Unmanned aerial vehicle information acquisition control method and system

Abstract

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle information acquisition control method and system, and the method comprises the following steps: determining coordinate systems of an accelerometer, a gyroscope and an unmanned aerial vehicle; the accelerometer and the gyroscope are calibrated; carrying out initial alignment on the accelerometer and the gyroscope; the accelerometer collects the acceleration of the unmanned aerial vehicle in the flight process, the gyroscope collects the angular velocity of the unmanned aerial vehicle in the flight process, and the global satellite navigation system collects the position and speed of the unmanned aerial vehicle in the flight process; resolving flight information, collected by an accelerometer, a gyroscope and a global satellite navigation system, in the flight process of the unmanned aerial vehicle; and error correction processing is carried out on the resolved flight information. The problems of low acquisition accuracy and the like in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, specifically to a method and system for UAV information acquisition and control. Background Technology

[0002] Inertial Navigation Systems (INS) are an autonomous navigation technology based on Newtonian mechanics. They use inertial sensors to measure the acceleration and angular velocity of a vehicle in real time, and calculate position, velocity, and attitude information through integration. Essentially, they autonomously calculate motion state through continuous integration of physical quantities (acceleration and angular velocity). They offer advantages such as providing real-time, continuous navigation data and suitability for high-speed, dynamic scenarios. However, they also have significant drawbacks: integration errors cause position deviations to accumulate over time, requiring external systems for error correction; and they are difficult to use alone to meet long-term, high-precision requirements.

[0003] A Global Navigation Satellite System (GNSS) is a space-based radio system that provides all-weather, high-precision positioning, navigation, and timing services to users worldwide through a constellation of satellites, such as BDS (BeiDou Navigation Satellite System), GPS (Global Positioning System), GLONASS, and GALILEO. It boasts advantages such as global coverage and high availability. However, it also has the following disadvantages: signal obstruction issues, such as those caused by tall buildings, tunnels, or mountainous areas, can easily lead to satellite signal interruptions, reduced positioning accuracy, or even failure. The urban canyon effect can amplify errors to tens of meters. Furthermore, it carries a high risk of electromagnetic interference and has insufficient anti-interference capabilities; strong electromagnetic environments such as high-voltage power lines and radar stations can cause signal distortion.

[0004] Unmanned aerial vehicles (UAVs), with their flexibility, efficiency, and low cost, have been widely used in various fields, such as agriculture, emergency response, logistics, energy, and infrastructure inspection. Given the diverse flight environments in which UAVs operate, improving their positioning and navigation accuracy has become a trend in technological advancement. However, existing UAV positioning and navigation technologies suffer from low accuracy in flight data collection and limitations in application scenarios. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention provides a method and system for unmanned aerial vehicle (UAV) information acquisition and control, which solves problems such as low acquisition accuracy in existing technologies.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A method for collecting and controlling information from an unmanned aerial vehicle (UAV) utilizes an inertial navigation system and a global satellite navigation system to collect flight information during the UAV's flight. The inertial navigation system includes accelerometers and gyroscopes that communicate with each other. The method includes the following steps: Determine the coordinate systems of the accelerometer, gyroscope, and UAV; Calibrate the accelerometer and gyroscope; Perform initial alignment of the accelerometer and gyroscope; Accelerometers collect the acceleration of the drone during flight, gyroscopes collect the angular velocity of the drone during flight, and global satellite navigation systems collect the position and velocity of the drone during flight. The flight information collected by accelerometers, gyroscopes, and global satellite navigation systems during the flight process of the UAV is processed. Error correction processing is performed on the calculated flight information.

[0007] As a preferred technical solution, the calculation of flight information collected by accelerometers, gyroscopes, and global navigation satellite systems during the flight of a UAV includes the following steps: The velocity is obtained by integrating the acceleration collected by the accelerometer once, and the position is obtained by integrating it twice. The attitude angle is obtained by integrating the angular velocity collected by the gyroscope once. The attitude angle is obtained by processing the angular velocity collected by the gyroscope using the quaternion difference method; The velocity obtained by one integration is subtracted from the velocity collected by the global satellite navigation system. If the absolute value of the difference is less than or equal to a set threshold, the average value of the two is taken as the calculated velocity; if the absolute value of the difference is greater than the set threshold, the velocity collected by the global satellite navigation system is taken as the calculated velocity. The position obtained by the second integration is subtracted from the position collected by the global satellite navigation system. If the absolute value of the difference is less than or equal to a set threshold, the average value of the two is taken as the calculated position; if the absolute value of the difference is greater than the set threshold, the position collected by the global satellite navigation system is taken as the calculated position. The attitude angle obtained by one integration and the attitude angle obtained by processing using the quaternion difference method are subtracted. If the absolute value of the difference is less than or equal to a set threshold, the average value of the two is taken as the solved attitude angle; if the absolute value of the difference is greater than the set threshold, the attitude angle obtained by processing using the quaternion difference method is taken as the solved attitude angle.

[0008] As a preferred technical solution, error correction processing of the calculated flight information includes the following steps: The Kalman filter method is used to estimate the error of the calculated velocity, position and / or attitude angles to obtain the velocity, position and / or attitude angle error values; The Kalman filter method is used to estimate the error of the accelerometer and / or gyroscope to obtain the error value of the accelerometer and / or gyroscope. If the accelerometer and / or gyroscope error value is ≤ a set threshold and the velocity, position, and / or attitude angle error value is ≤ a set threshold, then the calculated velocity, position, and / or attitude angle is used as the final velocity, position, and / or attitude angle. If the accelerometer and / or gyroscope error value is ≤ a set threshold and the velocity, position, and / or attitude angle error value is > a set threshold, then the calculated velocity, position, and / or attitude angle is corrected using the velocity, position, and / or attitude angle error values, and the corrected velocity, position, and / or attitude angle is used as the final velocity, position, and / or attitude angle. If the accelerometer and / or gyroscope error value is > a set threshold, then the calculated velocity, position, and / or attitude angle is discarded as the final velocity, position, and / or attitude angle, and the accelerometer and / or gyroscope re-acquire information.

