A surveying method for reducing surveying errors

Abstract

The application discloses a surveying and mapping method for reducing surveying and mapping errors, and particularly relates to the technical field of unmanned aerial vehicle surveying and mapping, and comprises the following steps: step S01, dividing a region into n sub-regions and calculating the number m of surveying reference points required by each region; step S02, taking each surveying reference point as an origin to obtain m groups of three-dimensional coordinates, aligning the m groups of three-dimensional coordinates, and calculating the position deviation Pmi of the surveying reference points; step S03, screening according to the threshold value of the surveying reference point deviation value to obtain a surveying reference point set A satisfying the threshold value, obtaining a position correction coefficient based on the reference point set A, and obtaining the three-dimensional coordinates of the corrected surveying points; step S04, using an unmanned aerial vehicle to carry a remote sensing device to obtain a remote sensing image of a sub-region, removing the noise of the remote sensing image through preprocessing to obtain processed remote sensing image information; and step S05, arranging and fusing the remote sensing image according to the corrected reference points according to the remote sensing image data after preprocessing.

Description

A surveying method to reduce surveying errors Technical Field

[0001] This invention relates to the field of surveying and mapping technology, and more specifically, to a surveying and mapping method for reducing surveying and mapping errors. Background Technology

[0002] Surveying and mapping is based on computer technology, optoelectronic technology, network communication technology, space science, and information science, and uses global navigation satellite positioning systems, remote sensing, and geographic information systems as core technologies. It obtains graphic and location information reflecting the shape of the ground by measuring existing feature points and boundaries, which is used for planning and design of engineering construction and administrative management.

[0003] Patent application CN106500674B discloses a surveying method based on municipal engineering, which solves the shortcomings of traditional geological surveying, such as low accuracy of geological spatial measurement schemes and large workload in the later stages, especially the difficulty of surveying under complex geological environments. The surveying method based on municipal engineering includes the following steps: determining the reference surface and reference points; drawing the distribution map of underground pipelines; static laser surveying on the ground; dynamic laser surveying on the ground; drawing a surveying model map of the surveying area. This invention is suitable for surveying municipal engineering projects with complex geological environments on both the ground and underground, improving the comprehensiveness and accuracy of municipal engineering surveying, reducing surveying blind spots, reducing errors, and is simple to operate and highly efficient.

[0004] The technical shortcomings of the above process are: 1. Repeated measurement of the error part is inefficient, and the judgment of data deviation is limited to the size of the data itself, which is insufficient for judgment; 2. The three-dimensional coordinate error of the measurement point is not considered when the data is fused, resulting in a large measurement error. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a surveying method for reducing surveying errors, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a surveying method for reducing surveying errors, comprising the following steps:

[0007] Step S01: Divide the region into n sub-regions, calculate the number of measurement reference points m required for each region, and the measurement reference points in each sub-region are discretely and uniformly distributed.

[0008] Step S02: Obtain the three-dimensional coordinates of the measurement reference points. Using each measurement reference point as the origin, obtain the three-dimensional coordinates of the remaining measurement reference points, resulting in m sets of three-dimensional coordinates. After aligning the m sets of three-dimensional coordinates, calculate the position deviation Pmi of the measurement reference points.

[0009] Step S03: Filter the measurement reference points according to the threshold of the deviation value to obtain a set A of measurement reference points that meet the threshold. Based on the set A of reference points, obtain the position correction coefficient λ2 of the sub-region and the position correction coefficient λ3 between regions to obtain the corrected three-dimensional coordinates of the measurement points.

[0010] Step S04: Obtain remote sensing image information for each sub-region. Using a drone equipped with remote sensing equipment, acquire remote sensing images of the sub-regions based on the calibrated three-dimensional coordinates of the measurement points. After preprocessing to remove noise from the remote sensing images, obtain the processed remote sensing image information and evaluate the effectiveness of the preprocessing process.

[0011] Step S05: Arrange and fuse the preprocessed remote sensing image data according to the corrected reference points to obtain a map image of the region.

