RU2798604C1 - Uav and method for performing aerial photography - Google Patents

Abstract

FIELD: UAVs.

SUBSTANCE: group of inventions relates to an unmanned aerial vehicle (UAV) and a method for performing aerial photography. The UAV comprises a flight controller, a camera, an on-board computer, a camera control module, a module for determining the quality of images taken in a certain way. To perform aerial photography, an image is obtained from the camera according to the interval specified in the flight mission. The number of images from the camera is determined and compared with a predetermined value. If this number has reached it and if the value of the image quality metric is less than a predetermined threshold value, a decision is made by the computer to re-calibrate and return to the shooting point.

EFFECT: improving the quality of images, accelerating and reducing the cost of resources for performing aerial photography of a given area with the required quality, increasing the accuracy of the orthomosaic, accelerating and reducing the cost of resources for the formation of an orthomosaic of the required quality.

6 cl, 7 dwg, 1 tbl

Description

The field of technology to which the invention belongs

The invention relates to photogrammetry, in particular to the implementation of aerial photography using a UAV.

State of the art

Unmanned aerial vehicles (UAVs, UAVs) are gradually becoming a part of everyday life - they carry out cargo delivery, various studies, analysis of the surrounding space, territory monitoring, object control, photo and video shooting, are used in rescue operations and find many other applications.

One of the popular areas of UAV application is the creation of an orthophotomap or aerial photography (APS) for geoinformation systems. The use of a UAV for these purposes makes it possible to increase the spatial resolution of the map up to several mm/pixel, which is impossible when constructing an orthomosaic using manned aviation or satellite imagery.

Regardless of the type of UAV and payload, a flight mission is formed that takes into account the required spatial resolution of images, camera characteristics, percentage of image overlap, flight speed, and much more. In addition to the flight mission, it is also necessary to set up the camera and set the optimal shutter speed, light sensitivity, and aperture settings. Most often, these parameters are set manually and the result directly depends on the experience of the operator. However, manually set parameters cannot guarantee a quality result due to problems such as:

- change of weather during the flight (low exposure);

- Incorrectly set shutter speed (blur pictures or low exposure);

- overlapping of images by precipitation (poor-quality image);

- sharp gusts of wind (shooting not at a given angle).

As a result, aerial photography has developed a practice in which, after the flight mission is completed and the UAV returns to the base, up to 10% of the captured images are rejected, and a new flight is performed to re-shoot poor-quality sections of the orthomosaic, which leads to an increase in time and costs for its creation. Some of the problem is solved by slower movement of the UAV and/or by obtaining more images, but this again leads to an increase in time and costs, and also places increased demands on the UAV, on the available flight time, on the camera, on the storage, on the processing unit, and however, some areas often end up being of poor quality and requiring re-shooting.

The essence of the invention

The present invention is directed to devices and methods to overcome at least some of the above disadvantages of the prior art.

In particular, it is proposed to determine the quality of the received images directly during the flight on board the UAV.

In one aspect of the present invention, an unmanned aerial vehicle (UAV) is provided, comprising:

a flight controller configured to control the flight of the UAV;

a camera configured to capture images of the area under the UAV; And

an on-board computer connected to the flight controller and to the camera and containing:

a camera control module, configured to control a camera to acquire images of the area in the process of performing aerial photography; And

a picture quality determination module, configured to determine the quality of images received from the camera;

wherein the on-board computer is configured to receive an image from the camera according to the interval specified in the flight mission, and decide on the need for re-calibration if it is determined by the on-board computer that the number of images received from the camera since the last calibration has reached a predetermined number , or if it is determined by the image quality detection module that the metric value of the current image is less than the quality metric threshold;

however, to perform a recalibration:

the camera control module is configured to initiate camera autofocus,

the on-board computer is configured to receive an additional image from the camera after autofocusing,

the image quality determination module is configured to determine a new quality metric reference value according to the additional image and update the quality metric threshold based on the new quality metric reference value.

In another aspect of the present invention, a method for performing aerial photography to be performed in a UAV is provided, comprising the steps of:

receiving an image from the camera according to the interval specified in the flight mission; And

if it is determined by the on-board computer that the number of images received from the camera since the last calibration has reached a predetermined number, or if it is determined by the image quality determination module that the current image metric value is less than the quality metric threshold, a decision is made by the on-board computer the need for recalibration;

while performing a recalibration comprises the steps of:

initiate camera autofocus using the camera control module,

receive an additional image from the camera after autofocusing,

determine a new reference value of the quality metric using the image quality determination module according to the additional image,

updating the quality metric threshold based on the new quality metric reference value.

