Airborne gimbal attitude solving method, device and equipment

Abstract

The application provides an airborne gimbal attitude solving method, device and equipment, and relates to the technical field of unmanned aerial vehicles, and comprises the following steps: acquiring flight control attitude data of an unmanned aerial vehicle carrier and joint angle data of an airborne gimbal carried by the unmanned aerial vehicle carrier, the joint angle data being the rotation angle of a camera assembly in different directions relative to the unmanned aerial vehicle carrier, and the camera assembly being installed on the airborne gimbal; determining a virtual gravity vector according to the flight control attitude data and the joint angle data, the virtual gravity vector being used for simulating an ideal gravity vector when there is no motion acceleration interference; and solving an attitude estimation result of the airborne gimbal according to the angular velocity data of the airborne gimbal and the virtual gravity vector, so as to control the attitude of the airborne gimbal based on the attitude estimation result. The application effectively alleviates the attitude solving error caused by motion acceleration interference and vibration noise, and significantly improves the stability and horizontal keeping ability of the gimbal picture of the unmanned aerial vehicle in the maneuvering flight.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to an airborne gimbal attitude calculation method, apparatus, and equipment. Background Technology

[0002] In drone operations such as aerial photography and inspection, the attitude stability of the airborne gimbal plays a crucial role in acquiring high-quality images. Currently, mainstream three-axis gimbals typically rely on an Inertial Measurement Unit (IMU) mounted on the camera to achieve attitude perception and closed-loop control. The IMU generally integrates a three-axis gyroscope and a three-axis accelerometer, and combines sensor fusion algorithms (such as complementary filtering and Kalman filtering) to calculate the gimbal's spatial orientation relative to the direction of gravity in real time, thereby driving the motors for attitude compensation.

[0003] However, this type of IMU-based attitude estimation method has the following errors:

[0004] (1) Accelerometers are susceptible to vibration and noise interference: The high-frequency vibration of the UAV body generated during flight is transmitted to the gimbal through the mechanical structure, resulting in a large number of non-gravity disturbance components in the acceleration signal measured by the IMU, which significantly reduces the signal-to-noise ratio and affects the accuracy of gravity direction identification.

[0005] (2) Observational confusion caused by motion acceleration: The accelerometer measures the specific force generated by the net external force on a unit mass, and its output includes both gravitational acceleration and the motion acceleration of the UAV. When the UAV is in a non-uniform flight state such as acceleration, deceleration or turning, the motion acceleration cannot be ignored. If the total acceleration is still directly used as the gravity vector for attitude calculation, it will introduce serious pitch and roll angle errors;

[0006] The two types of errors mentioned above together cause the gimbal attitude estimation results to deviate from the true horizontal reference, which means that the gimbal cannot maintain absolute horizontality during maneuvering and it is difficult to maintain the visual stability of the image. Summary of the Invention

[0007] In view of this, the purpose of the present invention is to provide an airborne gimbal attitude calculation method, apparatus and equipment, which effectively alleviates the attitude calculation error caused by motion acceleration interference and vibration noise, and significantly improves the stability and leveling ability of the gimbal image during UAV maneuvering flight.

[0008] In a first aspect, the present invention provides an airborne gimbal attitude calculation method, comprising:

[0009] Acquire flight control attitude data of the UAV carrier and joint angle data of the airborne gimbal mounted on the UAV carrier. The joint angle data is the rotation angle of the camera component relative to the UAV carrier in different orientations. The camera component is mounted on the airborne gimbal.

[0010] The virtual gravity vector is determined based on flight control attitude data and joint angle data. The virtual gravity vector is used to simulate the ideal gravity vector when there is no motion acceleration disturbance.

[0011] Based on the angular velocity data and virtual gravity vector of the airborne gimbal, the attitude estimation results of the airborne gimbal are calculated, and the attitude control of the airborne gimbal is performed based on the attitude estimation results.

[0012] In one implementation, determining the virtual gravity vector based on flight control attitude data and joint angle data includes:

[0013] Based on flight control attitude data and joint angle data, a chain rotation transformation model from the navigation coordinate system to the camera coordinate system is constructed.

[0014] By using a chain rotation transformation model, the actual gravity vector in the navigation coordinate system is projected onto the camera coordinate system to obtain a virtual gravity vector.

[0015] In one implementation, a chain-like rotation transformation model from the navigation coordinate system to the camera coordinate system is constructed based on flight control attitude data and joint angle data, including:

[0016] Based on flight control attitude data and joint angle data, a continuous transformation process is performed from the navigation coordinate system to the camera coordinate system to obtain multiple local rotation transformation matrices.

[0017] By performing chain multiplication on multiple local rotation transformation matrices, a chain rotation transformation model from the navigation coordinate system to the camera coordinate system is obtained.

