CN115657706B - Landform measurement method and system based on unmanned aerial vehicle - Google Patents
Landform measurement method and system based on unmanned aerial vehicle Download PDFInfo
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Abstract
The invention relates to the technical field of unmanned aerial vehicle mapping, and particularly discloses a geomorphic measurement method and system based on unmanned aerial vehicles, wherein the system comprises a region planning module, a route planning module, an image analysis module, a data processing module and a plurality of unmanned aerial vehicles; the area planning module is used for uniformly dividing the geomorphic measurement operation area into a plurality of grids; the route planning module is used for determining the number of unmanned aerial vehicles according to the number of grids, and planning and measuring routes according to the number of unmanned aerial vehicles; the unmanned aerial vehicle is used for flying according to a planned measurement route and shooting aerial images of an operation area; the image analysis module is also used for acquiring aerial images from the communication module, carrying out preliminary screening, judging whether the imaging quality meets the requirement, and if not, marking the corresponding grids; the data processing module is used for acquiring POS data from the communication module, performing aerial triangle calculation according to the aerial image, the POS data and coordinates of the image control points, and generating a digital orthographic image. By adopting the technical scheme of the invention, the measurement efficiency can be improved.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle mapping, in particular to a landform measurement method and system based on an unmanned aerial vehicle.
Background
The existing landform measurement mainly uses total station, RTK and other measurement equipment to measure elevation points on the ground, and uses related geographic information software to generate contour lines by measuring elevation points with certain density, but has low manual measurement efficiency, and some mountain and dense forest manual actual measurement is difficult.
With the development of unmanned aerial vehicle technology, unmanned aerial vehicle carries out low altitude photogrammetry and has the characteristics of high flexibility, high resolution of images, high integration of systems, low image acquisition cost and the like, and the low altitude photogrammetry is lower due to the fact that the photogrammetry is lower in height, and the shielding effect caused by cloud and fog is smaller, so that the unmanned aerial vehicle is widely applied to landform measurement in recent years.
However, the current unmanned aerial vehicle measurement depends on the operation of staff, has higher requirement on the operation capability of the staff, is easy to have the problems of missing or incomplete coverage of a shooting area caused by yaw, and needs secondary measurement; when the area of the area to be measured is large, the flying time is long, the working strength is high, and the required time is also large.
Therefore, there is a need for an unmanned aerial vehicle-based geomorphic measurement method and system that can improve measurement efficiency.
Disclosure of Invention
One of the purposes of the invention is to provide a geomorphic measurement system based on an unmanned aerial vehicle, which can improve measurement efficiency.
In order to solve the technical problems, the application provides the following technical scheme:
the unmanned aerial vehicle-based landform measurement system comprises a communication module, a region planning module, a route planning module, an image analysis module, a data processing module and a plurality of unmanned aerial vehicles;
the area planning module is used for determining a geomorphic measurement operation area according to the measurement task; uniformly dividing the operation area into a plurality of grids, setting image control points in the grids, receiving coordinates of the image control points and recording the coordinates;
the route planning module is used for determining the number of unmanned aerial vehicles according to the number of grids, planning a measurement route according to the number of unmanned aerial vehicles, and enabling the unmanned aerial vehicles to fly to cover all grids;
the route planning module is also used for transmitting the measurement route to the unmanned aerial vehicle through the communication module;
the unmanned aerial vehicle is used for flying according to a planned measurement route and shooting aerial images of an operation area; the camera is also used for shooting aerial images and transmitting the aerial images back to the communication module;
the image analysis module is also used for acquiring aerial images from the communication module, carrying out preliminary screening, judging whether the imaging quality meets the requirement, and if not, marking the corresponding grids;
the unmanned aerial vehicle is also used for sending POS data to the communication module;
the data processing module is used for acquiring POS data from the communication module and carrying out aerial triangle calculation according to the aerial image, the POS data and the coordinates of the image control points; and the method is also used for generating a digital orthophoto map according to the aerial triangle calculation result and the aerial image.
