Beidou surveying and mapping system positioning method and device

Abstract

The invention relates to the field of positioning adopting reflection of radio waves, in particular to a Beidou surveying and mapping system positioning method and device. Comprising the steps of obtaining three-dimensional point cloud data corresponding to each pixel point in each image shot by an unmanned aerial vehicle at each moment in a surveying and mapping process and unmanned aerial vehicle motion data, further obtaining surveying and mapping system point cloud data, dividing the surveying and mapping system point cloud data into a dense point cluster and a sparse point cluster, and performing deletion and addition operations respectively to obtain a sparse point cluster and a sparse point cluster; and obtaining a final updated dense point cluster and a final updated sparse point cluster, further generating an optimal reference triangular network of the point cloud data of the surveying and mapping system, determining the standard degree of any point cloud data according to the distance difference between any two real reference points, and determining the real coordinate of any point cloud data. Through multiple analysis of the image, the point cloud and Beidou positioning, the positioning coordinates of all positions in the surveying and mapping area can be more accurate, and the accuracy of the surveying and mapping file is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of positioning technologies using radio wave reflection, and particularly relates to a positioning method and device for a Beidou mapping system. Background Art

[0002] Surveying and mapping refers to measuring or surveying the topography and geological conditions of a certain area and drawing corresponding engineering images. Early surveying and mapping was mainly carried out manually using surveying instruments. With the development of unmanned aerial vehicle (UAV) technology, aerial surveying and mapping can quickly and efficiently survey an area. To ensure the actual use of engineering images, it is necessary to correspond the coordinates of each position in the surveyed area with the real positions, so as to ensure that whether it is mechanical use or building construction, bridges, etc. according to local conditions, it can be carried out accurately. The Beidou satellite navigation system can complete the precise positioning of the surveyed position through positioning and calculation algorithms and spatial position coordinates.

[0003] Existing problems: In the current positioning and calculation algorithms, due to the influence of various factors such as satellite and receiver clock errors, ionospheric and tropospheric delays, and multipath effects caused by reflectors, there are still certain errors in the obtained lidar coordinates. If the surveyed area has complex terrain, the positioning error is even greater. Then there will be a certain difference between the planned area construction in the surveying and mapping document and the real area, resulting in the need to repeatedly determine during the construction process, and even leading to safety problems during construction. Summary of the Invention

[0004] The present invention provides a positioning method and device for a Beidou mapping system to solve the existing problems.

[0005] A positioning method and device for a Beidou mapping system of the present invention adopt the following technical solutions: An embodiment of the present invention provides a positioning method for a Beidou mapping system, and the method includes the following steps: Obtain the three-dimensional point cloud data corresponding to each pixel point in each image captured by the UAV at each moment during the surveying and mapping process and the UAV motion data; use the three-dimensional point cloud data corresponding to all pixel points in all images to form the point cloud data of the mapping system; Divide the point cloud data of the mapping system into dense point clusters and sparse point clusters, perform deletion and addition operations on the dense point clusters and sparse point clusters, obtain the finally updated dense point clusters and sparse point clusters, and generate the best reference triangular network of the point cloud data of the mapping system according to the finally updated dense point clusters and sparse point clusters; Based on the 3D point cloud data corresponding to each pixel point in each image at each moment and the UAV motion data, determine the true reference point for each 3D point cloud data; the true reference point is the 3D point cloud data; in the optimal reference triangular network, obtain the stability between any two true reference points according to the distance difference between any two true reference points; based on the stability between any two true reference points and the distance difference between the corresponding point cloud data, obtain the standard degree of any point cloud data; determine the true coordinates of any point cloud data according to the standard degree of the any point cloud data; Complete the establishment of the surveying and mapping file based on the true coordinates.

[0006] Further, dividing the point cloud data of the surveying and mapping system into dense point clusters and sparse point clusters includes the following specific steps: In the point cloud data of the surveying and mapping system, take the point cloud data closest to the th point cloud data as the adjacent point of the th point cloud data, and record the Euclidean distance between the th point cloud data and the adjacent point of the th point cloud data as the closest distance of the th point cloud data ; Calculate the mean value of the closest distances of all point cloud data , when is greater than or equal to , take the th point cloud data as a sparse point, and when is less than , take the th point cloud data as a dense point; Form a dense point cluster with all dense points and a sparse point cluster with all sparse points.

[0007]

[0007] Further, performing deletion and addition operations on the dense point cluster and the sparse point cluster to obtain the final updated dense point cluster and sparse point cluster includes the following specific steps: Determine the deletion possibility of each point cloud data according to the distance relationship between each point cloud data and the adjacent point cloud data in the dense point cluster; Preset a first threshold ; in the dense point cluster, delete the point cloud data with the greatest deletion possibility to obtain a first updated dense point cluster; Obtain the deletion possibility of each point cloud data in the first updated dense point cluster according to the way of obtaining the deletion possibility of each point cloud data in the dense point cluster; In the first updated dense point cluster, delete the point cloud data with the greatest deletion possibility to obtain a second updated dense point cluster; And so on, until the maximum deletion probability in the t-th updated dense point cluster appears for the first time and is less than at which point it ends, and the t-th updated dense point cluster is used as the final updated dense point cluster; Select the point cloud data to be added to the sparse point cluster, and according to the method for obtaining the final updated dense point cluster, add the point cloud data to be added to the sparse point cluster to obtain the final updated sparse point cluster.

