CN116933471A - Unmanned aerial vehicle external parameter detection method and device - Google Patents

Unmanned aerial vehicle external parameter detection method and device Download PDF

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CN116933471A
CN116933471A CN202210350029.7A CN202210350029A CN116933471A CN 116933471 A CN116933471 A CN 116933471A CN 202210350029 A CN202210350029 A CN 202210350029A CN 116933471 A CN116933471 A CN 116933471A
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unmanned aerial
bearing surface
aerial vehicle
determining
value
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李鑫
王昌龙
庞勃
姚兴华
郭彦杰
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Beijing Sankuai Online Technology Co Ltd
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Beijing Sankuai Online Technology Co Ltd
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Abstract

The application relates to the technical field of unmanned aerial vehicles, in particular to a method and a device for detecting external parameters of an unmanned aerial vehicle. The method comprises the following steps: determining a first description value of a bearing surface according to unmanned aerial vehicle attitude data when the unmanned aerial vehicle is stationary on the bearing surface; determining a second description value of the bearing surface according to camera data of the bearing surface shot by the unmanned aerial vehicle in a flight state; and determining the external parameter error of the unmanned aerial vehicle according to the first description value and the second description value. According to the scheme provided by the embodiment of the application, the online detection of the unmanned aerial vehicle external parameters can be realized more simply, the external parameter error detection is carried out depending on the fixed environment information such as the bearing surface, and the detection result has higher robustness.

Description

Unmanned aerial vehicle external parameter detection method and device
Technical Field
The application relates to the technical field of unmanned aerial vehicles, in particular to a method and a device for detecting external parameters of an unmanned aerial vehicle.
Background
Cameras, inertial measurement units (Inertial Measurement Unit, IMU) or odometers, etc., are the primary sensing devices for the unmanned aerial vehicle to perceive its own state or flight environment. The accurate acquisition of the spatial position and posture conversion relation between the coordinate systems of the sensors, namely calibrating the external parameters of the sensors, is a necessary premise for fusing the data of the sensors. At present, an iteration optimization idea is mainly adopted to calibrate the unmanned aerial vehicle external parameters, namely, an objective function and an optimization equation related to the unmanned aerial vehicle external parameters are constructed, and an optimal solution of the unmanned aerial vehicle external parameters is obtained through optimization iteration of the objective function and the optimization equation. Although the unmanned aerial vehicle external parameters can be corrected in the flight state of the unmanned aerial vehicle, the unmanned aerial vehicle is required to keep rotating and translating motion for a certain time in order to enable the state estimation to meet the convergence requirement, and the accuracy of the unmanned aerial vehicle external parameters is affected by the change of the flight environment.
Disclosure of Invention
In view of the above, the embodiment of the application provides a method and a device for detecting the external parameters of the unmanned aerial vehicle, which can simply and conveniently realize the online detection of the external parameters of the unmanned aerial vehicle, and rely on the fixed environment information such as a bearing surface to detect the external parameters, so that the detection result has higher robustness.
In a first aspect, an embodiment of the present application provides a method for detecting an external parameter of an unmanned aerial vehicle, including:
determining a first description value of a bearing surface according to unmanned aerial vehicle attitude data when the unmanned aerial vehicle is stationary on the bearing surface;
determining a second description value of the bearing surface according to camera data of the bearing surface shot by the unmanned aerial vehicle in a flight state;
and determining the external parameter error of the unmanned aerial vehicle according to the first description value and the second description value.
Optionally, the determining, according to the unmanned aerial vehicle attitude data when the unmanned aerial vehicle is stationary on the bearing surface, the first description value of the bearing surface includes:
according to the unmanned aerial vehicle attitude data, determining a rotation matrix of the unmanned aerial vehicle in a world coordinate system
According to the rotation matrixAnd determining a normal vector true value of the bearing surface to serve as the first description value.
