CN113340307A - Unmanned aerial vehicle path planning method based on field division - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle path planning method based on field division, which comprises the following steps: field workers set appropriate flight parameters of the unmanned aerial vehicle, acquire the whole image of the investigation region and upload the whole image and the ground control point to a server; secondly, performing geometric correction processing on the image by using corresponding satellite base map data and ground control points by the personnel in the field, then performing contour detection and segmentation on the image based on an MCG segmentation algorithm, selecting an optimal segmentation scale to realize automatic extraction of the boundary of the plot, and then obtaining the central point of the plot by analyzing the topological relation and the geometric characteristics among the subregions by using a polygonal triangulation skeleton diagram; then, according to the photo coverage range of the low-flying unmanned aerial vehicle, removing the central point of the dense plot, and automatically generating a final air route; and finally, the internal worker transmits the generated route to the unmanned aerial vehicle end, and the external worker executes the acquired route to acquire an image of the investigation region.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle path planning method based on field division.
Background
With the increasing maturity of unmanned aerial vehicle technology, unmanned aerial vehicles have become the new strength in the agricultural condition monitoring technology. Compared with the traditional satellite remote sensing, the unmanned aerial vehicle has unique advantages in the aspects of flexibility and resolution, can be used for carrying out aerial photography of the unmanned aerial vehicle at any time according to the agricultural condition demand, is high in timeliness, and can be applied to different fields such as grain estimation, crop type identification, crop growth monitoring, crop pest monitoring and plant protection. However, in the application of the unmanned aerial vehicle to perform the task, due to the limitation of the battery energy carried by the unmanned aerial vehicle, it is necessary to plan the flight path of the unmanned aerial vehicle to perform the task quickly and effectively.
While there are many studies on the planning of unmanned aerial vehicle paths today, there are currently few routes planning for agricultural unmanned aerial vehicles, especially in a wide range of agricultural conditions. Currently, agricultural condition data collection is mostly carried out in an area to be surveyed, and a plurality of small-range survey samples are randomly selected to shoot unmanned aerial vehicle survey images. The investigation method has the problems of large human intervention, incomplete waypoint coverage, multiple shooting, missed shooting and the like. In addition, due to the fact that the referenced satellite map has time lag and is often inconsistent with the actual plot space distribution, the effectiveness and the working efficiency of unmanned aerial vehicle survey are greatly reduced, and the unmanned aerial vehicle path planning method based on the plot division is provided.
Disclosure of Invention
In view of the above, the present invention provides a method for planning a route of an unmanned aerial vehicle based on field division, so as to solve the technical problems described in the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
an unmanned aerial vehicle path planning method based on field division comprises the following steps:
step S1, setting relevant parameters of the unmanned aerial vehicle, acquiring a first image and a second image of a region to be surveyed through the unmanned aerial vehicle, and simultaneously acquiring ground control point information corresponding to the first image, wherein the first image, the corresponding ground control point information and the second image are transmitted to a server, the first image is an overall image of the region to be surveyed, which is acquired when the unmanned aerial vehicle is located in the air 100-500 m away from the ground, and the second image is a partial image of the region to be surveyed, which is acquired when the unmanned aerial vehicle is located in the air 15-20 m away from the ground;
s2, selecting a satellite base map corresponding to the area to be investigated, then acquiring the first image obtained in the S1 and corresponding ground control point information from the server, and performing image geometric correction processing on the first image by using the ground control point information and the selected affine transformation model to obtain a corrected first image;
step S3, processing the corrected first image through an MCG segmentation algorithm, and extracting the boundary of each plot in the image to obtain a boundary map;
step S4, analyzing the boundary map by utilizing a polygon triangulation skeleton map technology, acquiring the central point of each plot by analyzing the topological relation and the geometric characteristics among the plots in the boundary map, and finally removing the central points of the dense plots according to the coverage range of a second image acquired by the unmanned aerial vehicle to generate a final route which is transmitted to the unmanned aerial vehicle and the mobile terminal;
and S5, the unmanned aerial vehicle receives the flight route generated in the S3 to fly, and continuously acquires a second image of the area to be investigated in the process of flying.