[0008] Furthermore, the specific steps for respectively performing deletion and addition operations on the dense point cluster and the sparse point cluster to obtain the final updated dense point cluster and the sparse point cluster are as follows: Determine the deletion probability of each point cloud data according to the distance relationship between each point cloud data and its adjacent point cloud data in the dense point cluster; Preset a first threshold ; In the dense point cluster, delete the point cloud data with the maximum deletion probability to obtain the first updated dense point cluster; According to the method for obtaining the deletion probability of each point cloud data in the dense point cluster, obtain the deletion probability of each point cloud data in the first updated dense point cluster; In the first updated dense point cluster, delete the point cloud data with the maximum deletion probability to obtain the second updated dense point cluster; And so on, until the maximum deletion probability in the t-th updated dense point cluster appears for the first time and is less than at which point it ends, and the t-th updated dense point cluster is used as the final updated dense point cluster; Select the point cloud data to be added to the sparse point cluster, and according to the method for obtaining the final updated dense point cluster, add the point cloud data to be added to the sparse point cluster to obtain the final updated sparse point cluster.

[0009] Furthermore, the specific steps for determining the deletion probability of each point cloud data according to the distance relationship between each point cloud data and its adjacent point cloud data in the dense point cluster are as follows: For the -th point cloud data in the dense point cluster, obtain the average value of the nearest distances of all point cloud data in the dense point cluster ; Denote the difference between the nearest distance of the -th point cloud data in the dense point cluster and as the dispersion degree of the -th point cloud data; Denote the average value of the dispersion degrees of all point cloud data in the dense point cluster as the dispersion degree of the distances between all points and their adjacent points in the dense point cluster ; According to the obtaining method, obtain the dispersion degree of the distances between all points and their adjacent points in the dense point cluster after removing the -th point cloud data ; Denote the difference between and as the deletion possibility of the th point cloud data in the dense point cluster.

[0010] Furthermore, the steps for selecting the point cloud data to be added to the sparse point cluster are as follows: Generate a Delaunay triangular network of the point cloud data of the surveying and mapping system using the point-by-point insertion method; the Delaunay triangular network divides the point cloud data into several non-overlapping triangular structures; Obtain the triangular structure where each point cloud data in the sparse point cluster is located. For the th triangular structure in the sparse point cluster, denote the point cloud data closest to the circumcenter of the th triangle as the point cloud data to be added to the

[0011] Furthermore, the steps for determining the true reference point of each three-dimensional point cloud data according to the three-dimensional point cloud data corresponding to each pixel point in each image at each moment and the UAV motion data are as follows: The UAV motion data includes: the UAV flight altitude, the UAV roll angle, the error distance, and the true distance at each moment; Take the point cloud data corresponding to the central pixel point in each image as the reference point; At the th moment, obtain the arctangent value of the ratio of the error distance to the UAV flight altitude , calculate the tangent value of the sum of and the UAV roll angle, and take the difference between the product of the tangent value and the UAV flight altitude and the true distance as the distance corrected by the roll angle of the reference point at the th moment; Replace the roll angle with the UAV pitch angle, and obtain the distance corrected by the pitch angle of the reference point at the th moment according to the distance corrected by the roll angle of the reference point at the th moment; Take the mean of the distance corrected by the roll angle of the reference point at the th moment and the distance corrected by the pitch angle of the reference point as the corrected distance of the reference point at the th moment; Take the point cloud data other than the u-th reference point corresponding to the maximum authenticity among the authenticities of all point cloud data other than the u-th reference point as the true reference point of the u-th reference point.

[0012] Further, the obtaining of the stability between any two true reference points according to the distance difference between any two true reference points includes the following specific steps: All the point cloud data in the optimal reference triangular network are divided into several non-overlapping triangular structures; In the optimal reference triangular network, select any two true reference points and , count and the lengths of all sides of the triangular structures where all the point cloud data on the connecting line of and are located, denote them as the target side lengths, calculate the absolute value of the difference between each target side length and the mean value of all target side lengths, and take the ratio of the sum of the absolute values of the differences between all target side lengths and the mean value of all target side lengths to the number of all point cloud data on the connecting line of and as the stability of

[0013] Further, the obtaining of the standard degree of any point cloud data based on the stability between any two true reference points and the distance difference between the corresponding point cloud data includes the following specific steps: For the -th point cloud data in the point cloud data of the surveying and mapping system, first calculate the stability values of the true reference point of the -th point cloud data and the two nearest true reference points, and denote them as and respectively; denote the sum of the and as ; calculate the distances between the -th point cloud data and the two nearest point cloud data, and denote them as and respectively; denote the sum of the and as ; denote the product of the and as the standard degree of the -th point cloud data.

