aerial vehicle task equipment management and emergency control. In this application, a plurality of measuring units of unmanned aerial vehicle can be for but not limiting to Inertial Measurement Unit (IMU), and measuring unit's measured data derives from its sensor, including redundant sensor, for example: gyroscopes, accelerators, geomagnetic sensors, Global Positioning Systems (GPS), air pressure sensors, ultrasonic sensors, optical flow sensors, and the like. The acquired measurement data includes, but is not limited to, acceleration, angular velocity, euler angles (pitch, roll, yaw), altitude, and the like. If one sensor has a plurality of redundancies, the measurement data of each sensor is acquired, and if 5 gyroscopes are totally arranged, 5 groups of angular velocity data are acquired.

The data processing unit 320 is configured to perform filtering processing on the acquired multiple sets of measurement data to obtain a filtering result, where the filtering result includes preliminary attitude data corresponding to each set of measurement data and data health of each set of measurement data; and the system is used for screening a plurality of groups of preliminary attitude data according to the filtering result.

The filtering is an operation of filtering specific wave band frequencies in signals, is an important measure for inhibiting and preventing interference, and is a probability theory and a method for estimating another random process related to the random process according to the result of observing the random process. In this embodiment, the filtering algorithm may adopt one or a combination of several in the prior art according to the requirement for data, including but not limited to amplitude limiting filtering, median filtering, arithmetic mean filtering, recursive mean filtering, median mean filtering, amplitude limiting mean filtering, first-order lag filtering, weighted recursive mean filtering, debounce filtering, amplitude limiting debounce filtering, and the like.

And filtering the obtained multiple groups of measurement data to obtain a filtering result, wherein the filtering result comprises preliminary attitude data corresponding to each group of measurement data and the data health degree of each group of measurement data.

And carrying out filtering processing on a group of measurement data to obtain a group of filtering results, wherein the group of filtering results comprise but are not limited to angular velocity, acceleration, yaw angle, horizontal height and the like, the parameters determine a flight attitude of the unmanned aerial vehicle, and the group of data is initial attitude data corresponding to the group of filtering results. Because the hardware of the unmanned aerial vehicle has redundancy, if the same 5 groups of measurement units exist in parallel and run simultaneously, 5 groups of preliminary attitude data can be obtained after filtering treatment, and the parameters in each group of preliminary attitude data are the same and all comprise angular velocity, acceleration, yaw angle, horizontal height and the like.

Above-mentioned multiunit measuring unit that parallels, because sensor installation angle, operating condition or certain single sensor have factors such as trouble, the measured data of gathering are not totally reliable, in order to ensure unmanned aerial vehicle flight attitude measurement's accuracy, this application is in the in-process of carrying out filtering process to multiunit measured data, the data health degree to multiunit measured data is makeed statistics of, the data health degree can be regarded as the measured data quality of measuring the multiunit measuring unit of unmanned aerial vehicle is good or bad, can be used for one of the standard of follow-up unmanned aerial vehicle attitude estimation even, if can keep the measured data that data health degree is high, can reject some measured data that data health degree is very low, with the fail safe nature of the estimation of guarantee follow-up unmanned aerial vehicle flight attitude.

The calculation of the data health degree can adopt any one or a combination of several in the prior art, for example, the data health degree can be limited to be a numerical value in the range of 0-100, the initial data health degree of the measurement data is set to be 100, the final data health degree of the measurement data is calculated on the basis of the initial data health degree according to a preset rule, if a wild value exists in the measurement data, the final data health degree is reduced by 5 on the basis of the initial data health degree, and if a certain item of data is missing, the final data health degree of each measurement unit of the unmanned aerial vehicle is reduced by 20 on the basis of the initial data health degree, so that the final data health degree of each measurement unit of the unmanned aerial vehicle is obtained.

Screening a plurality of groups of preliminary attitude data according to the filtering result, wherein unhealthy and unreasonable data in the measurement data of the unmanned aerial vehicle measurement unit can be removed, and data with better quality can be further selected from all reasonable data to carry out subsequent steps so as to save the calculated amount.

The screening process can be performed according to the plurality of groups of preliminary posture data, for example, one or more data in the preliminary posture data are utilized, and if the number of the outliers in the angular velocity is judged to exceed the preset number, the group of preliminary posture data are removed.

The screening process can also be carried out according to the numerical health degree of the measurement data, for example, the measurement data with the preset data health degree of more than 60 and including 60 can be reserved, and the measurement data with the data health degree of less than 60 can be eliminated.

And the execution unit 330 is configured to fuse the screened preliminary attitude data, and use a fusion result as the attitude data of the unmanned aerial vehicle.

And 4, fusing the retained initial attitude data, and taking the fusion result as the attitude data of the unmanned aerial vehicle. Data fusion is an information processing technique that combines, correlates, and combines multiple sets or types of source data and information to obtain more accurate results.

The fusion method in the application can adopt one or more of the prior art, and can be direct fusion of data layers, for example, averaging all accelerations in each group is taken as the fusion result of the accelerations.

