A positioning method and device for a Beidou surveying and mapping system

Abstract

The present invention relates to the field of positioning using radio wave reflection, and specifically to a Beidou surveying and mapping system positioning method and device. The method comprises: obtaining three-dimensional point cloud data corresponding to each pixel point in each image taken by a drone at each moment in the surveying and mapping process, as well as drone motion data, thereby obtaining surveying and mapping system point cloud data; dividing the surveying and mapping system point cloud data into dense point clusters and sparse point clusters, and performing deletion and addition operations on each to obtain the final updated dense point clusters and sparse point clusters, thereby generating the optimal reference triangulation network of the surveying and mapping system point cloud data; determining the standardization of any point cloud data based on the distance difference between any two real reference points, and determining the real coordinates of any point cloud data. The present invention uses multiple analyses of images, point clouds, and Beidou positioning to make the positioning coordinates of all locations in the surveying and mapping area more accurate, thereby ensuring the accuracy of the surveying and mapping files.

Description

Technical Field

[0001] The present invention relates to the technical field of positioning using reflection of radio waves, and in particular to a Beidou surveying and mapping system positioning method and device. Background Art

[0002] Surveying and mapping refers to the process of measuring or investigating the topography and geology of an area and mapping it into corresponding engineering images. Early surveying and mapping was primarily conducted manually using surveying instruments. However, with the development of drone technology, airborne surveying and mapping has enabled rapid and efficient regional mapping. To ensure the practical use of engineering images, it is necessary to match the coordinates of each location in the surveyed area with their actual locations. This ensures that both the use of machinery and the construction of buildings and bridges tailored to local conditions can be carried out accurately. The Beidou satellite navigation system uses positioning algorithms and spatial coordinates to accurately locate surveyed locations.

[0003] Current positioning algorithms still suffer from errors in LiDAR coordinates due to factors such as satellite and receiver clock errors, ionosphere and troposphere delays, and multipath effects caused by reflective objects. Positioning errors are even greater if the survey area has complex terrain. Consequently, the planned area in the mapping document may differ from the actual area, requiring repeated adjustments during construction and even leading to safety issues. Summary of the Invention

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

[0005] The Beidou surveying and mapping system positioning method and device of the present invention adopt the following technical solutions:

[0006] An embodiment of the present invention provides a Beidou surveying and mapping system positioning method, the method comprising the following steps:

[0007] Obtain the 3D point cloud data corresponding to each pixel 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 pixels in all images;

[0008] 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. The optimal reference triangulation 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.

[0009] Determine the true reference point of each 3D point cloud data item based on the 3D point cloud data corresponding to each pixel point in each image at each moment and the drone motion data; the true reference point is the 3D point cloud data; obtain the stability between any two true reference points based on the distance difference between them in the optimal reference triangulation network; obtain the standardization of any point cloud data item based on the stability of any two true reference points and the distance difference between the corresponding point cloud data items; and determine the true coordinates of any point cloud data item based on the standardization of the arbitrary point cloud data item.

[0010] Complete the establishment of surveying and mapping files based on real coordinates.

[0011] Furthermore, the method of dividing the point cloud data of the surveying and mapping system into dense point clusters and sparse point clusters includes the following specific steps:

[0012] 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 point cloud data is recorded as The closest distance to the point cloud data ;

[0013] 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, recorded as dense points;

[0014] All dense points form a dense point cluster, and all sparse points form a sparse point cluster.

[0015] Furthermore, the deletion and addition operations are performed on the dense point clusters and the sparse point clusters to obtain the final updated dense point clusters and the sparse point clusters, including the following specific steps:

[0016] 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;

[0017] 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;

[0018] Obtaining the possibility of each point cloud data in the first updated dense point cluster to be deleted according to the method of obtaining the possibility of each point cloud data in the dense point cluster to be deleted;

[0019] 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;

[0020] And so on, until the first time the largest probability of deletion 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;

[0021] Select the point cloud data to be added of the sparse point cluster, and add the point cloud data 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.