[0014] Further, the determination of the true coordinates of any point cloud data according to the standard degree of any point cloud data includes the following specific steps: Calculate the standard degree of all point cloud data, record the point cloud data with the highest standard degree as the standard point, and record the standard degree of the standard point as ; Record the coordinate position of the th point cloud data as ; Record the coordinate position of the true reference point of the th point cloud data as ; The standard degree of the standard point and the th point cloud data's standard degree The difference is recorded as ; Let and The product is recorded as the corrected coordinate value of the th point cloud data. Add the corrected coordinate value and to get the true coordinate of the th point cloud data.

[0015] The present invention also proposes a positioning device for a Beidou surveying and mapping system, which adopts the above-mentioned Beidou surveying and mapping system positioning method. This device includes the following modules: Data acquisition module: used to obtain the three-dimensional point cloud data corresponding to each pixel point in each image captured by the unmanned aerial vehicle at each moment during the surveying and mapping process and the unmanned aerial vehicle motion data; use the three-dimensional point cloud data corresponding to all pixel points in all images to form the surveying and mapping system point cloud data; Optimal reference triangular network construction module: used to divide the surveying and mapping system point cloud data into dense point clusters and sparse point clusters, perform deletion and addition operations on the dense point clusters and sparse point clusters, obtain the final updated dense point clusters and sparse point clusters, and generate the optimal reference triangular network of the surveying and mapping system point cloud data according to the final updated dense point clusters and sparse point clusters; True coordinate acquisition module of point cloud data: used to determine the true reference point of each three-dimensional point cloud data according to the three-dimensional point cloud data corresponding to each pixel point in each image at each moment and the unmanned aerial vehicle motion data; the true reference point is the three-dimensional point cloud data; in the optimal reference triangular network, obtain the stability between any two true reference points according to the distance difference between any two true reference points; based on the stability between any two true reference points and the distance difference between the corresponding point cloud data, obtain the standard degree of any point cloud data; determine the true coordinate of any point cloud data according to the standard degree of the any point cloud data; Surveying and mapping file establishment module: used to complete the establishment of the surveying and mapping file based on the true coordinates.

[0016] The present invention has the following beneficial effects: In an embodiment of the present invention, three-dimensional point cloud data corresponding to each pixel point in each image captured by a drone at each moment during the surveying and mapping process and the drone motion data are obtained; the three-dimensional point cloud data corresponding to all pixel points in all images are used to form the point cloud data of the surveying and mapping system, and the point cloud data of the surveying and mapping system is divided into dense point clusters and sparse point clusters. Deletion and addition operations are performed on the dense point clusters and sparse point clusters to obtain the finally updated dense point clusters and sparse point clusters. According to the finally updated dense point clusters and sparse point clusters, an optimal reference triangular network of the point cloud data of the surveying and mapping system is generated. According to the three-dimensional point cloud data corresponding to each pixel point in each image captured by the drone at each moment and the drone motion data, the true reference point of each three-dimensional point cloud data is determined, and the true reference point is the three-dimensional point cloud data. The stability between any two true reference points is obtained according to the distance difference between any two true reference points. Based on the stability between any two true reference points and the distance difference between the corresponding point cloud data, the standard degree of any point cloud data is obtained. The true coordinates of any point cloud data are determined according to the standard degree of any point cloud data, and the establishment of the surveying and mapping file is completed based on the true coordinates. Through the multiple analyses of images, point clouds, and Beidou positioning, the present invention enables the positioning coordinates of all positions in the surveying and mapping area to be more accurate, ensuring the accuracy of the surveying and mapping file. Brief Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a flowchart of the steps of a Beidou surveying and mapping system positioning method provided by an embodiment of the present invention; Figure 2 It is a module structure diagram of a Beidou surveying and mapping system positioning device provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of the true reference point and error reference point of the image captured by the drone of the present invention. Detailed Embodiments

[0019] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, in conjunction with the accompanying drawings and preferred embodiments, a Beidou mapping system positioning method and device proposed according to the present invention, including its specific implementation manner, structure, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this invention belongs.

[0021] The following specifically describes the specific solutions of a Beidou mapping system positioning method and device provided by the present invention in conjunction with the accompanying drawings.

[0022] Please refer to Figure 1 , which shows a flowchart of the steps of a Beidou mapping system positioning method provided by an embodiment of the present invention. The method includes: Step S001: Obtain the three-dimensional point cloud data corresponding to each pixel point in each image captured by the unmanned aerial vehicle (UAV) at each moment during the mapping process, as well as the UAV motion data; the three-dimensional point cloud data corresponding to all pixel points in all images constitutes the mapping system point cloud data.

[0023] In this embodiment, the mapping system is carried by a UAV. The mapping system includes a UAV, a multi-sensor mapper, and a ground workstation. The multi-sensor mapper includes a global high-definition camera, a Beidou receiver, a three-dimensional lidar, and an inertial measurement unit. The ground workstation includes remote control of the mapping system, data preprocessing, and synchronization. Since the point cloud data of the lidar is used to construct a three-dimensional map of the surrounding environment, and the body coordinate system can obtain more accurate positioning and map construction, it is necessary to convert the lidar coordinates to the body coordinates.