[0012] Preferably, the calculation method for the number of measurement reference points m satisfies the formula , m>3, where S i q represents the area of ​​the region, s0 represents the preset area constant, and q represents the area of ​​the region. i The coefficient representing the degree of undulation of the region satisfies the formula , where hi represents the altitude of the sub-region. λ represents the average altitude of the sub-region, and λ1 represents the precision coefficient constant.

[0013] Preferably, as shown in Figure 2, the process of obtaining the position correction coefficient includes the following steps:

[0014] Step S11: Obtain the three-dimensional coordinates of the measurement reference points within the sub-region: Using each measurement reference point as the origin, measure the three-dimensional coordinates of other measurement reference points to obtain m sets of three-dimensional coordinates, denoted as ZM;

[0015] Step S12: Filter the measurement reference points in the sub-region: Take the geometric center of the sub-region as the origin, obtain m sets of transformed three-dimensional coordinates, calculate the deviation value Pmi of the transformed measurement reference point mi, set the threshold of the deviation value, and remove the measurement reference points that do not meet the threshold requirements.

[0016] Step S13: Obtain the correction coefficient λ2 of the sub-region: After obtaining the measurement reference points that meet the deviation values, calculate the correction coefficient λ2 between the measurement reference points;

[0017] Step S14: Obtain the correction coefficient λ3 between regions: Select the measurement reference point with the smallest deviation value from each sub-region, and obtain the correction coefficient of the region based on the measurement reference point.

[0018] Preferably, in step S12, the three-dimensional coordinates corresponding to the first measurement reference point are denoted as Z1"=[z11, z12, ..., z1m], and the three-dimensional coordinates corresponding to the m-th measurement reference point are denoted as Zm"=[zm1, zm2, ..., zmm]. The deviation value Pmi of the transformed measurement reference point mi is calculated, satisfying the formula... Where aj represents the three-dimensional coordinates of the reference point to be measured, bi represents the three-dimensional coordinates of the reference point to be measured, and a threshold for the deviation value is set to remove reference points that do not meet the threshold requirements.

[0019] Preferably, in step S13, the deviation value of each measurement reference point is recorded as pm1, Pm2, ..., Pmn, satisfying the formula .

[0020] Preferably, the correction coefficient λ3 between the regions satisfies the formula .

[0021] Preferably, the remote sensing equipment is mounted on a drone. The remote sensing equipment includes, but is not limited to, a high-resolution digital camera, a multispectral imager, a synthetic aperture radar, and an infrared scanner to acquire image information. The drone collects remote sensing images within a pre-set flight path, and the flight path is designed based on the calibrated coordinates of the measurement points.

[0022] Preferably, the preprocessing of the remote sensing data includes the following steps:

[0023] Step S21: Transform the image from the spatial domain to the frequency domain: Based on the Fourier transform, transform the image from the spatial domain to the frequency domain, satisfying the formula... ,in Where F(k, l) is the frequency domain value and f(i, j) is the spatial domain value, the frequency domain corresponding to the image information is obtained;

[0024] Step S22: Obtain the noise distribution model of the remote sensing image: Treat the remote sensing image data as an image composed of h rows and j columns of pixels. Perform Fourier transform on the pixels in the odd-numbered columns to obtain the noise distribution characteristics of the image information. The noise distribution model of the remote sensing image satisfies... , where i, j represent the number of rows and columns of the pixel position in the remote sensing image, f(i, j) represents the frequency domain of the pixel position in the remote sensing image, u(i+j) represents the actual image pixel, s(i+j) represents the error generated by the pixel spectrum, and n(i+j) represents the pixel noise error;

[0025] Step S23, Noise Removal: Based on the noise distribution model, the periodicity of noise in the remote sensing image is obtained. The frequency domain interval of the noise is obtained according to the Fourier transform formula. The frequency domain interval where the noise is located is removed using a filter.

[0026] Preferably, the effectiveness assessment of data preprocessing includes the following steps:

[0027] Step S31: Obtain the survey image with N pixels. Record the gray value of each pixel before data processing as Xi, and record the gray value of the pixel after image preprocessing as Yi.