Optionally, the determining, according to the camera data of the bearing surface captured by the unmanned aerial vehicle in the flight state, the second description value of the bearing surface includes:
establishing a bearing surface plane model according to the camera data;
and determining a normal measurement value of the bearing surface in a world coordinate system according to the bearing surface plane model to serve as the second description value.
Optionally, the determining a normal measurement value of the bearing surface in a world coordinate system according to the plane model of the bearing surface includes:
determining the normal vector of the bearing surface in a camera coordinate system according to the plane model of the bearing surface
The normal vector is processedAnd converting into the normal measurement value.
Optionally, the plane model of the bearing surface is determined according to a plane point cloud of the bearing surface shot by the unmanned aerial vehicle in a flight state, and a normal vector of the bearing surface in a camera coordinate system is determined according to the plane model of the bearing surfaceComprising the following steps:
determining a feature vector corresponding to the minimum feature value of the bearing surface plane model as the normal vector
Optionally, the bearing surface is provided with a marker, and the plane model of the bearing surface is based on the unmanned aerial vehicleDetermining marker corner points in the images of the markers shot in the flight state, and determining normal vectors of the bearing surface in a camera coordinate system according to the plane model of the bearing surfaceComprising the following steps:
determining a rotation matrix of a central coordinate system of the image of the marker relative to a camera coordinate system according to the marker corner points
The rotation matrix is processedConversion to the normal vector->
Optionally, said applying said normal vectorConverting to the normal measurement value, comprising:
according to the rotation matrix from the world coordinate system to the sensor coordinate system of the unmanned aerial vehicle in the flight stateAnd a rotation external parameter from the sensor coordinate system to the camera coordinate system +.>The normal vector->And converting into the normal measurement value.
Optionally, the determining the parameter error of the unmanned aerial vehicle according to the first description value and the second description value includes:
determining an external parameter error of the unmanned aerial vehicle according to the Euclidean distance between the first description value and the second description value;
and if the Euclidean distance is larger than a set threshold value, the external parameter error is an abnormal error.
Optionally, the determining the parameter error of the unmanned aerial vehicle according to the euclidean distance between the first description value and the second description value includes:
determining the average value of a plurality of second description values in a preset duration;
and determining the external parameter error according to the Euclidean distance between the first description value and the mean value.
In a second aspect, an embodiment of the present application provides a device for detecting an external parameter of an unmanned aerial vehicle, including:
the first determining module is used for determining a first description value of the bearing surface according to unmanned aerial vehicle attitude data when the unmanned aerial vehicle is stationary on the bearing surface;
the second determining module is used for determining a second description value of the bearing surface according to the camera data of the bearing surface shot by the unmanned aerial vehicle in the flight state;
and the external parameter correction module is used for determining the external parameter error of the unmanned aerial vehicle according to the first description value and the second description value.
In a third aspect, an embodiment of the present application provides a device for detecting an external parameter of an unmanned aerial vehicle, including: at least one processor; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method according to the first aspect or any embodiment of the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, where the computer readable storage medium includes a stored program, where the program when executed controls a device in which the computer readable storage medium is located to perform a method according to the first aspect or any embodiment of the first aspect.