[0024] Obtain the three-dimensional point cloud data, UAV flight altitude, UAV roll angle, error distance, and true distance corresponding to each pixel point in each image captured by the UAV at each moment during the mapping process.

[0025] The unmanned aerial vehicle (UAV) conducts mapping in an S-shaped pattern in the mapping area. During the mapping process, the lidar coordinates of each pixel point in each image captured by the UAV at each moment are obtained through lidar. The flight altitude of the UAV is obtained through a Beidou receiver. During the mapping process, the UAV takes pictures of the target area from multiple angles, thus obtaining multiple images. Then, based on the lidar coordinates of each point and combined with the aerial triangulation method, the camera coordinates of each pixel point in each image captured by the UAV at each moment are obtained. The external parameter calibration method is used to convert the camera coordinate system to the body coordinate system. Furthermore, the three-dimensional point cloud data corresponding to each pixel point in each image captured by the UAV at each moment in the body coordinate system is obtained.

[0026] The three-dimensional point cloud data corresponding to all pixel points in all images constitutes the point cloud data of the mapping system.

[0027] It should be noted that: the flight altitude of the UAV is constant, the composition of the three-dimensional point cloud data is (altitude, longitude, latitude), and the lidar collects 240,000 points per second. Obtaining the camera coordinates of each point in the image through the aerial triangulation method is a well-known technology, and the specific method will not be introduced here in detail. Using the external parameter calibration method to convert the camera coordinate system to the body coordinate system is a well-known technology, and the specific method will not be introduced here.

[0028] At the same time, the inertial measurement unit of the UAV obtains the motion data of the UAV such as acceleration, roll angle, pitch angle, and yaw angle in seconds. The UAV motion data includes: the flight altitude of the UAV, the roll angle of the UAV, the error distance, and the true distance.

[0029] The true distance of each pixel point in each image captured by the UAV at each moment is calculated through the ground sampling distance. Then, the error distance of each pixel point in each image captured by the UAV at each moment is obtained through the difference between the theoretical distance value and the true distance of each pixel point. The true distance and error distance of each point cloud data in the point cloud data of the mapping system can be obtained.

[0030] It should be noted that: the acquisition of the ground sampling distance and the calculation of the true distance of each pixel point in the image through the ground sampling distance are well-known technologies, and the specific method will not be introduced here.

[0031] Step S002: Divide the point cloud data of the mapping system into dense point clusters and sparse point clusters, perform deletion and addition operations on the dense point clusters and sparse point clusters, obtain the final updated dense point clusters and sparse point clusters, and generate the best reference triangular network of the point cloud data of the mapping system according to the final updated dense point clusters and sparse point clusters.

[0032] Furthermore, since the point cloud is discrete and lacks holistic analysis, triangulation is commonly used to construct the point cloud into a triangular mesh for surface reconstruction and model simplification. Reference points for locating data are retrieved in the triangular network, and based on the motion inertia and route of the drone, there is an association between these reference points. According to this association, the coordinates of the point cloud data can be corrected globally to improve the overall accuracy of the coordinates of the point cloud data.

[0033] The Delaunay triangular network of the point cloud data of the surveying and mapping system is generated using the point-by-point insertion method. The Delaunay triangular network divides the point cloud data into multiple non-overlapping triangular structures.

[0034] It should be noted that: Generating the Delaunay triangular network of point cloud data using the point-by-point insertion method is a well-known technology, and the specific method will not be introduced here.

[0035] Furthermore, in the point cloud data of the surveying and mapping system, the point cloud data closest to the -th point cloud data is denoted as the adjacent point of the -th point cloud data. The Euclidean distance between the -th point cloud data and the adjacent point of the -th point cloud data is denoted as the closest distance of the -th point cloud data .

[0036] Calculate the mean value of the closest distances of all point cloud data . When is greater than or equal to , the -th point cloud data is denoted as a sparse point. When is less than , the -th point cloud data is denoted as a dense point.

[0037] All dense points form a dense point cluster, and all sparse points form a sparse point cluster.

[0038] Furthermore, during the flight of the drone, since the speed of generating point cloud data is consistent, the faster the drone flies, the fewer the point cloud data in the unit area. To ensure the accuracy of the reference points of the coordinates of the point cloud data, more point cloud data should be added. In areas where the drone flies slowly, at the turning points, start, and end, the density of the point cloud data is greater, and the point cloud data should be deleted.

[0039] For the -th point cloud data in the dense point cluster, obtain the mean value of the closest distances of all point cloud data within the dense point cluster ; the closest distance of the -th point cloud data in the dense point cluster and The difference is denoted as the dispersion degree of the point cloud data of the th point; the mean value of the dispersion degrees of all the point cloud data within the dense point cluster is denoted as the dispersion degree of the distances between all the points within the dense point cluster and their adjacent points ; According to the acquisition method, acquire the dispersion degree of the distances between all the points within the dense point cluster and their adjacent points after removing the th point cloud data ; Denote the difference between and as the deletability possibility of the th point cloud data in the dense point cluster.