Further, the relevant parameters of the drone include: the flight height, the flight speed, the residence time of the unmanned aerial vehicle at each cruising point and the white balance of an onboard camera of the unmanned aerial vehicle; the unmanned aerial vehicle is provided with a terrain following system and carries a camera with a pixel value larger than or equal to 1200 ten thousand; and measuring coordinate information of a plurality of control points in different areas of the area to be investigated by the GNSS handset.
Further, the step S3 specifically includes:
s301, determining an optimal ground sampling distance, preprocessing the corrected first image by adopting a blocking strategy, and downsampling the first image by adopting a bilinear interpolation method, wherein the optimal ground sampling distance is determined by comparing accuracy of the obtained plot boundaries at different ground sampling distances; secondly, a blocking strategy is adopted to cut the sampled image into image tiles with the same pixel size;
step S302, carrying out contour detection and segmentation processing on the corrected first image and the first image preprocessed in the step S301 by adopting an MCG (micro computer graphics) segmentation algorithm, analyzing the accuracy rate of boundary extraction, selecting the optimal segmentation scale, and carrying out segmentation;
in step S303, the first image divided in step S202 is binarized to form a boundary map.
Further, in the step S4, firstly, the center-to-center and the proximity-to-center of the nodes in the skeleton map are defined, then the center-to-center degree of each node in the skeleton map is calculated according to the definition of the center-to-center and the proximity-to-center, and the node with the largest center-to-center degree is selected as the center point of the parcel;
the definition of the intercentrality is the number of skeleton paths passing through a certain node, and the expression is as follows:
in the formula (1), Cb(V) is expressed as the intercentricity, p, of a certain node VsV, t represents a path connecting the endpoints s, t and passing through node V;
the definition of the adjacent centrality is the reciprocal of the standard deviation of weighted length from a certain node to each end point skeleton branch, and the expression is as follows:
in the formula (2), Cc(V) is expressed as the proximity centrality of a certain node V, dw(V, s) represents the weighted length of the skeleton branch from the node V to each end point s, M represents the reciprocal of the weighted length, and the weight w of the corresponding side of the skeleton branch is the length, width or area.
Further, according to the coverage of the second image acquired by the unmanned aerial vehicle, removing a central point of the dense plot, and generating the route specifically includes: and calculating the overlapping degree between the second images shot by the unmanned aerial vehicle at any two central points, deleting the two corresponding central points with the overlapping degree being more than 0.3, reserving other central points, and performing shortest path planning on the other central points to generate the air route.
Further, the mobile terminal comprises a smart phone or a tablet computer.
Furthermore, field workers upload the flight paths generated in the step S4 to a tablet personal computer, a second image acquired by the unmanned aerial vehicle in the step S5 is transmitted to the tablet personal computer through a data transmission module, field workers check the second image and the flight paths displayed by the tablet personal computer in real time, if one or more images in the second image are found to be abnormal, a waypoint in the flight path corresponding to the abnormal image is marked through the tablet personal computer, the waypoint is transmitted to the unmanned aerial vehicle, and the unmanned aerial vehicle shoots the image of the waypoint again according to the acquired abnormal waypoint.
The invention has the beneficial effects that:
1) the unmanned aerial vehicle agricultural emotion inquiry path planning algorithm based on the field information has the advantages that the flight route of the unmanned aerial vehicle can be planned and investigated according to the actual distribution situation of the field, the investigation image of the unmanned aerial vehicle corresponding to each field is ensured to be obtained, and the situations of missed shooting, multi-shooting and the like are prevented.
2) The invention effectively solves the contradiction that the unmanned aerial vehicle has limited battery reserve, wide investigation range and heavy task in the actual field investigation. The accurate aerial photography point is arranged, so that the working efficiency of crop investigation is improved, the field investigation cost is greatly saved, and the practical application value is achieved.
Drawings
Fig. 1 is a schematic flow chart of an MCG segmentation algorithm provided in this embodiment.
Fig. 2 is a schematic view of the waypoint provided in this embodiment.
