CN118329009A - High-precision navigation and positioning system and method for rotor unmanned aerial vehicle - Google Patents
High-precision navigation and positioning system and method for rotor unmanned aerial vehicle Download PDFInfo
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Abstract
The invention discloses a high-precision navigation and positioning system and a method of a rotor unmanned aerial vehicle, wherein the system comprises the following steps: the system comprises a GNSS receiver, four-array-element anti-interference antennas, an inertial navigation system, an altimeter, a magnetic heading sensor and a visual system, wherein the GNSS receiver is connected with the four-array-element anti-interference antennas and used for differentially positioning signals of a plurality of navigation systems and calculating the position and the speed of the unmanned aerial vehicle; the inertial navigation system is internally provided with an inertial measurement unit and a navigation resolving unit, and the navigation resolving unit is connected with the GNSS receiver, the magnetic heading sensor, the vision system, the altimeter and the inertial measurement unit and is used for carrying out fusion resolving on the angular velocity and the acceleration measured by the inertial measurement unit, the heading angle measured by the magnetic heading sensor, the altitude calculated by the altimeter, the position and the speed calculated by the GNSS receiver or the position and the speed estimated by the vision system to obtain the current position, the gesture, the speed and the heading of the rotor unmanned aerial vehicle. According to the scheme, the unmanned aerial vehicle navigation precision and the environment adaptability can be improved.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle navigation, in particular to a rotor unmanned aerial vehicle high-precision navigation positioning system, a rotor unmanned aerial vehicle high-precision navigation positioning method, a rotor unmanned aerial vehicle high-precision navigation positioning computing device and a rotor unmanned aerial vehicle storage medium.
Background
The rotor unmanned aerial vehicle is generally provided with an autonomous navigation positioning system, and can autonomously perform course control and navigation in flight. The navigation positioning system is responsible for acquiring the current position, the gesture, the speed, the heading and other information of the rotor unmanned aerial vehicle, and is an essential system for realizing route tracking, position control and gesture control.
Although a rotorcraft navigational positioning system is generally capable of providing good flight control and position estimation, its accuracy and stability remain to be improved in complex environments or in severe weather conditions. In particular, in urban canyons, forests, and other areas where GPS signals are blocked, accurate positioning may be affected. How to effectively fuse various sensor data so that the sensor data can adapt to different environments is still a problem to be solved.
Disclosure of Invention
In order to solve the problems in the prior art, the scheme provides the high-precision navigation positioning system and method for the rotor unmanned aerial vehicle, and more accurate and reliable unmanned aerial vehicle positioning information can be provided by fusing and resolving various sensor data, so that the rotor unmanned aerial vehicle can stably work in a complex environment.
According to a first aspect of the present invention, there is provided a high-precision navigation and positioning system for a rotary-wing unmanned aerial vehicle, comprising: the system comprises a GNSS receiver, four-array-element anti-interference antennas, an inertial navigation system, an altimeter, a magnetic heading sensor and a visual system, wherein the GNSS receiver is connected with the four-array-element anti-interference antennas and is used for differentially positioning signals of a plurality of navigation systems received by the four-array-element anti-interference antennas simultaneously and calculating the position and the speed of the unmanned aerial vehicle; the inertial navigation system is internally provided with an inertial measurement unit and a navigation resolving unit, the navigation resolving unit is connected with the GNSS receiver, the magnetic heading sensor, the vision system, the altimeter and the inertial measurement unit, and the navigation resolving unit is used for carrying out fusion resolving on the angular velocity and acceleration measured by the inertial measurement unit, the heading angle measured by the magnetic heading sensor, the altitude calculated by the altimeter, the position and speed calculated by the GNSS receiver or the position and speed estimated by the vision system, and carrying out dynamic correction and error compensation on the sensor data based on an adaptive correction algorithm to obtain the current position, gesture, speed and heading information of the rotor unmanned aerial vehicle.
Through the technical scheme, the four-array-element anti-interference antenna is combined with the differential positioning technology, so that signal interference can be effectively resisted, the signal quality and positioning accuracy of the GNSS receiver are improved, and the GNSS receiver has good performance especially in a complex electromagnetic environment. Through multi-sensor fusion and calculation, the advantages of each sensor can be fully utilized, and high-precision positioning of the information such as the position, the speed, the gesture and the course of the unmanned aerial vehicle is realized.