[0022] Furthermore, the specific steps of 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:

[0023] 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;

[0024] 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;

[0025] Obtaining the possibility of each point cloud data in the first updated dense point cluster to be deleted according to the method of obtaining the possibility of each point cloud data in the dense point cluster to be deleted;

[0026] 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;

[0027] And so on, until the first time the largest probability of deletion 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;

[0028] Select the point cloud data to be added of the sparse point cluster, and add the point cloud data 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.

[0029] Furthermore, the method of determining the possibility of deletion of each point cloud data according to the distance relationship between each point cloud data and adjacent point cloud data in the dense point cluster includes the following specific steps:

[0030] 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 first 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 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 ;

[0031] according to How to obtain the The degree of dispersion of the distance between all points and adjacent points in the dense point cluster after the point cloud data ;

[0032] Will and The difference is recorded as the first The possibility of deleting individual point cloud data.

[0033] Furthermore, the step of selecting the point cloud data to be added to the sparse point cluster includes the following specific steps:

[0034] Generate a Delaunay triangulation network of the surveying and mapping system point cloud data using a point-by-point interpolation method; the Delaunay triangulation network divides the point cloud data into a plurality of non-overlapping triangle structures;

[0035] 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 triangle is recorded as the point cloud data to be added in the x-th triangle structure.

[0036] Furthermore, the method of determining the true 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 includes the following specific steps:

[0037] The UAV motion data includes: the UAV flight altitude, UAV roll angle, error distance and true distance at each moment;

[0038] Use the point cloud data corresponding to the central pixel in each image as the reference point;

[0039] In the At this moment, obtain the arctangent value of the ratio of the error distance to the flight height of the drone ,calculate The product of the tangent value and the drone's flight altitude minus the actual distance is used as the first The distance of the reference point at the moment is corrected inversely by the roll angle;

[0040] Replace the roll angle with the pitch angle of the drone, according to The reference point at the moment is corrected by the roll angle to obtain the distance The distance of the reference point at the moment is inversely corrected by the pitch angle;

[0041] The first The average of the distance of the reference point at the moment corrected by the roll angle and the distance of the reference point corrected by the pitch angle is recorded as Correction distance of the reference point at the moment;

[0042] 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;

[0043] The point cloud data except the u-th reference point corresponding to the maximum authenticity of the u-th reference point and all the point cloud data except the u-th reference point is used as the true reference point of the u-th reference point.

[0044] Furthermore, the step of obtaining the stability between any two real reference points based on the distance difference between the two real reference points includes the following specific steps:

[0045] All point cloud data in the optimal reference triangulated network are divided into a plurality of non-overlapping triangular structures;

[0046] In the best reference triangulation network, select any two real reference points and ,statistics and All the side lengths of the triangle structure where all the point cloud data are located 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 used as and stability.

[0047] Furthermore, the method 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 includes the following specific steps:

[0048] For the point cloud data in the surveying and mapping system Point cloud data, first calculate the The stability values of the true reference point of the point cloud data and the first two true reference points closest to it are respectively recorded as and ; and The sum is recorded as ; Calculate the The distance between a point cloud data and the two nearest point cloud data is recorded as and ; and The sum is recorded as ; and The product of The standardization of point cloud data .

[0049] Furthermore, the determining of the real coordinates of any point cloud data according to the standard degree of the arbitrary point cloud data includes the following specific steps:

[0050] 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 ;

[0051] 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 Hedi The standardization of point cloud data The difference is recorded as ;Will and The product of The corrected coordinate value of the point cloud data is Add up to get The real coordinates of the point cloud data.