Fig. 3 is a schematic flow chart of the unmanned aerial vehicle path planning method based on field division provided in this embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1 to fig. 3, the present embodiment provides an unmanned aerial vehicle path planning method based on field division, including the following steps:
step S1, the field personnel sets appropriate relevant parameters of the unmanned aerial vehicle according to actual conditions, the unmanned aerial vehicle acquires a first image and a second image of an area to be surveyed, meanwhile, a plurality of field personnel measure a plurality of control point coordinate information in different areas of the area to be surveyed through the GNSS handset, and the data transmission module transmits the first image, the corresponding ground control point information and the second image to the server, wherein the first image is an overall image of the area to be surveyed acquired when the unmanned aerial vehicle is located in the air 100-500 m away from the ground, and the second image is a partial image of the area to be surveyed acquired when the unmanned aerial vehicle is located in the air 15-20 m away from the ground.
Specifically, in this embodiment, the relevant parameters of the drone include: flying height, flying speed, the residence time of the unmanned aerial vehicle at each cruising point, and the white balance of the onboard camera of the unmanned aerial vehicle.
And S2, selecting a satellite base map corresponding to the area to be investigated by the insiders, then acquiring the first image obtained in the step S1 and corresponding ground control point information from the server, and performing image geometric correction processing on the first image by using the ground control points and the selected affine transformation model to obtain a corrected first image.
Specifically, in this embodiment, step S3 specifically includes:
step S301, determining an optimal ground sampling distance and preprocessing the corrected first image by adopting a blocking strategy. Adopting a bilinear interpolation method to carry out down-sampling on the first image, wherein the optimal ground sampling distance is determined by comparing the accuracy of the obtained plot boundaries under different ground sampling distances; secondly, a partitioning strategy is adopted to segment the sampled image into image tiles with the same pixel size.
Step S302, carrying out contour detection and segmentation processing on the corrected first image by adopting an MCG segmentation algorithm, analyzing the accuracy rate of boundary extraction, selecting an optimal segmentation scale, and carrying out segmentation;
in step S303, the first image divided in step S202 is binarized to form a boundary map.
And step S3, the interior worker processes the corrected first image through an MCG segmentation algorithm, and extracts the boundaries of all the plots in the image to obtain a boundary map.
Specifically, although the high-resolution image can clearly display the feature detail information, the improvement of the spatial resolution cannot guarantee the improvement of the accuracy of the boundary of the land parcel, and the key point is to find the spatial resolution of the feature matched with the dimension of the feature. High resolution unmanned aerial vehicle image breadth is big, is difficult to the direct processing, adopts the partitioning strategy, can overcome the difficult point that unmanned aerial vehicle image is difficult to the direct division under the big breadth, can improve the practicality again.
MCG is a fast and efficient contour detection and image segmentation algorithm. Firstly, the method is based on a structure forest edge detector to quickly detect the edge of an image, but the edge is a non-closed line segment at the moment; then, edges on a local image scale and a global image scale are considered through frequency spectrum division, a directional watershed transform is used for generating a closed region from the detected edges, and irrelevant edges in a textured region are eliminated to identify an initial contour; and finally, weighting each boundary and each region in a global manner, converting the size of each pixel into a boundary probability, and constructing a hypermetrological contour map defining layered segmentation.
Agricultural land used shows that the plot is regular relatively on high resolution unmanned aerial vehicle remote sensing image, and the plot size is not of uniform size, and detail information is very clear. And (3) carrying out contour detection by using an MCG algorithm to obtain an ultra-metric contour map, wherein the boundaries among all the blocks are clearly visible, and the fine boundaries in the same block are also visible. The value of the super-metric contour map represents the levels of the regions, the value range is [0, 1], the size of the segmented regions can be changed by changing the value of the super-metric contour map, so that the value of the super-metric contour map is defined as a scale k, the super-metric contour map is binarized to form a boundary map by controlling the size of the scale k, detailed information is removed, obvious boundaries are reserved, the problem of over-segmentation caused by inconsistent sizes of land parcels is solved, and the accuracy of land parcel boundary extraction is improved.