[0028] Step S32: Obtain the pixel similarity XSD of the image data before and after preprocessing, satisfying the formula ,in This represents the average grayscale value of the remote sensing image before preprocessing. This represents the average grayscale value of the preprocessed remote sensing image;

[0029] Step S33: Obtain the structural similarity JSD of the image data before and after preprocessing, satisfying the formula , where δxy represents the covariance of the gray values ​​of pixels x and y, δx represents the standard deviation of the gray values ​​of pixel X, and δy represents the standard deviation of the gray values ​​of pixel Y.

[0030] Step S34: Evaluate the effectiveness index ZX of the pretreatment, satisfying the formula When the preset threshold is met, it proves that the preprocessing is effective.

[0031] Preferably, the remote sensing image arrangement and fusion process is as follows: the three-dimensional coordinates of the measurement reference points of each region are fused to obtain a three-dimensional coordinate model of the region; the preprocessed remote sensing image data obtained from each measurement reference point is located in the three-dimensional coordinate model to complete the arrangement of the remote sensing images; and the pixel grayscale values ​​of the overlapping images are weighted and fused. Let the overlapping images come from reference points m1, m2, ... mm, and the fused overlapping pixel grayscale values ​​satisfy the formula... ,in This represents the pixel grayscale values ​​of the overlapping areas, completing the image arrangement and fusion.

[0032] The technical effects and advantages of this invention are as follows:

[0033] This invention obtains the three-dimensional coordinates of each sub-region based on measurement reference points. By correcting the measurement reference points, correction coefficients for the three-dimensional coordinates are obtained. Remote sensing image information of the surrounding environment is collected using the measurement reference points as a reference. Based on the Fourier transform series function, the collected data is denoised to obtain denoised remote sensing image information. The denoised data is then fused using a weighted fusion method with the reference points as a reference to complete the data fusion of remote sensing image data. By selecting and filtering the measurement reference points, mapping errors are reduced, thus solving the problems mentioned in the background technology. Attached Figure Description

[0034] Figure 1 is a flowchart of the method of the present invention.

[0035] Figure 2 is a flowchart of the calculation of the position correction coefficient of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0037] As used in this application, the terms "module" and "system" are intended to include computer-related entities, such as, but not limited to, hardware, firmware, hardware-software combination, software, or software in execution. For example, a module can be, but is not limited to, a process running on a processor, a processor, an object, an executable program, a thread in execution, a program, and / or a computer. For instance, an application running on a computing device and the computing device itself can both be modules. One or more modules may reside within a single process and / or thread in execution, and a module may reside on a single computer and / or be distributed across two or more computers.

[0038] Example 1

[0039] This embodiment provides a surveying method for reducing surveying errors, as shown in Figure 1, including the following steps:

[0040] Step S01: Divide the region into n sub-regions, calculate the number of measurement reference points m required for each region, and the measurement reference points in each sub-region are discretely and uniformly distributed.

[0041] Step S02: Obtain the three-dimensional coordinates of the measurement reference points. Using each measurement reference point as the origin, obtain the three-dimensional coordinates of the remaining measurement reference points, resulting in m sets of three-dimensional coordinates. After aligning the m sets of three-dimensional coordinates, calculate the position deviation Pmi of the measurement reference points.

[0042] Step S03: Filter the measurement reference points according to the threshold of the deviation value to obtain a set A of measurement reference points that meet the threshold. Based on the set A of reference points, obtain the position correction coefficient λ2 of the sub-region and the position correction coefficient λ3 between regions to obtain the corrected three-dimensional coordinates of the measurement points.

[0043] Step S04: Obtain remote sensing image information for each sub-region. Using a drone equipped with remote sensing equipment, acquire remote sensing images of the sub-regions based on the calibrated three-dimensional coordinates of the measurement points. After preprocessing to remove noise from the remote sensing images, obtain the processed remote sensing image information and evaluate the effectiveness of the preprocessing process.

[0044] Step S05: Arrange and fuse the preprocessed remote sensing image data according to the corrected reference points to obtain a map image of the region.