[0018] In one implementation, based on flight control attitude data and joint angle data, a continuous transformation process is performed from the navigation coordinate system to the camera coordinate system to obtain multiple local rotation transformation matrices, including:

[0019] Based on flight control attitude data and joint angle data, a first rotation transformation matrix from the navigation coordinate system to the UAV body coordinate system, a second rotation transformation matrix from the UAV body coordinate system to the gimbal coordinate system, and a third rotation transformation matrix from the gimbal coordinate system to the camera coordinate system are constructed sequentially.

[0020] The local rotation transformation matrix includes a first rotation transformation matrix, a second rotation transformation matrix, and a third rotation transformation matrix.

[0021] In one implementation, based on flight control attitude data and joint angle data, a first rotation transformation matrix from the navigation coordinate system to the UAV body coordinate system, a second rotation transformation matrix from the UAV body coordinate system to the gimbal coordinate system, and a third rotation transformation matrix from the gimbal coordinate system to the camera coordinate system are constructed sequentially, including:

[0022] Based on flight control attitude data, a first rotational transformation matrix is ​​constructed from the navigation coordinate system to the UAV body coordinate system;

[0023] Construct a second rotational transformation matrix from the UAV body coordinate system to the gimbal coordinate system;

[0024] Based on joint angle data, a third rotation transformation matrix is ​​constructed from the gimbal coordinate system to the camera coordinate system.

[0025] In one implementation, based on flight control attitude data, a first rotation transformation matrix is ​​constructed from the navigation coordinate system to the UAV body coordinate system, including:

[0026] According to the preset first rotation sequence, the UAV yaw angle, UAV pitch angle and UAV roll angle contained in the flight control attitude data are rotated around the specified axis of the navigation coordinate system in sequence to obtain the first rotation transformation matrix from the navigation coordinate system to the UAV body coordinate system.

[0027] Based on joint angle data, a third rotation transformation matrix is ​​constructed from the gimbal coordinate system to the camera coordinate system, including:

[0028] According to the preset second rotation sequence, the joint angle data, including the joint yaw angle, joint pitch angle, and joint roll angle, are rotated around the specified axis of the gimbal coordinate system in sequence to obtain the third rotation transformation matrix from the gimbal coordinate system to the camera coordinate system.

[0029] In one implementation, a chain multiplication process is performed on multiple local rotation transformation matrices to obtain a chain rotation transformation model from the navigation coordinate system to the camera coordinate system, including:

[0030] Chain multiplication is performed on the first, second, and third rotation transformation matrices to obtain a chain rotation transformation model from the navigation coordinate system to the camera coordinate system.

[0031] In one implementation, the attitude estimation result of the airborne gimbal is calculated based on the angular velocity data and virtual gravity vector of the airborne gimbal, including:

[0032] Acquire angular velocity data provided by the inertial measurement unit deployed on the airborne gimbal;

[0033] Based on angular velocity data and virtual gravity vectors, the attitude estimation results of the airborne gimbal are calculated.

[0034] Secondly, the present invention also provides an airborne gimbal attitude calculation device, comprising:

[0035] The data acquisition module is used to acquire the flight control attitude data of the UAV carrier and the joint angle data of the airborne gimbal mounted on the UAV carrier. The joint angle data is the rotation angle of the camera component relative to the UAV carrier in different orientations. The camera component is mounted on the airborne gimbal.

[0036] The virtual gravity determination module is used to determine the virtual gravity vector based on flight control attitude data and joint angle data. The virtual gravity vector is used to simulate the ideal gravity vector when there is no motion acceleration interference.

[0037] The gimbal attitude calculation module is used to calculate the attitude estimation results of the airborne gimbal based on the angular velocity data and virtual gravity vector of the airborne gimbal, so as to perform attitude control of the airborne gimbal based on the attitude estimation results.

[0038] Thirdly, the present invention also provides an electronic device including a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement any of the methods provided in the first aspect.

[0039] This invention provides an airborne gimbal attitude calculation method, apparatus, and device. First, it acquires flight control attitude data of an unmanned aerial vehicle (UAV) carrier and joint angle data of the airborne gimbal mounted on the UAV carrier. The joint angle data represents the rotation angles of a camera component relative to the UAV carrier in different orientations. The camera component is mounted on the airborne gimbal. Then, it determines a virtual gravity vector based on the flight control attitude data and joint angle data. This virtual gravity vector simulates the ideal gravity vector under conditions of no motion acceleration interference. Finally, it calculates the attitude estimation result of the airborne gimbal based on the angular velocity data of the airborne gimbal and the virtual gravity vector, and performs attitude control on the airborne gimbal based on the attitude estimation result. The above method abandons the traditional reliance on camera-side IMU accelerometer data, which is noisy and susceptible to motion interference. Instead, it integrates the flight control attitude data of the UAV carrier and the joint angle data of the airborne gimbal to reconstruct a virtual gravity vector. This virtual gravity vector is used to simulate the ideal gravity vector when there is no motion acceleration interference and serves as a key observation input for attitude calculation. This invention effectively alleviates the attitude calculation error caused by motion acceleration interference and vibration noise, and significantly improves the stability and leveling ability of the gimbal image during UAV maneuvering flight.

