UAV Mapping Method Based on Topographic and Geomorphic Mapping Accuracy

Abstract

The present invention discloses a UAV mapping method based on the mapping accuracy of topography and geomorphology. The present invention relates to the technical field of topographic mapping, and solves the problem that the mapping route planning of the UAV is unreasonable due to the lack of understanding of the specified topographic area. The present invention carefully processes multiple groups of remote sensing images. First, radiometric correction is carried out, which greatly eliminates the radiation deviation caused by atmospheric absorption, scattering, and sensor characteristics. In the pixel feature extraction link, the information of the pixel value of the point position and its surrounding eight pixel points is innovatively considered comprehensively to ensure that the same feature point positions can be accurately located in multiple groups of images. A series of fine operations based on the feature point positions, including the mean processing of the connection line lengths and the stretching and scaling of the image plane, properly correct the geometric differences between different images, and finally achieve the high-precision fusion of multiple groups of images. The generated fused image can more truly and comprehensively reflect the actual situation of the landform area to be surveyed.

Description

Technical Field

[0001] The present invention relates to the technical field of topographic surveying and mapping, and specifically to an unmanned aerial vehicle (UAV) surveying and mapping method based on the accuracy of topographic and geomorphic surveying and mapping. Background Art

[0002] Topographic surveying and mapping is a crucial basic geographical information acquisition task. It is like drawing a detailed "blueprint" for the earth's surface and plays an indispensable supporting role in the development of many fields.

[0003] From a professional perspective, topographic surveying and mapping covers a rich variety of technical means and processes. First is the data collection link. Among them, satellite remote sensing technology stands out. Various high-resolution satellites are like "thousand-mile eyes" in space, continuously sending back a large amount of remote sensing images to the ground. These images not only cover a wide range and can take in large areas at a glance, but also, with the help of advanced sensors, can capture ground object information in different bands, just like putting a colorful "information coat" on the earth's surface.

[0004] When conducting specific surveying and mapping of a designated topographic area, generally, relevant operators control the UAV to make the dispatched UAV conduct associated surveying and mapping of the designated topographic area, confirm the surveying and mapping images, so as to improve the specific surveying and mapping accuracy of the designated topographic area. In the actual surveying and mapping processing process, due to the lack of understanding of the designated topographic area, the surveying and mapping route planning of the UAV will be unreasonable. Moreover, when the UAV undergoes height difference changes, if the drop is large, it will cause great problems in the surveying and mapping accuracy of the UAV during surveying and mapping. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides an unmanned aerial vehicle surveying and mapping method based on the accuracy of topographic and geomorphic surveying and mapping, which solves the problem of unreasonable surveying and mapping route planning of the UAV caused by the lack of understanding of the designated topographic area.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An unmanned aerial vehicle surveying and mapping method based on the accuracy of topographic and geomorphic surveying and mapping, including the following steps:

[0007] Step 1: Obtain remote sensing images of the geomorphic area to be surveyed. There are multiple groups of remote sensing images. Confirm the pixel features associated with different points from the multiple groups of remote sensing images. Based on the confirmed pixel features, select the same-feature points from the multiple groups of remote sensing images. Then, based on the same-feature points determined in the multiple groups of remote sensing images, fuse the multiple groups of remote sensing images to confirm the fused image belonging to this geomorphic area to be surveyed. The specific method is as follows:

[0008] S11: From the multiple groups of remote sensing images associated with the corresponding geomorphic area to be surveyed, confirm the pixel features of the corresponding points in a single group of remote sensing images: Confirm the point pixel value of the corresponding point and label it as Xi where \(i\) represents different points within the corresponding remote sensing image, and the pixel values of the pixel points around this point are calibrated as \(H\). i-q where \(q = 1, 2, \cdots, 8\);

[0009] Based on the confirmed pixel value \(X\) of the point i and the pixel value \(H\) of the surrounding pixel points i-q , adopt: \(C_z\) i-q =\((X i -H i-q ) to confirm the pixel value difference \(C_z\) between this pixel point and the surrounding pixel points i-q , and then perform ratio processing on the different pixel value differences \(C_z\) associated with different surrounding pixel points i-q to confirm the ratio sequence, and use this ratio sequence as the pixel feature of this pixel point. Sequentially confirm and record the different pixel features associated with different points in the remote sensing image;