Optionally, in the high-precision navigation positioning system of the unmanned rotorcraft provided by the invention, a GNSS receiver is internally provided with a multi-frequency multi-mode dolphin series navigation baseband chip, and is used for simultaneously receiving BDS (Beidou navigation system), GPS (global positioning system), GLONASS (russian GALILEO navigation system) and GALILEO (european GALILEO navigation system) signals of a plurality of different frequency points, demodulating and decoding the received signals by adopting an adaptive beam forming algorithm, correcting errors of the receiver by utilizing position information of one or more reference base stations, and calculating to obtain longitude and latitude, altitude and speed information of the unmanned rotorcraft.
Optionally, in the rotor unmanned aerial vehicle high-precision navigation positioning system provided by the invention, the navigation resolving unit is used for resolving the gesture by using the angular velocity measured by the MEMS gyroscope and the acceleration measured by the accelerometer in the inertial measurement unit to obtain gesture information of the unmanned aerial vehicle, wherein the gesture information comprises a yaw angle, a pitch angle and a roll angle; the yaw angle obtained by combining the course angle information provided by the magnetic course sensor with the gesture solution is utilized to carry out course solution, so that the current course of the unmanned aerial vehicle is obtained; when an effective GNSS signal exists, the longitude and latitude and speed information calculated by the GNSS receiver and the speed information provided by the inertial measurement unit are fused and resolved by utilizing Kalman filtering, and when the effective GNSS signal does not exist, the position and posture information estimated by the vision system and the speed information provided by the inertial measurement unit are fused and resolved to obtain the position and speed information of the unmanned aerial vehicle.
Optionally, in the high-precision navigation positioning system of the rotor unmanned aerial vehicle provided by the invention, the magnetic heading sensor uses a SEC225 magnetic compass with a self-calibration function.
Optionally, in the high-precision navigation positioning system of the rotor unmanned aerial vehicle provided by the invention, the altimeter adopts a barometric pressure sensor MS5611 for measuring the atmospheric pressure and the temperature value so as to calculate the altitude of the unmanned aerial vehicle according to the relationship among the barometric pressure, the temperature and the altitude.
Optionally, in the high-precision navigation positioning system of the rotor unmanned aerial vehicle provided by the invention, the navigation resolving unit is used for reading correction parameters, uncorrected actual air pressure and actual temperature from the MS5611, wherein the correction parameters comprise pressure sensitivity, pressure compensation coefficient, temperature coefficient of pressure sensitivity, temperature coefficient of pressure compensation, reference temperature and temperature calculation coefficient; calculating a corrected temperature based on a temperature difference value between the actual temperature and the reference temperature and a temperature calculation coefficient: temp=2000+dt×c6/2 23, where TEMP is the corrected temperature, C6 is the temperature calculation coefficient, dT is the temperature difference, and the temperature difference is: dT=d2-c5×2 8, D2 is the uncorrected actual temperature, and C5 is the reference temperature; calculating a temperature compensated barometric pressure value based on the uncorrected actual barometric pressure, the actual temperature compensation, and the actual temperature sensitivity: Wherein P is the air pressure value after temperature compensation, D1 is the uncorrected actual air pressure, SENS is the actual temperature compensation, OFF is the actual temperature sensitivity, and the actual temperature compensation calculation formula is: sens=c2× 16+(C4*dT)/27, where C2 is a pressure compensation coefficient, C4 is a temperature coefficient of pressure compensation, and the actual temperature sensitivity calculation formula is: off=c1×2 15+(C3*dT)/28, where C1 is the pressure sensitivity and C3 is the temperature coefficient of the pressure sensitivity; and calculating according to the relation between the pressure and the height to obtain a pressure height value:
h is the air pressure height value.
Optionally, in the high-precision navigation positioning system of the unmanned rotorcraft provided by the invention, the vision system comprises one or more cameras, infrared sensors, a laser radar and a vision processor, wherein the cameras are used for shooting continuous images, the infrared sensors and the laser radar are used for providing additional vision information in night or insufficient-light environments, and the vision processor is used for processing and calculating the continuous images and the vision information by using a depth learning model to obtain vision data comprising the position and the speed of the unmanned rotorcraft relative to the ground.