[0040] Preset a first threshold , and take this as an example for description.

[0041] In the dense point cluster, delete the point cloud data with the greatest deletability possibility to obtain a first updated dense point cluster; According to the acquisition method of the deletability possibility of each point cloud data in the dense point cluster, acquire the deletability possibility of each point cloud data in the first updated dense point cluster; In the first updated dense point cluster, delete the point cloud data with the greatest deletability possibility to obtain a second updated dense point cluster; And so on, until it ends when the greatest deletability possibility in the t-th updated dense point cluster is less than for the first time, and take the t-th updated dense point cluster as the final updated dense point cluster.

[0042] Acquire the triangular structure where each point cloud data in the sparse point cluster is located. For the th triangular structure in the sparse point cluster, denote the point cloud data closest to the circumcenter of the th triangle as the to-be-added point cloud data of the x-th triangular structure (the to-be-added point cloud data is selected from the point cloud data of the surveying and mapping system); According to the above method, acquire the to-be-added point cloud data of all the triangular structures in the sparse point cluster.

[0043] For the th point cloud data in the sparse point cluster, acquire the mean value of the closest distances of all the point cloud data within the sparse point cluster ; Denote the difference between the closest distance of the th point cloud data in the sparse point cluster and as the dispersion degree of the th point cloud data; Denote the mean value of the dispersion degrees of all the point cloud data within the sparse point cluster as the dispersion degree of the distances between all the points within the sparse point cluster and their adjacent points ; According to the acquisition method, acquire the dispersion degree of the distances between all points and adjacent points within the sparse point cluster after adding the th point cloud data to be added; ; Denote the difference between and as the should - add possibility of the th point cloud data to be added in the sparse point cluster.

[0044] In the sparse point cluster, add the point cloud data to be added with the greatest should - add possibility to obtain the first sparse point cluster; According to the acquisition method of the should - add possibility of each point cloud data to be added in the sparse point cluster, acquire the should - add possibility of each point cloud data to be added in the first updated sparse point cluster; In the first updated sparse point cluster, add the point cloud data to be added with the greatest should - add possibility to obtain the second updated sparse point cluster; And so on, until it ends when for the first time the greatest should - add possibility in the f - th updated sparse point cluster is less than , and take the f - th updated sparse point cluster as the final updated sparse point cluster.

[0045] According to the final updated sparse point cluster and the final updated point cluster, update the Delaunay triangular network of the point cloud data of the mapping system, denoted as the optimal reference triangular network.

[0046] Step S003: According to the three - dimensional point cloud data corresponding to each pixel point in each image at each moment and the UAV motion data, determine the true reference point of each three - dimensional point cloud data; the true reference point is the three - dimensional point cloud data; in the optimal reference triangular network, obtain the stability between any two true reference points according to the distance difference between any two true reference points; based on the stability between any two true reference points and the distance difference between the corresponding point cloud data, obtain the standard degree of any point cloud data; determine the true coordinates of any point cloud data according to the standard degree of any point cloud data.

[0047] Furthermore, there is a corresponding relationship between the speed, distance of the UAV and the coordinates of the point cloud data, and there is also a corresponding relationship between the coordinates of the point cloud data and the points in the triangular network. Therefore, based on the distance of the UAV and the pitch angle, roll angle, the true reference point of any point cloud data can be obtained. As Figure 3 is a schematic diagram of the true reference point and the error reference point of the image captured by the UAV.

[0048] Take the point cloud data corresponding to the central pixel point in each image as the reference point.

[0049] The The method for obtaining the distance corrected by the roll angle for the reference point at a moment is as follows: At the moment, obtain the arctangent value of the ratio of the error distance to the flight altitude of the UAV , calculate the tangent value of the sum of and the roll angle of the UAV, and use the difference between the product of the tangent value and the flight altitude of the UAV and the true distance as the distance corrected by the roll angle for the reference point at the

[0050] Replace the roll angle with the pitch angle of the UAV, and according to the distance corrected by the roll angle for the reference point at the moment, obtain the distance corrected by the pitch angle for the reference point at the moment.

[0051] Denote the average value of the distance corrected by the roll angle for the reference point at the moment and the distance corrected by the pitch angle for the reference point as the corrected distance for the reference point at the moment.

[0052] Calculate the absolute value of the difference between the Euclidean distance between the u-th reference point and the k-th point cloud data other than the u-th reference point and the corrected distance of the u-th reference point as the authenticity between the u-th reference point and the k-th point cloud data other than the u-th reference point.

[0053] Take the point cloud data other than the u-th reference point corresponding to the maximum authenticity among the authenticities between the u-th reference point and all point cloud data other than the u-th reference point as the true reference point of the u-th reference point.

[0054] It should be noted that: if there are multiple maximum authenticities, any one of the maximum authenticities can be taken.