[0010] S12. Based on the pixel features associated with the corresponding points in the corresponding remote sensing image, identify the points with the same pixel features from multiple groups of remote sensing images, mark the identified points as feature points, confirm the pixel values of these feature points in different remote sensing images, and perform mean processing on several groups of pixel values regarding this feature point to determine the mean pixel, and use the determined mean pixel as the feature pixel of this feature point;

[0011] S13. Based on several groups of feature points confirmed within a single group of remote sensing images, confirm the connection lines between the corresponding adjacent feature points, and record the line length \(L\) of the connection lines. Then, confirm the same connection lines from multiple groups of remote sensing images, perform mean processing on the line lengths \(L\) of the determined several groups of connection lines to confirm the feature mean belonging to this adjacent feature point, and the feature points associated with the same connection lines are all the same;

[0012] Based on the feature mean determined between the corresponding adjacent feature points, perform planar stretching or scaling on multiple groups of remote sensing images, and stop when the line length of the connection lines associated with the corresponding adjacent feature points in each remote sensing image is consistent with the determined feature mean. Mark the remotely sensed image after planar adjustment as the image to be fused;

[0013] S14. Perform image fusion on multiple groups of images to be fused. Based on the determined feature points, align the same feature points in each image to be fused and perform image fusion to confirm the fused image. After fusion, adjust the pixel values associated with the corresponding feature points to the determined feature pixels;

[0014] Step 2. Based on the confirmed fused image, generate a mountain model for this fused image, and perform preliminary processing on the generated mountain model. Decompose the mountain model into several grid regions. The specific sub-steps are as follows:

[0015] S21. Generate a set of grid surfaces with several grids in each grid surface, and the area of each grid is the same and the area of the corresponding grid is a preset value. Then determine the bottom surface of the mountain model, which is parallel to the grid surface, and make the grid surface coincide with the bottom surface of the mountain model;

[0016] S22. Based on the determined grid surface, move the corresponding grid surface upward. During the movement, confirm the partial area of the outer surface of the mountain model that belongs to this grid surface, and label the confirmed partial area as the grid area. Therefore, the outer surface of the mountain model is decomposed into several grid areas;

[0017] Step 3. Based on several groups of grid areas confirmed on the upper surface of the mountain model, determine the grid characteristics of the grid areas. Then, based on the grid characteristics of different grid areas determined, perform an associated sorting on several groups of grid areas and select the best grid sequence. The specific sub-steps are as follows:

[0018] S31. Based on the bottom surface determined by the mountain model, confirm the relevant distance J of different points in the corresponding grid area from the bottom surface k , where k represents different points in the grid area, and perform a mean value processing on several groups of relevant distances J k confirmed for the corresponding grid area to confirm the grid characteristic TZ belonging to this grid area;

[0019] S32. Randomly select a group of grid areas from the determined several groups of edge grid areas as the starting area, and starting from the starting area, randomly select other grid areas as the subsequent grid areas and perform a random combination. After all the grid areas are combined, confirm the combination sequence, confirm the grid characteristic difference between adjacent grid areas in the combination sequence. The grid characteristic difference = |the grid characteristic of the previous group of grid areas - the grid characteristic of the next group of grid areas|, and sum up several groups of grid characteristic differences associated with this combination sequence to confirm the combination characteristic corresponding to this combination sequence;

[0020] Then confirm the combination characteristics of other combination sequences one by one;

[0021] Then randomly select other grid areas as the starting area and confirm the subsequent combination sequences, and synchronously confirm the combination characteristics associated with the corresponding several groups of combination sequences;

[0022] S33. Select the minimum value from the combination characteristics associated with several groups of combination sequences, and use the combination sequence associated with the minimum value as the best grid sequence;

[0023] Step 4: Based on the selected optimal grid sequence, identify the feature points corresponding to the grid areas within this optimal grid sequence, then connect the identified groups of feature points to confirm the surveying route associated with this optimal grid sequence, and control the drone with the determined optimal grid sequence and surveying route for on-site surveying. The specific sub-steps for determining the surveying route are as follows:

[0024] S41. Start processing step by step from the first grid area of the optimal grid sequence: Based on the confirmed grid features within the corresponding grid area and the different relevant distances J associated with different points within this grid area k , identify the center point of the corresponding grid area, calibrate it as the grid midpoint, use the grid midpoint as the center, and construct a set of radiation circles with a radius of R1, where R1 is a preset value. Mark the points located inside this radiation circle as the points to be selected, and perform a difference processing on the relevant distances of the points to be selected and the grid features of the corresponding grid area to confirm the relevant difference. The relevant difference = |J k - grid feature|. Select the minimum value from the confirmed groups of relevant differences, and mark the point to be selected associated with the minimum value as the surveying point;

[0025] S42. Confirm the surveying points for each corresponding grid area, connect the sequentially confirmed surveying points to confirm the surveying route, and control the drone based on the confirmed surveying route to enable the drone to conduct on-site surveying.

[0026] The present invention provides a drone surveying method based on the surveying accuracy of terrain and landforms. Compared with the prior art, it has the following beneficial effects:

[0027] Through meticulous processing of multiple groups (at least three groups) of remote sensing images, the present invention first performs radiometric correction, greatly eliminating the radiation deviation caused by atmospheric absorption, scattering, and sensor characteristics, restoring the true radiation situation of ground objects, and laying a foundation for subsequent accurate analysis; in the pixel feature extraction link, innovatively considering the pixel values of points and the information of the eight surrounding pixel points comprehensively, accurately defining pixel features through the difference ratio sequence, effectively overcoming the interference of different lighting conditions on image interpretation, and ensuring the accurate positioning of the same feature points in multiple groups of images; subsequent series of fine operations based on feature points, including the mean processing of the connection line length and the stretching and scaling of the image plane, properly correct the geometric differences between different images, and finally achieve the high-precision fusion of multiple groups of images. The generated fused image can more truly and comprehensively reflect the actual situation of the landform area to be surveyed;

[0028] Based on the grid area, through a unique grid feature calculation method, comprehensively considering the distance of points within the grid from the bottom surface and taking the average value as the key feature, the internal structural information of the mountain model is deeply explored; in the process of selecting the best grid sequence, a random combination and the sum of feature differences analysis are innovatively adopted to accurately screen out the best sequence with a gentle change in height difference from numerous possible combination sequences; based on the surveying and mapping route determined by this sequence, making full use of the concept of points and radiation circles within the grid, the surveying and mapping points are cleverly selected, so that when the drone conducts actual surveying and mapping, it does not need to frequently adjust the flight height greatly, but only needs to make small and precise adjustments. This not only greatly reduces the error caused by height adjustment, ensures the stability of the drone surveying and mapping accuracy, but also significantly improves the surveying and mapping efficiency, ensuring that the topographic data of the area to be surveyed can be obtained quickly and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0031] Please refer to Figure 1 , the present application provides a drone surveying and mapping method based on the surveying and mapping accuracy of terrain and landform, including the following steps:

[0032] Step 1: Obtain the remote sensing images of the area to be surveyed. There are multiple groups of remote sensing images, which are input by the operator himself. Confirm the pixel features associated with different points from the multiple groups of remote sensing images. Based on the confirmed pixel features, select the same-feature points from the multiple groups of remote sensing images. Then, based on the same-feature points determined in the multiple groups of remote sensing images, fuse the multiple groups of remote sensing images to confirm the fused image belonging to this area to be surveyed. Specifically, due to the influence of factors such as atmospheric absorption, scattering, and the characteristics of the sensor itself during the acquisition of remote sensing images, there are deviations in the radiation values of the images; perform radiometric correction on the multiple groups of remote sensing images respectively to eliminate these influences and make the brightness values of the images closer to the true radiation situation of the ground objects. Among them, the multiple groups include at least three groups. Through the specific fusion situation between the multiple groups of images, the overall performance effect of the corresponding fused image is improved;

[0033] Among them, the specific method for determining the fused image of this area to be surveyed is:

[0034] S11. From multiple sets of remote sensing images associated with the topographic area to be surveyed, confirm the pixel characteristics of the corresponding points within a single set of remote sensing images: confirm the pixel value of the corresponding point and label it as X i , where i represents different points within the corresponding remote sensing image, and label the pixel values of the pixel points around this point as H i-q , where q = 1, 2,..., 8. There are eight groups of pixel points around a group of pixel points. In the two-dimensional plane representation of a digital image, pixels are the basic units that make up the image, and they are arranged in a discrete grid form; usually, we can regard the image as a matrix composed of rows and columns, and each element (i.e., pixel) has its specific position in this matrix. Therefore, in the associated remote sensing image, the number of pixel points around a group of pixel points is 8. For the points at the edge of the image, there may not be eight surrounding points, so such points do not need to have their pixel characteristics confirmed. Because the surrounding points are insufficient, and in the remote sensing image, such points are located in the edge area, generally not belonging to the relevant characteristics of the mountain body and having no specific surveying value, so such points need to be confirmed;

[0035] Based on the confirmed point pixel value X i and the pixel values H of the surrounding pixel points i-q , use: Cz i-q = (X i - H i-q ) to confirm the pixel value difference Cz between this pixel point and the surrounding pixel points i-q . Then, perform ratio processing on the different pixel value differences Cz associated with different surrounding pixel points i-q to confirm the ratio sequence, and use this ratio sequence as the pixel characteristic of this pixel point (in different images, there are the same points. The pixel values generated under different lighting conditions may be different, but the change ratios between the corresponding pixel points are the same. Because the corresponding points are located at fixed positions and their positions do not change, the change ratios associated with the corresponding points must be the same value). Confirm and record the different pixel characteristics associated with different points in the remote sensing image in sequence;

[0036] S12. Based on the pixel characteristics associated with the corresponding points in the corresponding remote sensing image, identify the points with the same pixel characteristics from multiple sets of remote sensing images, and label the identified points as characteristic points (the pixel characteristics associated with these characteristic points are the same in multiple sets of remote sensing images). Confirm the pixel values of these characteristic points in different remote sensing images, and perform mean processing on the several groups of pixel values of these characteristic points to determine the mean pixel, and use the determined mean pixel as the characteristic pixel of these characteristic points (in multiple sets of remote sensing images, there are the same characteristic points, and the pixel values associated with these characteristic points are all different. Therefore, by using the mean processing method, the mean pixel can be better selected);

[0037] S13. Based on several groups of feature points confirmed within a single set of remote sensing images, confirm the connecting lines between corresponding adjacent feature points, record the line length L of the connecting lines, and then, for the same connecting lines confirmed from multiple sets of remote sensing images, perform a mean processing on the line lengths L of the determined several groups of connecting lines to confirm the feature mean belonging to this adjacent feature point. The feature points associated with the same connecting lines are all the same (that is, in each remote sensing image, the points associated with the same connecting line are the same feature points).

[0038] Based on the feature mean determined between corresponding adjacent feature points, perform a planar stretching or scaling on multiple sets of remote sensing images, and stop when the line length of the connecting line associated with the corresponding adjacent feature points in each remote sensing image is consistent with the determined feature mean. Mark the remotely sensed image after planar adjustment as the image to be fused.

[0039] S14. Perform image fusion on multiple sets of images to be fused. Based on the determined feature points, align the same feature points within each image to be fused and perform image fusion to confirm the fused image. After fusion, adjust the pixel values associated with the corresponding feature points to the determined feature pixels. Since the method of fusing multiple sets of basically identical images is relatively common in the prior art, it will not be elaborated here too much.

[0040] Step 2. Based on the confirmed fused image, generate a mountain model for this fused image, and perform preliminary processing on the generated mountain model. Decompose the mountain model into several grid regions. Among them, the method of generating a mountain model based on an image is relatively common in the prior art. Generally, a suitable edge detection algorithm is used to extract the edge information of the mountain from the fused image to outline the general contour. Common edge detection algorithms include the Canny edge detection algorithm, Sobel operator, etc. Among them, the Canny edge detection algorithm will first perform Gaussian filtering on the fused image to remove the noise in the image. Because noise will cause incorrect results in edge detection. For example, in a mountain image, noise points may be misjudged as edges. Gaussian filtering effectively smooths the image by performing weighted averaging on each pixel point in the image and its surrounding pixel points and determining the weights according to the Gaussian distribution, while retaining the edge information.

[0041] Among them, the specific sub-steps for performing preliminary processing on the generated mountain model are as follows:

[0042] S21. Generate a set of grid surfaces. There are several grids within the grid surface, and the area of each grid is the same, and the area of the corresponding grid is a preset value, which is determined by relevant operators according to experience. Then determine the bottom surface of this mountain model, and its bottom surface is parallel to the grid surface, so that the grid surface coincides with the bottom surface of the mountain model.