According to the embodiment of the application, when the unmanned aerial vehicle is stationary on the bearing surface and the unmanned aerial vehicle is in a flight state, the description values about the bearing surface are respectively acquired, so that the error of the unmanned aerial vehicle external parameters is determined according to the description values of the bearing surface. According to the scheme provided by the embodiment of the application, the external parameter error detection is carried out based on the fixed environment information such as the bearing surface, so that the influence of the atmospheric environment and the light environment on the accuracy of the detection result can be reduced as much as possible, and the robustness of the external parameter detection result of the unmanned aerial vehicle is improved. In addition, the implementation flow of the embodiment is simple, and the calculation resources of the unmanned aerial vehicle are saved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for detecting external parameters of an unmanned aerial vehicle according to an embodiment of the present application;
fig. 2 is a schematic diagram of an unmanned aerial vehicle resting on a bearing surface according to an embodiment of the present application;
fig. 3 is a schematic diagram of an unmanned aerial vehicle in a flight state according to an embodiment of the present application;
fig. 4 is a flowchart of another method for detecting external parameters of an unmanned aerial vehicle according to an embodiment of the present application;
fig. 5 is a flowchart of another method for detecting external parameters of an unmanned aerial vehicle according to an embodiment of the present application;
fig. 6 is a schematic diagram of two-dimensional code set on a bearing surface according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a device for detecting an external parameter of an unmanned aerial vehicle according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an unmanned aerial vehicle external parameter detection device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, a flowchart of a method for detecting unmanned aerial vehicle external parameters is provided in an embodiment of the present application. The execution main body of the method is an unmanned aerial vehicle, and specifically is processing equipment arranged on the unmanned aerial vehicle. As shown in fig. 1, the processing steps of the method include:
101, determining a first description value of the bearing surface according to unmanned aerial vehicle attitude data when the unmanned aerial vehicle is stationary on the bearing surface.
Referring to fig. 2, a schematic diagram of an unmanned aerial vehicle resting on a bearing surface according to an embodiment of the present application is shown. As shown in fig. 2, the bearing surface is a plane with a high parallelism, for example, the bearing surface is an unmanned aerial vehicle apron.
In some embodiments, determining the manner in which the drone is stationary at the bearing surface may include: and acquiring a height value of the unmanned aerial vehicle from the bearing surface, and determining that the unmanned aerial vehicle is in a static state on the bearing surface when the height value is smaller than a first threshold value. Alternatively, the first threshold may be set according to actual needs, for example, the first threshold is 0.5 meters. That is, when the height value of the unmanned aerial vehicle from the bearing surface is smaller than 0.5 meter, the unmanned aerial vehicle is determined to be stationary on the bearing surface.
In some embodiments, the unmanned aerial vehicle pose data may be acquired through the IMU when the unmanned aerial vehicle is stationary on the bearing surface. Specifically, the unmanned aerial vehicle attitude data may include unmanned aerial vehicle pitch angle, acceleration, angular velocity, and the like. Optionally, the first description value of the bearing surface may be determined according to the unmanned aerial vehicle attitude data acquired by the IMU.
In some embodiments, determining the first description value of the bearing surface according to the unmanned aerial vehicle attitude data when the unmanned aerial vehicle is stationary on the bearing surface may include: according to unmanned aerial vehicle attitude data when the unmanned aerial vehicle is stationary on the bearing surface, determining that the unmanned aerial vehicle is onRotation matrix of world coordinate systemAccording to the rotation matrix of the unmanned aerial vehicle in the world coordinate system +.>Determining the normal vector true value of the bearing surface>As a first descriptive value of the bearing surface.
In some embodiments, the rotation matrix in the world coordinate system is based on the droneDetermining the normal vector true value of the bearing surface>Comprising the following steps: according to the formula->Calculating normal vector true value of bearing surface>Wherein k is a unit vector of the unmanned aerial vehicle in the Z-axis direction under the world coordinate system, and is a known value.
It should be noted that, the unmanned aerial vehicle attitude data or the first description value of the bearing surface when the unmanned aerial vehicle is stationary on the bearing surface may be stored in the unmanned aerial vehicle in advance. When the unmanned aerial vehicle needs to perform external parameter detection in the flight state, the stored attitude data of the unmanned aerial vehicle or the first description value of the bearing surface can be directly called. Of course, after the unmanned aerial vehicle starts the external parameter detection function, unmanned aerial vehicle attitude data when the unmanned aerial vehicle is stationary on the bearing surface can be obtained, and the first description value can be calculated.
102, determining a second description value of the bearing surface according to camera data of the bearing surface shot by the unmanned aerial vehicle in the flight state.
In some embodiments, the drone photographs camera data of the bearing surface by looking down the camera in the flight state, and determines a second description value of the bearing surface from the camera data of the bearing surface.