In one embodiment, an additional image is acquired from a survey taken on the same flight without deviating from the flight mission route, immediately after the decision to recalibrate is made and the camera has autofocused.

In one embodiment, an additional image is obtained from an image taken on the same flight after the UAV returns to the point corresponding to the point where the image was taken, which was determined to be poor quality.

In one embodiment, the return of the UAV to the point of capturing an image that has been determined to be poor quality is performed based on a determination that the distance traveled or the elapsed time since the capture of that image does not allow sufficient overlap of the additional image with the last high quality image preceding the image that was determined to be of poor quality, on the route specified in the flight mission.

In one embodiment, the moment the sub-image is taken resets the interval count.

Technical result

The present invention improves the quality of images, accelerates and reduces the cost of resources for performing aerial photography of a given area with the required quality, improves the accuracy of the orthomosaic, accelerates and reduces the cost of resources for the formation of an orthomosaic of the required quality.

These and other advantages of the present invention will become apparent upon reading the following detailed description with reference to the accompanying drawings.

Brief description of the drawings

On FIG. 1 shows a block diagram of an apparatus according to the present invention.

On FIG. 2 shows a flowchart of the method according to the present invention.

On FIG. 3 shows an example of a flight mission.

On FIG. 4A-4D show a comparison of the results of a typical method and the method according to the present invention.

It should be understood that the figures may be represented schematically and not to scale and are intended primarily to improve understanding of the present invention.

Detailed description

As a UAV for the purposes of the present invention, for example, a drone, a quadrocopter, a multicopter, an unmanned helicopter, a vertical takeoff and landing aircraft (VTOL) or any other aircraft on which the proposed device is installed or can be installed can be considered.

An unmanned aerial vehicle (UAV) may contain a body, propellers, engines, a landing gear, an engine control module, a flight controller, a transceiver, a navigation module, an energy source, a camera, a set of sensors. The specific set of UAV elements and their arrangement are not limited by the present invention.

The UAV may also contain an inertial measurement unit.

The set of sensors may include, but is not limited to, a gyroscope, an accelerometer, a barometer, a magnetometer, and so on. Which sensors are installed on the UAV depends on the purpose of the UAV and the functions it performs.

The energy source can be a battery for supplying electrical power to the UAV onboard facilities. In other embodiments, the energy source may be kerosene, hydrogen, a fuel cell, gasoline, diesel fuel, or other fuel sources designed to power UAV propulsion systems and electronics.

Engines are designed to convert energy from an energy source into mechanical energy and can be made, for example, in the form of internal combustion engines (ICE) or electric motors. Electric motors can be made, for example, in the form of brushless motors.

The propellers can be in the form of propellers, in particular clockwise rotating propellers (pushing propellers) or counterclockwise rotating propellers (pulling propellers). At the same time, optionally, pushing and/or pulling propellers in any combination can be used in the UAV. Propellers can be made of two or more blades. Propellers can be made of metal, plastic or composite materials such as carbon fiber. The UAV can be made with a single propeller (for example, with a pusher propeller) or with multiple propellers.

A transceiver is a device responsible for receiving and transmitting radio signals sent to and from an aircraft. The transceiver may comprise a video transmission system including a video transmitter and a video receiver.

The engine control module controls the operation of the engine through actuators in order to increase efficiency and can be made in the form of an electronic speed controller, idle speed controller, valve timing controller, etc.

The navigation module may include a satellite navigation module that, based on data received from a global navigation satellite system (GNSS) such as GPS, GLONASS, BeiDou, Galileo, QZSS, determines the position (geographical coordinates), direction, speed and yaw angle of the UAV and synchronizes time.

aerial photography

The UAV can perform flight missions aimed at aerial photography in order to form an orthophoto. In the present invention, it is proposed to determine the quality of the received images directly during the flight on board the UAV. This requires the integration of an additional onboard computer on the UAV with the developed camera control system.

An example connection structure is shown in Fig. 1. In particular, the on-board computer 120 is connected to the autopilot (flight controller) 110 via a data interface (for example, via UART), which is connected to the camera 130 via a data interface (for example, via USB), controlling it and receiving and processing images from her. To this end, the on-board computer 120 includes a camera control module 140 and a module 150 for determining image quality.