Step S4, the interior personnel analyze the boundary map by utilizing a polygon triangulation skeleton map technology, obtain the central point of each plot by analyzing the topological relation and the geometric characteristics among the plots in the boundary map, finally remove the central point of the dense plot according to the coverage range of a second image obtained by the unmanned aerial vehicle, refer to fig. 2, generate a route, and transmit the route to the unmanned aerial vehicle and the mobile terminal through a data transmission module;
specifically, in this embodiment, based on the polygon triangulation skeleton diagram structure, the centrality theory in the graph theory field is used for reference, the centrality measurement of the skeleton diagram vertices is defined, and then the skeleton vertices with higher centrality are obtained as the shape center of the planar object.
In graph theory, intercentrality is a shortest path-based measure of centrality of nodes in a graph, expressed by the number of all shortest paths through a vertex.
The patent considers that the skeleton path among visual feature points reflects the consistency of the shape visual feature part, so that the intermediate centrality of the nodes in the skeleton graph is defined as the number of skeleton paths passing through the nodes.
Define 1 the intercentrality of a skeleton graph node V: the number of skeleton paths through V. The calculation formula is as follows:
in the formula (1), Cb(V) is expressed as the intercentricity, p, of a certain node VsV, t are the connecting terminals s,t and passes through node V;
the proximity centrality in graph theory is a centrality measure of a node in another graph, and is obtained by calculating the reciprocal of the sum of the shortest path lengths from the node to all other nodes in the graph. In consideration of the balance of the feeling of the proximity of the skeleton point to each visual feature point, the present embodiment defines the reciprocal of the standard deviation of the length of the skeleton branches from the skeleton map node to the skeleton map end point as the proximity centrality of the skeleton map node.
Defining 2 the proximity centrality of a skeleton graph node V: weighted length d of the skeleton branches from V to the respective end points Sw(V, S) reciprocal of standard deviation M. The calculation formula is as follows:
in the formula (2), Cc(V) is expressed as the proximity centrality of a certain node V, dw(V, s) represents the weighted length of the skeleton branch from the node V to each end point s, M represents the reciprocal of the weighted length, and the weight w of the corresponding side of the skeleton branch is the length, width or area.
And (3) obtaining the centrality degree of each node on the triangulation skeleton diagram by calculating the centrality of the nodes in the skeleton diagram, sequencing the centrality degrees, and obtaining the point with the largest centrality degree to be used as the shape central point of the region.
Further, according to the coverage of the second image acquired by the unmanned aerial vehicle, removing a central point of the dense plot, and generating the route specifically includes: and calculating the overlapping degree between the second images shot by the unmanned aerial vehicle at any two central points, deleting the two corresponding central points with the overlapping degree being more than 0.3, reserving other central points, and performing shortest path planning on the other central points to generate the air route.
And S5, the unmanned aerial vehicle receives the flight route generated in the S3 to fly, and continuously acquires a second image of the area to be investigated in the process of flying.
Specifically, in this embodiment, the mobile terminal includes a smart phone or a tablet computer, and the data transmission module specifically includes a 4G or 5G mobile cellular network.
Specifically, in this embodiment, the field worker uploads the route generated in step S4 to the tablet personal computer, the second image acquired by the unmanned aerial vehicle in step S5 is transmitted to the tablet personal computer through the data transmission module, the field worker checks the second image and the route displayed by the tablet personal computer in real time, if it is found that one or more of the second images are abnormal, the point in the route corresponding to the abnormal image is marked through the tablet personal computer, and the point is transmitted to the unmanned aerial vehicle, and the unmanned aerial vehicle retakes an image of the point according to the acquired abnormal point.
The invention is not described in detail, but is well known to those skilled in the art.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (7)
1. An unmanned aerial vehicle path planning method based on field division is characterized by comprising the following steps:
step S1, setting relevant parameters of the unmanned aerial vehicle, acquiring a first image and a second image of a region to be surveyed through the unmanned aerial vehicle, and simultaneously acquiring ground control point information corresponding to the first image, wherein the first image, the corresponding ground control point information and the second image are transmitted to a server, the first image is an overall image of the region to be surveyed, which is acquired when the unmanned aerial vehicle is located in the air 100-500 m away from the ground, and the second image is a partial image of the region to be surveyed, which is acquired when the unmanned aerial vehicle is located in the air 15-20 m away from the ground;
s2, selecting a satellite base map corresponding to the area to be investigated, then acquiring the first image obtained in the S1 and corresponding ground control point information from the server, and performing image geometric correction processing on the first image by using the ground control point information and the selected affine transformation model to obtain a corrected first image;
step S3, processing the corrected first image through an MCG segmentation algorithm, and extracting the boundary of each plot in the image to obtain a boundary map;
step S4, analyzing the boundary map by utilizing a polygon triangulation skeleton map technology, acquiring the central point of each plot by analyzing the topological relation and the geometric characteristics among the plots in the boundary map, and finally removing the central points of the dense plots according to the coverage range of a second image acquired by the unmanned aerial vehicle to generate a final route which is transmitted to the unmanned aerial vehicle and the mobile terminal;
and S5, the unmanned aerial vehicle receives the flight route generated in the S3 to fly, and continuously acquires a second image of the area to be investigated in the process of flying.