[0045] Furthermore, the calculation method for the number of measurement reference points m satisfies the formula... , m>3, where S i q represents the area of ​​the region, s0 represents the preset area constant, and q represents the area of ​​the region. i The coefficient representing the degree of undulation of the region satisfies the formula , where hi represents the altitude of the sub-region. λ represents the average altitude of the sub-region, and λ1 represents the precision coefficient constant.

[0046] Furthermore, as shown in Figure 2, the process of obtaining the position correction coefficient includes the following steps:

[0047] Step S11: Obtain the three-dimensional coordinates of the measurement reference points within the sub-region: Using each measurement reference point as the origin, measure the three-dimensional coordinates of other measurement reference points to obtain m sets of three-dimensional coordinates, denoted as ZM;

[0048] Step S12: Filter the measurement reference points in the sub-region: Take the geometric center of the sub-region as the origin, obtain m sets of transformed three-dimensional coordinates, calculate the deviation value Pmi of the transformed measurement reference point mi, set the threshold of the deviation value, and remove the measurement reference points that do not meet the threshold requirements.

[0049] Step S13: Obtain the correction coefficient λ2 of the sub-region: After obtaining the measurement reference points that meet the deviation values, calculate the correction coefficient λ2 between the measurement reference points;

[0050] Step S14: Obtain the correction coefficient λ3 between regions: Select the measurement reference point with the smallest deviation value from each sub-region, and obtain the correction coefficient of the region based on the measurement reference point.

[0051] Further, in step S12, the three-dimensional coordinates corresponding to the first measurement reference point are denoted as Z1"=[z11, z12, ..., z1m], and the three-dimensional coordinates corresponding to the m-th measurement reference point are denoted as Zm"=[zm1, zm2, ..., zmm]. The deviation value Pmi of the transformed measurement reference point mi is calculated, satisfying the formula... Where aj represents the three-dimensional coordinates of the reference point to be measured, bi represents the three-dimensional coordinates of the reference point to be measured, and a threshold for the deviation value is set to remove reference points that do not meet the threshold requirements.

[0052] Furthermore, in step S13, the deviation value of each measurement reference point is recorded as pm1, Pm2, ..., Pmn, satisfying the formula .

[0053] Furthermore, the correction coefficient λ3 between the regions satisfies the formula .

[0054] Furthermore, the remote sensing equipment is mounted on the UAV, and the remote sensing equipment includes, but is not limited to, a high-resolution digital camera, a multispectral imager, a synthetic aperture radar, and an infrared scanner to acquire image information. The UAV collects remote sensing images within a preset flight path, and the flight path is designed based on the calibrated coordinates of the measurement points.

[0055] Furthermore, the preprocessing of the remote sensing data includes the following steps:

[0056] Step S21: Transform the image from the spatial domain to the frequency domain: Based on the Fourier transform, transform the image from the spatial domain to the frequency domain, satisfying the formula... ,in Where F(k, l) is the frequency domain value and f(i, j) is the spatial domain value, the frequency domain corresponding to the image information is obtained;

[0057] Step S22: Obtain the noise distribution model of the remote sensing image: Treat the remote sensing image data as an image composed of h rows and j columns of pixels. Perform Fourier transform on the pixels in the odd-numbered columns to obtain the noise distribution characteristics of the image information. The noise distribution model of the remote sensing image satisfies... , where i, j represent the number of rows and columns of the pixel position in the remote sensing image, f(i, j) represents the frequency domain of the pixel position in the remote sensing image, u(i+j) represents the actual image pixel, s(i+j) represents the error generated by the pixel spectrum, and n(i+j) represents the pixel noise error;

[0058] Step S23, Noise Removal: Based on the noise distribution model, the periodicity of noise in the remote sensing image is obtained. The frequency domain interval of the noise is obtained according to the Fourier transform formula. The frequency domain interval where the noise is located is removed using a filter.

[0059] Further, the effectiveness evaluation of data preprocessing includes the following steps:

[0060] Step S31: Obtain the survey image with N pixels. Record the gray value of each pixel before data processing as Xi, and record the gray value of the pixel after image preprocessing as Yi.