[0040] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0041] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0042] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0043] Figure 1 A flowchart illustrating an airborne gimbal attitude calculation method provided in an embodiment of the present invention;

[0044] Figure 2 This is a flowchart illustrating another method for calculating the attitude of a gimbal, provided in an embodiment of the present invention.

[0045] Figure 3 This is a schematic diagram of the structure of an airborne gimbal attitude calculation device provided in an embodiment of the present invention;

[0046] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Currently, traditional gimbal attitude calculation methods suffer from problems such as accelerometer sensitivity to high-frequency vibration noise, difficulty in distinguishing between gravitational acceleration and motion acceleration leading to attitude errors, and image tilting and decreased stability during UAV maneuvering. To address these issues, this invention provides an airborne gimbal attitude calculation method, apparatus, and device that effectively mitigates attitude calculation errors caused by motion acceleration interference and vibration noise, significantly improving the stability and leveling capability of the gimbal image during UAV maneuvering.

[0049] To facilitate understanding of this embodiment, a detailed description of an airborne gimbal attitude calculation method disclosed in this embodiment of the invention will be provided first. (See [link to relevant documentation]). Figure 1The diagram shows a flowchart of an airborne gimbal attitude calculation method, which mainly includes the following steps S102 to S106:

[0050] Step S102: Obtain the flight control attitude data of the UAV carrier and the joint angle data of the airborne gimbal mounted on the UAV carrier.

[0051] In one example, the system can receive flight control attitude data provided by a flight control system (hereinafter referred to as flight control) deployed on the UAV carrier. This flight control attitude data includes the carrier's yaw angle, pitch angle, and roll angle. It can also receive joint angle data provided by a linear Hall sensor integrated on the airborne gimbal. This joint angle data represents the rotation angle of the camera assembly relative to the UAV carrier in different orientations, denoted as joint yaw angle, joint pitch angle, and joint roll angle. The camera assembly is mounted on the airborne gimbal.

[0052] Step S104: Determine the virtual gravity vector based on the flight control attitude data and joint angle data.

[0053] The virtual gravity vector is used to simulate the ideal gravity vector when there is no motion acceleration disturbance. In one example, based on flight control attitude data and joint angle data, a complete transformation relationship from the navigation coordinate system to the camera coordinate system can be constructed through a series of chain multiplications of direction cosine matrices (DCM). This complete transformation relationship is denoted as the chain rotation transformation model (that is, the chain rotation transformation model is used to describe the complete transformation relationship from the navigation coordinate system to the camera coordinate system). The actual gravity vector in the navigation coordinate system is projected onto the camera coordinate system using this chain rotation transformation matrix to obtain the virtual gravity vector.

[0054] Step S106: Based on the angular velocity data and virtual gravity vector of the airborne gimbal, calculate the attitude estimation result of the airborne gimbal, and perform attitude control on the airborne gimbal based on the attitude estimation result.

[0055] The angular velocity data can be gyroscope data provided by the IMU at the camera end. In one example, attitude fusion algorithms such as Mahony complementary filtering or Kalman filtering are used to calculate the attitude estimation result of the airborne gimbal based on the angular velocity data and virtual gravity vector. Based on the attitude estimation result, control signals for the three-axis motors in the gimbal are generated to drive the airborne gimbal to counteract the shaking of the aircraft and maintain the stability of the camera components.

[0056] The airborne gimbal attitude calculation method provided in this invention abandons the traditional reliance on camera-side IMU accelerometer data, which is noisy and susceptible to motion interference. Instead, it integrates the flight control attitude data of the UAV carrier with the joint angle data of the airborne gimbal to reconstruct a virtual gravity vector. This virtual gravity vector is used to simulate the ideal gravity vector when there is no motion acceleration interference and serves as the key observation input for attitude calculation. This invention effectively alleviates the attitude calculation error caused by motion acceleration interference and vibration noise, and significantly improves the stability and leveling ability of the gimbal image during UAV maneuvering flight.

[0057] For ease of understanding, this embodiment of the invention provides a specific implementation method for airborne gimbal attitude calculation, see [link to relevant documentation]. Figure 2 The flowchart of another method for calculating the attitude of a gimbal is shown, including: Step 1, acquiring flight control attitude data provided by the UAV's flight control system; Step 2, acquiring joint angle data of the airborne gimbal; Step 3, constructing a chain rotation transformation model based on the flight control attitude data and joint angle data; Step 4, calculating the virtual gravity vector in the camera coordinate system using the chain rotation transformation model; Step 5, updating the attitude data of the airborne gimbal using the virtual gravity vector; Step 6, outputting the stable attitude control motor.

[0058] The specific implementation process is as follows:

[0059] Step 1: Obtain flight attitude data provided by the UAV's flight control system.

