CN112837378B - Aerial camera attitude external dynamic calibration and mapping method based on multi-unmanned aerial vehicle formation - Google Patents
Aerial camera attitude external dynamic calibration and mapping method based on multi-unmanned aerial vehicle formation Download PDFInfo
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Abstract
An aerial camera attitude external dynamic calibration and mapping method based on multi-unmanned aerial vehicle formation belongs to the technical field of image geographic information calibration. According to the invention, in the aerial image of the multi-unmanned aerial vehicle formation host camera, the global absolute coordinate information of the multi-frame collaborative unmanned aerial vehicle and the pixel position information of the multi-frame collaborative unmanned aerial vehicle in the main unmanned aerial vehicle camera picture are combined, and the attitude view angle of the main unmanned aerial vehicle camera is calculated in an inversion mode, so that mapping is performed, and the problem of low precision in the prior art is solved. The unmanned aerial vehicle aerial photographing system is low in dependence on image characteristic points, high in mapping precision, high in dynamic photographing precision and high in efficiency, and the dependence of the traditional image splicing technology on available image characteristics is broken through.
Description
Technical Field
The invention belongs to the technical field of image geographic information calibration, and particularly relates to a multi-unmanned aerial vehicle formation aerial image geographic information calibration method independent of flight attitude.
Background
For aerial images lacking image features which can be effectively identified, namely lacking identifiable and referenced optical features, an image stitching method based on feature matching is often invalid, and indexes such as accuracy and consistency of environment identification, detection and modeling are seriously affected. The attitude-free data has higher requirements on precision, efficiency, frequency, instantaneity and the like, requires higher hardware cost and is not suitable for unmanned aerial vehicle systems with low configuration. At the same time, achieving time synchronization, spatial synchronization is a difficult problem.
When facing a complex aerial photographing environment, many characteristic points are included in the images to be spliced, but when facing a lake aerial photographing, the image characteristics in the aerial photographing object are sparse, the requirements of the airborne optical mapping on the attitude precision of the aerial photographing image are high, and the positioning precision of the inertial sensor and the compass in the traditional calibration method cannot meet the requirements of the aerial photographing mapping with high precision, so that the method cannot be completed, and a method with high precision is needed to be searched to solve the problem in the prior art.
Disclosure of Invention
The invention provides a multi-machine formation aerial image geographic information calibration method independent of flight gestures, which is used for constructing an unmanned aerial vehicle aerial system with low dependence on image characteristic points, high mapping precision, high dynamic shooting precision and high efficiency, and breaks through the dependence of the traditional image splicing technology on available characteristics of images.
The method is realized by a dynamic flying unmanned aerial vehicle collaborative formation calibration system, and the calibration system comprises a main unmanned aerial vehicle and more than three collaborative unmanned aerial vehicles; the calibration and mapping method comprises the following steps:
And firstly, reasonably forming a queue and planning a flight path for the main unmanned aerial vehicle and the cooperative unmanned aerial vehicle, so that the cooperative unmanned aerial vehicle is ensured to be in an image range shot by the main unmanned aerial vehicle and form a certain space geometrical relationship with the main unmanned aerial vehicle. Global absolute coordinate information (i.e., GPS positioning, etc.) of the multiple collaborative drones corresponds to the multiple real space geometry. And the unmanned aerial vehicles are time-synchronized and coordinates are shared synchronously in real time.