Referring to fig. 3, a schematic diagram of an unmanned aerial vehicle in a flight state according to an embodiment of the present application is provided. As shown in fig. 3, the timing of capturing the bearing surface camera data when the unmanned aerial vehicle is in a flight state may include: when the height value of the unmanned aerial vehicle from the bearing surface is larger than or equal to a first threshold value and smaller than a second threshold value, the unmanned aerial vehicle shoots camera data of the bearing surface through the downward-looking camera. Optionally, the second threshold may be set according to actual needs, where the second threshold is smaller than the maximum flying height of the unmanned aerial vehicle, for example, the second threshold is 8 meters. In some embodiments, during the change of the unmanned aerial vehicle from the stationary state to the takeoff state, camera data of the bearing surface is photographed by the downward-looking camera when the takeoff height is greater than or equal to the first threshold value and less than the second threshold value. In some embodiments, in the process of changing the unmanned aerial vehicle from the normal flight state to the falling state, when the height of the unmanned aerial vehicle after falling is greater than or equal to a first threshold value and less than a second threshold value, camera data of the bearing surface is shot through the downward-looking camera.
In some embodiments, after the bearing surface camera data shot by the unmanned aerial vehicle in the flight state is obtained, a bearing surface plane model can be established according to the camera data, and then a normal measurement value of the bearing surface in a world coordinate system can be determined according to the bearing surface plane model. The normal measurement value of the bearing surface in the world coordinate system can be used as the second description value of the bearing surface.
In some embodiments, determining a normal measurement of the bearing surface in a world coordinate system from the bearing surface planar model includes: determining the normal vector of the bearing surface in a camera coordinate system according to the plane model of the bearing surfaceNormal vector of the bearing surface in the camera coordinate system +.>Conversion to a socketNormal measurement of the surface in world coordinate System +.>
In some embodiments, the normal vector of the bearing surface in the camera coordinate systemConversion into a normal measurement value of the support surface in the world coordinate system +.>Comprising the following steps: according to the rotation matrix of the unmanned aerial vehicle from the world coordinate system to the sensor coordinate system in the flight state +.>Rotation profile R from sensor coordinate system to camera coordinate system bc Normal vector of camera coordinate system>Normal measurement value converted into world coordinate system +.>
And 103, determining the external parameter error of the unmanned aerial vehicle according to the first description value and the second description value of the bearing surface.
The bearing surface is used as a fixed environment for the unmanned aerial vehicle to park, and the bearing surface description values measured in different modes are kept consistent in theory. Based on the principle, the embodiment of the application respectively acquires the description value of the bearing surface when the unmanned aerial vehicle is stationary on the bearing surface and the unmanned aerial vehicle is in a flight state. The first description value and the second description value of the bearing surface are respectively determined based on data acquired by the unmanned aerial vehicle IMU and the downward-looking camera. If the IMU of the unmanned aerial vehicle and the external parameter calibration of the downward-looking camera are accurate, the first description value and the second description value are consistent; if the unmanned aerial vehicle external parameter error is larger, then there is also great error between the first descriptive value and the second descriptive value. Based on the above, the embodiment of the application can determine the error of the unmanned aerial vehicle external parameter according to the first description value and the second description value.
In some embodiments, the unmanned aerial vehicle's extrinsic error may be determined from the euclidean distance between the first descriptive value and the second descriptive value. When the Euclidean distance between the first description value and the second description value is smaller, the external parameter error of the unmanned aerial vehicle is smaller; when the Euclidean distance between the first description value and the second description value is larger, the unmanned aerial vehicle external parameter is larger in error, and the unmanned aerial vehicle external parameter needs to be calibrated again.
In some embodiments, when calculating the euclidean distance between the first description value and the second description value, the second description values of the plurality of bearing surfaces may be calculated according to the camera data collected in the preset time period. And calculating the Euclidean distance between the first description value and the average value of the second description values.
According to the embodiment of the application, the external parameter error detection is carried out based on the fixed environment information such as the bearing surface, so that the influence of the atmospheric environment and the light environment on the accuracy of the detection result can be reduced as much as possible, and the robustness of the external parameter detection result of the unmanned aerial vehicle is improved.