On-board computer 120 may be implemented as a device that includes a processor, memory, and data interfaces. The memory may contain a set of instructions that, when executed by the processor, cause the processor to perform the functions of the camera control module 140 and the image quality determination module 150 . In another embodiment, the on-board computer can be implemented as a device that contains a controller, data interfaces and physically implemented (for example, in the form of chips) separate modules 140 for controlling the camera and 150 for determining the quality of images. Various combinations of the above are also possible.

The type of camera and flight controller that can be used for the purposes of the present invention is not limited in this description and is determined by the requirements of a particular application. Accordingly, the present invention can be embodied in a wide variety of embodiments and can be integrated into many existing UAVs.

The camera control unit 140 is configured to control the camera of the UAV to acquire images of the area while performing aerial photography.

The image quality determination module 150 is configured to determine the quality of images obtained with the UAV camera directly during the flight (in the process of performing aerial photography) on board the UAV.

Also, the on-board computer 120 may include an image set generation module (not shown in FIG. 1) configured to generate an image set from images acquired by the UAV camera to form an orthomosaic. To this end, the imaging set module contains a memory.

A method 200 for performing aerial photography according to the present invention is shown in FIG. 2 and may be as follows.

At step 210, a camera image is obtained according to the interval set in the flight mission. The interval can be set by specific points in time, by the elapsed time or distance traveled since the last snapshot, by a location (point) on the route specified in the flight mission, etc.

Next, at step 220, it is determined by the on-board computer whether the number of images received from the camera since the last calibration has reached a predetermined number.

At step 230, it is determined by the image quality determination module whether the metric value of the current image is lower than the quality metric threshold. The quality metric threshold at the beginning of the mission may be predetermined by the user or may be generated by calibration based on the first reference image taken with autofocus.

Steps 220 and 230 may be performed in parallel or sequentially in any order. If both checks at steps 220 and 230 are negative, then the method proceeds to step 240 where the on-board computer decides that calibration and re-survey is not required, and then the aerial survey continues normally, proceeding to step 290, where the on-board computer the computer waits for the next image taken according to the interval set in the flight mission, and the loop is completed.

When the next image arrives, the method again performs step 210 and checks at steps 220 and 230. Aerial photography is performed until the flight mission is completed, that is, until all the necessary images are obtained. There are also cases of emergency interruption of aerial photography due to UAV malfunctions, low battery, etc., however, they do not belong to the normal mode of aerial photography and are not considered in the present invention.

If it is determined at step 220 that the number of images received from the camera since the last calibration has reached a predetermined number, or if it is determined at step 230 that the current image metric value is less than the quality metric threshold, then the method proceeds to step 250, where the on-board computer decides whether a recalibration is necessary.

Performing a recalibration contains the following steps.

At step 260, the camera control module initiates camera autofocus, and the camera performs autofocus.

In step 270, the on-board computer receives an additional image from the camera after autofocusing, and the image quality determination module determines a new quality metric reference value according to the additional image.

In step 280, the image quality determination module updates the quality metric threshold based on the new quality metric reference value and the method proceeds to step 290 where the flight computer waits for the next image captured according to the interval specified in the flight mission and the loop closes.

Thus, a timely determination of a low-quality image is performed directly on board the UAV in a mode close to real time, and a new image is formed to replace or in addition to the low-quality image, which, due to autofocusing, has a high quality and is further taken as a reference.

The Image Quality Detection Module may apply various suitable methods to determine the image quality. It should be noted that for the purposes of the present invention, most of the known methods are unsuitable for one reason or another (some methods require user interaction, some are not suitable for speed, some are suitable only for moving objects, some require model training, some are not universal and depend from data, etc.). The SIFT methods, the Laplace differential operator, and the scaled Laplace differential operator demonstrate the highest performance in terms of resources and time spent, combined with accuracy and universality.

In particular, the SIFT method searches and compares between neighboring images of key points found by building a pyramid of Gaussians, and then the difference of Gaussians. This method provides high image quality (by maximizing the number of high-quality key points, which is advantageous precisely in aerial photography and the construction of an orthophotomap or digital terrain model) and at the same time requires relatively high computing power and processing time, which, under the conditions of a survey task and instant processing on board the UAV may not be available for some implementations.

An alternative to SIFT is the Laplace differential operator. Since it is a sliding window highlighting changes in the intensity of each pixel, the speed of this method is much higher than the search for key points using SIFT, which makes this method suitable in case of limited computing power, since the metrics of these methods have similar values after normalization.