[0052] The present invention also proposes a Beidou surveying and mapping system positioning device, which adopts the above-mentioned Beidou surveying and mapping system positioning method. The device includes the following modules:

[0053] Data acquisition module: used to obtain the 3D point cloud data corresponding to each pixel 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 pixels in all images constitute the surveying and mapping system point cloud data;

[0054] The optimal reference triangulated network construction module is 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 triangulated network of the surveying and mapping system point cloud data based on the final updated dense point clusters and sparse point clusters;

[0055] The real coordinate acquisition module for point cloud data is used to determine the real reference point of each 3D point cloud data based on the 3D point cloud data corresponding to each pixel point in each image at each moment and the drone motion data; the real reference point is the 3D point cloud data; in the optimal reference triangulation network, the stability between any two real reference points is obtained based on the distance difference between them; the standardization of any point cloud data is obtained based on the stability of any two real reference points and the distance difference between the corresponding point cloud data; and the real coordinates of any point cloud data are determined based on the standardization of any point cloud data;

[0056] Surveying and mapping file creation module: used to complete the creation of surveying and mapping files based on real coordinates.

[0057] The present invention has the following beneficial effects:

[0058] In an embodiment of the present invention, the three-dimensional point cloud data corresponding to each pixel point in each image taken by the drone at each moment in 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 constitute the surveying and mapping system point cloud data; the three-dimensional point cloud data corresponding to all pixel points in all images constitute the surveying and mapping system point cloud data; the surveying and mapping system point cloud data is divided into dense point clusters and sparse point clusters; the dense point clusters and sparse point clusters are deleted and added to obtain the final updated dense point clusters and sparse point clusters; and the optimal point cloud data of the surveying and mapping system is generated according to the final updated dense point clusters and sparse point clusters. The triangulated network is referenced, and based on the three-dimensional point cloud data corresponding to each pixel in each image captured by the drone at each moment and the drone motion data, the real reference point of each three-dimensional point cloud data is determined, where the real reference point is the three-dimensional point cloud data. The stability between any two real reference points is obtained based on the distance difference between the two real reference points. Based on the stability of the 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 based on the standardization of the arbitrary point cloud data, and the establishment of the surveying and mapping file is completed based on the real coordinates. Through multiple analyses of images, point clouds, and Beidou positioning, the present invention makes the positioning coordinates of all locations in the surveying and mapping area more accurate, ensuring the accuracy of the surveying and mapping file. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 A flowchart of a Beidou surveying and mapping system positioning method provided by one embodiment of the present invention;

[0061] Figure 2 A module structure diagram of a Beidou surveying and mapping system positioning device provided by one embodiment of the present invention;

[0062] Figure 3 Schematic diagram of the true reference points and error reference points of the image captured by the drone of the present invention. DETAILED DESCRIPTION

[0063] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a Beidou mapping system positioning method and device according to the present invention, including its specific implementation, structure, features, and effectiveness. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0064] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0065] The following describes in detail a Beidou surveying and mapping system positioning method and device provided by the present invention with reference to the accompanying drawings.

[0066] See also Figure 1 , which shows a flowchart of the steps of a Beidou surveying and mapping system positioning method provided by one embodiment of the present invention, the method comprising:

[0067] Step S001: Acquire the three-dimensional 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 three-dimensional point cloud data corresponding to all pixels in all images.

[0068] In this embodiment, a drone-mounted mapping system is used. The system consists of a drone, a multi-sensor mapping instrument, and a ground workstation. The multi-sensor mapping instrument includes a global high-definition camera, a Beidou receiver, a 3D LiDAR, and an inertial measurement unit. The ground workstation is responsible for remote control of the mapping system, data preprocessing, and synchronization. Because the LiDAR point cloud data is used to construct a 3D map of the surrounding environment, and the aircraft coordinate system allows for more accurate positioning and mapping, it is necessary to convert the LiDAR coordinates into aircraft coordinates.

[0069] Obtain the three-dimensional point cloud data, drone flight altitude, drone roll angle, error distance, and true distance corresponding to each pixel point in each image taken by the drone at each moment in the surveying and mapping process.

[0070] The drone surveys the area in an S-shaped pattern. During the surveying process, the lidar coordinates of each pixel in each image captured by the drone at each moment are obtained using lidar. The drone's flight altitude is determined using a Beidou receiver. During the surveying process, the drone captures the target area from multiple angles, acquiring multiple images. Based on the lidar coordinates of each point, aerial triangulation is then used to determine the camera coordinates of each pixel in each image captured by the drone at each moment. External calibration is used to convert the camera coordinate system to the aircraft's body coordinate system. This results in 3D point cloud data corresponding to each pixel in each image captured by the drone at each moment, in body coordinates.