[0061] Step S32: Obtain the pixel similarity XSD of the image data before and after preprocessing, satisfying the formula ,in This represents the average grayscale value of the remote sensing image before preprocessing. This represents the average grayscale value of the preprocessed remote sensing image;

[0062] Step S33: Obtain the structural similarity JSD of the image data before and after preprocessing, satisfying the formula , where δxy represents the covariance of the gray values ​​of pixels x and y, δx represents the standard deviation of the gray values ​​of pixel X, and δy represents the standard deviation of the gray values ​​of pixel Y.

[0063] Step S34: Evaluate the effectiveness index ZX of the pretreatment, satisfying the formula When the preset threshold is met, it proves that the preprocessing is effective.

[0064] Furthermore, the remote sensing image arrangement and fusion process is as follows: the three-dimensional coordinates of the measurement reference points in each region are fused to obtain a three-dimensional coordinate model of the region; the preprocessed remote sensing image data obtained from each measurement reference point is located in the three-dimensional coordinate model to complete the arrangement of the remote sensing images; and the pixel grayscale values ​​of the overlapping images are weighted and fused. Let the overlapping images come from reference points m1, m2, ... mm, and the fused overlapping pixel grayscale values ​​satisfy the formula... ,in This represents the pixel grayscale values ​​of the overlapping areas, completing the image arrangement and fusion.

[0065] The embodiments of the present invention are merely one implementation method and are not intended to specifically limit the scope of protection of the present invention.

[0066] In conclusion, 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 spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A surveying method for reducing surveying errors, characterized in that: The process includes the following steps: Step S01: Divide the region into n sub-regions, calculate the number m of measurement reference points required for each region, and ensure that the measurement reference points in each sub-region are discretely and uniformly distributed; Step S02: Obtain the three-dimensional coordinates of the measurement reference points, and obtain the three-dimensional coordinates of the remaining measurement reference points with each measurement reference point as the origin, resulting in m sets of three-dimensional coordinates. After aligning the m sets of three-dimensional coordinates, calculate the position deviation Pmi of the measurement reference points; Step S03: Filter the measurement reference points according to a threshold value to obtain a set A of measurement reference points that meet the threshold value, and obtain the position deviation Pmi based on the reference point set A. Step S04: Set correction coefficients, including sub-region position correction coefficient λ2 and inter-region position correction coefficient λ3, to obtain the corrected three-dimensional coordinates of the measurement points; Step S05: Obtain remote sensing image information for each sub-region. Using a UAV equipped with remote sensing equipment, acquire remote sensing images of the sub-regions based on the corrected three-dimensional coordinates of the measurement points. After preprocessing to remove noise from the remote sensing images, obtain processed remote sensing image information, and evaluate the effectiveness of the preprocessing process; Step S06: Arrange and fuse the preprocessed remote sensing image data according to the corrected reference points to obtain the mapped image of the region.

2. The surveying method for reducing surveying errors according to claim 1, characterized in that: The calculation method for the number m of measurement reference points satisfies the formula , m>3, where S i S0 represents the area of ​​the region, and q represents the preset area constant. i The coefficient representing the degree of undulation of the region satisfies the formula , where hi represents the altitude of the sub-region. λ represents the average altitude of the sub-region, and λ1 represents the preset precision coefficient constant.

3. The surveying method for reducing surveying errors according to claim 1, characterized in that: The calculation process of the sub-region position correction coefficient λ2 and the inter-region position correction coefficient λ3 includes the following steps: Step S11: Obtain the three-dimensional coordinates of the measurement reference points within the sub-region: Taking each measurement reference point as the origin, measure the three-dimensional coordinates of other measurement reference points to obtain m sets of three-dimensional coordinates, denoted as ZM; Step S12: Filter the measurement reference points within the sub-region: Taking the geometric center of the sub-region as the origin, obtain m sets of transformed three-dimensional coordinates. The three-dimensional coordinates corresponding to the first measurement reference point are denoted as Z1"=[z11, z12, ..., z1m], and the three-dimensional coordinates corresponding to the m-th measurement reference point are denoted as Zm"=[zm1, zm2, ..., zmm]. Calculate the deviation value Pmi of the transformed measurement reference point mi, satisfying the formula... ,in Represents the three-dimensional coordinates of the reference point to be measured. The three-dimensional coordinates of the measurement reference points are represented, a threshold for the deviation value is set, and measurement reference points that do not meet the threshold requirements are removed; Step S13: Obtain the correction coefficient λ2 of the sub-region: After obtaining the measurement reference points that meet the deviation value, the correction coefficient λ2 between the measurement reference points is calculated, and the deviation value of each measurement reference point is recorded as pm1, Pm2, ..., Pmn, satisfying the formula Step S14: Obtain the correction coefficient λ3 between regions: Select the measurement reference point with the smallest deviation value from each sub-region, and obtain the correction coefficient of the region based on the measurement reference point.