[0009] As a preferred technical solution, the initial alignment of the accelerometer and gyroscope includes the following steps: The gravity vector is measured using an accelerometer, and the Earth's rotation angular velocity vector is measured using a gyroscope. The initial attitude matrix from the carrier coordinate system to the geographic coordinate system is calculated using a two-vector attitude determination method. The measured gravity vector and the measured Earth's rotation angular velocity vector are used as two vectors in the two-vector attitude determination method. The position and velocity of the accelerometer, as well as the position and velocity of the gyroscope, are measured using a global satellite navigation system. The measured position and velocity of the accelerometer are converted into the gravity vector of the accelerometer, and then the difference between the gravity vector measured by the accelerometer is used to obtain the gravity measurement error. The position and velocity of the gyroscope are converted into the rotational angular velocity vector of the gyroscope, and then the difference between the rotational angular velocity vector of the Earth measured by the gyroscope is used to obtain the angular velocity measurement error. Using gravity measurement error and angular velocity measurement error as state elements, an inertial navigation state model is constructed to output state result values; The state result values ​​are processed by Kalman filtering, and the filtered state result values ​​are used to replace the gravity vector measured by the accelerometer and the Earth's rotation angular velocity vector measured by the gyroscope.

[0010] As a preferred technical solution, the initial alignment of the accelerometer and gyroscope also includes the following steps: Using gravity measurement error and angular velocity measurement error as error values, and the measured positions and velocities of the accelerometer and gyroscope as true values, an error observation model is constructed to output the observed values; where, observed value = error value + true value; The observations are processed by Kalman filtering. The difference between the filtered observations and the state result is calculated. If the difference is less than or equal to a set threshold, the filtered observations are used to replace the gravity vector measured by the accelerometer and the Earth's rotation angular velocity vector measured by the gyroscope.

[0011] As a preferred technical solution, when performing error correction processing on the calculated flight information, the acceleration after error correction is: ; in, b represents the acceleration after error correction. a I3 represents the zero bias error of the accelerometer, M represents the identity matrix, and M represents the zero bias error of the accelerometer. a This represents the scaling factor error of the inertial navigation system. ω represents the acceleration collected by the accelerometer. a This indicates the measurement noise of the accelerometer.

[0012] As a preferred technical solution, M a The calculation formula is: ; in, This indicates the scaling factor of the accelerometer on the X-axis. This indicates the scaling factor of the accelerometer on the Y-axis. This indicates the scaling factor of the accelerometer on the Z-axis. This indicates the cross-coupling error between the X and Y axes of the accelerometer. This indicates the cross-coupling error between the X and Z axes of the accelerometer. This indicates the cross-coupling error between the Y and X axes of the accelerometer. This indicates the cross-coupling error between the Y and Z axes of the accelerometer. This indicates the cross-coupling error between the Z-axis and X-axis of the accelerometer. This indicates the cross-coupling error between the Z-axis and Y-axis of the accelerometer.

[0013] As a preferred technical solution, when performing error correction processing on the calculated flight information, the angular velocity after error correction is: ; in, b represents the angular velocity after error correction. g M represents the zero bias error of the gyroscope. g This represents the cross-coupling error of the inertial navigation system. G represents the angular velocity collected by the gyroscope. g Indicates the gravity-sensitive bias value, ω g This indicates the measurement noise of the gyroscope.

[0014] As a preferred technical solution, M g The calculation formula is: ; in, This represents the scaling factor of the gyroscope on the X-axis. This indicates the scaling factor of the gyroscope on the Y-axis. This indicates the scaling factor of the gyroscope on the Z-axis. This represents the cross-coupling error between the X and Y axes of the gyroscope. This represents the cross-coupling error between the X and Z axes of the gyroscope. This represents the cross-coupling error between the Y-axis and X-axis of the gyroscope. This represents the cross-coupling error between the Y and Z axes of the gyroscope. This represents the cross-coupling error between the Z-axis and X-axis of the gyroscope. This represents the cross-coupling error between the Z-axis and Y-axis of the gyroscope.

[0015] The advantages of adopting the above-mentioned preferred technical solution are: considering multiple elements to determine the scale factor error inertial navigation system, and more accurately calculating the angular velocity after error correction.

[0016] A drone information acquisition and control system, used to implement the aforementioned drone information acquisition and control method, includes the following modules connected in sequence: The coordinate system determination module is used to determine the coordinate system of the accelerometer, gyroscope, and UAV. The calibration module is used to calibrate accelerometers and gyroscopes. The initial alignment module is used for: initial alignment of the accelerometer and gyroscope; The data acquisition module is used to: enable the accelerometer to acquire the acceleration of the UAV during flight, enable the gyroscope to acquire the angular velocity of the UAV during flight, and enable the global satellite navigation system to acquire the position and velocity of the UAV during flight; The calculation module is used to calculate the flight information collected by the accelerometer, gyroscope, and global satellite navigation system during the flight process of the UAV. The error correction module is used to perform error correction processing on the calculated flight information.

