A method and system for topographic mapping image restoration based on lidar

Abstract

This invention relates to the field of topographic mapping image processing, specifically to a method and system for topographic mapping image restoration based on lidar. The invention obtains local density based on the data distribution and reflected signal intensity surrounding the topographic mapping image, thereby filtering out all sparse data. It then determines the stability of density changes based on the distribution of sparse data around the sparse data; it obtains the coplanarity of the data based on the spatial distribution of point cloud data around the sparse data, and combines this with a density uniformity index to obtain all noise data. The noise data is then removed to obtain a denoised topographic image, which is then used for restoration. This invention can remove all noise data, resulting in an accurate topographic mapping image, thereby improving the restoration effect of the topographic mapping image.

Description

Technical Field

[0001] This invention relates to the field of topographic mapping image processing, specifically to a method and system for topographic mapping image restoration based on lidar. Background Technology

[0002] LiDAR (Light Detection and Ranging) is a radar system that detects the position, velocity, and other characteristics of a target by emitting a laser beam. Its working principle involves sending a detection signal (laser beam) towards the target and then comparing the received signal reflected back from the target (target echo) with the transmitted signal to obtain relevant information about the target. LiDAR has wide applications in topographic mapping, offering advantages over traditional methods such as high precision and low cost. The resulting topographic mapping images are discrete point sets based on a three-dimensional point cloud, accurately representing the shape and location of objects and providing detailed environmental physical information.

[0003] LiDAR can generate noise points during topographic mapping due to various reasons, such as environmental interference, systematic errors, and multipath reflections. These noise points reduce the overall accuracy of point cloud data. Current technologies often use clustering algorithms to analyze all point cloud data in topographic mapping images, identify and remove noise, and then restore the denoised topographic mapping image. However, in reality, certain areas, such as forest areas or densely vegetated urban areas, may exhibit uneven signal reflection intensity or return time. This results in sparse point cloud data in these areas, making it difficult to distinguish between sparse point cloud data and noisy data. Denoising in these areas can easily lead to loss of detail in the point cloud data, thus affecting the restoration of the topographic mapping image. Summary of the Invention

[0004] To address the issue that in real-world scenarios, some areas may exhibit uneven signal reflection intensity or return time of lidar, resulting in sparse point cloud data in these areas. This sparse point cloud data is difficult to distinguish from noisy data, easily leading to loss of detail during denoising and thus affecting the restoration of topographic mapping images. The present invention aims to provide a lidar-based method and system for topographic mapping image restoration. The specific technical solution is as follows: A lidar-based method for topographic mapping image restoration, comprising: acquiring a topographic mapping image; acquiring all point cloud data in the topographic mapping image and the reflected signal intensity of the lidar corresponding to each point cloud data; randomly selecting one point cloud data in the topographic mapping image as a reference point cloud data; and determining the spatial distribution of the point cloud data within a preset space of the reference point cloud data. The local compactness of the reference point cloud data is obtained by considering the spatial distribution and the intensity of the reflected signal. All density-sparse data are then selected from all point cloud data based on this local compactness. One density-sparse data is randomly selected as the reference sparse data. The density change stability of the reference sparse data is obtained based on the distribution characteristics of other density-sparse data within a preset space of the reference sparse data. The data coplanarity of the reference sparse data is obtained based on the spatial distribution of the point cloud data within the preset space of the reference sparse data. The density uniformity index of the reference sparse data is obtained based on the density change stability and the data coplanarity. All noise data is obtained based on the density uniformity index of each density-sparse data. The noise data in the topographic mapping image is removed to obtain a denoised topographic image. The denoised topographic image is then repaired.

[0005] Furthermore, the method for obtaining the local compactness includes: obtaining the local compactness according to a local compactness calculation formula, the local compactness calculation formula being as follows: Where, Indicates the sequence number of other point cloud data within the preset space, excluding the reference point cloud data; Indicates the local compactness of the reference point cloud data; Indicates the volume of the preset space; Indicates the number of other point cloud data within the preset space; Indicates the reference point cloud data and the first point within the preset space. The distance between other point cloud data; Indicates the first [number] in the preset space The reflected signal strength of other point cloud data; This indicates the intensity of the reflected signal from the reference point cloud data.

[0006] Furthermore, the method for obtaining all density-sparse data includes: taking point cloud data with a local compactness not greater than a preset first threshold as density-sparse data.