The fusion in the application can also be a fusion of feature layers, for example, the feature information such as angular velocity, acceleration, yaw angle, horizontal height and the like in each group of measurement data is comprehensively analyzed and processed, the fusion of the feature layers has the advantages of realizing considerable information compression and being beneficial to real-time processing, and the extracted features are directly related to decision analysis, so that the fusion result can give out feature information required by the decision analysis to the maximum extent.

The fusion in the application can also be a decision-making level fusion, namely, a preliminary conclusion on an observed target is established according to data of different types of sensors, and then decision-making level fusion judgment is carried out through association processing, and a joint inference result is finally obtained.

In an embodiment of the present application, in the above apparatus, the data processing unit 320 is configured to perform complementary filtering on each set of measurement data, respectively, to obtain an angular velocity drift amount of each set of measurement data and preliminary attitude data corresponding to each set of measurement data; and determining the data health degree of each group of measurement data according to the angular speed drift amount.

In an embodiment of the present application, in the above apparatus, the data processing unit 320 is configured to perform preprocessing on each set of measurement data, where the preprocessing includes timestamp alignment and/or low-pass filtering; and the system is used for comparing the preprocessed groups of measurement data and determining the data health degree of each group of measurement data according to the comparison result.

In one embodiment of the present application, in the above apparatus, the measurement data includes angular velocity and/or acceleration, the data processing unit 320 is configured to determine a difference between two moduli of angular velocity, and determine a outlier and a normal value in each angular velocity according to a comparison result between each obtained difference and a preset angular velocity threshold; and/or determining the difference value of the modes of every two accelerations, and determining the wild value and the normal value in each acceleration according to the comparison result of each obtained difference value and a preset acceleration threshold value.

In an embodiment of the present application, in the above apparatus, the data processing unit 320 is configured to determine a preliminary posture data set according to the data health degree, and screen out two sets of preliminary posture data with the shortest euclidean distance from the preliminary posture data set.

In an embodiment of the present application, in the above apparatus, the executing unit 330 is configured to determine a fusion weight of each screened primary attitude data according to a data health degree of measurement data corresponding to the primary attitude data; and fusing the screened preliminary attitude data according to a weighted least square method based on the fusion weight.

In an embodiment of the present application, in the above apparatus, the execution unit 330 is further configured to perform navigation of the drone according to the attitude data of the drone.

It should be noted that the unmanned aerial vehicle attitude measurement device in the foregoing embodiment may be respectively used to execute the unmanned aerial vehicle attitude measurement method in the foregoing embodiment, and therefore, specific description is not given one by one.

According to the technical scheme, the measurement data of the plurality of measurement units of the unmanned aerial vehicle are acquired; filtering the obtained multiple groups of measurement data to obtain a filtering result, wherein the filtering result comprises preliminary attitude data corresponding to each group of measurement data and the data health degree of each group of measurement data; screening a plurality of groups of preliminary attitude data according to the filtering result; and fusing the screened initial attitude data, and taking a fusion result as the attitude data of the unmanned aerial vehicle. The beneficial effect of this application lies in: under the condition that the hardware of the unmanned aerial vehicle is not required to be modified, the posture of the unmanned aerial vehicle can be quickly and accurately measured by utilizing the redundancy on the hardware of the flight control system, the stability and the accuracy of the unmanned aerial vehicle flight control system in the aspect of navigation are obviously improved, especially when a single measuring unit breaks down, a data source can be timely switched, and the crash accident of the unmanned aerial vehicle is avoided; and the calculation amount is small, the code implementation method is simple and convenient, and the practicability is stronger.

It should be noted that:

the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. In addition, this application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the present application.

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various application aspects. However, the disclosed method should not be interpreted as reflecting an intention that: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, application is directed to less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.

Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a drone attitude measurement device according to embodiments of the present application. The present application may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present application may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

For example, fig. 4 shows a schematic structural diagram of a drone according to one embodiment of the present application. The drone 400 includes a processor 410 and a memory 420 arranged to store computer executable instructions (computer readable program code). The memory 420 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. The memory 420 has a storage space 430 storing computer readable program code 431 for performing any of the method steps described above. For example, the storage space 430 for storing the computer readable program code may include respective computer readable program codes 431 for respectively implementing various steps in the above method. The computer readable program code 431 can be read from or written to one or more computer program products. These computer program products comprise a program code carrier such as a hard disk, a Compact Disc (CD), a memory card or a floppy disk. Such a computer program product is typically a computer readable storage medium such as described in fig. 5. FIG. 5 shows a schematic diagram of a computer-readable storage medium according to an embodiment of the present application. The computer readable storage medium 500 stores computer readable program code 431 for performing the steps of the method according to the present application, which is readable by the processor 410 of the drone 400, which when the computer readable program code 431 is executed by the drone 400, causes the drone 400 to perform the steps of the method described above, and in particular the computer readable program code 431 stored by the computer readable storage medium may perform the method shown in any of the embodiments described above. The computer readable program code 431 may be compressed in a suitable form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.