[0043] S22. Based on the determined grid surfaces, move the corresponding grid surfaces upward. During the movement, confirm the partial areas on the outer surface of the mountain model that belong to this grid surface, and label the confirmed partial areas as grid areas. Therefore, the outer surface of the mountain model is decomposed into several grid areas;

[0044] Specifically, the corresponding grid surface is parallel to the bottom surface of the corresponding mountain model. After the grid surface is generated, it can be moved upward. When moving upward, it is horizontally upward. Then, during the movement, the relevant areas of the mountain model covered by the corresponding grid surface can be determined, so as to determine the specific grid areas. The relevant parameters associated with each different mountain grid area are all different;

[0045] Step 3. Based on several groups of grid areas confirmed on the upper surface of the mountain model, determine the grid characteristics of the grid areas. Then, based on the grid characteristics of the determined different grid areas, perform an associated sorting on several groups of grid areas, and select the best grid sequence. Specifically, different sorting methods have different characteristic displays. Therefore, when performing sorting, there are several different sorting methods, and each different sorting method corresponds to a different grid sequence, so that the corresponding best grid sequence can be confirmed;

[0046] Among them, the specific sub-steps for selecting the best grid sequence are:

[0047] S31. Based on the determined bottom surface of the mountain model, confirm the relevant distance J from different points in the corresponding grid area to the bottom surface k , where k represents different points in the grid area. Perform an averaging process on several groups of relevant distances J k confirmed for the corresponding grid area, and confirm the grid characteristic TZ belonging to this grid area;

[0048] S32. Randomly select a group of grid areas from the determined several groups of edge grid areas as the starting area, and starting from the starting area, randomly select other grid areas as subsequent grid areas, and perform random combinations. After all the grid areas are combined, confirm the combination sequence. Confirm the grid characteristic difference between adjacent grid areas within the combination sequence. The grid characteristic difference = |the grid characteristic of the previous group of grid areas - the grid characteristic of the next group of grid areas|, and sum up several groups of grid characteristic differences associated with this combination sequence to confirm the combination characteristic corresponding to this combination sequence;

[0049] Then, confirm the combination characteristics of other combination sequences one by one;

[0050] Randomly select other grid areas as the starting area again, and confirm the subsequent combination sequences, and simultaneously confirm the combination characteristics associated with the corresponding several groups of combination sequences;

[0051] S33. Select the minimum value from the combined features associated with several groups of combined sequences, and use the combined sequence associated with the minimum value as the optimal grid sequence;

[0052] Specifically, since the outer surface of the mountain model has been decomposed into several grid regions, there are different grid features between each grid region, that is, relevant parameters such as height differences. Then, such height differences need to be feature-identified. From the several groups of combined sequences and the several groups of combined features associated therewith, several groups of grid feature differences are identified and summed up, and the combined features associated with the corresponding combined sequences are identified. The selected optimal combined sequence is relatively gentle in the overall change of the height difference, which is convenient for the drone to perform relevant surveys. There is no need to always perform specific adjustments for the up and down height differences, and only fine-tuning is required, which can effectively ensure the surveying accuracy of the drone during surveying, so as to ensure the overall surveying accuracy of the corresponding geomorphic terrain area. Moreover, when the drone is actually surveying, it is necessary to adjust the actual flight height of the drone in real time based on the height difference between the drone and the ground. When the planned route is in the lowest state in terms of height difference, for the drone, the difference in the adjusted parameters will also be relatively low, and there will be no error due to large-scale adjustment, which can effectively ensure the surveying accuracy during surveying and improve the overall effect of the geomorphic terrain area.