According to a second aspect of the present invention, there is provided a high-precision navigation positioning method for a rotary-wing unmanned aerial vehicle, which is executed based on the high-precision navigation positioning system for a rotary-wing unmanned aerial vehicle according to the first aspect of the present invention, the method comprising:
acquiring angular velocity and acceleration measured by an inertial measurement unit, a heading angle measured by a magnetic heading sensor and a height calculated by an altimeter; carrying out attitude calculation by using the angular speed and the acceleration to obtain attitude information of the unmanned aerial vehicle, wherein the attitude information comprises a yaw angle, a pitch angle and a roll angle;
the yaw angle obtained by combining the course angle with the gesture solution is utilized to carry out course solution, so as to obtain the current course of the unmanned aerial vehicle;
Judging whether a GNSS receiver receives a valid GNSS signal;
if the effective GNSS signals exist, fusion and calculation are carried out on longitude and latitude, altitude and speed information measured by a GNSS receiver, speed information provided by an inertial measurement unit and altitude provided by an altimeter by utilizing Kalman filtering, so that the current position and speed of the unmanned aerial vehicle are obtained;
if no effective GNSS signal exists, the position and the speed information of the unmanned aerial vehicle estimated based on the vision system are fused and solved with the speed information provided by the inertia measurement unit and the height provided by the altimeter by utilizing Kalman filtering, so that the current position and the speed of the unmanned aerial vehicle are obtained;
and correcting and compensating errors of the parameters of the inertial navigation system and the parameters of each sensor according to the combined navigation result.
According to a third aspect of the present invention, there is provided a computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor performing the method of high precision navigational positioning of a rotorcraft as in the first aspect.
According to a fourth aspect of the present invention, there is provided a computer readable storage medium comprising a computer program stored with instructions executable by a processor to load and execute the method of high precision navigational positioning of a rotorcraft according to the first aspect.
According to the high-precision navigation and positioning system and method for the rotor unmanned aerial vehicle, provided by the invention, the advantages of each sensor can be fully utilized through multi-sensor fusion calculation including GNSS, inertial navigation, magnetic heading sensor, vision system and the like, the positioning precision is improved, the system error is reduced, and the stability and accuracy of the system under different environments and working conditions are maintained.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 illustrates a block diagram of a computing device 100 according to one embodiment of the invention;
FIG. 2 illustrates a structural schematic diagram of a high-precision navigation and positioning system for a rotorcraft, according to one embodiment of the present invention;
Fig. 3 shows a flow diagram of a method 300 for high-precision navigational positioning of a rotary-wing drone, according to one embodiment of the present invention.
Detailed Description
In order to ensure that the rotor unmanned aerial vehicle can provide reliable and accurate navigation and positioning information under various environmental conditions, the scheme provides a rotor unmanned aerial vehicle high-precision navigation positioning system and a positioning method, and an MEMS gyroscope, an accelerometer, a magnetic heading sensor, an altimeter, a GNSS receiver and a vision system are adopted, and the accuracy and the robustness of navigation positioning can be improved by fusing data of various sensors, and particularly, the rotor unmanned aerial vehicle high-precision navigation positioning system can still effectively navigate under the condition of no GNSS signals.
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
FIG. 1 illustrates a block diagram of a computing device 100 according to one embodiment of the invention. As shown in fig. 1, computing device 100 may include memory 106 and processor 104. The memory bus 108 may be used for communication between the processor 104 and the system memory 106.
Memory 106 may include an operating system 120, applications 122, and program data 124. The application 122 may be arranged to execute instructions on an operating system by the one or more processors 104 using the program data 124. The application 122 includes program instructions for implementing various user-desired functions.
When the computing device 100 starts up running, the processor 104 reads the program instructions of the operating system 120 from the memory 106 and executes them. Applications 122 run on top of operating system 120, utilizing interfaces provided by operating system 120 and underlying hardware to implement various user-desired functions. When a user launches the application 122, the application 122 is loaded into the memory 106, and the processor 104 reads and executes the program instructions of the application 122 from the memory 106.
Computing device 100 also includes storage device 132 and output device 142, storage device 132 being coupled to storage interface bus 134. And an interface bus 140 that facilitates communication from various interface devices (e.g., an output device 142, a peripheral interface 144, and a communication device 146) via bus/interface controller 130.