[0060] In one implementation, the gimbal controller receives flight attitude data from the UAV flight control system in real time via a communication bus (such as a CAN bus or UART interface). This attitude data is typically represented in Euler angles, including the UAV's yaw angle. UAV pitch angle and the roll angle of the drone The aforementioned flight control attitude data is generated by the flight control system through the fusion of data from multiple sources, including GPS (Global Positioning System), IMU, and barometer, and processed by filtering and estimation algorithms. It has high accuracy and dynamic stability and can reliably reflect the actual attitude state of the UAV in three-dimensional space.

[0061] Step 2: Obtain the joint angle data of the airborne gimbal.

[0062] In one implementation, linear Hall sensors integrated into the three motion axis motors (pitch, roll, and yaw) of the airborne gimbal are used to detect the precise rotation angle of each axis motor rotor in real time, which is recorded as joint angle data. Based on this joint angle data, the relative rotation state of the camera assembly with respect to the UAV body (or gimbal base) is determined. The joint angle data is expressed in Euler angle form and is defined as follows: joint yaw angle. Joint pitch angle and joint roll angle .

[0063] Step 3: Construct a chained rotation transformation model based on flight control attitude data and joint angle data. In one implementation, a chained rotation transformation model from the navigation coordinate system to the camera coordinate system can be constructed based on flight control attitude data and joint angle data. Specifically, this includes:

[0064] Step 3.1: Based on the flight control attitude data and joint angle data, perform continuous transformation processing from the navigation coordinate system to the camera coordinate system to obtain multiple local rotation transformation matrices.

[0065] The local rotation transformation matrix includes a first rotation transformation matrix from the navigation coordinate system to the UAV body coordinate system, a second rotation transformation matrix from the UAV body coordinate system to the gimbal coordinate system, and a third rotation transformation matrix from the gimbal coordinate system to the camera coordinate system. Therefore, step 3.1 can be understood as constructing the first rotation transformation matrix from the navigation coordinate system to the UAV body coordinate system, the second rotation transformation matrix from the UAV body coordinate system to the gimbal coordinate system, and the third rotation transformation matrix from the gimbal coordinate system to the camera coordinate system, based on the flight control attitude data and joint angle data.

[0066] In specific implementation, it includes:

[0067] (a) Based on flight control attitude data (including UAV yaw angle) UAV pitch angle and the roll angle of the drone Construct the first rotation transformation matrix from the navigation coordinate system (NED) to the unmanned aerial vehicle body coordinate system (FRD). .

[0068] In one implementation, the UAV yaw angle, UAV pitch angle, and UAV roll angle, contained in the flight control attitude data, are sequentially rotated around a specified axis of the navigation coordinate system according to a preset first rotation sequence, resulting in a first rotation transformation matrix from the navigation coordinate system to the UAV body coordinate system. An exemplary first rotation sequence is the ZYX rotation sequence. In this embodiment, the Euler angle representation method of the ZYX rotation sequence (i.e., yaw-pitch-roll) is used to decompose the attitude transformation from the navigation coordinate system (NED) to the body coordinate system (FRD) into three basic rotation steps. Specifically, firstly, the UAV yaw angle is rotated around the Z-axis (vertical axis, pointing downwards) of the navigation coordinate system. This allows the carrier to adjust its heading in the horizontal plane; subsequently, the UAV's pitch angle is rotated around the new Y-axis (right-hand axis) after the first rotation. This reflects the changes in the aircraft's forward and backward tilt; finally, it rotates the UAV's roll angle around the updated X-axis (forward axis). This completes the modeling of the lateral roll posture. The mathematical expression is as follows:

[0069] ;

[0070] The basic rotation matrices are:

[0071] ;

[0072] ;

[0073] = .

[0074] (b) Construct the second rotation transformation matrix from the UAV body coordinate system (FRD) to the gimbal coordinate system (Base). This matrix is ​​a preset constant rotation matrix, the value of which is determined by the installation position and orientation of the gimbal on the body. It is used to compensate for the initial attitude deviation between the two coordinate systems and to achieve the reference alignment of the coordinate definition.

[0075] (c) Based on joint angle data (including joint yaw angle) Joint pitch angle and joint roll angle Construct a third rotation transformation matrix from the gimbal coordinate system (Base) to the camera coordinate system (Camera). .

[0076] In one implementation, following a preset second rotation sequence, the joint angle data, including the joint yaw angle, joint pitch angle, and joint roll angle, are sequentially rotated around a specified axis of the gimbal coordinate system to obtain a third rotation transformation matrix from the gimbal coordinate system to the camera coordinate system. The second rotation sequence is a ZYX rotation sequence. In this embodiment, the Euler angle representation method using the ZYX rotation sequence (i.e., yaw-pitch-roll) decomposes the attitude transformation from the gimbal coordinate system (Base) to the camera coordinate system (Camera) into three basic rotation steps. Specifically, firstly, the joint yaw angle is rotated around the Z-axis of the gimbal coordinate system. This allows for camera orientation adjustment in the horizontal plane; subsequently, based on the new coordinate system formed by the first rotation, the joint pitch angle is rotated around its Y-axis. This allows for the camera's line of sight to move vertically in the vertical plane; finally, in the coordinate system after the two aforementioned rotations, the roll angle of the rotation joint is determined about the current X-axis. This completes the tilt compensation of the camera around the optical axis. The mathematical expression is as follows:

[0077] ;

[0078] The basic rotation matrices are:

[0079] ;

[0080] ;

[0081] = .