The basic scheme principle and the beneficial effects are as follows:
in the scheme, a working area is divided into a plurality of grids, phase control points are arranged in the grids, and then a plurality of unmanned aerial vehicles are adopted for aerial photography, so that the unmanned aerial vehicles fly to cover all the grids, and the aerial photographic images are ensured not to be missed; compared with measurement by using a single unmanned aerial vehicle, the efficiency of measurement can be effectively improved. And a measurement route is automatically generated, and the unmanned aerial vehicle automatically flies according to the planned measurement route, so that the workload of personnel is reduced. Preliminary screening is carried out on aerial images, grids with imaging quality not meeting requirements are marked, and follow-up supplementary shooting is facilitated. Compared with the situation that after the whole aerial photography is finished and returned, the aerial photography image is found to have a problem, the time for measuring the working area in the reciprocating landform can be saved, and the measuring efficiency is further improved.
Further, the survey route includes a departure point, a mission starting point, a mission route, and a mission ending point.
Further, the last unmanned aerial vehicle is further used for sending the position and the residual electric quantity to the communication module after finishing the flight of the mission route;
the route planning module is also used for acquiring the position and the residual electric quantity of the last unmanned aerial vehicle from the communication module, and planning a retest route according to the position of the last unmanned aerial vehicle and the marked grids so that the retest route covers all the marked grids; judging whether the residual electric quantity of the last unmanned aerial vehicle can finish the flight of the retest route, and if so, sending the retest route to the last unmanned aerial vehicle; if the mission course can not be completed, the retest course is regenerated and sent to the unmanned aerial vehicle which firstly completes the mission course flight and returns to the departure point.
If the last unmanned aerial vehicle finishes the flight of the mission route, the residual electric quantity is sufficient, the grid with the imaging quality not meeting the requirement can be directly retested, and the whole measurement time can be shortened. When the residual electric quantity of the last unmanned aerial vehicle is insufficient, retest is carried out by the unmanned aerial vehicle which firstly completes the mission route to fly and returns to the departure point, and a certain time can be saved.
The second object of the present invention is to provide a geomorphic measurement method based on an unmanned aerial vehicle, comprising the steps of:
s1, determining a geomorphic measurement operation area according to a measurement task; uniformly dividing the operation area into a plurality of grids;
s2, setting image control points in the grid, measuring coordinates of the image control points and recording the coordinates;
s3, determining the number of unmanned aerial vehicles according to the number of grids, and planning a measurement route according to the number of unmanned aerial vehicles so that the unmanned aerial vehicles fly to cover all grids;
s4, enabling the unmanned aerial vehicle to fly according to a mission route, and shooting aerial images of the operation area through a camera carried by the unmanned aerial vehicle;
s5, receiving aerial images of the unmanned aerial vehicle in real time, performing primary screening, judging whether the imaging quality meets the requirement, and if not, marking a corresponding grid;
s7, acquiring POS data of the unmanned aerial vehicle after the unmanned aerial vehicle finishes the flight of all the measurement routes, and performing aerial triangle calculation according to the aerial image, the POS data and coordinates of the image control points;
and S8, generating a digital orthophoto map according to the aerial triangle calculation result and the aerial image.
In the scheme, a working area is divided into a plurality of grids, phase control points are arranged in the grids, and then a plurality of unmanned aerial vehicles are adopted for aerial photography, so that the unmanned aerial vehicles fly to cover all the grids, and the aerial photographic images are ensured not to be missed; compared with measurement by using a single unmanned aerial vehicle, the efficiency of measurement can be effectively improved. And a measurement route is automatically generated, and the unmanned aerial vehicle automatically flies according to the planned measurement route, so that the workload of personnel is reduced. Preliminary screening is carried out on aerial images, grids with imaging quality not meeting requirements are marked, and follow-up supplementary shooting is facilitated. Compared with the situation that after the whole aerial photography is finished and returned, the aerial photography image is found to have a problem, the time for measuring the working area in the reciprocating landform can be saved, and the measuring efficiency is further improved.
Further, in step S4, the unmanned aerial vehicle flying according to the mission route specifically includes: the unmanned aerial vehicle takes off at the take-off point, flies to the task starting point, flies according to the task route, and returns to the take-off point to land after reaching the task ending point.