[0017] Compared with the prior art, the present invention has the following advantages: (1) This invention achieves control by collecting flight information from multiple sources such as accelerometers, gyroscopes, and global satellite navigation systems. It uses the global satellite navigation system to correct the measurement errors of accelerometers and gyroscopes. The positioning accuracy is improved by steps such as coordinate system determination, calibration, initial alignment, acquisition, calculation, and error correction. Moreover, it combines the information acquisition of accelerometers, gyroscopes, and global satellite navigation systems, which has the advantages of both inertial navigation systems and global satellite navigation systems, significantly expanding the application scenarios. At the same time, it has a high degree of automation. By determining the coordinate system of accelerometers, gyroscopes, and UAVs, the conversion of flight data, time and space synchronization are achieved, which facilitates the vector analysis of motion trajectory in the later stage. The calibration process is convenient for correcting the zero bias and scaling factor errors of the accelerometers and gyroscopes themselves, improving the acquisition accuracy. The initial alignment determines the relative relationship between the carrier coordinate system and the navigation coordinate system, providing an initial reference for the inertial navigation system. The acquisition accuracy is further improved by calculating and error correction of the acquired flight information. (2) From the perspective of data acquisition equipment, the speed and position obtained from different data sources are compared. If the absolute value of the difference between the two is less than or equal to the set threshold, it indicates that the error is within the acceptable set range. The average value of the two is taken as the data after calculation, which is conducive to improving the reliability of the data. If the absolute value of the difference between the two is greater than the set threshold, it indicates that the error has exceeded the acceptable range. It may be caused by the large cumulative error of the accelerometer and gyroscope. In this case, the data collected by the more accurate global satellite navigation system is taken as the data after calculation, which is more conducive to ensuring accuracy and reducing the probability of error. From the perspective of calculation method, the attitude angles obtained by different calculation methods are compared. If the absolute value of the difference between the two is less than or equal to the set threshold, it indicates that the error is within the acceptable set range. The average value of the two is taken as the data after calculation, which is conducive to improving the reliability of the data. If the absolute value of the difference between the two is greater than the set threshold, it indicates that the error has exceeded the acceptable range. Since the quaternion difference method does not involve a continuous integration process, there is no long-term drift problem caused by numerical approximation in the integration method. Therefore, in this case, the attitude angle obtained by the quaternion difference method is more accurate. (3) The Kalman filtering method is more accurate in estimating the error of accelerometers and gyroscopes and has strong real-time performance. If the instrument error is within the acceptable range, the calculated data or the data after error correction is selected as the final speed, position and / or attitude angle based on the comparison between the speed, position and / or attitude angle error values ​​and the set threshold. If the instrument error exceeds the acceptable range, it may be due to a large cumulative error. In this case, the calculated data is discarded and re-acquired. This data selection method ensures the accuracy of the final speed, position and / or attitude angle. (4) The data obtained from the two methods (gravity vector and Earth rotation angular velocity vector measured by the inertial navigation system, and gravity vector and Earth rotation angular velocity vector measured and converted by the global satellite navigation system) are compared and subtracted to obtain the measurement error. The measurement error is used as a state element to construct the inertial navigation state model and output the state result value, which facilitates timely correction, observation and updating, improves the accuracy of the initial alignment, and provides a precision guarantee for the accuracy of the data acquisition. Kalman filtering eliminates noise, which is more conducive to ensuring the accuracy of the data. (5) The error observation model outputs observation values, which further facilitates timely correction, observation and updating. Moreover, the model's fault tolerance is improved through difference calculation and vector replacement operation. (6) Considering various error factors, the acceleration and angular velocity after error correction are calculated more accurately. Attached Figure Description

[0018] Figure 1 This is a flowchart of an unmanned aerial vehicle (UAV) information acquisition and control method according to the present invention; Figure 2 This is a schematic diagram of the strapdown calculation module. Figure 3 This is a flowchart of the combined navigation process; Figure 4 This is a schematic diagram of the combined navigation signal flow. Detailed Implementation

[0019] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0020] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0021] Example 1 like Figures 1 to 4 As shown, a method for information acquisition and control of an unmanned aerial vehicle (UAV) utilizes an inertial navigation system and a global satellite navigation system to collect flight information during the UAV's flight. The inertial navigation system includes accelerometers and gyroscopes that communicate with each other, and includes the following steps: Determine the coordinate systems of the accelerometer, gyroscope, and UAV; Calibrate the accelerometer and gyroscope; Perform initial alignment of the accelerometer and gyroscope; Accelerometers collect the acceleration of the drone during flight, gyroscopes collect the angular velocity of the drone during flight, and global satellite navigation systems collect the position and velocity of the drone during flight. The flight information collected by accelerometers, gyroscopes, and global satellite navigation systems during the flight process of the UAV is processed. Error correction processing is performed on the calculated flight information.

[0022] The beneficial effects of this invention are as follows: This invention enables control through flight information acquisition from multiple sensors, including accelerometers, gyroscopes, and global navigation satellite systems (GNSS). It utilizes GNSS to correct measurement errors of the accelerometers and gyroscopes. The steps of coordinate system determination, calibration, initial alignment, acquisition, calculation, and error correction improve positioning accuracy. Furthermore, by combining information acquisition from accelerometers, gyroscopes, and GNSS, it leverages the advantages of both inertial navigation systems and GNSS, significantly expanding application scenarios while maintaining a high degree of automation. Determining the coordinate systems of the accelerometers, gyroscopes, and the UAV enables flight data conversion, temporal and spatial synchronization, and facilitates subsequent vector analysis of the motion trajectory. The calibration process facilitates the correction of errors such as zero bias and scaling factor of the accelerometers and gyroscopes, improving acquisition accuracy. Initial alignment determines the relative relationship between the carrier coordinate system and the navigation coordinate system, providing an initial reference for the inertial navigation system. The calculation and error correction of the acquired flight information further enhances acquisition accuracy.

[0023] As a preferred technical solution, the calculation of flight information collected by accelerometers, gyroscopes, and global navigation satellite systems during the flight of a UAV includes the following steps: The velocity is obtained by integrating the acceleration collected by the accelerometer once, and the position is obtained by integrating it twice. The attitude angle is obtained by integrating the angular velocity collected by the gyroscope once. The attitude angle is obtained by processing the angular velocity collected by the gyroscope using the quaternion difference method; The velocity obtained by one integration is subtracted from the velocity collected by the global satellite navigation system. If the absolute value of the difference is less than or equal to a set threshold, the average value of the two is taken as the calculated velocity; if the absolute value of the difference is greater than the set threshold, the velocity collected by the global satellite navigation system is taken as the calculated velocity. The position obtained by the second integration is subtracted from the position collected by the global satellite navigation system. If the absolute value of the difference is less than or equal to a set threshold, the average value of the two is taken as the calculated position; if the absolute value of the difference is greater than the set threshold, the position collected by the global satellite navigation system is taken as the calculated position. The attitude angle obtained by one integration and the attitude angle obtained by processing using the quaternion difference method are subtracted. If the absolute value of the difference is less than or equal to a set threshold, the average value of the two is taken as the solved attitude angle; if the absolute value of the difference is greater than the set threshold, the attitude angle obtained by processing using the quaternion difference method is taken as the solved attitude angle.

[0024] The beneficial effects of adopting the above-mentioned preferred technical solution are as follows: From the perspective of data acquisition equipment, comparing the velocity and position obtained from different data sources, if the absolute value of the difference is less than or equal to a set threshold, it indicates that the error is within an acceptable set range. Taking the average of the two values ​​as the calculated data improves data reliability. If the absolute value of the difference is greater than the set threshold, it indicates that the error has exceeded the acceptable range, possibly due to large cumulative errors from accelerometers or gyroscopes. In this case, using data from a more accurate global navigation satellite system (GNSS) as the calculated data further ensures accuracy and reduces the probability of errors. From the perspective of calculation methods, comparing the attitude angles obtained from different calculation methods, if the absolute value of the difference is less than or equal to a set threshold, it indicates that the error is within an acceptable set range. Taking the average of the two values ​​as the calculated data improves data reliability. If the absolute value of the difference is greater than the set threshold, it indicates that the error has exceeded the acceptable range. Since the quaternion difference method does not involve continuous integration and does not suffer from long-term drift problems caused by numerical approximation in integration methods, the attitude angles obtained by the quaternion difference method are more accurate in this case.