[0082] Step 3.2 involves performing chain multiplication on multiple local rotation transformation matrices to obtain a chain rotation transformation model from the navigation coordinate system to the camera coordinate system. Specifically, this involves performing chain multiplication on the first, second, and third rotation transformation matrices to obtain the chain rotation transformation model from the navigation coordinate system to the camera coordinate system. The specific expression is shown below: = its transpose This is a chain rotation transformation model from the camera coordinate system to the navigation coordinate system. The chain multiplication method used in this embodiment of the invention has the following effects: (1) The direction of gravity in the navigation coordinate system is always vertically downward, and its magnitude and direction are known and are not affected by motion disturbances; through step-by-step coordinate transformation, the theoretical component of the gravity vector in the camera coordinate system can be accurately calculated, thereby providing a stable attitude observation benchmark without motion acceleration aliasing; (2) Each link of the chain transformation corresponds to a clear physical entity and observable measurement, the flight control attitude data has global reference and long-term stability, the angle data of each axis joint is measured by a linear Hall sensor, which has high vibration resistance and zero drift characteristics, and the installation relationship between the gimbal base and the body is a fixed rotation determined by calibration; the transformations at each level are decoupled from each other, avoiding the nonlinear superposition and propagation of multi-source errors; (3) The chain operation form of the direction cosine matrix used is simple, does not require trigonometric function iteration or inverse function solution, has low computational load, can be executed stably at high frequency, and meets the requirements of airborne gimbal for real-time and deterministic attitude calculation.

[0083] Step 4: Using the chain rotation transformation model, the actual gravity vector in the navigation coordinate system is projected onto the camera coordinate system to obtain the virtual gravity vector.

[0084] In practice, in the navigation coordinate system (NED), the actual gravity vector is constant and has a defined direction, and its components are represented as a constant acceleration downward along the Z-axis. ,in The standard gravitational acceleration is taken as approximately 9.81 m / s². A chain-like rotational transformation model from the navigation coordinate system to the camera coordinate system is used. The actual gravity vector is then transformed into the camera coordinate system, specifically by left-multiplying the actual gravity vector by the chain-like rotation transformation model. We obtain its projection components on the X, Y, and Z axes of the camera coordinate system, denoted as The expression is as follows: = Calculated from this It characterizes the ideal acceleration components of theoretical gravity along the X, Y, and Z axes of the camera coordinate system under the current attitude. Since it is derived solely from attitude transformation and is not affected by aircraft maneuvers, external disturbances, or mechanical vibrations, it possesses "pure" characteristics, containing no motion acceleration or sensor noise components, and can serve as a high-precision attitude reference.

[0085] Step 5: Update the attitude data of the airborne gimbal using a virtual gravity vector. In one implementation, angular velocity data provided by the inertial measurement unit deployed on the airborne gimbal is acquired; using an attitude fusion algorithm, the attitude estimation result of the airborne gimbal is calculated based on the angular velocity data and the virtual gravity vector.

[0086] In this embodiment of the invention, the virtual gravity vector, as an external observation input, replaces the original measurement data collected by the camera-side IMU accelerometer in traditional attitude calculation, and is input together with the gyroscope angular velocity data provided by the same IMU into the attitude fusion algorithm (such as Mahony complementary filter or extended Kalman filter) for processing.

[0087] Since this virtual gravity vector is an ideal gravity component derived from high-precision global attitude and multi-level coordinate transformation, it is unaffected by aircraft maneuvers, vibration interference, and sensor noise, exhibiting good stability and physical consistency. Therefore, compared to measured acceleration signals that are easily contaminated by dynamic disturbances, it can provide a more reliable attitude observation reference for attitude fusion algorithms. Based on this, the attitude solver can effectively suppress attitude drift and instantaneous deviations caused by accelerometer malfunctions, significantly improving the accuracy and convergence speed of pitch and roll attitude estimation. Especially under non-static conditions such as UAV acceleration, deceleration, or encountering airflow disturbances, it can still stably output the camera's true attitude relative to the horizontal plane, thus providing highly reliable attitude support for subsequent image stabilization, target tracking, and geographic pointing functions.

[0088] Step 6: Output stable attitude control motor.

[0089] In this embodiment of the invention, the attitude solver outputs high-precision attitude estimation results, including real-time attitude components of the camera in the pitch, roll, and yaw directions. This attitude estimation result serves as feedback input to generate control commands for the three-axis motors. By driving the motors on each axis of the gimbal to perform reverse compensation movements, the swaying and disturbances generated by the UAV body during flight are actively counteracted, thereby achieving high-precision stabilization of the camera's line of sight.