Further, in step S5, the battery of the unmanned aerial vehicle that completes the mission route to fly and returns to the departure point is replaced, and after the last unmanned aerial vehicle completes the flight of the mission route, the retest route is planned according to the position of the last unmanned aerial vehicle and the marked grid, so that the retest route covers all the marked grids; judging whether the residual electric quantity of the last unmanned aerial vehicle can complete the flight of the retest route, if so, sending the retest route to the last unmanned aerial vehicle, and if not, regenerating the retest route and sending the retest route to the unmanned aerial vehicle which firstly completes the flight of the mission route.
If the last unmanned aerial vehicle finishes the flight of the mission route, the residual electric quantity is sufficient, the grid with the imaging quality not meeting the requirement can be directly retested, and the whole measurement time can be shortened. When the residual electric quantity of the last unmanned aerial vehicle is insufficient, retest is carried out by the unmanned aerial vehicle which firstly completes the mission route to fly and returns to the departure point, and a certain time can be saved.
Further, in the step S1, the area of the divided grid is smaller than the area of the coverage area of the Yu Shanzhang aerial image.
The grids on the aerial image are partially overlapped, so that the full coverage of the geomorphic measurement operation area is ensured.
Further, in the step S3, an upper limit of the number of unmanned aerial vehicles is set, and when the calculated number of unmanned aerial vehicles is greater than the upper limit of the number of unmanned aerial vehicles, the upper limit of the number of unmanned aerial vehicles is used as the number of task unmanned aerial vehicles for practical use;
when planning and measuring the route, the unmanned aerial vehicle quantity that obtains of calculation is greater than unmanned aerial vehicle's quantity upper limit, makes unmanned aerial vehicle pass through many times flight in order to cover all grids.
The situation that the geomorphic measurement operation area is too large and the carried unmanned aerial vehicle is insufficient to cover all grids is avoided.
Drawings
FIG. 1 is a flowchart of a geomorphic measurement method based on an unmanned aerial vehicle;
FIG. 2 is a schematic diagram of a survey route in accordance with a first embodiment;
fig. 3 is a schematic diagram of planning a measurement route by a group of two unmanned aerial vehicles in the second embodiment;
fig. 4 is a schematic diagram of a measurement route of the same group of unmanned aerial vehicles in the second embodiment photographing the marked grid.
Detailed Description
The following is a further detailed description of the embodiments:
example 1
The unmanned aerial vehicle-based landform measurement system comprises a communication module, a region planning module, a route planning module, an image analysis module, a data processing module and a plurality of unmanned aerial vehicles and controllers corresponding to the unmanned aerial vehicles; wherein the number of unmanned aerial vehicles is not less than 2.
Unmanned aerial vehicle includes unmanned aerial vehicle body and camera that unmanned aerial vehicle body carried.
The area planning module is used for determining a geomorphic measurement operation area according to the measurement task; uniformly dividing the operation area into a plurality of grids, and numbering the grids; the system is also used for setting image control points in the grid, receiving coordinates of the image control points and recording the coordinates;
the route planning module is used for determining the number of unmanned aerial vehicles according to the number of grids, planning and measuring routes according to the number of unmanned aerial vehicles, and enabling the unmanned aerial vehicles to fly to cover all grids.
The route planning module is also used for sending the measurement route to the controller through the communication module, and the controller sends the measurement route to the unmanned aerial vehicle body.
The unmanned aerial vehicle body flies according to the planned measurement route, and aerial images of the operation area are shot through the camera carried by the unmanned aerial vehicle.
The unmanned aerial vehicle body is also used for transmitting the aerial image shot by the camera back to the controller, and the aerial image is sent to the communication module by the controller;
the image analysis module is also used for acquiring aerial images from the communication module, carrying out preliminary screening, judging whether the imaging quality meets the requirement, and if not, marking the corresponding grids.
The measuring route comprises a departure point, a task starting point, a task route and a task ending point; wherein the flight of the mission route covers the gateway.