Secondly, keeping the formation and the height of the main unmanned aerial vehicle and the cooperative unmanned aerial vehicle unchanged, synchronously flying according to a planned flight path, and shooting the cooperative unmanned aerial vehicle and a mapping area by the main unmanned aerial vehicle at a coordinate point to be mapped;
Thirdly, after shooting is completed, as the shapes of the two geometric relations are directly related to the attitude view angle of the host camera, the pixel position information and the global absolute coordinate information of the multi-frame collaborative unmanned aerial vehicle in the image shot by the host unmanned aerial vehicle camera are combined to obtain the attitude view angle of the host unmanned aerial vehicle camera; firstly, identifying pixel position information of a plurality of collaborative unmanned aerial vehicles in a camera image of a main unmanned aerial vehicle to determine a multi-machine geometric relationship in a camera view angle, secondly, determining a multi-machine actual space geometric relationship based on global absolute coordinate information of the plurality of collaborative unmanned aerial vehicles, and then, inverting and calculating a posture view angle of the main unmanned aerial vehicle camera by utilizing projection transformation relationship of the two geometric relationships, wherein the posture view angle comprises a pitch angle, a roll angle and a yaw angle;
And fourthly, in the acquired shooting image of the host camera, the pixel position information of the cooperative unmanned aerial vehicle in the image of the host unmanned aerial vehicle and the global positioning coordinates thereof are utilized, and the global coordinates of the host camera and the attitude view angle determined in the third step are combined, and the global absolute coordinate information corresponding to the pixels except the pixels of the cooperative unmanned aerial vehicle in the image is converted by performing rotational orthographic projection processing on the pixels of the image, so that the processing of the original image is completed.
And fifthly, combining global absolute coordinate data of aerial photographing data, and splicing the orthographic projection image data after multi-unmanned aerial vehicle formation and multi-frame aerial photographing mapping inversion to form complete image information and coordinate consistency mapping data under a unified coordinate system. And the method adopts a Riemann manifold space description mode to calibrate the image and coordinate data in the manifold space, so that the calibration precision and consistency of the image pixels can be effectively improved.
Further, the unmanned aerial vehicle is four rotor unmanned aerial vehicle.
Further, four rotor unmanned aerial vehicle includes organism carbon fiber support, motor, carbon fiber screw blade, chargeable lithium cell, wireless data transmission communication module, GPS module, pixhawk flight control module and firefly camera cloud platform. The functions of the wireless data transmission communication module, the GPS module, the Pixhawk flight control module and the firefly camera cradle head are as follows:
and a wireless data transmission communication module: the unmanned aerial vehicle transmits the aerial photo, GPS information of the aerial photo and attitude data of the unmanned aerial vehicle in real time; data sharing among unmanned aerial vehicles is carried out so as to keep a matrix type;
And a GPS module: determining the position and posture data of the unmanned aerial vehicle through a GPS;
the Pixhawk flight control module: automatically maintaining the normal flight attitude of the aircraft;
firefly camera cradle head: balance and stabilize the camera, and adjust the pitching angle of the camera.
The invention has the beneficial effects that: according to the invention, in the aerial image of the multi-unmanned aerial vehicle formation host camera, the global absolute coordinate information of the multi-frame collaborative unmanned aerial vehicle and the pixel position information of the multi-frame collaborative unmanned aerial vehicle in the main unmanned aerial vehicle camera picture are combined, and the attitude view angle (pitch angle, roll angle and yaw angle) of the main unmanned aerial vehicle camera is calculated in an inversion mode, so that mapping is performed, and the problem of low precision in the prior art is solved.
Drawings
Fig. 1 is a geometric relationship diagram of projections of a main unmanned aerial vehicle, a collaborative unmanned aerial vehicle and a collaborative unmanned aerial vehicle on a photo.
Fig. 2 is a schematic diagram of the positions of the cooperating unmanned aerial vehicles in the image when the main unmanned aerial vehicle camera is not in use.
Detailed Description
The invention achieves the effects by the design of the software program and the transformation and configuration of the hardware equipment.