[0025] As a preferred technical solution, error correction processing of the calculated flight information includes the following steps: The Kalman filter method is used to estimate the error of the calculated velocity, position and / or attitude angles to obtain the velocity, position and / or attitude angle error values; The Kalman filter method is used to estimate the error of the accelerometer and / or gyroscope to obtain the error value of the accelerometer and / or gyroscope. If the accelerometer and / or gyroscope error value is ≤ a set threshold and the velocity, position, and / or attitude angle error value is ≤ a set threshold, then the calculated velocity, position, and / or attitude angle is used as the final velocity, position, and / or attitude angle. If the accelerometer and / or gyroscope error value is ≤ a set threshold and the velocity, position, and / or attitude angle error value is > a set threshold, then the calculated velocity, position, and / or attitude angle is corrected using the velocity, position, and / or attitude angle error values, and the corrected velocity, position, and / or attitude angle is used as the final velocity, position, and / or attitude angle. If the accelerometer and / or gyroscope error value is > a set threshold, then the calculated velocity, position, and / or attitude angle is discarded as the final velocity, position, and / or attitude angle, and the accelerometer and / or gyroscope re-acquire information.

[0026] The advantages of adopting the above-mentioned preferred technical solution are: the Kalman filter method provides relatively accurate error estimation for accelerometers and gyroscopes, with strong real-time performance; if the instrument error is within an acceptable range, the calculated data or error-corrected data is selected as the final velocity, position, and / or attitude angle based on the comparison between the velocity, position, and / or attitude angle error values ​​and the set thresholds; if the instrument error exceeds the acceptable range, it may indicate a large cumulative error, in which case the calculated data is discarded and re-acquired. This data selection method ensures the accuracy of the final velocity, position, and / or attitude angle. Error correction can be achieved using existing technology, therefore its working principle and process are not further elaborated upon.

[0027] As a preferred technical solution, the initial alignment of the accelerometer and gyroscope includes the following steps: The gravity vector is measured using an accelerometer, and the Earth's rotation angular velocity vector is measured using a gyroscope. The initial attitude matrix from the carrier coordinate system to the geographic coordinate system is calculated using a two-vector attitude determination method. The measured gravity vector and the measured Earth's rotation angular velocity vector are used as two vectors in the two-vector attitude determination method. The position and velocity of the accelerometer, as well as the position and velocity of the gyroscope, are measured using a global satellite navigation system. The measured position and velocity of the accelerometer are converted into the gravity vector of the accelerometer, and then the difference between the gravity vector measured by the accelerometer is used to obtain the gravity measurement error. The position and velocity of the gyroscope are converted into the rotational angular velocity vector of the gyroscope, and then the difference between the rotational angular velocity vector of the Earth measured by the gyroscope is used to obtain the angular velocity measurement error. Using gravity measurement error and angular velocity measurement error as state elements, an inertial navigation state model is constructed to output state result values; The state result values ​​are processed by Kalman filtering, and the filtered state result values ​​are used to replace the gravity vector measured by the accelerometer and the Earth's rotation angular velocity vector measured by the gyroscope.

[0028] The beneficial effects of adopting the above-mentioned preferred technical solution are as follows: The measurement error is obtained by comparing and subtracting the data obtained from the two methods (gravity vector and Earth rotation angular velocity vector measured by the inertial navigation system, and gravity vector and Earth rotation angular velocity vector measured and converted by the global satellite navigation system). This measurement error is used as a state element to construct an inertial navigation state model and output state result values, facilitating timely correction, observation, and updates, improving the accuracy of initial alignment, and providing a precision guarantee for the accuracy of data acquisition. Kalman filtering eliminates noise, further ensuring data accuracy. The conversion here can be achieved using existing technology; therefore, its working principle and process are not described in further detail.

[0029] As a preferred technical solution, the initial alignment of the accelerometer and gyroscope also includes the following steps: Using gravity measurement error and angular velocity measurement error as error values, and the measured positions and velocities of the accelerometer and gyroscope as true values, an error observation model is constructed to output the observed values; where, observed value = error value + true value; The observations are processed by Kalman filtering. The difference between the filtered observations and the state result is calculated. If the difference is less than or equal to a set threshold, the filtered observations are used to replace the gravity vector measured by the accelerometer and the Earth's rotation angular velocity vector measured by the gyroscope.

[0030] The advantages of adopting the above-mentioned preferred technical solution are: the error observation model outputs observation values, which further facilitates timely correction, observation and updating, and also improves the model's fault tolerance through difference calculation and vector replacement operations.

[0031] As a preferred technical solution, when performing error correction processing on the calculated flight information, the acceleration after error correction is: ; in, b represents the acceleration after error correction. a I3 represents the zero bias error of the accelerometer, M represents the identity matrix, and M represents the zero bias error of the accelerometer. a This represents the scaling factor error of the inertial navigation system. ω represents the acceleration collected by the accelerometer. a This indicates the measurement noise of the accelerometer.

[0032] The beneficial effect of adopting the above-mentioned preferred technical solution is that it can more accurately calculate the acceleration after error correction by considering various error influencing factors.

[0033] As a preferred technical solution, M a The calculation formula is: ; in, This indicates the scaling factor of the accelerometer on the X-axis. This indicates the scaling factor of the accelerometer on the Y-axis. This indicates the scaling factor of the accelerometer on the Z-axis. This indicates the cross-coupling error between the X and Y axes of the accelerometer. This indicates the cross-coupling error between the X and Z axes of the accelerometer. This indicates the cross-coupling error between the Y and X axes of the accelerometer. This indicates the cross-coupling error between the Y and Z axes of the accelerometer. This indicates the cross-coupling error between the Z-axis and X-axis of the accelerometer. This indicates the cross-coupling error between the Z-axis and Y-axis of the accelerometer.