The last unmanned aerial vehicle is further used for sending the position and the residual electric quantity to the controller after finishing the flight of the mission route, and the controller sends the position and the residual electric quantity to the communication module;
the route planning module is also used for acquiring the position and the residual electric quantity of the last unmanned aerial vehicle from the communication module, and planning a retest route according to the position of the last unmanned aerial vehicle and the marked grids so that the retest route covers all the marked grids; judging whether the residual electric quantity of the last unmanned aerial vehicle can complete the flight of the retest route, if so, sending the retest route to the last unmanned aerial vehicle, and if not, regenerating the retest route and sending the retest route to the unmanned aerial vehicle which firstly completes the flight of the mission route and returns to the departure point.
The unmanned aerial vehicle body is also used for sending POS data to the communication module through the controller;
the data processing module is also used for acquiring POS data from the communication module and carrying out aerial triangle calculation according to the aerial image, the POS data and the coordinates of the image control points; and the method is also used for generating DOM according to the aerial triangle calculation result and the aerial image.
As shown in fig. 1, the unmanned aerial vehicle-based landform measurement method of the embodiment includes the following steps:
s1, determining a geomorphic measurement operation area according to a measurement task; uniformly dividing the operation area into a plurality of grids, and numbering the grids; specific:
calculating the flight height and the coverage area of a single Zhang Hang shot image according to the ground resolution P (in meters) required by a task, the sensor resolution (a) and the lens field angle theta of a camera mounted on the unmanned aerial vehicle;
in this embodiment, the fly height H (in meters) is calculated by the following formula:
the side length (X Y) of the coverage area of Shan Zhanghang shots was also calculated by the following formula:
X＝a*P
Y＝b*P
the size of the grid is determined from the coverage area of the Shan Zhanghang image. In this embodiment, the grid area is smaller than the coverage area of the Yu Shanzhang aerial image.
S2, setting image control points in the grid, measuring coordinates of the image control points through an RTK measuring instrument and recording the coordinates; the image control points can be set in all grids, and the phase control points can be set by selecting a plurality of grids.
S3, determining the number of unmanned aerial vehicles according to the number of the grids, planning a measurement route according to the number of the unmanned aerial vehicles, and enabling the unmanned aerial vehicles to fly to cover all the grids. Specifically, the total flight mileage is calculated according to the number of grids, and then the number of unmanned aerial vehicles is calculated according to the total flight mileage and the single flight mileage of a single unmanned aerial vehicle.
The single flight mileage of the unmanned aerial vehicle is obtained through historical measurement tasks. Specifically, the flight mileage of the unmanned aerial vehicle when the electric quantity is 20% is recorded, and then the average value of all recorded data is calculated to obtain the estimated single flight mileage. In order to ensure the accuracy of estimating the single flight mileage, the altitude, wind power, flight speed and load should be ensured to be in the same interval.
In this embodiment, the upper limit of the number of unmanned aerial vehicles is also set, and when the calculated number of unmanned aerial vehicles is greater than the upper limit of the number of unmanned aerial vehicles, the upper limit of the number of unmanned aerial vehicles is used as the number of task unmanned aerial vehicles in actual use. The upper limit of the number of unmanned aerial vehicles is determined according to unmanned aerial vehicles used by the measuring task, and the upper limit of the number is larger than or equal to 2.
When planning a measurement route, if the number of unmanned aerial vehicles on the task does not meet the condition of completing the total flight mileage once, namely, the calculated number of unmanned aerial vehicles is larger than the upper limit of the number of unmanned aerial vehicles, the unmanned aerial vehicles fly for multiple times to cover all grids; and if the number of the unmanned aerial vehicles meets the condition of completing the total flight mileage at a time, enabling the unmanned aerial vehicles to cover all grids through single flight.
As shown in fig. 2, specifically, first, a flying spot is determined at the edge of the work area; and then, distributing an covered grid for each unmanned aerial vehicle, and determining a task starting point, a task route and a task ending point. The flying spot can be comprehensively selected according to factors such as whether personnel conveniently arrive, whether the ground is level or not and the like. The task starting point refers to the position before the unmanned aerial vehicle enters the grid, and the task ending point refers to the position after the unmanned aerial vehicle leaves the grid.