Furthermore, the embodiment of the application detects the external parameters of the unmanned aerial vehicle in the take-off process or the landing process of the unmanned aerial vehicle, has simple realization flow, and does not need to occupy the computing resources when the unmanned aerial vehicle works aloft.
Further, when unmanned aerial vehicle external parameter detection is carried out in the unmanned aerial vehicle take-off process, unmanned aerial vehicle external parameter detection can be completed in a short time of unmanned aerial vehicle take-off, and instant warning can be realized when external parameter is abnormal.
Referring to fig. 4, a flowchart of another method for detecting unmanned aerial vehicle external parameters is provided in an embodiment of the present application. In the embodiment, the plane model of the bearing surface is determined according to the plane point cloud of the bearing surface shot by the unmanned aerial vehicle in the flight state. As shown in fig. 4, after the unmanned aerial vehicle turns on the external parameter detection function, the method steps executed include:
and 201, determining the height value of the unmanned aerial vehicle from the bearing surface according to the data acquired by the height sensor. Alternatively, the height sensor may be a barometer, an ultrasonic sensor, GPS, or the like. The height value of the unmanned aerial vehicle from the bearing surface can be determined according to the data acquired by the height sensor.
202, determining a normal vector true value of the bearing surface according to unmanned aerial vehicle attitude data acquired by the IMU when the height value is smaller than a first threshold value
The first threshold value is smaller, and the first threshold value is used for judging whether the unmanned aerial vehicle is static or approximately static on the bearing surface. Alternatively, the first threshold value may be determined according to the body height of the unmanned aerial vehicle, and for example, the body height or the body height of 0.5 times may be determined as the first threshold value. If the height of the unmanned aerial vehicle from the bearing surface is smaller than a first threshold value, determining that the unmanned aerial vehicle is static or approximately static on the bearing surface, and determining a normal vector true value of the bearing surface according to unmanned aerial vehicle attitude data acquired by the IMU
203, when the height value is greater than or equal to the first threshold value and less than the second threshold value, shooting the plane point cloud of the bearing surface through the camera. Optionally, the plane point cloud of the bearing surface can be shot by a binocular camera arranged on the unmanned aerial vehicle.
204, establishing a bearing surface plane model based on the plane point cloud. Optionally, after the plane point cloud of the bearing surface is acquired, a bearing surface plane model may be established based on a ransac algorithm.
205, determining the feature vector corresponding to the minimum feature value of the plane model of the bearing surface as the normal vector of the bearing surface in the camera coordinate system
206, normal vector of bearing surface under camera coordinate systemConversion to normal of bearing surface in world coordinate systemMeasurement value +.>
Wherein, the liquid crystal display device comprises a liquid crystal display device, the rotation matrix of the unmanned aerial vehicle in the world coordinate system in the flying state is shown, and k is a unit vector of the unmanned aerial vehicle in the Z-axis direction in the world coordinate system.
Alternatively, the following formula may be usedConversion to->
Wherein, the liquid crystal display device comprises a liquid crystal display device,the rotation matrix from the world coordinate system to the sensor coordinate system at the flight time tau of the unmanned aerial vehicle; r is R bc Is a rotation external parameter from the sensor coordinate system to the camera coordinate system.
207 according to the normal vector true value of the bearing surfaceAnd normal measurement value +.>And determining the external parameter error of the unmanned aerial vehicle by the Euclidean distance d, and if the Euclidean distance is larger than a set threshold value, determining the external parameter error of the unmanned aerial vehicle as an abnormal error.
In some embodiments of the present application, in some embodiments,
in the embodiment of the application, the unmanned aerial vehicle attitude data is assumed to be accurately measured, so that the change of the vector distance d mainly comes from R bc Andwhen->When the solution is better, the unmanned aerial vehicle external parameter R can be evaluated through the vector distance d bc When d is larger, the error exists in the rotation external parameter, and the calibration is needed to be carried out again.