[0034] The advantages of adopting the above-mentioned preferred technical solution are: considering multiple elements to determine the influence factors of the proportional factor error of the inertial navigation system, and more accurately calculating the acceleration after error correction.

[0035] As a preferred technical solution, when performing error correction processing on the calculated flight information, the angular velocity after error correction is: ; in, b represents the angular velocity after error correction. g M represents the zero bias error of the gyroscope. g This represents the cross-coupling error of the inertial navigation system. G represents the angular velocity collected by the gyroscope. g Indicates the gravity-sensitive bias value, ω g This indicates the measurement noise of the gyroscope.

[0036] The advantages of adopting the above-mentioned preferred technical solution are: considering various error influencing factors, the angular velocity after error correction is calculated more accurately.

[0037] As a preferred technical solution, M g The calculation formula is: ; in, This represents the scaling factor of the gyroscope on the X-axis. This indicates the scaling factor of the gyroscope on the Y-axis. This indicates the scaling factor of the gyroscope on the Z-axis. This represents the cross-coupling error between the X and Y axes of the gyroscope. This represents the cross-coupling error between the X and Z axes of the gyroscope. This represents the cross-coupling error between the Y-axis and X-axis of the gyroscope. This represents the cross-coupling error between the Y and Z axes of the gyroscope. This represents the cross-coupling error between the Z-axis and X-axis of the gyroscope. This represents the cross-coupling error between the Z-axis and Y-axis of the gyroscope.

[0038] The advantages of adopting the above-mentioned preferred technical solution are: considering multiple elements to determine the scale factor error inertial navigation system, and more accurately calculating the angular velocity after error correction.

[0039] A drone information acquisition and control system, used to implement the aforementioned drone information acquisition and control method, includes the following modules connected in sequence: The coordinate system determination module is used to determine the coordinate system of the accelerometer, gyroscope, and UAV. The calibration module is used to calibrate accelerometers and gyroscopes. The initial alignment module is used for: initial alignment of the accelerometer and gyroscope; The data acquisition module is used to: enable the accelerometer to acquire the acceleration of the UAV during flight, enable the gyroscope to acquire the angular velocity of the UAV during flight, and enable the global satellite navigation system to acquire the position and velocity of the UAV during flight; The calculation module is used to calculate the flight information collected by the accelerometer, gyroscope, and global satellite navigation system during the flight process of the UAV. The error correction module is used to perform error correction processing on the calculated flight information.

[0040] Example 2 like Figures 1 to 4 As shown, based on Example 1, this example provides a more detailed implementation method.

[0041] Based on the research of multi-source tightly integrated flight navigation fusion positioning technology, we developed autonomous flight three-dimensional real-time route planning technology and UAV real-time intelligent flight control technology to capture ground points and obtain terrain data results. This allows us to calculate the distance between the UAV and the ground and obstacles, control the speed and direction of the UAV, autonomously fly along the predetermined target, adjust the planned route in real time, change the interval strip of the route in the survey area, ensure that the flight trajectory and scanning angle meet the accuracy requirements of the data results, improve the data collection integrity of unknown terrain and environmental areas, reduce flight risks, and provide reliable technical support for intelligent reconnaissance and inspection.

[0042] This invention relates to hardware for deeply coupling multi-source positioning data. It addresses the accuracy and payload requirements of the acquired data, selecting appropriate sensors such as inertial measurement units, lidar, high-resolution cameras, and GNSS positioning systems, and integrating a combined navigation and positioning system to provide hardware support for the multi-source positioning data coupling algorithm. A lightweight and compact control module and data recording mode are developed to achieve tight integration of the control module and data recording, ensuring the normal operation of the multi-source sensors.

[0043] Develop a prototype of an intelligent pod that integrates multiple sensors.

[0044] Based on the project's requirements for data acquisition accuracy and payload capacity, the performance of components such as the inertial measurement unit, lidar, high-resolution camera, GNSS positioning system, and core control board was selected to provide hardware support for the intelligent pod integrated system. Next, the control system was designed, and the touch sensor module, GNSS positioning module, and power supply system within the intelligent pod were developed and designed. Finally, the intelligent pod structure of the UAV system was designed and simulated, a pre-production simulation model was built, and based on the integration requirements of multi-source sensors, simulated tests were conducted on payload loading, endurance testing, and structural strength.

[0045] Multi-source sensor integration algorithm: This invention discloses a high-precision real-time inertial / GNSS tightly coupled navigation technology and a real-time high-performance lidar SLAM technology. By fusing data from lidar, imagery, inertial measurement units, and GNSS multi-source sensors, it achieves high-precision pose measurement on the one hand, and high-precision real-time mapping on the other hand, in order to obtain a high-precision real-time point cloud that reconstructs the three-dimensional environment of the scene, while improving the positioning accuracy of UAV autonomous flight.

[0046] (1) High-precision real-time inertial / GNSS tightly integrated navigation technology High-precision real-time integrated navigation technology based on raw inertial / GNSS observations. This technology involves: 1) achieving unbiased acquisition of high-precision sensor data through research on static and dynamic sensor calibration techniques; 2) achieving dynamic acquisition of high-precision initial pose information adaptable to complex environments through research on high-precision dynamic initial alignment; 3) acquiring high-precision satellite clock errors, receiver clock errors, ionospheric correction errors, tropospheric correction errors, and real-time satellite position errors through high-precision preprocessing and calculation techniques for raw GNSS information, maximizing compensation for various errors in the calculation process and making real-time navigation measurements more accurate; and 4) achieving high-precision real-time pose acquisition through high-dimensional, disturbance-resistant, and nonlinear inertial integrated navigation calculations.

[0047] The algorithm includes modules for determining the coordinate system, sensor calibration, inertial navigation, GNSS processing, and integrated navigation, as detailed below. Figures 1 to 4 As shown.

[0048] SINS initial alignment utilizes data measured by the accelerometer and gyroscope in the inertial measurement unit (IMU) to calculate the carrier's attitude, velocity, and position information through algorithms. A high-precision gyroscope can measure the Earth's rotation speed (approximately 15° / h), while the accelerometer can measure Earth's gravity. Therefore, under stationary conditions, a high-precision IMU can determine its own attitude without external assistance.

[0049] (2) IMU sensor error model scheme The accelerometer and gyroscope of the IMU sensor have zero bias error, scaling factor error, cross-coupling error and a certain degree of random noise.