An intermediate option is the scaled Laplace differential operator, which is based on the Laplace differential operator, but for the final estimate, a scaling function is calculated that reduces the original image by a factor of four (two on each of the axes). Since this method calculates the ratio of metrics on two images, one of which is obviously lower in quality, the described metric returns values in the range from 0 to 1, which allows using it without taking into account previous data and obtaining an independent metric. However, it is important to note that this method takes longer than the usual Laplace differential operator, since it calculates the blur value for twice as many images. However, the execution time is still significantly faster than the keypoint calculation using the SIFT method.

Because image quality may degrade as the image is taken, the present invention regularly recalibrates even if the image quality determination module considers that a number of consecutive images are good. This avoids gradual deterioration in quality and maintains high image performance throughout the mission. The new threshold value can be calculated, for example, using the formula threshold=Q(i)*tolerance, where Q(i) is the calculated quality metric of the last received image, and the tolerance is a value that takes into account errors in the measurement of the metric (set during initialization, for example, by may be 0.8 by default).

The choice of quality determination method should be based on the computing power of the final UAV, and the final decision is made depending on the requirements of a particular application.

On-board image processing eliminates the loss of imagery data during flight without the need for operator intervention during flight. Unlike typical methods, when the operator sets fixed shooting parameters before the flight, the proposed method guarantees high-quality images, which prevents or at least minimizes re-shooting.

Additional image acquisition can be performed within the same flight without deviating from the route set in the flight mission, immediately after the decision to recalibrate is made and the camera has autofocused. If the interval between frames specified in the flight mission is large enough, and the speed of image quality determination is high enough, then in this case the overlapped loss between images will be small and acceptable for orthomosaic construction without the need to stop the UAV.

An additional image can be taken within the same flight after the UAV returns to the point corresponding to the point where the image was taken, which was determined to be of poor quality. Such an approach may require the suspension of the planned survey for the flight mission and the return of the UAV to the previous point, which may be required when the loss that has occurred between the images is already relatively large and cannot be painlessly replenished with a new image without returning. However, it is still a much faster and more accurate way to generate a set of high quality images compared to traditional methods where on-board image quality detection and poor quality image recapture are not provided.

The return of the UAV to the point of capture of an image that was determined to be poor quality can be performed based on the determination that the distance traveled or the elapsed time since the capture of this image does not allow sufficient overlap of the additional image with the last high-quality image preceding the image that was determined to be poor quality. , on the route specified in the flight mission. This approach increases automation and allows the use of relatively more time-consuming methods for determining image quality, but with higher performance. In some implementations, this choice may be preferable (for example, when the computing power of the UAV is high, and the expected shooting conditions suggest that the proportion of low-quality images will be small, and you will not often have to go back for reshoots).

The moment the sub-image is taken may reset the countdown of the interval between shots. Since the sub-image is high quality, it may not be necessary to shoot close in time with the interval counting from the previous main image determined to be poor quality, as this would result in excessive overlap between the sub-image and the following main image. This reduces the number of frames required and increases the speed of aerial photography, while still guaranteeing improved image quality.

An orthomosaic can be formed from a set of terrain images acquired by the aforementioned UAV aerial photography method. Namely, orthorectification of images, calculation of cutlines, and stitching of orthorectified images by cutlines can be performed to thereby form an orthomosaic. EFFECT: improved orthomosaic accuracy, acceleration and reduction of resource costs for the formation of an orthomosaic of the required quality.

Example

To test the present invention, a DJI Mavic quadcopter was used, on which a Sony A6000 (16mm) camera was installed through dampers. Raspberry Pi 4 8Gb single board computer with Ubuntu 20.04 / ROS Melodic OS was used as an onboard computer. A single flight mission was created with the automatic shooting mode after a given distance from the next. characteristics:

- survey area 0.09 sq km;

- flight altitude 100 meters;

- flight speed 10 m/s;

- Percentage of overlapping vertically and horizontally 70%;

- Estimated spatial resolution of 2.4 cm/pixel (taking into account the characteristics of the camera);

- Estimated distance between images 30m;

- Estimated interval between shots 3 sec;

- number of photos 150.

The flight mission drawing is shown in Fig. 3. It should be noted that during testing the wind was 5 m/s with gusts up to 7 m/s. The results obtained were processed in the program for creating an orthomosaic, the data were stitched at the same quality settings and the degree of data filtration. The results of the quality of the resulting orthomosaic are presented in Table 1.