[0071] The point cloud data of the surveying and mapping system is composed of the three-dimensional point cloud data corresponding to all pixels in all images.

[0072] It should be noted that the drone's flight altitude is constant, and the 3D point cloud data consists of (altitude, longitude, and latitude). The lidar collects 240,000 points per second. Obtaining the camera coordinates of each point in the image through aerial triangulation is a well-known technique, and the specific method is not described here. Converting the camera coordinate system to the aircraft's coordinate system using extrinsic calibration is also a well-known technique, and the specific method is not described here.

[0073] At the same time, the drone's inertial measurement unit (IMU) collects drone motion data such as acceleration, roll angle, pitch angle, and yaw angle in seconds. This data includes the drone's flight altitude, roll angle, error distance, and true distance.

[0074] The ground sampling distance is used to calculate the true distance of each pixel in each image captured by the drone at each moment. The difference between the theoretical distance and the true distance for each pixel is then used to determine the error distance for each pixel in each image captured by the drone at each moment. This provides the true distance and error distance for each point in the surveying and mapping system's point cloud data.

[0075] It should be noted that obtaining the ground sampling distance and calculating the true distance of each pixel in the image using the ground sampling distance are well-known technologies, and the specific methods will not be introduced here.

[0076] Step S002: 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 the sparse point clusters to obtain final updated dense point clusters and sparse point clusters, and generate the best reference triangulation network of the surveying and mapping system point cloud data based on the final updated dense point clusters and the sparse point clusters.

[0077] Furthermore, since point clouds are discrete and lack holistic analysis, triangulation is often used to construct point clouds into triangular meshes for surface reconstruction and model simplification. Reference points for positioning data are retrieved in the triangular network, and based on the UAV's motion inertia and route, there is a correlation between these reference points. Based on this correlation, the coordinates of the point cloud data can be corrected as a whole to improve the overall accuracy of the coordinates of the point cloud data.

[0078] A point-by-point interpolation method is used to generate a Delaunay triangulation network of point cloud data of a surveying and mapping system, wherein the Delaunay triangulation network divides the point cloud data into a plurality of non-overlapping triangle structures.

[0079] It should be noted that the Delaunay triangulation network for generating point cloud data using a point-by-point interpolation method is a well-known technique, and the specific method will not be introduced here.

[0080] Furthermore, 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 point cloud data is recorded as The closest distance to the point cloud data .

[0081] 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.

[0082] All dense points form a dense point cluster, and all sparse points form a sparse point cluster.

[0083] Furthermore, during a drone's flight, the speed at which point cloud data is generated remains constant. Therefore, the faster the drone flies, the less point cloud data is generated per unit area. To ensure the accuracy of the reference points for the point cloud data's coordinates, more point cloud data should be generated. In areas where the drone flies slower, such as during turns, at the start, and at the end of a flight, the density of point cloud data is higher, and point cloud data should be deleted.

[0084] 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 first 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 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 ;

[0085] according to How to obtain the The degree of dispersion of the distance between all points and adjacent points in the dense point cluster after the point cloud data ;

[0086] Will and The difference is recorded as the first The possibility of deleting individual point cloud data.

[0087] Preset first threshold , take this as an example to describe.

[0088] 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;

[0089] Obtaining the possibility of each point cloud data in the first updated dense point cluster to be deleted according to the method of obtaining the possibility of each point cloud data in the dense point cluster to be deleted;

[0090] 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;

[0091] And so on, until the first time the largest probability of deletion in the tth update dense point cluster is less than The process ends when t th updated dense point cluster is taken as the final updated dense point cluster.