[0050] In this invention, the IMU sensor error is modeled in the following form: ; in, b represents the acceleration after error correction. a I3 represents the zero bias error of the accelerometer, M represents the identity matrix, and M represents the zero bias error of the accelerometer. a This represents the scaling factor error of the inertial navigation system. ω represents the acceleration collected by the accelerometer. a This indicates the measurement noise of the accelerometer.

[0051] M a The calculation formula is: ; in, This indicates the scaling factor of the accelerometer on the X-axis. This indicates the scaling factor of the accelerometer on the Y-axis. This indicates the scaling factor of the accelerometer on the Z-axis. This indicates the cross-coupling error between the X and Y axes of the accelerometer. This indicates the cross-coupling error between the X and Z axes of the accelerometer. This indicates the cross-coupling error between the Y and X axes of the accelerometer. This indicates the cross-coupling error between the Y and Z axes of the accelerometer. This indicates the cross-coupling error between the Z-axis and X-axis of the accelerometer. This indicates the cross-coupling error between the Z-axis and Y-axis of the accelerometer.

[0052] ; in, b represents the angular velocity after error correction. g M represents the zero bias error of the gyroscope. g This represents the cross-coupling error of the inertial navigation system. G represents the angular velocity collected by the gyroscope. g This represents the gravity-sensitive bias value (this error arises because accelerations along all three axes can affect the true value of the same gyroscope measurement), ω. g This indicates the measurement noise of the gyroscope.

[0053] M g The calculation formula is: ; in, This represents the scaling factor of the gyroscope on the X-axis. This indicates the scaling factor of the gyroscope on the Y-axis. This indicates the scaling factor of the gyroscope on the Z-axis. This represents the cross-coupling error between the X and Y axes of the gyroscope. This represents the cross-coupling error between the X and Z axes of the gyroscope. This represents the cross-coupling error between the Y-axis and X-axis of the gyroscope. This represents the cross-coupling error between the Y and Z axes of the gyroscope. This represents the cross-coupling error between the Z-axis and X-axis of the gyroscope. This represents the cross-coupling error between the Z-axis and Y-axis of the gyroscope.

[0054] (2) Control system design For multi-source sensor integrated systems, the system control module is essentially the brain of the entire system, undertaking very complex and crucial functions: receiving and processing input signals from sensors such as lidar, image information modules, GNSS, and IMU; monitoring system status; and outputting control signals to the lower unit (motor driver) to control the motor motion mechanism. Considering the requirements of a lightweight and compact system design and the characteristics of data reception, recording, and transmission from each sensor, a well-designed control system is needed that can handle high-speed signals, has ample computing power, moderate energy consumption, strong scalability, rich interfaces, good shock resistance, modular construction, and reserved space. This will achieve tight integration of the control module and data recording, and also simplify future hardware upgrades.

[0055] Inertial measurement units, GNSS systems, lidar, high-resolution cameras, and other multi-source sensors primarily collect raw data for positioning. Further processing and application of this raw data requires a system control module for recording and transmission. The system control module is the core component of the multi-source sensor integrated system, using a high-performance microprocessor to control positioning data acquisition, combined positioning algorithm processing, and positioning result transmission. The result transmission section transmits the high-precision combined positioning results processed by the central processing unit to the corresponding terminal or cloud, enabling data exchange and transmission via wireless LAN, Bluetooth, or LTE.

[0056] (3) Structural design Integrating sensors such as inertial measurement units, GNSS positioning systems, lidar, and optical cameras onto a UAV platform requires comprehensive consideration of the UAV platform's overall balance, flight interference and obstruction of the payloads, and the overall structural design of each payload. The structural design must be reasonable and reliable, facilitating equipment assembly and disassembly, and ensuring efficient operation and maintenance. This includes achieving rigid connections between payloads, a rigid connection for the attitude measurement system, and optimized design of the overall payload weight. The shape, weight, and installation location of each payload must be optimized to allow for flexible remounting and conversion between different mounting structures.

[0057] (3) GNSS / INS integrated navigation scheme This module primarily performs IMU (Inertial Measurement Unit) / GPS (Global Positioning System) integrated processing, providing POS (Position and Orientation) information to other modules. The module preprocesses the IMU data and combines it with real-time GPS navigation output information synchronized with the time. The processing includes initial alignment, strapdown calculation, Kalman filtering, and data feedback correction, ultimately calculating the position and attitude information. The IMU / GPS integrated navigation system module flow is as follows: Figure 3 As shown, the signal flow of the IMU / GPS integrated navigation system is as follows: Figure 4 As shown.

[0058] More specifically, as follows: 1) Initial Alignment IMU data is used for initial SINS alignment, providing highly accurate initial position, velocity, and attitude information for subsequent SINS / GPS combined filtering. Alignment methods include static alignment, dynamic alignment, and transfer alignment.

[0059] The initial alignment of the accelerometer and gyroscope includes the following steps: The gravity vector is measured using an accelerometer, and the Earth's rotation angular velocity vector is measured using a gyroscope. The initial attitude matrix from the carrier coordinate system to the geographic coordinate system is calculated using a two-vector attitude determination method. The measured gravity vector and the measured Earth's rotation angular velocity vector are used as two vectors in the two-vector attitude determination method. The position and velocity of the accelerometer, as well as the position and velocity of the gyroscope, are measured using a global satellite navigation system. The measured position and velocity of the accelerometer are converted into the gravity vector of the accelerometer, and then the difference between the gravity vector measured by the accelerometer is used to obtain the gravity measurement error. The position and velocity of the gyroscope are converted into the rotational angular velocity vector of the gyroscope, and then the difference between the rotational angular velocity vector of the Earth measured by the gyroscope is used to obtain the angular velocity measurement error. Using gravity measurement error and angular velocity measurement error as state elements, an inertial navigation state model is constructed to output state result values; The state result values ​​are processed by Kalman filtering, and the filtered state result values ​​are used to replace the gravity vector measured by the accelerometer and the Earth's rotation angular velocity vector measured by the gyroscope.

[0060] Using gravity measurement error and angular velocity measurement error as error values, and the measured positions and velocities of the accelerometer and gyroscope as true values, an error observation model is constructed to output the observed values; where, observed value = error value + true value; The observations are processed by Kalman filtering. The difference between the filtered observations and the state result is calculated. If the difference is less than or equal to a set threshold, the filtered observations are used to replace the gravity vector measured by the accelerometer and the Earth's rotation angular velocity vector measured by the gyroscope.