Peripheral interface 144 may include a serial interface controller 154 and a parallel interface controller 156, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 158. The communication device 146 may include a network controller 160 that may be arranged to facilitate communication with one or more other computing devices 162 via one or more communication ports 164 over a network communication link. In computing device 100 according to the present invention, application 122 includes instructions for performing the rotorcraft high-precision navigational positioning method 300 of the present invention.
Fig. 2 shows a schematic structural diagram of a high-precision navigation and positioning system of a rotary-wing drone according to one embodiment of the present invention. As shown in fig. 2, the high-precision navigation positioning system includes: the system comprises a GNSS receiver, a four-array element anti-interference antenna, an inertial navigation system, an altimeter, a magnetic heading sensor and a visual system.
The GNSS receiver is connected with the four-array-element anti-interference antenna and used for carrying out differential positioning on the signals of the navigation systems received by the four-array-element anti-interference antenna simultaneously and calculating the position and the speed of the unmanned aerial vehicle.
A GNSS (global navigation satellite system) receiver is a device capable of receiving satellite signals such as GPS, GLONASS, galileo and beidou at the same time, and by calculating time and position information of these satellites, information such as longitude, latitude and altitude of the position where the receiver is located is determined. The GNSS receiver is internally provided with a multi-frequency multi-mode dolphin series navigation baseband chip, BDS (Beidou navigation system), GPS (global positioning system), GLONASS (Russian Galileo navigation system) and GALILEO (European Galileo navigation system) signals of a plurality of different frequency points can be received at the same time, the received signals are demodulated and decoded by adopting an adaptive beam forming algorithm, the errors of the receiver are corrected by utilizing the position information of one or more reference base stations, and longitude and latitude, altitude and speed information of the unmanned aerial vehicle are obtained through calculation.
The multi-array element antenna design can realize the suppression of multipath interference. By designing the arrangement mode and the phase relation of antenna elements, the ultra-high gain of the four-array-element anti-interference antenna in a specific direction can be realized, and the self-adaptive beam forming algorithm automatically adjusts the direction of the receiving antenna according to the characteristics of the received signals so as to maximize the receiving intensity of the perceived signals.
The reference base station is a ground station of known location for providing a reference signal to correct for errors in the receiver, and the distance and direction relationship between the reference base station and the drone is calculated. The longitude and latitude and the altitude information of the unmanned aerial vehicle can be calculated by using the distance relation between the reference base station and the unmanned aerial vehicle through triangulation or other positioning algorithms. Meanwhile, as the position of the reference base station is known, the speed information of the unmanned aerial vehicle can be calculated by utilizing the time delay information of the received signal.
The inertial navigation system is internally provided with an inertial measurement unit and a navigation resolving unit, and the navigation resolving unit is connected with the GNSS receiver, the magnetic heading sensor, the vision system, the altimeter and the inertial measurement unit.
The magnetic heading sensor is used for measuring the direction, namely the heading angle, of the unmanned aerial vehicle relative to the earth magnetic field, and can assist navigation and flight attitude control. In one embodiment of the invention, the magnetic heading sensor may use a SEC225 magnetic compass with self-calibration to automatically correct for deviations in the magnetic compass during unmanned aerial vehicle flight.
The altimeter is used for measuring the height of the unmanned aerial vehicle relative to the ground, and is realized through the principles of air pressure, laser radar and the like. In one embodiment of the invention, atmospheric pressure and temperature values are collected using a barometric pressure altitude sensor MS5611 to calculate the altitude of the drone from the relationship of barometric pressure, temperature, and altitude.
Specifically, the navigation resolving unit is configured to read correction parameters including C1 (pressure sensitivity), C2 (pressure compensation coefficient), C3 (temperature coefficient of pressure sensitivity), C4 (temperature coefficient of pressure compensation), C5 (reference temperature Tref), C6 (temperature calculation coefficient), uncorrected actual air pressure, and actual temperature from the PROM of the MS 5611. The uncorrected actual air pressure D1 and the actual temperature D2 can be read directly through the SPI interface.
First, a corrected temperature is calculated based on a temperature difference between an actual temperature and a reference temperature and a temperature calculation coefficient: temp=2000+dt×c6/2 23, where TEMP is the corrected temperature (in ℃), C6 is the temperature calculation coefficient, dT is the temperature difference, and the temperature difference is: dT=d2-c5×2 8, D2 is the uncorrected actual temperature and C5 is the reference temperature.