[0092] Get the triangle structure of each point cloud data in the sparse point cluster. A triangular structure will be The point cloud data with the closest distance to the circumcenter of the triangle is recorded as the point cloud data to be added of the x-th triangle structure (the point cloud data to be added is selected from the point cloud data of the surveying and mapping system);

[0093] According to the above method, the point cloud data to be added of all triangular structures in the sparse point cluster is obtained.

[0094] For the sparse point cluster Point cloud data, get the mean of the nearest distance of all point cloud data in the sparse point cluster ; The first 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 of the discreteness of all point cloud data in the sparse point cluster is recorded as the discreteness of the distance between all points in the sparse point cluster and the adjacent points ;

[0095] according to How to obtain the first The degree of dispersion of the distance between all points and adjacent points in the sparse point cluster after adding point cloud data ;

[0096] Will and The difference is recorded as the first The possibility of adding point cloud data should be increased.

[0097] In the sparse point cluster, add the point cloud data with the greatest possibility to be added to obtain the first sparse point cluster;

[0098] According to the method for obtaining the possibility of each point cloud data to be added in the sparse point cluster, obtaining the possibility of each point cloud data to be added in the first updated sparse point cluster;

[0099] In the first updated sparse point cluster, the point cloud data to be added that has the greatest possibility of being added is added to obtain a second updated sparse point cluster;

[0100] And so on, until the first time the maximum increase probability in the fth updated sparse point cluster is less than The process ends when the fth updated sparse point cluster is taken as the final updated sparse point cluster.

[0101] According to the final updated sparse point cluster and the final updated point cluster, the Delaunay triangulation network of the point cloud data of the surveying and mapping system is updated and recorded as the best reference triangulation network.

[0102] Step S003: Determine the real reference point of each three-dimensional point cloud data based on 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 is the three-dimensional point cloud data; in the optimal reference triangulation network, obtain the stability between any two real reference points based on the distance difference between any two real reference points; obtain 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; determine the real coordinates of any point cloud data based on the standardization of any point cloud data.

[0103] Furthermore, there is a correspondence between the speed and distance of the drone and the coordinates of the point cloud data, and there is also a correspondence between the coordinates of the point cloud data and the points in the triangulation network. Therefore, based on the distance, pitch angle, and roll angle of the drone, the real reference point of any point cloud data can be obtained. Figure 3 Schematic diagram of the true reference points and error reference points for drone-captured images.

[0104] The point cloud data corresponding to the central pixel in each image is used as the reference point.

[0105] No. The distance of the reference point at the moment corrected by the roll angle is obtained as follows:

[0106] In the At this moment, obtain the arctangent value of the ratio of the error distance to the flight height of the drone ,calculate The product of the tangent value and the drone's flight altitude minus the actual distance is used as the first The distance of the reference point at a moment in time that is inversely corrected by the roll angle.

[0107] Replace the roll angle with the pitch angle of the drone, according to The reference point at the moment is corrected by the roll angle to obtain the distance The distance of the reference point at a moment in time corrected inversely by the pitch angle.

[0108] The first The average of the distance of the reference point at the moment corrected by the roll angle and the distance of the reference point corrected by the pitch angle is recorded as The corrected distance of the reference point at that moment.

[0109] 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.

[0110] The point cloud data except the u-th reference point corresponding to the maximum authenticity of the u-th reference point and all the point cloud data except the u-th reference point is used as the true reference point of the u-th reference point.

[0111] It should be noted that if there are multiple maximum truths, any one of them is selected.

[0112] Set the true reference point of all pixels in the image corresponding to the u-th reference point as the true reference point of the u-th reference point.

[0113] What needs to be explained is: the angle between the error distance of the u-th reference point and the flight altitude of the drone And the true distance of the u-th reference point There is the following relationship between them:

[0114]

[0115] Where, represents the distance of the u-th reference point reversely corrected by the roll angle; represents the inverse tangent function; represents the roll angle; Indicates the flight altitude of the drone; Indicates the true distance of the u-th reference point; represents the angle between the error distance of the u-th reference point and the flight altitude of the UAV;

[0116] Thus we can get ;in, Tangent function.