Referring to fig. 5, a flowchart of another method for detecting unmanned aerial vehicle external parameters is provided in an embodiment of the present application. In this embodiment, the bearing surface is provided with a marker. The unmanned aerial vehicle shoots an image of a marker of the bearing surface in a flight state, and establishes a plane model of the bearing surface according to marker corner points in the image of the marker shot in the flight state. As shown in fig. 5, after the unmanned aerial vehicle turns on the external parameter detection function, the method includes the following steps:
and 301, determining the height value of the unmanned aerial vehicle from the bearing surface according to the data acquired by the height sensor.
302, determining a normal vector true value of the bearing surface according to unmanned aerial vehicle attitude data acquired by the IMU when the height value is smaller than a first threshold value
303, when the height value is greater than or equal to the first threshold value and less than the second threshold value, shooting an image of the marker set on the bearing surface by the camera. As shown in fig. 6, the markers set on the bearing surface may be two-dimensional codes. When the height value is greater than or equal to the first threshold value and less than the second threshold value, an image of the two-dimensional code can be photographed by the monocular camera.
And 304, identifying marker corner points from the shot images of the markers, and establishing a bearing surface plane model according to the identified marker corner points. Optionally, when the image of the marker shot by the monocular camera is an image of the two-dimensional code, four corner points of the outermost dimension of the two-dimensional code in the two-dimensional code image are identified, and a bearing surface plane model is built based on coordinates of the four corner points in a camera coordinate system.
305, determining a rotation matrix of a central coordinate system of the image of the marker relative to a camera coordinate system based on marker corner points identified from the marker imageOptionally, after four corner points at the outermost periphery of the two-dimensional code are identified from the two-dimensional code image, a rotation matrix of a two-dimensional code center coordinate system relative to a camera system can be solved through PNP algorithm>
306 according to the rotation matrixDetermining the normal vector of the support surface in the camera coordinate system>Alternatively, it is possible to apply the formula +.>Will->Conversion to->k is a unit vector of the unmanned aerial vehicle in the Z-axis direction under the world coordinate system.
307, normal vector of bearing surface under camera coordinate systemConversion into a normal measurement value of the support surface in the world coordinate system +.>
308 according to the normal vector true value of the bearing surfaceAnd normal measurement value +.>And determining the external parameter error of the unmanned aerial vehicle by the Euclidean distance d, and if the Euclidean distance is larger than a set threshold value, determining the external parameter error of the unmanned aerial vehicle as an abnormal error.
Corresponding to the detection method of the unmanned aerial vehicle external parameters, the embodiment of the application also provides a detection device of the unmanned aerial vehicle external parameters. Those skilled in the art will appreciate that these means may be configured by the steps taught by the present solution using commercially available hardware components. Fig. 7 is a schematic structural diagram of an unmanned aerial vehicle external parameter detection device according to an embodiment of the present application. As shown in fig. 7, the apparatus includes: a first determining module 401, configured to determine a first description value of a bearing surface according to unmanned aerial vehicle pose data when the unmanned aerial vehicle is stationary on the bearing surface; a second determining module 402, configured to determine a second description value of the bearing surface according to camera data of the bearing surface captured by the unmanned aerial vehicle in a flight state; and the external parameter correction module 403 is configured to determine an external parameter error of the unmanned aerial vehicle according to the first description value and the second description value.
The unmanned aerial vehicle external parameter detection device provided by the embodiment of the application can execute the unmanned aerial vehicle external parameter detection method related to the embodiment shown in fig. 1 to 6. For parts of the embodiment which are not described in detail, reference may be made to the relevant description of the embodiment shown in fig. 1 to 6. The implementation process and technical effects of this technical solution are described in the embodiments shown in fig. 1 to 6, and are not described herein.