[0055] Set the true reference point of all pixel points in the image corresponding to the u-th reference point as the true reference point of the u-th reference point.

[0056] It should be noted that: the included angle between the error distance of the u-th reference point and the flight altitude of the UAV and the true distance of the u-th reference point have the following relationship: In the formula, represents the distance corrected by the roll angle for the u-th reference point; represents the arctangent function; represents the roll angle; represents the flight altitude of the UAV; Denote the included angle between the error distance of the \(u\)-th reference point and the flight altitude of the UAV; Thus, we can obtain ; where Tangent function.

[0057] Furthermore, there are certain fluctuations in the accuracy of the point cloud data coordinates. First, a certain set of standard points should be selected as the benchmark to correct the coordinates of other point cloud data. In the triangular network, the positions inside the sparse point cluster class and the dense point cluster class are relatively stable, and the more the triangle approaches an equilateral triangle, the more stable the area, the more stable the flight of the UAV, the higher the reference degree of the reference points, and the smaller the distance difference between the distances of the reference points and the coordinates of the corresponding point cloud data, the higher the accuracy of the coordinates of the point cloud data, and the more it should be used as the benchmark point.

[0058] In the optimal reference triangular network, select any two true reference points and , and count and All the side lengths of the triangular structures where all the point cloud data are located on the connecting line of, denote as the target side lengths, calculate the absolute value of the difference between each target side length and the mean value of all the target side lengths, and take the sum of the absolute values of the differences between all the target side lengths and the mean value of all the target side lengths and and The ratio of the number of all the point cloud data on the connecting line of is used as and Stability.

[0059] For the \(i\)-th point cloud data in the point cloud data of the surveying and mapping system, first calculate the stability values of the true reference point of the \(i\)-th point cloud data and the two closest true reference points, denoted as respectively. Denote the sum of the and and as and as . Calculate the distances between the \(i\)-th point cloud data and the two closest point cloud data, denoted as and and respectively. Denote the sum of the and as . Denote the product of the and as the standard degree of the \(i\)-th point cloud data .

[0060] Calculate the standard degrees of all the point cloud data, denote the point cloud data with the highest standard degree as the standard point, and denote the standard degree of the standard point as .

[0061] Furthermore, for other point cloud data except the standard points, the lower the standard degree, the more it should refer to the position information of its corresponding true reference points.

[0062] Denote the coordinate position of the th point cloud data as . Denote the coordinate position of the true reference point of the th point cloud data as . The difference between the standard degree of the standard point and the standard degree of the th point cloud data is denoted as . Denote the product of and as the corrected coordinate value of the th point cloud data. Add the corrected coordinate value and to obtain the true coordinate of the th point cloud data.

[0063] Step S004: Complete the establishment of the mapping file based on the true coordinates.

[0064] After calculating the true coordinates of all point cloud data, fit the best reference triangular network by the polynomial reconstruction method, and then complete the reconstruction of the mapping area to generate the mapping file.

[0065] It should be noted that: Using the polynomial reconstruction method to reconstruct the triangular network in this embodiment is a well-known technology, and no specific introduction is made here. Generating the mapping file based on the mapping area is a well-known technology, and no specific introduction is made here.

[0066] Second, please refer to Figure 2 , which shows a positioning device of a Beidou mapping system provided by an embodiment of the present invention. The device includes the following modules: Data acquisition module: used to obtain the three-dimensional point cloud data corresponding to each pixel point in each image captured by the unmanned aerial vehicle at each moment during the mapping process and the unmanned aerial vehicle motion data; and form the mapping system point cloud data with the three-dimensional point cloud data corresponding to all pixel points in all images; Best reference triangular network construction module: used to divide the mapping system point cloud data into dense point clusters and sparse point clusters, perform deletion and addition operations on the dense point clusters and sparse point clusters, obtain the final updated dense point clusters and sparse point clusters, and generate the best reference triangular network of the mapping system point cloud data according to the final updated dense point clusters and sparse point clusters; True coordinate acquisition module for point cloud data: used to determine the true reference point of each three-dimensional point cloud data according to the three-dimensional point cloud data corresponding to each pixel point in each image at each moment and the UAV motion data; the true reference point is the three-dimensional point cloud data; in the optimal reference triangular network, obtain the stability between any two true reference points according to the distance difference between any two true reference points; based on the stability between any two true reference points and the distance difference between the corresponding point cloud data, obtain the standard degree of any point cloud data; determine the true coordinates of any point cloud data according to the standard degree of the any point cloud data; Mapping file establishment module: used to complete the establishment of the mapping file based on the true coordinates.