4. The surveying method for reducing surveying errors according to claim 1, characterized in that: The correction coefficient λ3 between the regions satisfies the formula 。 5. The surveying method for reducing surveying errors according to claim 1, characterized in that: The remote sensing equipment is mounted on the UAV and includes, but is not limited to, a high-resolution digital camera, a multispectral imager, a synthetic aperture radar, and an infrared scanner to acquire image information. The UAV collects remote sensing images within a preset flight path, and the flight path is designed based on the calibrated coordinates of the measurement points.

6. The surveying method for reducing surveying errors according to claim 1, characterized in that: The preprocessing of the remote sensing image includes the following steps: Step S21, transforming the image from the spatial domain to the frequency domain: Based on Fourier transform, the image is transformed from the spatial domain to the frequency domain, satisfying the formula ,in Where F(k, l) is the frequency domain value; Step S22: Obtain the noise distribution model of the remote sensing image: Treat the remote sensing image data as an image composed of h rows and j columns of pixels, take the pixels in the odd-numbered columns and perform Fourier transform to obtain the noise distribution characteristics of the image information. The noise distribution model of the remote sensing image satisfies Where i and j represent the row and column numbers of the pixel positions in the remote sensing image, f(i,j) represents the frequency domain of the remote sensing image pixel positions, u(i+j) represents the actual image pixel, s(i+j) represents the error generated by the pixel spectrum, and n(i+j) represents the pixel noise error; Step S23, noise removal: Based on the noise distribution model, the periodicity of the noise in the remote sensing image is obtained, and the frequency domain interval of the noise is obtained according to the Fourier transform formula. The frequency domain interval where the noise is located is removed using a filter.

7. The surveying method for reducing surveying errors according to claim 1, characterized in that: The effectiveness evaluation of data preprocessing includes the following steps: Step S31, obtaining a survey image with N pixels, denoting the grayscale value of each pixel before data processing as Xi, and denoting the grayscale value of each pixel after image preprocessing as Yi; Step S32, obtaining the pixel similarity XSD of the image data before and after preprocessing, satisfying the formula... ,in This represents the average grayscale value of the remote sensing image before preprocessing. This represents the average grayscale value of the preprocessed remote sensing image; Step S33: Obtain the structural similarity JSD of the image data before and after preprocessing, satisfying the formula... , where δ XY δ represents the covariance of the grayscale values ​​of pixels X and Y. X δ represents the standard deviation of the grayscale values ​​of pixel X. Y The standard deviation of the pixel Y grayscale value is represented by the formula; Step S34: Evaluate the effectiveness index ZX of the preprocessing, satisfying the formula. When the preset threshold is met, it proves that the preprocessing is effective.

8. The surveying method for reducing surveying errors according to claim 1, characterized in that: The remote sensing image arrangement and fusion process is as follows: The three-dimensional coordinates of the measurement reference points in each region are fused to obtain a three-dimensional coordinate model of the region. The pre-processed remote sensing image data obtained from each measurement reference point is then located within the three-dimensional coordinate model to complete the arrangement of the remote sensing images. The pixel grayscale values ​​of the overlapping images are then weighted and fused. Let the overlapping images originate from reference points m1, m2, ... mm, and the fused overlapping pixel grayscale values ​​satisfy the formula... ,in This represents the pixel grayscale values ​​of the overlapping areas, completing the image arrangement and fusion.

Images