Table 1

Quality indicators Typical method Proposed method number of shots per mission (actual/calculated) 150/150 160/150 counted shots 137 150 number of anchor points 17564 25975 root mean square error in plan (m) 0.975 0.614 spatial resolution 0.024556 0.016228

The table shows that in both flights the drone took all the photos in the given coordinates along the route. However, when flying according to the standard method, in the process of image processing, the software considered 13 out of 150 images unsuitable, which is 8.6% of the total flight mission. The losses were mainly associated with blurring due to sudden gusts of wind, while the constant wind was compensated by the UAV control system along the entire route and did not introduce significant distortions into the imagery. Also, some of the received images had small defects in the form of insignificant blurring or overexposure, but were suitable for processing by the software.

The proposed method made it possible to identify 10 blurry photographs during the flight and retake them with a minimum delay. In the end, this resulted in a slight loss of overlap in the shooting areas, but did not greatly affect the final result.

From the data obtained, it was possible to collect a dense cloud of points. Each point has a confidence factor, which is displayed in a different color: green - excellent, blue - good, orange - satisfactory, red - unsatisfactory. The results of the same area captured by different methods are presented in Fig. 4A-4D. In particular, in FIG. 4A shows a photogrammetric model constructed in a typical manner, FIG. 4B shows a point cloud confidence map obtained in a typical way, FIG. 4C shows a photogrammetric model constructed by the proposed method, FIG. 4D shows the confidence map of the point cloud obtained by the proposed method.

As can be seen from the obtained images, the proposed method made it possible to significantly reduce the number of artifacts on the constructed model, as well as to eliminate unnecessary artifacts due to the absence of blurry images, while it was not necessary to perform a new flight to take additional images of low-quality areas.

Application

Devices and methods according to the present invention can be used to autonomously perform aerial photography using a UAV and based on this aerial photography, which can be used in autonomous monitoring for the tasks of protecting territories, farmland, in geographic information systems, in cartography, etc.

Additional Implementation Features

Various illustrative blocks and modules described in connection with the disclosure herein may be implemented or executed by a general purpose processor, digital signal processor (DSP), application specific integrated circuit (ASIC), field programmable gate array (FPGA), or other programmable logic device (PLD), discrete logic element or transistor logic, discrete hardware components, or any combination of the foregoing, designed to perform the functions described in this document. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices (eg, a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors together with a DSP core, or any other similar configuration).

Some blocks or modules, individually or collectively, may be, for example, a computer, and include a processor that is configured to call and execute computer programs from memory to perform method steps or functions of blocks or modules in accordance with embodiments of the present invention. According to embodiments, the device may further include a memory. The processor may call and execute computer programs from memory to execute the method. The memory may be a separate device independent of the processor or may be integrated with the processor. The memory may store code, instructions, commands, and/or data for execution on a set of one or more processors of the device described. Codes, instructions, commands may direct the processor to perform the steps of a method or device function.

The functions described in this document may be implemented in hardware, software running on one or more processors, firmware, or any combination of the foregoing, as applicable. The hardware and software implementing the functions may also be physically located in different locations, including such a distribution that parts of the functions are implemented in different physical locations, that is, distributed processing or distributed computing may be performed.

If the amount of data is large, multi-threaded data processing can be performed, which in a simple representation can be expressed in the fact that the entire set of data to be processed is divided into a set of subsets, and each processor core performs processing in relation to its assigned data subset.

The above memory may be volatile or non-volatile memory, or may include both volatile and non-volatile memory. One of ordinary skill in the art will also understand that when referring to memory and storage of data, programs, codes, instructions, commands, and the like, it is understood that there is a machine-readable (or computer-readable, processor-readable) storage medium. A computer-readable storage medium can be any available medium that can be used to carry or store a desired piece of program code in the form of instructions or data structures, and that can be accessed by a computer, processor, or other general purpose or special purpose processing device. .

By way of example, and not limitation, computer-readable media may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electronically erasable programmable read-only memory (EEPROM), flash memory. , Random Access Memory (RAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDR SDRAM) , increased speed synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct access bus random access memory (DR RAM), and the like.

The information and signals described herein may be represented using any of a variety of different technologies. For example, the data, instructions, commands, information, signals, bits, symbols, and elementary signals that may be exemplified in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combinations of the above.

The processor may include one or more processors. At the same time, one or more processors may be a general purpose processor such as a central processing unit (CPU), an application processor (AP) and the like, a graphics processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU) etc.