Then, the temperature-compensated air pressure value is calculated based on the uncorrected actual air pressure, the actual temperature compensation, and the actual temperature sensitivity:
wherein P is the air pressure value after temperature compensation, D1 is the uncorrected actual air pressure, SENS is the actual temperature compensation, OFF is the actual temperature sensitivity, and the actual temperature compensation calculation formula is: sens=c2× 16+(C4*dT)/27, where C2 is a pressure compensation coefficient, C4 is a temperature coefficient of pressure compensation, and the actual temperature sensitivity calculation formula is: off=c1×2 15+(C3*dT)/28, where C1 is the pressure sensitivity and C3 is the temperature coefficient of the pressure sensitivity.
Finally, calculating according to the relation between the pressure and the height to obtain a pressure height value H:
In one embodiment of the invention, the vision system may include one or more cameras for capturing successive images, infrared sensors and lidar for providing additional visual information during night or low light conditions, and a vision processor for processing and computing the successive images and visual information using a deep learning model to obtain visual data including the position and velocity of the drone relative to the ground.
In an environment lacking global satellite navigation signals or unstable satellite signals, the position of the unmanned aerial vehicle is estimated and tracked by utilizing a visual odometer or a simultaneous positioning and map construction technology. For example, the vision system can estimate pose change of the unmanned aerial vehicle according to a continuous image sequence captured by the camera, and by analyzing vision characteristics between adjacent images and utilizing a motion estimation algorithm (such as optical flow, characteristic matching and the like), a motion track in a three-dimensional space can be calculated, so that estimation and tracking of the position of the unmanned aerial vehicle are realized.
The inertial measurement unit provides a gyroscope and an accelerometer for measuring angular velocity and acceleration information of the unmanned aerial vehicle. The navigation resolving unit is used for carrying out fusion resolving on the angular speed and acceleration measured by the inertial measuring unit, the heading angle measured by the magnetic heading sensor, the altitude calculated by the altimeter, the position and speed calculated by the GNSS receiver or the position and speed estimated by the vision system, and carrying out real-time correction and error compensation on the data of each sensor to obtain the current position, attitude, speed and heading information of the rotor unmanned aerial vehicle.
Specifically, the navigation resolving unit is used for resolving the gesture by using the angular speed measured by the MEMS gyroscope and the acceleration measured by the accelerometer in the inertial measurement unit to obtain gesture information of the unmanned aerial vehicle, wherein the gesture information comprises a yaw angle, a pitch angle and a roll angle; and (3) performing course resolving by using the course angle information provided by the magnetic course sensor and the yaw angle obtained by combining the gesture resolving, so as to obtain the current course of the unmanned aerial vehicle.
When an effective GNSS signal exists, the longitude and latitude and speed information calculated by the GNSS receiver and the speed information provided by the inertial measurement unit are fused and resolved by utilizing Kalman filtering or extended Kalman filtering, and when the effective GNSS signal does not exist, the position and posture information estimated by the vision system and the speed information provided by the inertial measurement unit are fused and resolved to obtain the position and speed information of the unmanned aerial vehicle.
The navigation solution unit can dynamically correct and error compensate the data of each sensor through the self-adaptive correction algorithm. For example, drift of the inertial measurement unit is corrected by positional information provided by an external sensor, deviation of the magnetic heading sensor is corrected by a geomagnetic field model, and the like.
Fig. 3 shows a flow diagram of a method 300 for high-precision navigational positioning of a rotary-wing drone, according to one embodiment of the present invention. As shown in fig. 3, the angular velocity and acceleration measured by the inertial measurement unit, the heading angle measured by the magnetic heading sensor, and the altitude calculated by the altimeter are acquired.
And then, carrying out attitude calculation by using the angular speed and the acceleration to obtain the attitude information of the unmanned aerial vehicle, wherein the attitude information comprises a yaw angle, a pitch angle and a roll angle.
For example, angular velocity and acceleration data acquired from an Inertial Measurement Unit (IMU) are calibrated and filtered to reduce the effects of sensor errors and noise. Euler integration is used for converting angular velocity into gesture change, and the acceleration information is combined to eliminate the influence of gravity, so that the current gesture matrix of the unmanned aerial vehicle is obtained. And extracting yaw angle, pitch angle and roll angle from the gesture matrix or quaternion.