[0061] 2) Strapdown calculation Using the acceleration and angular rate information output by the IMU, and taking the results of rapid and accurate initial alignment as the initial values ​​for calculation, the position, velocity, and attitude information are calculated according to Newton's laws of motion for moving objects on Earth. The navigation error estimated by filtering is then used to correct the inertial navigation results. The principle of the strapdown calculation module is as follows: Figure 2 As shown.

[0062] The solution includes the following steps: The velocity is obtained by integrating the acceleration collected by the accelerometer once, and the position is obtained by integrating it twice. The attitude angle is obtained by integrating the angular velocity collected by the gyroscope once. The attitude angle is obtained by processing the angular velocity collected by the gyroscope using the quaternion difference method; The velocity obtained by one integration is subtracted from the velocity collected by the global satellite navigation system. If the absolute value of the difference is less than or equal to a set threshold, the average value of the two is taken as the calculated velocity; if the absolute value of the difference is greater than the set threshold, the velocity collected by the global satellite navigation system is taken as the calculated velocity. The position obtained by the second integration is subtracted from the position collected by the global satellite navigation system. If the absolute value of the difference is less than or equal to a set threshold, the average value of the two is taken as the calculated position; if the absolute value of the difference is greater than the set threshold, the position collected by the global satellite navigation system is taken as the calculated position. The attitude angle obtained by one integration and the attitude angle obtained by processing using the quaternion difference method are subtracted. If the absolute value of the difference is less than or equal to a set threshold, the average value of the two is taken as the solved attitude angle; if the absolute value of the difference is greater than the set threshold, the attitude angle obtained by processing using the quaternion difference method is taken as the solved attitude angle.

[0063] 3) Filtering Based on the Kalman filter method, the position and velocity output from GPS are used as measurement information to estimate the error of strapdown inertial navigation. Since the position error of strapdown navigation is a function of attitude error and inertial device error, the attitude error and inertial device error can be indirectly estimated by estimating the position and velocity errors of inertial navigation.

[0064] 4) Closed-loop error controller Based on the parameters estimated by filtering, a closed-loop error controller is used to reset the strapdown inertial navigator. After resetting, the position and velocity accuracy of the strapdown inertial navigation system can be consistent with that of GPS. The estimated inertial device errors are used to compensate for the device data before strapdown navigation, achieving the purpose of online calibration and improving the attitude accuracy of the combined filtering of the SINS / GPS integrated navigation system.

[0065] Error correction processing includes the following steps: The Kalman filter method is used to estimate the error of the calculated velocity, position and / or attitude angles to obtain the velocity, position and / or attitude angle error values; The Kalman filter method is used to estimate the error of the accelerometer and / or gyroscope to obtain the error value of the accelerometer and / or gyroscope. If the accelerometer and / or gyroscope error value is ≤ a set threshold and the velocity, position, and / or attitude angle error value is ≤ a set threshold, then the calculated velocity, position, and / or attitude angle is used as the final velocity, position, and / or attitude angle. If the accelerometer and / or gyroscope error value is ≤ a set threshold and the velocity, position, and / or attitude angle error value is > a set threshold, then the calculated velocity, position, and / or attitude angle is corrected using the velocity, position, and / or attitude angle error values, and the corrected velocity, position, and / or attitude angle is used as the final velocity, position, and / or attitude angle. If the accelerometer and / or gyroscope error value is > a set threshold, then the calculated velocity, position, and / or attitude angle is discarded as the final velocity, position, and / or attitude angle, and the accelerometer and / or gyroscope re-acquire information.

[0066] As described above, the present invention can be implemented well.

[0067] In this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0068] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0069] Although embodiments of the present invention have been shown and described herein, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

[0070] All features disclosed in all embodiments of this specification, or steps in all methods or processes implied in the disclosure, may be combined and / or extended or replaced in any way, except for mutually exclusive features and / or steps.

[0071] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Based on the technical essence of the present invention, any simple modifications, equivalent substitutions, and improvements made to the above embodiments within the spirit and principles of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for information acquisition and control of unmanned aerial vehicles (UAVs), characterized in that, Flight information of the UAV during flight is collected using an inertial navigation system and a global satellite navigation system. The inertial navigation system includes accelerometers and gyroscopes that communicate with each other. The process includes the following steps: Determine the coordinate systems of the accelerometer, gyroscope, and UAV; Calibrate the accelerometer and gyroscope; Perform initial alignment of the accelerometer and gyroscope; Accelerometers collect the acceleration of the drone during flight, gyroscopes collect the angular velocity of the drone during flight, and global satellite navigation systems collect the position and velocity of the drone during flight. The flight information collected by accelerometers, gyroscopes, and global satellite navigation systems during the flight process of the UAV is processed. Error correction processing is performed on the calculated flight information.

2. The method for information acquisition and control of unmanned aerial vehicles according to claim 1, characterized in that, The steps involved in processing flight information collected by accelerometers, gyroscopes, and global navigation satellite systems during the flight of a UAV include: The velocity is obtained by integrating the acceleration collected by the accelerometer once, and the position is obtained by integrating it twice. The attitude angle is obtained by integrating the angular velocity collected by the gyroscope once. The attitude angle is obtained by processing the angular velocity collected by the gyroscope using the quaternion difference method; The velocity obtained by one integration is subtracted from the velocity collected by the global satellite navigation system. If the absolute value of the difference is less than or equal to a set threshold, the average value of the two is taken as the calculated velocity; if the absolute value of the difference is greater than the set threshold, the velocity collected by the global satellite navigation system is taken as the calculated velocity. The position obtained by the second integration is subtracted from the position collected by the global satellite navigation system. If the absolute value of the difference is less than or equal to a set threshold, the average value of the two is taken as the calculated position; if the absolute value of the difference is greater than the set threshold, the position collected by the global satellite navigation system is taken as the calculated position. The attitude angle obtained by one integration and the attitude angle obtained by processing using the quaternion difference method are subtracted. If the absolute value of the difference is less than or equal to a set threshold, the average value of the two is taken as the solved attitude angle; if the absolute value of the difference is greater than the set threshold, the attitude angle obtained by processing using the quaternion difference method is taken as the solved attitude angle.