[0090] The entire processing flow is executed cyclically in the controller at a fixed period, which is synchronously triggered by a hardware timer or a real-time operating system clock to ensure that each step runs continuously according to a predetermined sequence. Through this closed-loop control mechanism, the system can continuously update the attitude estimation and dynamically adjust the gimbal position, maintaining the spatial pointing stability of the imaging device under various dynamic conditions, and achieving stable shooting and accurate observation in all weather and all scenarios.

[0091] In summary, the embodiments of the present invention have at least the following characteristics:

[0092] (1) Effectively eliminates the interference of motion acceleration and vibration noise on attitude calculation:

[0093] This invention replaces the traditional approach of relying on actual measurements obtained from the camera-side IMU accelerometer by constructing a virtual gravity vector based on multi-level coordinate transformation. This virtual gravity vector is derived from the standard gravity in the navigation coordinate system through precise rotational transformation, reflecting only the theoretical projection of Earth's gravity in the camera coordinate system. It does not contain any non-gravitational acceleration components caused by aircraft maneuvers (such as acceleration, turning, and ascent / descent), nor is it affected by high-frequency vibrations transmitted by mechanical structures or sensor noise.

[0094] Therefore, the key interference factors that cause attitude misjudgment are eliminated from the source, and the problems of attitude drift and pitch / roll angle distortion caused by the inability of accelerometers to distinguish between real gravity and motion acceleration in dynamic environments are overcome in the existing technology, which significantly improves the physical consistency and long-term stability of attitude calculation results.

[0095] (2) Significantly improves the attitude stability accuracy of the gimbal under UAV maneuvering flight conditions:

[0096] This invention makes full use of the high-precision global attitude information output by the flight control system. This information is obtained by the fusion calculation of multiple source sensors such as GPS, IMU, and barometer. It still has excellent orientation keeping capability and horizontal reference reliability under long-term operation and complex motion conditions, which is far superior to the camera's local IMU that relies solely on inertial integration.

[0097] By incorporating this high-quality attitude as a reference into the gimbal control system, the gimbal can accurately reconstruct the camera's spatial orientation relative to the horizontal plane even during rapid ascents, sharp turns, or violent maneuvers such as encountering gusts of wind. This enables higher-precision line-of-sight stabilization control, effectively preventing visual anomalies such as tilting, shaking, or horizon shift in the image, and significantly improving shooting quality and user experience.

[0098] (3) Reduces reliance on camera-side IMU performance, providing good cost control and design flexibility:

[0099] Since the core attitude correction mechanism of this invention no longer relies on the raw data from the camera-side accelerometer, the requirements for the accuracy, stability, and noise immunity of the IMU device mounted on the camera module are significantly reduced. The system can use a lower-cost, mid-to-low-end IMU module that prioritizes gyroscope performance, focusing on high-frequency angular velocity acquisition, without the need for expensive, high-performance six-axis or nine-axis sensors to compensate for acceleration noise.

[0100] At the same time, it reduces the complex filtering algorithms and additional computing resources required to suppress spurious acceleration signals, further simplifying the embedded software architecture and shortening the development cycle. This feature is particularly suitable for consumer drones, action cameras, and lightweight aerial photography equipment, and has significant cost optimization potential and industrialization value.

[0101] Based on the foregoing embodiments, this invention provides an airborne gimbal attitude calculation device, see [link to previous document]. Figure 3 The diagram shows a structural schematic of an airborne gimbal attitude calculation device, which mainly includes the following parts:

[0102] The data acquisition module 302 is used to acquire the flight control attitude data of the UAV carrier and the joint angle data of the airborne gimbal mounted on the UAV carrier. The joint angle data is the rotation angle of the camera component relative to the UAV carrier in different directions. The camera component is mounted on the airborne gimbal.

[0103] The virtual gravity determination module 304 is used to determine the virtual gravity vector based on flight control attitude data and joint angle data. The virtual gravity vector is used to simulate the ideal gravity vector when there is no motion acceleration interference.

[0104] The gimbal attitude calculation module 306 is used to calculate the attitude estimation result of the airborne gimbal based on the angular velocity data and virtual gravity vector of the airborne gimbal, so as to perform attitude control of the airborne gimbal based on the attitude estimation result.

[0105] The airborne gimbal attitude calculation device provided in this invention abandons the traditional reliance on camera-side IMU accelerometer data, which is noisy and susceptible to motion interference. Instead, it integrates the flight control attitude data of the UAV carrier with the joint angle data of the airborne gimbal to reconstruct a virtual gravity vector. This virtual gravity vector is used to simulate the ideal gravity vector when there is no motion acceleration interference and serves as the key observation input for attitude calculation. This invention effectively alleviates the attitude calculation error caused by motion acceleration interference and vibration noise, and significantly improves the stability and leveling ability of the gimbal image during UAV maneuvering flight.