And performing course resolving by using the yaw angle obtained by combining the course angle with the gesture resolving to obtain the current course of the unmanned aerial vehicle. The heading angle is continuously changed along with the movement of the unmanned aerial vehicle. Therefore, it is necessary to update the estimated value of the heading angle in real time according to the movement state of the unmanned aerial vehicle and possible external disturbance.
Then, judging whether a GNSS receiver receives a valid GNSS signal or not;
If the GNSS signals are effective, fusion and calculation are carried out on longitude and latitude, altitude and speed information measured by the GNSS receiver, speed information provided by the inertial measurement unit and altitude information provided by the altimeter by utilizing Kalman filtering, and a combined navigation result comprising the current position, speed and gesture of the unmanned aerial vehicle is obtained.
If no effective GNSS signal exists, the position and the speed information of the unmanned aerial vehicle estimated based on the vision system are fused and solved with the speed information provided by the inertia measurement unit and the altitude information provided by the altimeter by utilizing Kalman filtering, and a combined navigation result comprising the current position, the speed and the gesture of the unmanned aerial vehicle is obtained.
And finally, correcting and compensating errors of the parameters of the inertial navigation system and the parameters of each sensor according to the combined navigation result.
According to the rotor unmanned aerial vehicle high-precision navigation positioning system and method, the advantages of the sensors can be fully utilized through multi-sensor fusion calculation, including GNSS, inertial navigation, magnetic heading sensors, a visual system and the like, positioning precision is improved, system errors are reduced, and stability and accuracy of the system under different environments and working conditions are maintained.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
As used herein, unless otherwise specified the use of the ordinal terms "first," "second," "third," etc., to describe a general object merely denote different instances of like objects, and are not intended to imply that the objects so described must have a given order, either temporally, spatially, in ranking, or in any other manner.
Claims (10)
1. A rotor unmanned aerial vehicle high accuracy navigation positioning system, characterized in that includes: the system comprises a GNSS receiver, four-array-element anti-interference antennas, an inertial navigation system, an altimeter, a magnetic heading sensor and a visual system, wherein the GNSS receiver is connected with the four-array-element anti-interference antennas and is used for differentially positioning signals of a plurality of navigation systems received by the four-array-element anti-interference antennas simultaneously and calculating the position and the speed of the unmanned aerial vehicle; the inertial navigation system is internally provided with an inertial measurement unit and a navigation resolving unit, the navigation resolving unit is connected with the GNSS receiver, the magnetic heading sensor, the vision system, the altimeter and the inertial measurement unit, and the navigation resolving unit is used for carrying out fusion resolving on angular velocity and acceleration measured by the inertial measurement unit, heading angle measured by the magnetic heading sensor, altitude calculated by the altimeter, position and speed calculated by the GNSS receiver or position and speed estimated by the vision system, and carrying out dynamic correction and error compensation on data of each sensor based on an adaptive correction algorithm to obtain current position, gesture, speed and heading information of the rotor unmanned aerial vehicle.
2. The high-precision navigation positioning system of the rotary-wing unmanned aerial vehicle according to claim 1, wherein the GNSS receiver is internally provided with a multi-frequency multi-mode dolphin series navigation baseband chip and is used for simultaneously receiving BDS, GPS, GLONASS, GALILEO signals with a plurality of different frequency points, demodulating and decoding the received signals by adopting an adaptive beam forming algorithm, correcting errors of the GNSS receiver by utilizing position information of one or more reference base stations, and calculating longitude and latitude, altitude and speed information of the unmanned aerial vehicle.
3. The high-precision navigation and positioning system of the rotary-wing unmanned aerial vehicle according to claim 1, wherein the navigation resolving unit is used for resolving a gesture by using an angular velocity measured by a MEMS gyroscope and an acceleration measured by an accelerometer in the inertial measurement unit to obtain gesture information of the unmanned aerial vehicle, and the gesture information comprises a yaw angle, a pitch angle and a roll angle; the yaw angle obtained by combining the course angle information provided by the magnetic course sensor with the gesture solution is utilized to carry out course solution, so that the current course of the unmanned aerial vehicle is obtained; when an effective GNSS signal exists, the longitude and latitude and speed information calculated by the GNSS receiver and the speed information provided by the inertial measurement unit are fused and resolved by utilizing Kalman filtering, and when the effective GNSS signal does not exist, the position and posture information estimated by the vision system and the speed information provided by the inertial measurement unit are fused and resolved to obtain the position and speed information of the unmanned aerial vehicle.