[0117] Furthermore, the accuracy of point cloud data coordinates fluctuates to a certain extent, so a certain standard point should be selected as a benchmark to correct the coordinates of other point cloud data. In the triangulation network, the positions inside sparse point clusters and dense point clusters are relatively stable, and the closer the triangle is to an equilateral triangle, the more stable the area, the more stable the drone's flight, the higher the degree of reference point reliability, and the smaller the difference between the distance between the reference points and the distance between the corresponding point cloud data coordinates, the higher the accuracy of the point cloud data coordinates, and the more it should be used as a benchmark point.

[0118] In the best reference triangulation network, select any two real reference points and ,statistics and All the side lengths of the triangle structure where all the point cloud data are located 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 used as and stability.

[0119] For the point cloud data in the surveying and mapping system Point cloud data, first calculate the The stability values of the true reference point of the point cloud data and the first two true reference points closest to it are respectively recorded as and . and The sum is recorded as Calculate the The distance between a point cloud data and the two nearest point cloud data is recorded as and . and The sum is recorded as . and The product of The standardization of point cloud data .

[0120] 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 .

[0121] Furthermore, for point cloud data other than standard points, the lower the standardization degree, the more it should refer to the position information of its corresponding real reference point.

[0122] 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 The standard degree of the standard point Hedi The standardization of point cloud data The difference is recorded as .Will and The product of The corrected coordinate value of the point cloud data is Add up to get The real coordinates of the point cloud data.

[0123] Step S004: Complete the creation of the surveying and mapping file based on the real coordinates.

[0124] After calculating the true coordinates of all point cloud data, the best reference triangulation network is fitted using the polynomial reconstruction method to complete the reconstruction of the surveying area and generate the surveying file.

[0125] It should be noted that the polynomial reconstruction method used in this embodiment to reconstruct the triangulated network is a well-known technique and will not be described in detail here. The generation of mapping files based on the mapping area is a well-known technique and will not be described in detail here.

[0126] Second, see Figure 2 , which shows a Beidou surveying and mapping system positioning device provided by an embodiment of the present invention, the device includes the following modules:

[0127] Data acquisition module: used to obtain the 3D point cloud data corresponding to each pixel 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 pixels in all images constitute the surveying and mapping system point cloud data;

[0128] The optimal reference triangulated network construction module is 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 triangulated network of the surveying and mapping system point cloud data based on the final updated dense point clusters and sparse point clusters;

[0129] The real coordinate acquisition module for point cloud data is used to determine the real reference point of each 3D point cloud data based on the 3D point cloud data corresponding to each pixel point in each image at each moment and the drone motion data; the real reference point is the 3D point cloud data; in the optimal reference triangulation network, the stability between any two real reference points is obtained based on the distance difference between them; the standardization of any point cloud data is obtained based on the stability of any two real reference points and the distance difference between the corresponding point cloud data; and the real coordinates of any point cloud data are determined based on the standardization of any point cloud data;

[0130] Surveying and mapping file creation module: used to complete the creation of surveying and mapping files based on real coordinates.

[0131] Thus, the present invention is completed. In summary, in an embodiment of the present invention, three-dimensional point cloud data corresponding to each pixel in each image captured by a drone at each moment in the surveying and mapping process and drone motion data are obtained, surveying and mapping system point cloud data is formed using the three-dimensional point cloud data corresponding to all pixels in all images, the surveying and mapping system point cloud data is divided into dense point clusters and sparse point clusters, the dense point clusters and sparse point clusters are deleted and added to obtain final updated dense point clusters and sparse point clusters, and based on the final updated dense point clusters and sparse point clusters, an optimal reference triangulation network for the surveying and mapping system point cloud data is generated, and based on the three-dimensional point cloud data corresponding to each pixel in each image captured by the drone at each moment in the surveying and mapping process and drone motion data, a true reference point for each three-dimensional point cloud data is determined, the true reference point being the three-dimensional point cloud data, the stability between any two true reference points is obtained based on the distance difference between the two true reference points, the standardization of any point cloud data is obtained based on the stability of the two true reference points and the distance difference between the corresponding point cloud data, the true coordinates of any point cloud data are determined based on the standardization of the arbitrary point cloud data, and the establishment of a surveying and mapping file is completed based on the true coordinates. Through multiple analyses of images, point clouds, and Beidou positioning, the present invention makes the positioning coordinates of all locations in the surveying and mapping area more accurate, thus ensuring the accuracy of the surveying and mapping files.