[0106] In one implementation, the gimbal attitude calculation module 306 is specifically used for:

[0107] Based on flight control attitude data and joint angle data, a chain rotation transformation model from the navigation coordinate system to the camera coordinate system is constructed.

[0108] By using a chain rotation transformation model, the actual gravity vector in the navigation coordinate system is projected onto the camera coordinate system to obtain a virtual gravity vector.

[0109] In one implementation, the gimbal attitude calculation module 306 is specifically used for:

[0110] Based on flight control attitude data and joint angle data, a continuous transformation process is performed from the navigation coordinate system to the camera coordinate system to obtain multiple local rotation transformation matrices.

[0111] By performing chain multiplication on multiple local rotation transformation matrices, a chain rotation transformation model from the navigation coordinate system to the camera coordinate system is obtained.

[0112] In one implementation, the gimbal attitude calculation module 306 is specifically used for:

[0113] Based on flight control attitude data and joint angle data, a first rotation transformation matrix from the navigation coordinate system to the UAV body coordinate system, a second rotation transformation matrix from the UAV body coordinate system to the gimbal coordinate system, and a third rotation transformation matrix from the gimbal coordinate system to the camera coordinate system are constructed sequentially.

[0114] The local rotation transformation matrix includes a first rotation transformation matrix, a second rotation transformation matrix, and a third rotation transformation matrix.

[0115] In one implementation, the gimbal attitude calculation module 306 is specifically used for:

[0116] Based on flight control attitude data, a first rotational transformation matrix is ​​constructed from the navigation coordinate system to the UAV body coordinate system;

[0117] Construct a second rotational transformation matrix from the UAV body coordinate system to the gimbal coordinate system;

[0118] Based on joint angle data, a third rotation transformation matrix is ​​constructed from the gimbal coordinate system to the camera coordinate system.

[0119] In one implementation, the gimbal attitude calculation module 306 is specifically used for:

[0120] According to the preset first rotation sequence, the UAV yaw angle, UAV pitch angle and UAV roll angle contained in the flight control attitude data are rotated around the specified axis of the navigation coordinate system in sequence to obtain the first rotation transformation matrix from the navigation coordinate system to the UAV body coordinate system.

[0121] In one implementation, the gimbal attitude calculation module 306 is specifically used for:

[0122] According to the preset second rotation sequence, the joint angle data, including the joint yaw angle, joint pitch angle, and joint roll angle, are rotated around the specified axis of the gimbal coordinate system in sequence to obtain the third rotation transformation matrix from the gimbal coordinate system to the camera coordinate system.

[0123] In one implementation, the gimbal attitude calculation module 306 is specifically used for:

[0124] Chain multiplication is performed on the first, second, and third rotation transformation matrices to obtain a chain rotation transformation model from the navigation coordinate system to the camera coordinate system.

[0125] In one implementation, the gimbal attitude calculation module 306 is specifically used for:

[0126] Acquire angular velocity data provided by the inertial measurement unit deployed on the airborne gimbal;

[0127] Using an attitude fusion algorithm, the attitude estimation results of the airborne gimbal are calculated based on angular velocity data and virtual gravity vector.

[0128] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0129] This invention provides an electronic device, specifically, the electronic device includes a processor and a memory; the memory stores a computer program, which, when run by the processor, executes the method described in any of the above embodiments.

[0130] Figure 4 The present invention provides a schematic diagram of the structure of an electronic device 100, which includes a processor 40, a memory 41, a bus 42 and a communication interface 43. The processor 40, the communication interface 43 and the memory 41 are connected through the bus 42. The processor 40 is used to execute executable modules, such as computer programs, stored in the memory 41.

[0131] The memory 41 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 43 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0132] Bus 42 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0133] The memory 41 is used to store programs. After receiving an execution instruction, the processor 40 executes the program. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 40 or implemented by the processor 40.

[0134] Processor 40 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 40 or by instructions in software form. Processor 40 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 41. The processor 40 reads the information in memory 41 and, in conjunction with its hardware, completes the steps of the above method.

[0135] The computer program product of the readable storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.

[0136] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0137] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for calculating the attitude of an airborne gimbal, characterized in that, include: The flight control attitude data of the UAV carrier and the joint angle data of the airborne gimbal mounted on the UAV carrier are obtained. The joint angle data are the rotation angles of the camera component relative to the UAV carrier in different orientations. The camera component is mounted on the airborne gimbal. Determining a virtual gravity vector based on the flight control attitude data and the joint angle data includes: constructing a chain rotation transformation model from the navigation coordinate system to the camera coordinate system based on the flight control attitude data and the joint angle data; and using the chain rotation transformation model to project the actual gravity vector in the navigation coordinate system onto the camera coordinate system to obtain a virtual gravity vector, wherein the virtual gravity vector is used to simulate the ideal gravity vector when there is no motion acceleration interference. Based on the angular velocity data of the airborne gimbal and the virtual gravity vector, the attitude estimation result of the airborne gimbal is calculated, and the attitude control of the airborne gimbal is performed based on the attitude estimation result.