[0007] Further, the method for obtaining the stability of density change includes: establishing a spatial rectangular coordinate system centered on the reference sparse data; using the plane formed by every two coordinate axes as an initial plane to obtain all initial planes; arbitrarily selecting one initial plane as a target plane, and continuously rotating the target plane along any coordinate axis forming the plane by a preset angle until the preset space of the reference sparse data is divided into a preset number of equal-volume spaces by the target plane; obtaining the stability of density change based on the distribution of sparse data within the equal-volume spaces, wherein the formula for calculating the stability of density change is as follows: Where, This indicates the stability of density changes in the reference sparse data; Indicates the number of initial planes; Indicates the preset quantity of spaces of equal volume; Indicates the first The first initial plane rotation constitutes the... The local compactness of reference sparse data in an equal-volume space; Indicates the first The first initial plane rotation constitutes the... The local compactness of reference sparse data in an equal-volume space; It represents the variance of the local compactness of the reference sparse data within all equal-volume spaces formed by all initial plane rotations.

[0008] Further, the method for obtaining the coplanarity of the data includes: determining a reference plane by using the reference sparse data and any two other density-sparse data, and obtaining all reference planes where the reference sparse data is located; taking the density-sparse data other than the density-sparse data used to determine each reference plane within a preset space as the sparse points to be compared for each reference plane; and obtaining the coplanarity of the data according to the data coplanarity calculation formula, which is shown below: Where, Indicates the degree of data coplanarity in the reference sparse data; Indicates the number of reference planes; Indicates the first The number of sparse points to be compared outside the reference plane; Indicates the first The first reference plane outside the reference plane The sparse points to be compared with the first... Vertical distance between reference planes; Indicates the first The first reference plane outside the reference plane The degree of local compactness of the sparse points to be compared; Indicates the local compactness of the reference sparse data; Indicates the first The first reference plane outside the reference plane The distance between each sparse point to be compared and the reference sparse data; Indicates determining the first The sequence number of the first other density-sparse data in the reference plane; Indicates determining the first The index of the second other density-sparse data in the reference plane; Indicates determining the first The first reference plane The other density sparse data and the first The distance between other sparse data points; Indicates determining the first The first reference plane The intensity of reflected signals from other sparse data; Indicates determining the first The first reference plane The intensity of reflected signals from other sparse data; This represents the absolute value function.

[0009] Furthermore, the method for obtaining the density uniformity index includes: normalizing the ratio between the stability of the density change of the reference sparse data and the coplanarity of the data to obtain the density uniformity index of the reference sparse data.

[0010] Furthermore, the method for obtaining the noise data includes: using sparse data with a density uniformity index not less than a preset second threshold as noise data.

[0011] A lidar-based topographic mapping image restoration system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the lidar-based topographic mapping image restoration method described above.

[0012] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described lidar-based topographic mapping image restoration method.

[0013] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the lidar-based terrain mapping image restoration method described above.

[0014] This invention has the following beneficial effects: It acquires topographic mapping images; to analyze the point cloud data in the topographic mapping images to obtain all noise data, it first acquires all point cloud data in the topographic mapping images and the reflected signal intensity of the lidar corresponding to each point cloud data; since the reflected signal intensity of point cloud data in vegetated areas differs significantly from other areas, and the point cloud data is relatively sparse, it combines analysis to obtain the local compactness of each point cloud data, and filters all density-sparse data from all point cloud data based on the local compactness; since the actual density-sparse data and noise data have different distribution characteristics and density variation characteristics, it analyzes the density variation stability and data coplanarity of each density-sparse data in a preset space, and obtains the density uniformity index of the reference sparse data based on the density variation stability and data coplanarity of each density-sparse data, and uses the density uniformity index to find all noise data. The noise data in the topographic mapping images is removed to obtain denoised topographic images; the denoised topographic images are then repaired. This invention can remove all noise data, obtain accurate topographic mapping images, and thus improve the repair effect of topographic mapping images. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a topographic mapping image restoration method based on lidar, provided as an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a lidar-based topographic mapping image restoration method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, 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 pertains.

[0019] The following description, in conjunction with the accompanying drawings, details a specific scheme for a lidar-based topographic mapping image restoration method and system provided by the present invention.

[0020] Please see Figure 1 This illustrates a method for topographic mapping image restoration based on lidar according to an embodiment of the present invention. The method includes: step S1: acquiring topographic mapping image; acquiring all point cloud data in the topographic mapping image and the reflection signal intensity of lidar corresponding to each point cloud data.

[0021] This invention is primarily applied to the restoration of topographic mapping images. Therefore, the topographic mapping images are first acquired for subsequent analysis. Since the 3D point cloud data in the topographic mapping images contains noisy data, which may be difficult to distinguish from actual sparse data points, and since sparse data points are caused by uneven signal reflection intensity or return time of the lidar in certain areas, it is necessary to analyze the radar reflection signal corresponding to each point cloud data. Therefore, in this invention, all point cloud data in the topographic mapping image and the reflection signal intensity of the lidar corresponding to all point cloud data are acquired.

[0022] In one embodiment of the present invention, terrain data of the area to be mapped is acquired using a lidar system mounted on a drone, and corresponding terrain mapping images are generated. It should be noted that the method for acquiring the terrain mapping images can be set by the implementer and is not limited here.