A main unmanned aerial vehicle is at 500 meters high altitude for image shooting. Three collaborative unmanned aerial vehicles are positioned at 100 meters in the image range right below the main unmanned aerial vehicle. And reasonably forming three collaborative unmanned aerial vehicles to form a spatial geometric relationship. After the first image is shot by the main unmanned aerial vehicle, the four unmanned aerial vehicles translate simultaneously, and high-speed continuous shooting is performed. And (3) inverting other images by utilizing GPS coordinates of three collaborative unmanned aerial vehicles, and finally splicing the multi-unmanned aerial vehicle and multi-frame aerial mapping inversion data to form complete image information and coordinate consistency mapping data under a unified coordinate system. Therefore, the geographic information calibration of the multi-machine formation aerial image independent of the flight attitude is realized. Software operation part:
1. Setting a multi-unmanned aerial vehicle formation specific operation: right clicking selects "Connection optics" in Mission Planner ground stations and connects 4 unmanned aerial vehicles to Mission Planner ground stations by adopting TCP, selecting "Swarm" in "Temp", setting up main unmanned aerial vehicle, and other unmanned aerial vehicles will automatically enter "Guided" mode. Any slave is selected, takeoff is clicked, 3 slaves take off to a set height, because the slave is a four-rotor unmanned aerial vehicle, the slave can be directly set to 100 meters, then the slave is switched to a main unmanned aerial vehicle, a right button is clicked on a map interface, takeoff is selected, the slave can be directly lifted to 500 meters, the slave can take off to a low altitude, and then climbing is performed. In the Control panel, a master unmanned aerial vehicle and a slave can be controlled to form a required formation, and then the other slaves can automatically keep the formation and follow the master unmanned aerial vehicle to execute tasks only by controlling the master unmanned aerial vehicle.
2. Multiple unmanned aerial vehicle formation landing operations: and controlling the main unmanned aerial vehicle to return to the upper position of the falling point, keeping the original formation by the rest auxiliary unmanned aerial vehicles to return to the upper position of the falling point along with the main unmanned aerial vehicle, then, independently controlling each auxiliary unmanned aerial vehicle to drop, and finally, dropping the main unmanned aerial vehicle.
3. Time synchronization and space synchronization problems among multiple unmanned aerial vehicles: after the multi-unmanned aerial vehicle is formed into a team, the time synchronization problem is solved well, and under absolute time, the GPS coordinates of the master unmanned aerial vehicle and each slave are unified. The absolute time corresponding to the photo photographed by the master unmanned aerial vehicle can be calculated only by calculating the relative time difference between the master unmanned aerial vehicle and any slave computer display screen. As for the space synchronization problem, the algorithm of the multi-unmanned aerial vehicle formation has better robustness, GPS coordinates and pose data of the master unmanned aerial vehicle can be calculated through combination of images and a slave GPS, calculated data and flight control log data are compared, and relative errors can be calculated to ensure that space synchronization and mapping accuracy are achieved.
4. Image stitching algorithm: in the photo shot by the master unmanned aerial vehicle, three slaves are the optical characteristics of the image, and after the GPS coordinate data of the slaves in each image are uniformly processed, the image splicing based on the optical characteristics can be carried out by adopting a simple algorithm, and the higher precision can be kept.
5. Improvement of precision: the accuracy of GPS positioning can be cooperatively improved by adopting a laser radar calibration or RTK positioning method.
In terms of hardware, the present patent invention chooses to use a quad-rotor unmanned aerial vehicle rather than a fixed-wing unmanned aerial vehicle. The four rotor unmanned aerial vehicle is easy to refit, easy to operate and has great advantages in the aspect of water area environment monitoring relative to the fixed-wing unmanned aerial vehicle. The fixed-wing unmanned aerial vehicle is suitable for large-area aerial photography and aerial survey, can not achieve accurate stay fixed points, is high in flight speed, and is not suitable for high-quality acquisition of static images. The multi-aircraft formation aerial image geographic information calibration method which does not depend on the flight attitude is used, the requirements on the acquisition of geographic image information of the water area environment are fixed-point and static image acquisition, and the four-rotor unmanned aerial vehicle can better meet the requirements, so that the four-rotor unmanned aerial vehicle is adopted.
The four-rotor unmanned aerial vehicle hardware part used by the invention comprises a machine body carbon fiber support, a motor, a carbon fiber propeller blade, a rechargeable lithium battery, a wireless data transmission communication module, a GPS module, a Pixhawk flight control module, a firefly camera holder and the like.