4. The rotary-wing drone high-precision navigational positioning system of claim 1, wherein the magnetic heading sensor uses a SEC225 magnetic compass with self-calibration functionality.
5. The high-precision navigational positioning system of a rotary-wing unmanned aerial vehicle according to claim 1, wherein the altimeter employs a barometric pressure altitude sensor MS5611 for measuring the barometric pressure and temperature values to calculate the altitude of the unmanned aerial vehicle from the relationship of barometric pressure, temperature and altitude.
6. The high-precision navigation and positioning system of the rotary-wing unmanned aerial vehicle according to claim 5, wherein the navigation resolving unit is configured to read correction parameters, uncorrected actual air pressure and actual temperature from the MS5611, the correction parameters including pressure sensitivity, pressure compensation coefficient, temperature coefficient of pressure sensitivity, temperature coefficient of pressure compensation, reference temperature, temperature calculation coefficient; calculating a corrected temperature based on a temperature difference between an actual temperature and a reference temperature and the temperature calculation coefficient: temp=2000+dt×c6/2 23, where TEMP is the corrected temperature, C6 is the temperature calculation coefficient, dT is the temperature difference, and the temperature difference is: dT=d2-c5×2 8, D2 is the uncorrected actual temperature, and C5 is the reference temperature; calculating a temperature compensated barometric pressure value based on the uncorrected actual barometric pressure, the actual temperature compensation, and the actual temperature sensitivity: wherein P is the air pressure value after temperature compensation, D1 is the uncorrected actual air pressure, SENS is the actual temperature compensation, OFF is the actual temperature sensitivity, and the actual temperature compensation calculation formula is: sens=c2× 16+(C4*dT)/27, where C2 is a pressure compensation coefficient, C4 is a temperature coefficient of pressure compensation, and the actual temperature sensitivity calculation formula is: off=c1×2 15+(C3*dT)/28, where C1 is the pressure sensitivity and C3 is the temperature coefficient of the pressure sensitivity; and calculating according to the relation between the pressure and the height to obtain a pressure height value:
h is the air pressure height value.
7. The rotary-wing drone high-precision navigational positioning system of claim 1, wherein the vision system comprises one or more cameras for capturing successive images, infrared sensors and lidar for providing additional visual information in night or low-light environments, and a vision processor for processing and computing the successive images and visual information using a deep learning model to obtain visual data including the position and speed of the drone relative to the ground.
8. A method of high-precision navigation positioning of a rotorcraft, performed on the basis of a high-precision navigation positioning system of a rotorcraft as claimed in any one of claims 1 to 7, comprising:
Acquiring angular velocity and acceleration measured by an inertial measurement unit, a heading angle measured by a magnetic heading sensor and a height calculated by an altimeter; carrying out attitude calculation by using the angular speed and the acceleration to obtain attitude information of the unmanned aerial vehicle, wherein the attitude information comprises a yaw angle, a pitch angle and a roll angle;
Performing course resolving by utilizing the yaw angle obtained by combining the course angle with the gesture resolving to obtain the current course of the unmanned aerial vehicle;
Judging whether a GNSS receiver receives a valid GNSS signal;
if the effective GNSS signals exist, fusion and calculation are carried out on longitude and latitude, altitude and speed information measured by a GNSS receiver, speed information provided by an inertial measurement unit and altitude provided by an altimeter by utilizing Kalman filtering, so that the current position and speed of the unmanned aerial vehicle are obtained;
if no effective GNSS signal exists, the position and the speed information of the unmanned aerial vehicle estimated based on the vision system are fused and solved with the speed information provided by the inertia measurement unit and the height provided by the altimeter by utilizing Kalman filtering, so that the current position and the speed of the unmanned aerial vehicle are obtained;
and correcting and compensating errors of the parameters of the inertial navigation system and the parameters of each sensor according to the combined navigation result.
9. A computing device, comprising:
At least one processor; and a memory storing program instructions, wherein the program instructions are configured to be adapted to be executed by the at least one processor, the program instructions comprising instructions for performing the rotorcraft high-precision navigational positioning method according to claim 8.
10. A readable storage medium storing program instructions that, when read and executed by a computing device, cause the computing device to perform the rotorcraft high-precision navigational positioning method according to claim 8.
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