It should be understood that although terms such as "first", "second", "third" and the like may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, areas, layers and/or sections should not be limited by these terms. These terms are only used to distinguish one element, component, region, layer, or section from another element, component, region, layer, or section. Thus, a first element, component, region, layer, or section may be referred to as a second element, component, region, layer, or section without departing from the scope of the present invention. As used herein, the term "and/or" includes any and all combinations of one or more of the respective listed positions. Elements mentioned in the singular do not exclude the plurality of elements, unless otherwise specified.

The functionality of an element specified in the description or claims as a single element may be practiced by means of several components of the device, and conversely, the functionality of elements indicated in the description or claims as several separate elements may be practiced by means of a single component.

In one embodiment, the elements/blocks/modules of the proposed device are in a common housing, can be placed on the same frame/structure/printed circuit board/crystal and are structurally connected to each other through assembly (assembly) operations and functionally through communication lines. The mentioned communication lines or channels, unless otherwise indicated, are typical communication lines known to specialists, the material implementation of which does not require creative efforts. The communication link may be a wire, a set of wires, a bus, a track, a wireless link (inductive, RF, infrared, ultrasonic, etc.). Communication protocols over communication lines are known to those skilled in the art and are not disclosed separately.

The functional connection of elements should be understood as a connection that ensures the correct interaction of these elements with each other and the implementation of one or another functionality of the elements. Particular examples of functional communication may be communication with the ability to exchange information, communication with the ability to transmit electric current, communication with the ability to transmit mechanical motion, communication with the ability to transmit light, sound, electromagnetic or mechanical vibrations, etc. The specific type of functional connection is determined by the nature of the interaction of the mentioned elements, and, unless otherwise indicated, is provided by well-known means, using principles well-known in the art.

Although exemplary embodiments have been described in detail and shown in the accompanying drawings, it should be understood that such embodiments are illustrative only and are not intended to limit the present invention, and that the present invention should not be limited to the particular arrangements and structures shown and described, since a person skilled in the art on the basis of the information set forth in the description and the knowledge of the prior art may be obvious various other modifications and embodiments of the invention without going beyond the essence and scope of this invention.

Claims (23)

1. Unmanned aerial vehicle (UAV), containing: a flight controller configured to control the flight of the UAV; a camera configured to capture images of the area under the UAV; And an on-board computer connected to the flight controller and to the camera and containing: a camera control module, configured to control a camera to acquire images of the area in the process of performing aerial photography; And a picture quality determination module, configured to determine the quality of images received from the camera; wherein the on-board computer is configured to receive an image from the camera according to the interval specified in the flight mission, and decide on the need for re-calibration if it is determined by the on-board computer that the number of images received from the camera since the last calibration has reached a predetermined number , or if it is determined by the image quality detection module that the metric value of the current image is less than the quality metric threshold; however, to perform a recalibration: the camera control module is configured to initiate camera autofocus, the on-board computer is configured to receive an additional image from the camera after autofocusing, the image quality determination module is configured to determine a new quality metric reference value according to the additional image and update the quality metric threshold based on the new quality metric reference value. 2. A method for performing aerial photography to be performed in a UAV and containing the steps in which: receiving an image from the camera according to the interval specified in the flight mission; And if it is determined by the on-board computer that the number of images received from the camera since the last calibration has reached a predetermined number, or if it is determined by the image quality determination module that the current image metric value is less than the quality metric threshold, a decision is made by the on-board computer the need for recalibration; while performing a recalibration comprises the steps of: initiate camera autofocus using the camera control module, receive an additional image from the camera after autofocusing, determine a new reference value of the quality metric using the image quality determination module according to the additional image, updating the quality metric threshold based on the new quality metric reference value. 3. The method according to claim 2, in which an additional image is obtained as a result of shooting performed within the same flight without deviating from the route specified in the flight mission, immediately after the decision to recalibrate is made and the camera is autofocused. 4. The method according to claim 2, in which the additional image is obtained as a result of shooting performed within the same flight after the UAV returns to the point corresponding to the shooting point of the image, which was determined to be of poor quality. 5. The method according to claim 4, wherein the return of the UAV to the shooting point of the image, which was determined to be of poor quality, is performed on the basis of determining that the distance traveled or the elapsed time since the capture of this image does not allow sufficient overlap of the additional image with the last high-quality image , preceding the image that was determined to be poor quality, on the route specified in the flight mission. 6. The method according to claim 2, wherein the moment of capturing the additional image resets the interval count.

Images