3. The method for information acquisition and control of unmanned aerial vehicles according to claim 2, characterized in that, The error correction process for the calculated flight information includes the following steps: The Kalman filter method is used to estimate the error of the calculated velocity, position and / or attitude angles to obtain the velocity, position and / or attitude angle error values; The Kalman filter method is used to estimate the error of the accelerometer and / or gyroscope to obtain the error value of the accelerometer and / or gyroscope. If the accelerometer and / or gyroscope error value is ≤ a set threshold and the velocity, position, and / or attitude angle error value is ≤ a set threshold, then the calculated velocity, position, and / or attitude angle is used as the final velocity, position, and / or attitude angle. If the accelerometer and / or gyroscope error value is ≤ a set threshold and the velocity, position, and / or attitude angle error value is > a set threshold, then the calculated velocity, position, and / or attitude angle is corrected using the velocity, position, and / or attitude angle error values, and the corrected velocity, position, and / or attitude angle is used as the final velocity, position, and / or attitude angle. If the accelerometer and / or gyroscope error value is > a set threshold, then the calculated velocity, position, and / or attitude angle is discarded as the final velocity, position, and / or attitude angle, and the accelerometer and / or gyroscope re-acquire information.

4. The method for information acquisition and control of unmanned aerial vehicles according to claim 1, characterized in that, The initial alignment of the accelerometer and gyroscope includes the following steps: The gravity vector is measured using an accelerometer, and the Earth's rotation angular velocity vector is measured using a gyroscope. The initial attitude matrix from the carrier coordinate system to the geographic coordinate system is calculated using a two-vector attitude determination method. The measured gravity vector and the measured Earth's rotation angular velocity vector are used as two vectors in the two-vector attitude determination method. The position and velocity of the accelerometer, as well as the position and velocity of the gyroscope, are measured using a global satellite navigation system. The measured position and velocity of the accelerometer are converted into the gravity vector of the accelerometer, and then the difference between the gravity vector measured by the accelerometer is used to obtain the gravity measurement error. The position and velocity of the gyroscope are converted into the rotational angular velocity vector of the gyroscope, and then the difference between the rotational angular velocity vector of the Earth measured by the gyroscope is used to obtain the angular velocity measurement error. Using gravity measurement error and angular velocity measurement error as state elements, an inertial navigation state model is constructed to output state result values; The state result values ​​are processed by Kalman filtering, and the filtered state result values ​​are used to replace the gravity vector measured by the accelerometer and the Earth's rotation angular velocity vector measured by the gyroscope.

5. The method for unmanned aerial vehicle (UAV) information acquisition and control according to claim 4, characterized in that, The initial alignment of the accelerometer and gyroscope also includes the following steps: Using gravity measurement error and angular velocity measurement error as error values, and the measured positions and velocities of the accelerometer and gyroscope as true values, an error observation model is constructed to output the observed values; where, observed value = error value + true value; The observations are processed by Kalman filtering. The difference between the filtered observations and the state result is calculated. If the difference is less than or equal to a set threshold, the filtered observations are used to replace the gravity vector measured by the accelerometer and the Earth's rotation angular velocity vector measured by the gyroscope.

6. A method for collecting and controlling information from an unmanned aerial vehicle (UAV) according to any one of claims 2 to 5, characterized in that, When performing error correction processing on the calculated flight information, the acceleration after error correction is: ； in, b represents the acceleration after error correction. a I3 represents the zero bias error of the accelerometer, M represents the identity matrix, and M represents the zero bias error of the accelerometer. a This represents the scaling factor error of the inertial navigation system. ω represents the acceleration collected by the accelerometer. a This indicates the measurement noise of the accelerometer.

7. The method for unmanned aerial vehicle (UAV) information acquisition and control according to claim 6, characterized in that, M a The calculation formula is: ； in, This indicates the scaling factor of the accelerometer on the X-axis. This indicates the scaling factor of the accelerometer on the Y-axis. This indicates the scaling factor of the accelerometer on the Z-axis. This indicates the cross-coupling error between the X and Y axes of the accelerometer. This indicates the cross-coupling error between the X and Z axes of the accelerometer. This indicates the cross-coupling error between the Y and X axes of the accelerometer. This indicates the cross-coupling error between the Y and Z axes of the accelerometer. This indicates the cross-coupling error between the Z-axis and X-axis of the accelerometer. This indicates the cross-coupling error between the Z-axis and Y-axis of the accelerometer.

8. The method for information acquisition and control of unmanned aerial vehicles according to claim 7, characterized in that, When performing error correction processing on the calculated flight information, the angular velocity after error correction is: ； in, b represents the angular velocity after error correction. g M represents the zero bias error of the gyroscope. g This represents the cross-coupling error of the inertial navigation system. G represents the angular velocity collected by the gyroscope. g ω represents the gravity-sensitive bias value. g This indicates the measurement noise of the gyroscope.

9. The method for information acquisition and control of a UAV according to claim 8, characterized in that, M g The calculation formula is: ； in, This represents the scaling factor of the gyroscope on the X-axis. This represents the scaling factor of the gyroscope on the Y-axis. This represents the scaling factor of the gyroscope on the Z-axis. This represents the cross-coupling error between the X and Y axes of the gyroscope. This represents the cross-coupling error between the X and Z axes of the gyroscope. This represents the cross-coupling error between the Y-axis and X-axis of the gyroscope. This represents the cross-coupling error between the Y and Z axes of the gyroscope. This represents the cross-coupling error between the Z-axis and X-axis of the gyroscope. This represents the cross-coupling error between the Z-axis and Y-axis of the gyroscope.

10. A UAV information acquisition and control system, characterized in that, A method for implementing UAV information acquisition and control as described in any one of claims 1 to 9 comprises the following modules connected in sequence: The coordinate system determination module is used to determine the coordinate system of the accelerometer, gyroscope, and UAV. The calibration module is used to calibrate accelerometers and gyroscopes. The initial alignment module is used for: initial alignment of the accelerometer and gyroscope; The data acquisition module is used to: enable the accelerometer to acquire the acceleration of the UAV during flight, enable the gyroscope to acquire the angular velocity of the UAV during flight, and enable the global satellite navigation system to acquire the position and velocity of the UAV during flight; The calculation module is used to calculate the flight information collected by the accelerometer, gyroscope, and global satellite navigation system during the flight process of the UAV. The error correction module is used to perform error correction processing on the calculated flight information.

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