It should be understood that the division of the modules of the unmanned aerial vehicle external reference detection device shown in fig. 7 is merely a division of logic functions, and may be fully or partially integrated into a physical entity or may be physically separated. And these modules may all be implemented in software in the form of calls by the processing element; or can be realized in hardware; it is also possible that part of the modules are implemented in the form of software called by the processing element and part of the modules are implemented in the form of hardware. For example, the first determining module 401 and the second determining module 402 may be separately established processing elements, or may be integrated in a chip of the electronic device. The implementation of the other modules is similar. In addition, all or part of the modules can be integrated together or can be independently implemented. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.
For example, the modules above may be one or more integrated circuits configured to implement the methods above, such as: one or more specific integrated circuits (Application Specific Integrated Circuit; hereinafter ASIC), or one or more microprocessors (Digital Singnal Processor; hereinafter DSP), or one or more field programmable gate arrays (Field Programmable Gate Array; hereinafter FPGA), etc. For another example, the modules may be integrated together and implemented in the form of a System-On-a-Chip (SOC).
Fig. 8 is a schematic structural diagram of an unmanned aerial vehicle external parameter detection device according to an embodiment of the present application. The detection device is deployed on an unmanned aerial vehicle. As shown in fig. 8, the drone is in the form of a general purpose computing device. The components of the drone may include, but are not limited to: one or more processors 510, a communication interface 520, a memory 530, and a communication bus 540 that connects the various system components (including the memory 530, the communication interface 520, and the processing unit 510).
Communication bus 540 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include industry Standard architecture (Industry Standard Architecture; hereinafter ISA) bus, micro channel architecture (Micro Channel Architecture; hereinafter MAC) bus, enhanced ISA bus, video electronics standards Association (Video Electronics Standards Association; hereinafter VESA) local bus, and peripheral component interconnect (Peripheral Component Interconnection; hereinafter PCI) bus.
Electronic devices typically include a variety of computer system readable media. Such media can be any available media that can be accessed by the electronic device and includes both volatile and nonvolatile media, removable and non-removable media.
Memory 530 may include computer system readable media in the form of volatile memory, such as random access memory (Random Access Memory; hereinafter: RAM) and/or cache memory. The electronic device may further include other removable/non-removable, volatile/nonvolatile computer system storage media. The memory 530 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the unmanned aerial vehicle exogenous detection methods of the embodiments of the present application as described in connection with fig. 1-6.
A program/utility having a set (at least one) of program modules may be stored in the memory 530, such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules generally execute the unmanned aerial vehicle external parameter detection method according to the embodiments shown in fig. 1 to 6.
The processor 510 executes a program stored in the memory 530 to perform various functional applications and data processing, for example, to implement the unmanned aerial vehicle external parameter detection method according to the embodiment shown in fig. 1 to 6 of the present specification.
In a specific implementation, the present application also provides a computer storage medium, where the computer storage medium may store a program, where the program may include some or all of the steps in the embodiments provided by the present application when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random-access memory (random access memory, RAM), or the like.
In a specific implementation, an embodiment of the present application further provides a computer program product, where the computer program product contains executable instructions, where the executable instructions when executed on a computer cause the computer to perform some or all of the steps in the above method embodiments.
In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relation of association objects, and indicates that there may be three kinds of relations, for example, a and/or B, and may indicate that a alone exists, a and B together, and B alone exists. Wherein A, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of the following" and the like means any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.
Those of ordinary skill in the art will appreciate that the various elements and algorithm steps described in the embodiments disclosed herein can be implemented as a combination of electronic hardware, computer software, and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In several embodiments provided by the present application, any of the functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely exemplary embodiments of the present application, and any person skilled in the art may easily conceive of changes or substitutions within the technical scope of the present application, which should be covered by the present application. The protection scope of the present application shall be subject to the protection scope of the claims.

Claims (12)

1. The unmanned aerial vehicle external parameter detection method is characterized by comprising the following steps of:
determining a first description value of a bearing surface according to unmanned aerial vehicle attitude data when the unmanned aerial vehicle is stationary on the bearing surface;
determining a second description value of the bearing surface according to camera data of the bearing surface shot by the unmanned aerial vehicle in a flight state;
and determining the external parameter error of the unmanned aerial vehicle according to the first description value and the second description value.