[0053] Step 4. Based on the selected optimal grid sequence, identify the feature points of the corresponding grid regions within this optimal grid sequence, then connect the identified several groups of feature points to identify the surveying route associated with this optimal grid sequence, and control the drone to perform on-site surveying with the determined optimal grid sequence and surveying route. Among them, the specific sub-steps for determining the surveying route are as follows:

[0054] S41. Start processing step by step from the first grid region of the optimal grid sequence: Based on the grid features identified within the corresponding grid region and the different relevant distances J associated with different points within this grid region k , identify the center point of the corresponding grid region (the center point of the grid region has been pre-calibrated and can be directly extracted and identified), calibrate it as the grid midpoint, use the grid midpoint as the center of the circle, and construct a set of radiation circles with a radius of R1, where R1 is a preset value, and its specific value is determined by the operator according to experience. Mark the points located inside this radiation circle as the points to be selected, and perform difference processing on the relevant distances of the points to be selected and the grid features of the corresponding grid region to identify the relevant differences. The relevant difference = |J k - grid feature|. Select the minimum value from the identified several groups of relevant differences, and mark the point to be selected associated with the minimum value as the surveying point;

[0055] S42. Confirm the survey points for each corresponding grid area, connect the several survey points confirmed in sequence, confirm the survey route, and control the drone based on the confirmed survey route to enable the drone to conduct on-site surveys.

[0056] Based on the grid area, through a unique grid feature calculation method, comprehensively consider the distance of the points in the grid from the bottom surface and take the average value as the key feature, deeply excavating the internal structure information of the mountain model. In the process of selecting the best grid sequence, innovatively adopt random combination and the sum analysis of feature differences to accurately screen out the best sequence with a gentle change in height difference from numerous possible combination sequences. Based on the survey route determined by this sequence, make full use of the concept of points and radiation circles in the grid, and cleverly select survey points, so that when the drone conducts actual surveys, there is no need to frequently adjust the flight height significantly, and only minor precise adjustments are required. This not only greatly reduces the errors caused by height adjustment, ensures the stability of the drone survey accuracy, but also significantly improves the survey efficiency, ensuring that the topographic data of the to-be-surveyed landform area can be obtained quickly and accurately.

[0057] Some of the data in the above formula are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0058] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The UAV mapping method based on topographic mapping accuracy is characterized by: The following steps are involved: Step 1: Acquire remote sensing images of the landform area to be surveyed, where there are multiple groups of remote sensing images, confirm pixel features associated with different points in the multiple groups of remote sensing images, select points with the same features from the multiple groups of remote sensing images based on the confirmed pixel features, and then fuse the multiple groups of remote sensing images based on the points with the same features determined in the multiple groups of remote sensing images to confirm the fused image belonging to the landform area to be surveyed; Step 2: Based on the confirmed fused image, a mountain model of the fused image is generated, and the generated mountain model is preliminarily processed to decompose the mountain model into a number of grid areas; Step 3: Based on several groups of grid areas confirmed on the upper surface of the mountain model, the grid features of the grid areas are determined, and then based on the determined grid features of different grid areas, several groups of grid areas are sorted in association to select the best grid sequence. The specific sub-steps are as follows: S31. Based on the bottom surface determined by the mountain model, determine the relevant distance J from different points in the corresponding grid area to the bottom surface. k , where k represents different points in the grid area, and the corresponding groups of related distances J confirmed in the grid area k Perform mean processing to confirm the grid feature TZ belonging to this grid area; S32, randomly selecting a group of grid areas from the determined groups of edge grid areas as the starting area, and starting from the starting area, randomly selecting other grid areas as subsequent grid areas, and randomly combining them, until all the grid areas are combined to confirm the combination sequence, confirm the grid feature difference between adjacent grid areas in the combination sequence, and the grid feature difference = |grid feature of the previous group of grid areas - grid feature of the next group of grid areas|, and summing the grid feature differences of the groups associated with the combination sequence to confirm the combination feature corresponding to the combination sequence; Then confirm the combination features of other combination sequences one by one; Then randomly select other grid areas as the starting areas, and confirm the subsequent combination sequences, and simultaneously confirm the combination features associated with the corresponding groups of combination sequences; S33, selecting a minimum value from the combination features associated with the plurality of groups of combination sequences, and taking the combination sequence associated with the minimum value as the optimal grid sequence; Step 4: Based on the selected optimal grid sequence, confirm the feature points of the corresponding grid area in this optimal grid sequence, then connect the confirmed groups of feature points, confirm the surveying and mapping route associated with this optimal grid sequence, and control the drone to conduct field surveying using the determined optimal grid sequence and surveying route.