2. The airborne gimbal attitude calculation method according to claim 1, characterized in that, Based on the flight control attitude data and the joint angle data, a chain-like rotation transformation model from the navigation coordinate system to the camera coordinate system is constructed, including: Based on the flight control attitude data and the joint angle data, a continuous transformation process is performed from the navigation coordinate system to the camera coordinate system to obtain multiple local rotation transformation matrices; A chain multiplication process is performed on multiple local rotation transformation matrices to obtain a chain rotation transformation model from the navigation coordinate system to the camera coordinate system.

3. The airborne gimbal attitude calculation method according to claim 2, characterized in that, Based on the flight control attitude data and the joint angle data, a continuous transformation process is performed from the navigation coordinate system to the camera coordinate system to obtain multiple local rotation transformation matrices, including: Based on the flight control attitude data and the joint angle data, a first rotation transformation matrix from the navigation coordinate system to the UAV body coordinate system, a second rotation transformation matrix from the UAV body coordinate system to the gimbal coordinate system, and a third rotation transformation matrix from the gimbal coordinate system to the camera coordinate system are constructed sequentially. The local rotation transformation matrix includes the first rotation transformation matrix, the second rotation transformation matrix, and the third rotation transformation matrix.

4. The airborne gimbal attitude calculation method according to claim 3, characterized in that, Based on the flight control attitude data and the joint angle data, a first rotation transformation matrix from the navigation coordinate system to the UAV body coordinate system, a second rotation transformation matrix from the UAV body coordinate system to the gimbal coordinate system, and a third rotation transformation matrix from the gimbal coordinate system to the camera coordinate system are constructed sequentially, including: Based on the flight control attitude data, a first rotation transformation matrix is ​​constructed from the navigation coordinate system to the UAV body coordinate system; Construct a second rotational transformation matrix from the UAV body coordinate system to the gimbal coordinate system; Based on the joint angle data, a third rotation transformation matrix is ​​constructed from the gimbal coordinate system to the camera coordinate system.

5. The airborne gimbal attitude calculation method according to claim 4, characterized in that, Based on the flight control attitude data, a first rotation transformation matrix is ​​constructed from the navigation coordinate system to the UAV body coordinate system, including: According to the preset first rotation sequence, the UAV yaw angle, UAV pitch angle and UAV roll angle contained in the flight control attitude data are rotated around the specified axis of the navigation coordinate system in sequence to obtain the first rotation transformation matrix from the navigation coordinate system to the UAV body coordinate system. Based on the joint angle data, a third rotation transformation matrix is ​​constructed from the gimbal coordinate system to the camera coordinate system, including: According to the preset second rotation sequence, the joint angle data, including the joint yaw angle, joint pitch angle, and joint roll angle, are rotated around the specified axis of the gimbal coordinate system in sequence to obtain the third rotation transformation matrix from the gimbal coordinate system to the camera coordinate system.

6. The airborne gimbal attitude calculation method according to claim 3, characterized in that, A chain multiplication process is performed on multiple local rotation transformation matrices to obtain a chain rotation transformation model from the navigation coordinate system to the camera coordinate system, including: The first rotation transformation matrix, the second rotation transformation matrix, and the third rotation transformation matrix are subjected to chain multiplication to obtain a chain rotation transformation model from the navigation coordinate system to the camera coordinate system.

7. The airborne gimbal attitude calculation method according to claim 1, characterized in that, Based on the angular velocity data of the airborne gimbal and the virtual gravity vector, the attitude estimation result of the airborne gimbal is calculated, including: Obtain angular velocity data provided by the inertial measurement unit deployed on the airborne gimbal; Based on the angular velocity data and the virtual gravity vector, the attitude estimation result of the airborne gimbal is calculated.

8. An airborne gimbal attitude calculation device, characterized in that, include: The data acquisition module is used to acquire flight control attitude data of the UAV carrier and joint angle data of the airborne gimbal mounted on the UAV carrier. The joint angle data is the rotation angle of the camera component relative to the UAV carrier in different orientations. The camera component is mounted on the airborne gimbal. The virtual gravity determination module is used to determine a virtual gravity vector based on the flight control attitude data and the joint angle data, including: constructing a chain rotation transformation model from the navigation coordinate system to the camera coordinate system based on the flight control attitude data and the joint angle data; and using the chain rotation transformation model to project the actual gravity vector in the navigation coordinate system to the camera coordinate system to obtain a virtual gravity vector, wherein the virtual gravity vector is used to simulate the ideal gravity vector when there is no motion acceleration interference. The gimbal attitude calculation module is used to calculate the attitude estimation result of the airborne gimbal based on the angular velocity data of the airborne gimbal and the virtual gravity vector, so as to perform attitude control on the airborne gimbal based on the attitude estimation result.

9. An electronic device, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1 to 7.

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