2. The method of claim 1, wherein determining the first description value for the bearing surface based on the unmanned aerial vehicle pose data when the unmanned aerial vehicle is stationary on the bearing surface comprises:
according to the unmanned aerial vehicle attitude data, determining a rotation matrix of the unmanned aerial vehicle in a world coordinate system
According to the rotation matrixAnd determining a normal vector true value of the bearing surface to serve as the first description value.
3. The method according to claim 1, wherein the determining the second description value of the bearing surface according to the camera data of the bearing surface photographed by the unmanned aerial vehicle in the flight state includes:
establishing a bearing surface plane model according to the camera data;
and determining a normal measurement value of the bearing surface in a world coordinate system according to the bearing surface plane model to serve as the second description value.
4. A method according to claim 3, wherein said determining a normal measurement of said bearing surface in a world coordinate system from said bearing surface planar model comprises:
determining the normal vector of the bearing surface in a camera coordinate system according to the plane model of the bearing surface
The normal vector is processedAnd converting into the normal measurement value.
5. The method of claim 4, wherein the plane model of the bearing surface is determined according to a plane point cloud of the bearing surface photographed by the unmanned aerial vehicle in a flight state, and wherein the normal vector of the bearing surface in a camera coordinate system is determined according to the plane model of the bearing surfaceComprising the following steps:
determining a feature vector corresponding to the minimum feature value of the bearing surface plane model as the normal vector
6. The method according to claim 4, wherein the bearing surface is provided with a marker, the bearing surface plane model is determined according to a marker corner point in an image of the marker taken by the unmanned aerial vehicle in a flight state, and the normal vector of the bearing surface in a camera coordinate system is determined according to the bearing surface plane modelComprising the following steps:
determining a rotation matrix of a central coordinate system of the image of the marker relative to a camera coordinate system according to the marker corner points
The rotation matrix is processedConversion to the normal vector->
7. The method of any one of claims 4 to 6, wherein said applying said normal vectorConverting to the normal measurement value, comprising:
according to the instituteRotation matrix from world coordinate system to sensor coordinate system of unmanned aerial vehicle in flight stateAnd a rotation external parameter from the sensor coordinate system to the camera coordinate system +.>The normal vector->And converting into the normal measurement value.
8. The method of claim 1, wherein the determining the unmanned aerial vehicle's exogenous error based on the first descriptive value and the second descriptive value comprises:
determining an external parameter error of the unmanned aerial vehicle according to the Euclidean distance between the first description value and the second description value;
and if the Euclidean distance is larger than a set threshold value, the external parameter error is an abnormal error.
9. The method of claim 8, wherein the determining the unmanned aerial vehicle's exogenous error based on the euclidean distance between the first descriptive value and the second descriptive value comprises:
determining the average value of a plurality of second description values in a preset duration;
and determining the external parameter error according to the Euclidean distance between the first description value and the mean value.
10. Detection device of unmanned aerial vehicle external parameters, its characterized in that includes:
the first determining module is used for determining a first description value of the bearing surface according to unmanned aerial vehicle attitude data when the unmanned aerial vehicle is stationary on the bearing surface;
the second determining module is used for determining a second description value of the bearing surface according to the camera data of the bearing surface shot by the unmanned aerial vehicle in the flight state;
and the external parameter correction module is used for determining the external parameter error of the unmanned aerial vehicle according to the first description value and the second description value.
11. Detection device of unmanned aerial vehicle external parameters, its characterized in that includes:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1-9.
12. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored program, wherein the program, when run, controls a device in which the computer readable storage medium is located to perform the method of any one of claims 1 to 9.
CN202210350029.7A 2022-04-02 2022-04-02 Unmanned aerial vehicle external parameter detection method and device Pending CN116933471A (en)

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Applications Claiming Priority (1)

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