Claims (7)
1. The method is characterized in that the calibration method is realized by a dynamic flying unmanned aerial vehicle collaborative formation calibration system, and the calibration system comprises a main unmanned aerial vehicle and more than three collaborative unmanned aerial vehicles; the calibration and mapping method comprises the following steps:
Firstly, reasonably forming a queue and planning a flight path for a main unmanned aerial vehicle and a cooperative unmanned aerial vehicle, so that the cooperative unmanned aerial vehicle is ensured to be in an image range shot by the main unmanned aerial vehicle and form a certain space geometrical relationship with the main unmanned aerial vehicle; the global absolute coordinate information of the multiple collaborative unmanned aerial vehicles corresponds to the geometrical relationship of the actual space of the multiple unmanned aerial vehicles; each unmanned aerial vehicle is time-synchronized and coordinates are synchronously shared in real time;
Secondly, keeping the formation and the height of the main unmanned aerial vehicle and the cooperative unmanned aerial vehicle unchanged, synchronously flying according to a planned flight path, and shooting the cooperative unmanned aerial vehicle and a mapping area by the main unmanned aerial vehicle at a coordinate point to be mapped;
thirdly, after shooting is completed, combining pixel position information of the multiple collaborative unmanned aerial vehicles in an image shot by the main unmanned aerial vehicle camera and global absolute coordinate information of the multiple collaborative unmanned aerial vehicles to obtain a posture view angle of the main unmanned aerial vehicle camera; firstly, identifying pixel position information of a plurality of collaborative unmanned aerial vehicles in a camera image of a main unmanned aerial vehicle to determine a multi-machine geometric relationship in a camera view angle, secondly, determining a multi-machine actual space geometric relationship based on global absolute coordinate information of the plurality of collaborative unmanned aerial vehicles, and then, inverting and calculating a posture view angle of the main unmanned aerial vehicle camera by utilizing projection transformation relationship of the two geometric relationships, wherein the posture view angle comprises a pitch angle, a roll angle and a yaw angle;
Fourth, in the acquired shooting image of the host camera, the pixel position information of the cooperative unmanned aerial vehicle in the image of the host unmanned aerial vehicle and the global positioning coordinates thereof are utilized, and the global coordinates of the host camera and the attitude view angle determined in the third step are combined, and global absolute coordinate information corresponding to the pixels except the pixels of the cooperative unmanned aerial vehicle in the image is converted through rotary orthographic projection processing on the pixels of the image, so that the processing of an original image is completed;
And fifthly, combining global absolute coordinate data of aerial photographing data, and splicing the orthographic projection image data after multi-unmanned aerial vehicle formation and multi-frame aerial photographing mapping inversion to form complete image information and coordinate consistency mapping data in a global absolute coordinate system.
2. The method for dynamically calibrating and mapping the attitude of an aerial camera based on multi-unmanned aerial vehicle formation according to claim 1, wherein the unmanned aerial vehicles are all four-rotor unmanned aerial vehicles.
3. The method for dynamically calibrating and mapping the attitude of an aerial camera based on multi-unmanned aerial vehicle formation according to claim 1, wherein the four-rotor unmanned aerial vehicle comprises a body carbon fiber support, a motor, a carbon fiber propeller blade, a rechargeable lithium battery, a wireless data transmission communication module, a GPS module, a Pixhawk flight control module and a firefly camera cradle head.
4. An aerial camera pose external dynamic calibration and mapping method based on multi-unmanned aerial vehicle formation according to claim 3, wherein the function of the wireless data transmission communication module is as follows: the unmanned aerial vehicle transmits the aerial photo, GPS information of the aerial photo and attitude data of the unmanned aerial vehicle in real time; data sharing among unmanned aerial vehicles is carried out to keep the array type.
5. A method for external dynamic calibration and mapping of the pose of an aerial camera based on multi-unmanned aerial vehicle formation according to claim 3, wherein the GPS module functions: and determining the position and the attitude data of the unmanned aerial vehicle and the load through a GPS.
6. A method for external dynamic calibration and mapping of the pose of an aerial camera based on multi-unmanned aerial vehicle formation according to claim 3, wherein the Pixhawk flight control module functions: and automatically maintaining the normal flight attitude of the aircraft.
7. An aerial camera pose external dynamic calibration and mapping method based on multi-unmanned aerial vehicle formation according to claim 3, wherein the firefly camera cradle head functions: balance and stabilize the camera, and adjust the pitching angle of the camera.
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