[0067] So far, the present invention is completed. To sum up, in the embodiment of the present invention, the three-dimensional point cloud data corresponding to each pixel point in each image captured by the UAV at each moment during the mapping process and the UAV motion data are obtained, and the point cloud data of the mapping system is composed of the three-dimensional point cloud data corresponding to all pixel points in all images. The point cloud data of the mapping system is divided into dense point clusters and sparse point clusters, deletion and addition operations are performed on the dense point clusters and sparse point clusters to obtain the final updated dense point clusters and sparse point clusters. According to the final updated dense point clusters and sparse point clusters, an optimal reference triangular network of the point cloud data of the mapping system is generated. According to the three-dimensional point cloud data corresponding to each pixel point in each image captured by the UAV at each moment during the mapping process and the UAV motion data, the true reference point of each three-dimensional point cloud data is determined. The true reference point is the three-dimensional point cloud data. The stability between any two true reference points is obtained according to the distance difference between any two true reference points. Based on the stability between any two true reference points and the distance difference between the corresponding point cloud data, the standard degree of any point cloud data is obtained. The true coordinates of any point cloud data are determined according to the standard degree of the any point cloud data. The mapping file is established based on the true coordinates. Through the multiple analyses of images, point clouds, and Beidou positioning, the present invention enables the positioning coordinates of all positions in the mapping area to be more accurate and ensures the accuracy of the mapping file.

[0068] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A Beidou surveying and mapping system positioning method, characterized in that: The method comprises the following steps: Obtain the 3D point cloud data corresponding to each pixel point in each image taken by the drone at each moment in the surveying and mapping process, as well as the drone motion data; construct the surveying and mapping system point cloud data with the 3D point cloud data corresponding to all pixel points in all images; The point cloud data of the surveying and mapping system is divided into dense point clusters and sparse point clusters, and the dense point clusters and the sparse point clusters are deleted and added to obtain the final updated dense point clusters and the sparse point clusters, and the best reference triangulated network of the point cloud data of the surveying and mapping system is generated according to the final updated dense point clusters and the sparse point clusters; According to the three-dimensional point cloud data corresponding to each pixel point in each image at each moment and the drone motion data, the real reference point of each three-dimensional point cloud data is determined; the real reference point is the three-dimensional point cloud data; in the best reference triangulation network, the stability between any two real reference points is obtained according to the distance difference between any two real reference points; based on the stability of any two real reference points and the distance difference between the corresponding point cloud data, the standardization of any point cloud data is obtained; the real coordinates of any point cloud data are determined according to the standardization of any point cloud data; Complete the establishment of surveying and mapping files based on real coordinates; The specific steps of obtaining the stability between any two real reference points according to the distance difference between any two real reference points are as follows: All point cloud data in the optimal reference triangular network are divided into a plurality of non-overlapping triangular structures; In the best reference triangulation network, select any two real reference points and ,statistics and All the side lengths of the triangular structure of all point cloud data on the connecting line are recorded as target side lengths, and the absolute value of the difference between each target side length and the mean of all target side lengths is calculated. The sum of the absolute values ​​of the differences between all target side lengths and the mean of all target side lengths is added to and The ratio of the number of all point cloud data on the connecting line is and stability.

2. A Beidou surveying and mapping system positioning method according to claim 1, characterized in that: The specific steps of dividing the point cloud data of the surveying and mapping system into dense point clusters and sparse point clusters are as follows: In the point cloud data of the surveying and mapping system, The point cloud data closest to the point cloud data is recorded as The adjacent points of the point cloud data are Point cloud data and The Euclidean distance of adjacent points of the point cloud data is recorded as The closest distance to the point cloud data ; Calculate the mean of the closest distances of all point cloud data ,when Greater than or equal to When point cloud data, recorded as sparse points, when Less than When Point cloud data are recorded as dense points; All dense points form dense point clusters, and all sparse points form sparse point clusters.

3. A Beidou surveying and mapping system positioning method according to claim 1, characterized in that: The specific steps of performing deletion and addition operations on the dense point clusters and the sparse point clusters to obtain the final updated dense point clusters and the sparse point clusters are as follows: Determine the possibility of deleting each point cloud data according to the distance relationship between each point cloud data and adjacent point cloud data in the dense point cluster; Preset first threshold ; In the dense point cluster, delete the point cloud data with the greatest possibility of being deleted to obtain the first updated dense point cluster; According to the method for obtaining the possibility of each point cloud data in the dense point cluster being deleted, obtaining the possibility of each point cloud data in the first updated dense point cluster being deleted; In the first updated dense point cluster, the point cloud data with the greatest possibility of being deleted is deleted to obtain the second updated dense point cluster; This continues until the first time that the largest probability of being deleted in the tth update dense point cluster is less than The process ends when the tth updated dense point cluster is used as the final updated dense point cluster; Select the point cloud data to be added of the sparse point cluster, and add the point cloud data to be added to the sparse point cluster according to the method of obtaining the final updated dense point cluster to obtain the final updated sparse point cluster.

4. A Beidou surveying and mapping system positioning method according to claim 3, characterized in that: The specific steps of determining the possibility of deleting each point cloud data according to the distance relationship between each point cloud data and adjacent point cloud data in the dense point cluster are as follows: For the dense point cluster Point cloud data, get the mean of the nearest distance of all point cloud data in the dense point cluster ; The dense point cluster The closest distance to the point cloud data and The difference is recorded as The degree of discreteness of the point cloud data; the mean value of the discreteness of all point cloud data in the dense point cluster is recorded as the discreteness of the distance between all points in the dense point cluster and the adjacent points. ; according to How to obtain the method to remove the The degree of dispersion of the distance between all points and adjacent points in the dense point cluster after the point cloud data ; Will and The difference is recorded as the first The possibility of deleting point cloud data.