S4, enabling the unmanned aerial vehicle to take off at a take-off point, then flying to a task starting point, flying according to a task route, and returning to the take-off point to land after reaching a task ending point. According to the flight of the mission route, shooting aerial images of the operation area through a camera carried by the unmanned aerial vehicle;
s5, receiving aerial images of the unmanned aerial vehicle in real time, performing primary screening, judging whether the imaging quality meets the requirement, and if not, marking a corresponding grid; in this embodiment, the meeting of imaging quality requirements means that the image is clear and there is no large area reflection.
Changing a battery of the unmanned aerial vehicle which firstly completes the flight of the mission route and returns to the departure point, and planning a retest route according to the position of the last unmanned aerial vehicle and the marked grid after the last unmanned aerial vehicle completes the flight of the mission route, so that the retest route covers all the marked grids; judging whether the residual electric quantity of the last unmanned aerial vehicle can complete the flight of the retest route, if so, sending the retest route to the last unmanned aerial vehicle, and if not, regenerating the retest route and sending the retest route to the unmanned aerial vehicle which firstly completes the flight of the mission route.
S6, after the unmanned aerial vehicle finishes the flight of all the measuring routes and the retesting routes, POS data of the unmanned aerial vehicle are obtained, wherein the POS data comprise GPS data and IMU data, and the IMU data comprise heading angles, pitch angles and rolling angles. Performing aerial triangle calculation according to the aerial image, POS data and coordinates of the image control points;
s7, generating DOM (digital orthophoto map) according to the aerial triangle calculation result and the aerial image.
Example two
The difference between the present embodiment and the first embodiment is that, in the method of the present embodiment, in step S1, the topography of the marked grid is further divided according to the pre-stored satellite map of the working area, for example, the topography includes forests, grasslands, water surfaces, artificial facilities, etc.;
in the step S2, the current meteorological conditions are recorded, wherein the meteorological conditions comprise wind speed, cloud layer type, cloud layer height, illumination intensity and air temperature;
in step S3, calculating the probability that the imaging quality of each grid does not meet the requirement according to the current meteorological conditions and the existing topographic imaging probability database, counting the number of grids with the probability of not meeting the requirement higher than 70%, judging whether the number is larger than a first threshold, and generating alarm information if the number is larger than the first threshold; in this embodiment, the alarm information is that the current meteorological condition is not suitable for geomorphic measurement. In this embodiment, the first threshold is 20% of the current grid number. The second threshold is 10% of the current grid number.
If greater than the second threshold and less than the first threshold; step S3 of the first embodiment is continued, in principle, to split the working area in whole, so that each of the robots covers the grid of one of the working areas.
If the number is smaller than the second threshold, planning a measurement route, and when determining grids which each unmanned aerial vehicle needs to cover, setting the grid interval which each unmanned aerial vehicle needs to cover, and dividing two unmanned aerial vehicles which cover adjacent grids into the same group according to a mode of one group of two unmanned aerial vehicles. As shown in fig. 3, for example, the number 1 unmanned aerial vehicle and the number 2 unmanned aerial vehicle are one group, and the number 3 unmanned aerial vehicle and the number 4 unmanned aerial vehicle are one group.
In step S5, the terrain imaging probability database is updated according to the imaging quality of the aerial image, specifically, the grids of the same terrain of the same unmanned aerial vehicle under the same meteorological conditions are counted, the imaging quality meets the requirement and the quantity of the grids which do not meet the requirement are calculated, and the probability that the imaging quality does not meet the requirement of the grids of each terrain under the same meteorological conditions is calculated. In this embodiment, the same meteorological conditions refer to that the wind speed, the cloud layer type, the cloud layer height, the illumination intensity and the air temperature are in the same interval. Each section needs to be set according to actual conditions, for example, the wind speed section needs to refer to the wind resistance of the unmanned aerial vehicle.