[0132] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection 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 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 pixels 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. The optimal reference triangulation 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. Determine the true reference point of each 3D point cloud data item based on the 3D point cloud data corresponding to each pixel point in each image at each moment and the drone motion data; the true reference point is the 3D point cloud data; obtain the stability between any two true reference points based on the distance difference between them in the optimal reference triangulation network; obtain the standardization of any point cloud data item based on the stability of any two true reference points and the distance difference between the corresponding point cloud data items; and determine the true coordinates of any point cloud data item based on the standardization of the arbitrary point cloud data item. 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 the two real reference points are as follows: All point cloud data in the optimal reference triangulated 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 triangle structure where all the point cloud data are located 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 used as and stability.

2. A BeiDou surveying and mapping system positioning method according to claim 1, characterized in that: The specific steps of dividing the surveying and mapping system point cloud data 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 point cloud data is recorded as The closest distance to the point cloud data ; Calculate the mean of the nearest 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, recorded as dense points; All dense points form a dense point cluster, and all sparse points form a sparse point cluster.

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; Obtaining the possibility of each point cloud data in the first updated dense point cluster to be deleted according to the method of obtaining the possibility of each point cloud data in the dense point cluster to be 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; And so on, until the first time the largest probability of deletion 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 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 deletion of 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 first 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 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 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 individual 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 to the sparse point cluster are as follows: Generate a Delaunay triangulation network of the surveying and mapping system point cloud data 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 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 true 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; Use the point cloud data corresponding to the central pixel in each image as the reference point; In the At this moment, obtain the arctangent value of the ratio of the error distance to the flight height of the drone ,calculate The product of the tangent value and the drone's flight altitude minus the actual distance is used as the first The distance of the reference point at the moment is corrected inversely by the roll angle; Replace the roll angle with the pitch angle of the drone, according to The reference point at the moment is corrected by the roll angle to obtain the distance 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 moment corrected by the roll angle and the distance of the reference point corrected by the pitch angle is recorded as 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 except the u-th reference point corresponding to the maximum authenticity of the u-th reference point and all the point cloud data except the u-th reference point is used 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 method 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 includes the following specific steps: For the point cloud data in the surveying and mapping system Point cloud data, first calculate the The stability values of the true reference point of the point cloud data and the first two true reference points closest to it are respectively recorded as and ; and The sum is recorded as ; Calculate the The distance between a point cloud data and the two nearest point cloud data is recorded as and ; and The sum is recorded as ; and The product of The standardization of point cloud data .

8. The Beidou surveying and mapping system positioning method according to claim 1, characterized in that: The specific steps of determining the real coordinates of arbitrary point cloud data according to the standard degree of the arbitrary 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 Hedi The standardization of point cloud data The difference is recorded as ;Will and The product of The corrected coordinate value of the point cloud data is 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 according to 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 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 pixels in all images constitute the surveying and mapping system point cloud data; The optimal reference triangulated network construction module is 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 triangulated network of the surveying and mapping system point cloud data based on the final updated dense point clusters and sparse point clusters; The real coordinate acquisition module for point cloud data is used to determine the real reference point of each 3D point cloud data based on the 3D point cloud data corresponding to each pixel point in each image at each moment and the drone motion data; the real reference point is the 3D point cloud data; in the optimal reference triangulation network, the stability between any two real reference points is obtained based on the distance difference between them; the standardization of any point cloud data is obtained based on the stability of any two real reference points and the distance difference between the corresponding point cloud data; and the real coordinates of any point cloud data are determined based on the standardization 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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