2. The method for unmanned aerial vehicle mapping based on topographic mapping accuracy according to claim 1 is characterized in that: In the step 1, the specific method of determining the fused image of the landform area to be surveyed is: S11, from the multiple groups of remote sensing images associated with the landform area to be surveyed, confirm the pixel features of the corresponding points in a single group of remote sensing images: confirm the point pixel value of the corresponding point and mark it as X i , where i represents the corresponding point in the remote sensing image, and the pixel value of the surrounding pixels of this point is calibrated as H i-q , where q=1, 2, ..., 8; Based on the confirmed point pixel value X i And the pixel values ​​H of the surrounding pixels i-q , using: Cz i-q =(X i -H i-q ) Confirm the pixel value difference Cz between this pixel and the surrounding pixels i-q , and then the different pixel value differences Cz associated with different surrounding pixels i-q Perform ratio processing, confirm the ratio sequence, use this ratio sequence as the pixel feature of the pixel point, and confirm and record the different pixel features associated with different points in the remote sensing image in turn; S12, based on the pixel features associated with the corresponding points in the corresponding remote sensing images, identifying points with the same pixel features from multiple groups of remote sensing images, marking the identified points as feature points, confirming the pixel values ​​of the feature points in different remote sensing images, and performing mean processing on the confirmed groups of pixel values ​​about the feature points to determine the mean pixel, and using the determined mean pixel as the feature pixel of the feature point; S13, based on the several groups of feature points confirmed in the single group of remote sensing images, confirm the connecting lines between the corresponding adjacent feature points, and record the line length L of the connecting lines, and then perform mean processing on the line length L of the several groups of connecting lines confirmed from the multiple groups of remote sensing images, and confirm the feature mean value belonging to the adjacent feature points, and the feature points associated with the same connecting lines are all the same; Based on the feature mean determined between corresponding adjacent feature points, multiple groups of remote sensing images are stretched or scaled in the plane, and the process stops when the length of the connecting lines associated with the corresponding adjacent feature points in each remote sensing image is consistent with the determined feature mean, and the remote sensing images after the plane adjustment are calibrated as the images to be fused.

3. The method for unmanned aerial vehicle mapping based on topographic mapping accuracy according to claim 2 is characterized in that: The step one also includes: S14, fusing multiple groups of images to be fused, aligning the same feature points in each image to be fused based on the determined feature points, and performing image fusion to confirm the fused image, and adjusting the pixel values ​​associated with the corresponding feature points after fusion to the determined feature pixels.

4. The method for unmanned aerial vehicle mapping based on topographic mapping accuracy according to claim 1 is characterized in that: In the step 2, the specific sub-steps of performing initial processing on the generated mountain model are: S21, generating a set of grid surfaces, wherein the grid surfaces contain a plurality of grids, and the area of ​​each grid is consistent, and the area of ​​the corresponding grid is a preset value, and then determining the bottom surface of the mountain model, wherein the bottom surface is parallel to the grid surface, and the grid surface is overlapped with the bottom surface of the mountain model; S22. Based on the determined grid surface, the corresponding grid surface is moved upward, and during the movement, it is confirmed that the outer surface of the mountain model belongs to a partial area of ​​the grid surface, and the confirmed partial area is marked as a grid area, so the outer surface of the mountain model is decomposed into a plurality of grid areas.

5. The method for unmanned aerial vehicle mapping based on topographic mapping accuracy according to claim 1 is characterized in that: In step 4, the specific sub-steps for determining the surveying route are: S41, starting from the first grid area of ​​the best grid sequence and gradually performing correlation processing: based on the grid features confirmed in the corresponding grid area and the different correlation distances J associated with different points in the grid area k , confirm the center point of the corresponding grid area, mark it as the grid midpoint, take the grid midpoint as the center, construct a set of radiating circles with a radius of R1, where R1 is a preset value, mark the points inside this radiating circle as the points to be selected, and perform difference processing on the relevant distance of the points to be selected and the grid features of the corresponding grid area, confirm the relevant difference, and its relevant difference = |J k -Grid feature|, select the minimum value from the confirmed groups of related differences, and mark the point to be selected associated with the minimum value as the surveying and mapping point; S42, confirming the surveying and mapping points for each corresponding grid area, connecting several surveying and mapping points confirmed in sequence, confirming the surveying and mapping route, and controlling the UAV based on the confirmed surveying and mapping route to enable the UAV to perform field surveying and mapping.

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