If the grid number which does not meet the requirement probability and is higher than 70% is larger than the second threshold value and smaller than the first threshold value, planning a retest route according to the step S5 of the first embodiment;
if the number of grids which do not meet the requirement and have the probability higher than 70% is smaller than a second threshold, the marking grids obtained by the unmanned aerial vehicles in the same group are sent to another unmanned aerial vehicle in the same group, when the other unmanned aerial vehicle in the same group flies to the adjacent grids of the marking grids, the other unmanned aerial vehicle in the same group flies to the marking grids from the adjacent grids to shoot, then the adjacent grids are returned, or the unmanned aerial vehicle in the same group flies to the next grid along the mission route of the unmanned aerial vehicle in the same group and shoots, and then the unmanned aerial vehicle in the same group returns to the grids of the mission route of the unmanned aerial vehicle in the same group. As shown in fig. 4, for example, the I4 grid shot by the No. 2 unmanned aerial vehicle is marked, that is, the imaging quality of the I,4 grid aerial image shot by the No. 2 unmanned aerial vehicle does not meet the requirement, the No. 1 unmanned aerial vehicle flies from the I3 grid to the I4 grid on the basis of the original mission route, continues to fly along the mission route of the No. 2 unmanned aerial vehicle to the J4 grid for shooting, and returns to the J3 to continue the flight of the original mission route.
When the grid number which does not meet the requirement and the probability is higher than 70% is smaller than a second threshold, the aerial image with the imaging quality which does not meet the requirement is found, and the unmanned aerial vehicles in the same group are subjected to the supplementary shooting in time, so that the measurement time can be effectively saved.
The foregoing is merely an embodiment of the present invention, the present invention is not limited to the field of this embodiment, and the specific structures and features well known in the schemes are not described in any way herein, so that those skilled in the art will know all the prior art in the field before the application date or priority date, and will have the capability of applying the conventional experimental means before the date, and those skilled in the art may, in light of the teaching of this application, complete and implement this scheme in combination with their own capabilities, and some typical known structures or known methods should not be an obstacle for those skilled in the art to practice this application. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present invention, and these should also be considered as the scope of the present invention, which does not affect the effect of the implementation of the present invention and the utility of the patent. The protection scope of the present application shall be subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (3)
1. The unmanned aerial vehicle-based landform measurement method uses a unmanned aerial vehicle-based landform measurement system, wherein the system comprises a communication module, a region planning module, a route planning module, an image analysis module, a data processing module and a plurality of unmanned aerial vehicles;
the area planning module is used for determining a geomorphic measurement operation area according to the measurement task; uniformly dividing the operation area into a plurality of grids, setting image control points in the grids, receiving coordinates of the image control points and recording the coordinates;
the route planning module is used for determining the number of unmanned aerial vehicles according to the number of grids, planning a measurement route according to the number of unmanned aerial vehicles, and enabling the unmanned aerial vehicles to fly to cover all grids;
the route planning module is also used for transmitting the measurement route to the unmanned aerial vehicle through the communication module;
the unmanned aerial vehicle is used for flying according to a planned measurement route and shooting aerial images of an operation area; the camera is also used for shooting aerial images and transmitting the aerial images back to the communication module;
the image analysis module is also used for acquiring aerial images from the communication module, carrying out preliminary screening, judging whether the imaging quality meets the requirement, and if not, marking the corresponding grids;
the unmanned aerial vehicle is also used for sending POS data to the communication module;
the data processing module is used for acquiring POS data from the communication module and carrying out aerial triangle calculation according to the aerial image, the POS data and the coordinates of the image control points; the method is also used for generating a digital orthophoto map according to the aerial triangle calculation result and the aerial image;
the measuring route comprises a departure point, a task starting point, a task route and a task ending point;
the last unmanned aerial vehicle is further used for sending the position and the residual electric quantity to the communication module after finishing the flight of the mission route;
the route planning module is also used for acquiring the position and the residual electric quantity of the last unmanned aerial vehicle from the communication module, and planning a retest route according to the position of the last unmanned aerial vehicle and the marked grids so that the retest route covers all the marked grids; judging whether the residual electric quantity of the last unmanned aerial vehicle can finish the flight of the retest route, and if so, sending the retest route to the last unmanned aerial vehicle; if the operation can not be completed, regenerating a retest route and sending the retest route to the unmanned aerial vehicle which firstly completes the mission route to fly and returns to the departure point, and the method is characterized by further comprising the following steps:
s1, determining a geomorphic measurement operation area according to a measurement task; uniformly dividing the operation area into a plurality of grids; dividing the topography of the marked grid according to a pre-stored satellite map of the operation area;
s2, setting image control points in the grid, measuring coordinates of the image control points and recording the coordinates; recording current meteorological conditions, wherein the meteorological conditions comprise wind speed, cloud layer type, cloud layer height, illumination intensity and air temperature;
s3, calculating the probability that the imaging quality of each grid does not meet the requirement according to the current meteorological conditions and the existing topographic imaging probability database, counting the number of grids with the probability of not meeting the requirement higher than 70%, judging whether the number is larger than a first threshold, and generating alarm information if the number is larger than the first threshold;
if greater than the second threshold and less than the first threshold; determining the number of unmanned aerial vehicles according to the number of grids, and planning a measurement route according to the number of unmanned aerial vehicles so that the unmanned aerial vehicles fly to cover all grids;
if the number is smaller than the second threshold, planning a measurement route, and when determining grids which each unmanned aerial vehicle needs to cover, arranging the grids which each unmanned aerial vehicle needs to cover at intervals, and dividing two unmanned aerial vehicles which cover adjacent grids into the same group according to a mode of one group of two unmanned aerial vehicles;
s4, enabling the unmanned aerial vehicle to fly according to a mission route, and shooting aerial images of the operation area through a camera carried by the unmanned aerial vehicle; the unmanned aerial vehicle specifically comprises the following steps of: the unmanned aerial vehicle takes off at a take-off point, flies to a task starting point, flies according to a task route, and returns to the take-off point to land after reaching a task ending point;
s5, receiving aerial images of the unmanned aerial vehicle in real time, performing primary screening, judging whether the imaging quality meets the requirement, and if not, marking a corresponding grid; updating a terrain imaging probability database according to the imaging quality condition of the aerial image; the battery of the unmanned aerial vehicle which finishes the mission route flight at first and returns to the departure point is replaced;
if the grid number which does not meet the requirement and has the probability higher than 70% is larger than a second threshold value and smaller than a first threshold value, planning a retest route according to the position of the last unmanned aerial vehicle and the marked grids after the last unmanned aerial vehicle finishes the flight of the mission route, so that the retest route covers all the marked grids; judging whether the residual electric quantity of the last unmanned aerial vehicle can complete the flight of the retest route, if so, sending the retest route to the last unmanned aerial vehicle, and if not, regenerating the retest route and sending the retest route to the unmanned aerial vehicle which completes the flight of the mission route at first;
if the number of grids which do not meet the requirement and have the probability higher than 70% is smaller than a second threshold, the marking grids obtained by the unmanned aerial vehicles in the same group are sent to another unmanned aerial vehicle in the same group, when the other unmanned aerial vehicle in the same group flies to the adjacent grids of the marking grids, the other unmanned aerial vehicle in the same group flies to the marking grids from the adjacent grids to shoot, then the adjacent grids are returned, or the unmanned aerial vehicle in the same group flies to the next grid along the mission route of the unmanned aerial vehicle in the same group and shoots, and then the grids of the mission route of the unmanned aerial vehicle in the same group are returned;
and S7, acquiring POS data of the unmanned aerial vehicle after the unmanned aerial vehicle finishes the flight of all the measurement routes, and performing aerial triangle calculation according to the aerial image, the POS data and coordinates of the image control points.
2. The unmanned aerial vehicle-based geomorphic measurement method of claim 1, wherein: in the step S1, the area of the divided grid is smaller than the area of the coverage area of the Yu Shanzhang aerial image.
3. The unmanned aerial vehicle-based geomorphic measurement method of claim 2, wherein: in the step S3, an upper limit of the number of unmanned aerial vehicles is set, and when the calculated number of unmanned aerial vehicles is greater than the upper limit of the number of unmanned aerial vehicles, the upper limit of the number of unmanned aerial vehicles is used as the number of actually used task unmanned aerial vehicles;
when planning and measuring the route, the unmanned aerial vehicle quantity that obtains of calculation is greater than unmanned aerial vehicle's quantity upper limit, makes unmanned aerial vehicle pass through many times flight in order to cover all grids.
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