5. A Beidou surveying and mapping system positioning method according to claim 3, characterized in that: The specific steps of selecting the point cloud data to be added of the sparse point cluster are as follows: Generate a Delaunay triangulation network of point cloud data of a surveying and mapping system using a point-by-point interpolation method; the Delaunay triangulation network divides the point cloud data into a plurality of non-overlapping triangle structures; Get the triangle structure of each point cloud data in the sparse point cluster. A triangular structure will be The point cloud data closest to the circumcenter of the x-th triangle is recorded as the point cloud data to be added in the x-th triangle structure.

6. A Beidou surveying and mapping system positioning method according to claim 1, characterized in that: The specific steps of determining the real reference point of each 3D point cloud data according to the 3D point cloud data corresponding to each pixel point in each image at each moment and the UAV motion data are as follows: The UAV motion data includes: the UAV flight altitude, UAV roll angle, error distance and true distance at each moment; The point cloud data corresponding to the central pixel in each image is used as the reference point; In the At this moment, obtain the inverse tangent value of the ratio of the error distance to the flight altitude of the drone ,calculate The product of the tangent value and the flight height of the drone minus the difference between the actual distance is taken as the first The distance from the reference point at the moment to the reverse correction of the roll angle; Replace the roll angle with the pitch angle of the drone, according to The distance of the reference point at the moment is reversely corrected by the roll angle to obtain the The distance of the reference point at the moment is inversely corrected by the pitch angle; The first The average of the distance of the reference point at the time point corrected by the roll angle and the distance of the reference point corrected by the pitch angle is recorded as The correction distance of the reference point at the moment; Calculate the absolute value of the difference between the Euclidean distance between the u-th reference point and the k-th point cloud data excluding the u-th reference point and the corrected distance of the u-th reference point as the authenticity of the u-th reference point and the k-th point cloud data excluding the u-th reference point; The point cloud data other than the u-th reference point corresponding to the maximum authenticity of the u-th reference point and all the point cloud data other than the u-th reference point is taken as the true reference point of the u-th reference point.

7. A Beidou surveying and mapping system positioning method according to claim 1, characterized in that: The specific steps of obtaining the standardization of any point cloud data based on the stability of any two real reference points and the distance difference between the corresponding point cloud data are as follows: For the point cloud data in the surveying and mapping system Point cloud data, first calculate the The stability values ​​of the real reference point of the point cloud data and the first two closest real reference points are recorded as and ; and The sum is ; Calculate the The distance between a point cloud data and the two nearest point cloud data is recorded as and ; and The sum is ; and The product of The standardization of point cloud data .

8. A Beidou surveying and mapping system positioning method according to claim 1, characterized in that: The specific steps of determining the real coordinates of any point cloud data according to the standard degree of any point cloud data are as follows: Calculate the standardization of all point cloud data, record the point cloud data with the highest standardization as the standard point, and record the standardization of the standard point as ; The first The coordinate position of the point cloud data is recorded as ; The coordinate position of the real reference point of the point cloud data is recorded as ; Standard degree of standard point and The standardization of point cloud data The difference is recorded as ;Will and The product of The corrected coordinate values ​​of the point cloud data are Add up to get The real coordinates of the point cloud data.

9. A Beidou surveying and mapping system positioning device, using a Beidou surveying and mapping system positioning method as claimed in any one of claims 1 to 8, characterized in that: The device includes the following modules: Data acquisition module: used to obtain the 3D point cloud data corresponding to each pixel point in each image taken by the drone at each moment in the surveying and mapping process, as well as the drone motion data; the 3D point cloud data corresponding to all pixel points in all images constitute the surveying and mapping system point cloud data; The best reference triangular network construction module is used to divide the point cloud data of the surveying and mapping system into dense point clusters and sparse point clusters, perform deletion and addition operations on the dense point clusters and sparse point clusters, obtain the final updated dense point clusters and sparse point clusters, and generate the best reference triangular network of the point cloud data of the surveying and mapping system according to the final updated dense point clusters and sparse point clusters; The real coordinate acquisition module of the point cloud data is used to determine the real reference point of each 3D point cloud data according to the 3D point cloud data corresponding to each pixel point in each image at each moment and the UAV motion data; the real reference point is the 3D point cloud data; in the best reference triangulation network, the stability between any two real reference points is obtained according to the distance difference between any two real reference points; based on the stability of any two real reference points and the distance difference between the corresponding point cloud data, the standard degree of any point cloud data is obtained; the real coordinates of any point cloud data are determined according to the standard degree of any point cloud data; Surveying and mapping file creation module: used to complete the creation of surveying and mapping files based on real coordinates.

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