2. The method of claim 1, wherein the parameters related to the drone include: the flight height, the flight speed, the residence time of the unmanned aerial vehicle at each cruising point and the white balance of an onboard camera of the unmanned aerial vehicle; the unmanned aerial vehicle is provided with a terrain following system and carries a camera with a pixel value larger than or equal to 1200 ten thousand; and measuring coordinate information of a plurality of control points in different areas of the area to be investigated by the GNSS handset.
3. The unmanned aerial vehicle path planning method based on field division according to claim 2, wherein the step S3 specifically includes:
s301, determining an optimal ground sampling distance, preprocessing the corrected first image by adopting a blocking strategy, and downsampling the first image by adopting a bilinear interpolation method, wherein the optimal ground sampling distance is determined by comparing accuracy of the obtained plot boundaries at different ground sampling distances; secondly, a blocking strategy is adopted to cut the sampled image into image tiles with the same pixel size;
step S302, carrying out contour detection and segmentation processing on the corrected first image and the first image preprocessed in the step S301 by adopting an MCG (micro computer graphics) segmentation algorithm, analyzing the accuracy rate of boundary extraction, selecting the optimal segmentation scale, and carrying out segmentation;
in step S303, the first image divided in step S202 is binarized to form a boundary map.
4. The unmanned aerial vehicle path planning method based on field block division according to claim 3, wherein in step S4, the intercentricity and the adjacent centricity of the nodes in the skeleton map are defined first, then the centricity degree of each node in the skeleton map is calculated according to the definition of the intercentricity and the adjacent centricity, and the node with the largest centricity degree is selected as the center point of the field block;
the definition of the intercentrality is the number of skeleton paths passing through a certain node, and the expression is as follows:
in the formula (1), Cb(V) is expressed as the intercentricity, p, of a certain node VsV, t represents a path connecting the endpoints s, t and passing through node V;
the definition of the adjacent centrality is the reciprocal of the standard deviation of weighted length from a certain node to each end point skeleton branch, and the expression is as follows:
in the formula (2), Cc(V) is expressed as the proximity centrality of a certain node V, dw(V, s) represents the weighted length of the skeleton branch from the node V to each end point s, M represents the reciprocal of the weighted length, and the weight w of the corresponding side of the skeleton branch is the length, the width orArea.
5. The unmanned aerial vehicle path planning method based on field division according to claim 4, wherein the removing of the dense land center points according to the coverage of the second image acquired by the unmanned aerial vehicle and the generating of the route specifically comprise: and calculating the overlapping degree between the second images shot by the unmanned aerial vehicle at any two central points, deleting the two corresponding central points with the overlapping degree being more than 0.3, reserving other central points, and performing shortest path planning on the other central points to generate the air route.
6. The unmanned aerial vehicle path planning method based on field division of claim 5, wherein the mobile terminal comprises a smart phone or a tablet computer.
7. The unmanned aerial vehicle path planning method based on field division as claimed in claim 6, wherein the field worker uploads the route generated in step S4 to the tablet computer, the second image acquired by the unmanned aerial vehicle in step S5 is transmitted to the tablet computer through the data transmission module, the field worker checks the second image and the route displayed by the tablet computer in real time, if one or more of the second images is found to be abnormal, the point in the route corresponding to the abnormal image is marked by the tablet computer, and is transmitted to the unmanned aerial vehicle, and the unmanned aerial vehicle retakes the image of the point according to the acquired abnormal point.
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