[0023] Step S2: Select any point cloud data from the topographic mapping image as the reference point cloud data; obtain the local compactness of the reference point cloud data based on the spatial distribution and reflection signal intensity of the point cloud data within the preset space; and filter all sparse point cloud data based on the local compactness.

[0024] In reality, certain areas in topographic mapping images may exhibit uneven signal reflection intensity or return time for lidar, such as forest areas and densely vegetated urban areas. This results in sparse point cloud data in these areas, making it difficult to distinguish between sparse point cloud data and noise data. Since noise data in topographic mapping images is characterized by significant and prominent distance differences between noise data and surrounding point cloud data, this embodiment of the invention obtains the local density of the reference point cloud data based on the spatial distribution and reflected signal intensity of the point cloud data within a preset space. This local density is then used to filter sparse data, facilitating the subsequent identification of noise data.

[0025] Preferably, in one embodiment of the present invention, the method for obtaining the local compactness includes: obtaining the local compactness according to a local compactness calculation formula, the local compactness calculation formula being as follows: Where, Indicates the sequence number of other point cloud data within the preset space, excluding the reference point cloud data; Indicates the local compactness of the reference point cloud data; Indicates the volume of the preset space; Indicates the number of other point cloud data within the preset space; Indicates the reference point cloud data and the first point within the preset space. The distance between other point cloud data; Indicates the first [number] in the preset space The reflected signal strength of other point cloud data; Indicates the reflected signal strength of the reference point cloud data; This represents the normalization function.

[0026] In the formula for calculating local density, the distance between the reference point cloud data and every other point cloud data within the preset space is used. The larger the value, the less continuous the reference point cloud data is, meaning the more isolated the reference point cloud data is; and the greater the difference in reflected signal intensity between the reference point cloud data and every other point cloud data in the preset space. The larger the value, the more prominent the reference point cloud data is within the preset space; Perform cumulative summation, sum value The larger the value, the greater the difference between the reference point cloud data and other point cloud data within the preset space; in this case, the smaller the local compactness of the reference point cloud data. The point cloud data density within the preset space of the reference point cloud data... The smaller the value, the less compact the reference point cloud data is within the preset space.

[0027] In one embodiment of the present invention, a spherical space with a radius of 10 meters, centered on the reference point cloud data, is used as the preset space. It should be noted that the preset space can be set arbitrarily and is not limited here.

[0028] Preferably, in one embodiment of the present invention, the method for obtaining all density-sparse data includes: using point cloud data with a local compactness not greater than a preset first threshold as density-sparse data. In one embodiment of the present invention, the preset first threshold is set to 0.2. It should be noted that the preset first threshold can be set arbitrarily and is not limited here.

[0029] At this point, all density-sparse data in the topographic mapping imagery has been obtained.

[0030] Step S3: Select any density-sparse data as reference sparse data; based on the distribution characteristics of other density-sparse data within the preset space of the reference sparse data, obtain the stability of the density change of the reference sparse data; based on the spatial distribution of the point cloud data within the preset space of the reference sparse data, obtain the coplanarity of the reference sparse data; based on the stability of the density change and the coplanarity of the reference sparse data, obtain the density uniformity index of the reference sparse data; obtain all noise data based on the density uniformity index of each density-sparse data.

[0031] In practice, the reflected signal intensity of lidar during topographic mapping is uneven due to vegetation cover. Therefore, topographic images often contain sparse point cloud data that resembles noise data, and conventional methods cannot distinguish between real sparse point cloud data and noise data. In areas with abundant vegetation, most lidar signal beams are blocked by leaves, with only a few reaching the ground or branches. The reflected signal intensity of these unblocked beams is similar but differs significantly from surrounding beams, resulting in sparse density data. However, the surfaces of the ground and branches are usually relatively flat, so sparse density data with similar reflected signal intensity are mostly located in similar planes. Therefore, the density distribution of sparse density data around vegetation cover is uneven. In one embodiment of this invention, the density variation and distribution of sparse density data around each sparse density data point are analyzed.

[0032] Preferably, in one embodiment of the present invention, the stability of the density change of the reference sparse data is obtained based on the distribution characteristics of other density sparse data within the preset space of the reference sparse data. Specifically, this includes: establishing a spatial rectangular coordinate system with the reference sparse data as the center; taking the plane formed by each pair of coordinate axes as the initial plane, obtaining all the initial planes, that is, obtaining the initial plane xy where the x-axis and y-axis are located, the initial plane xz where the x-axis and z-axis are located, and the initial plane yz where the y-axis and z-axis are located.

[0033] Choose any initial plane as the target plane, and continuously rotate the target plane along any coordinate axis that makes up the plane by a preset angle until the preset space of the reference sparse data is divided into a preset number of equal-volume spaces by the target plane. In one embodiment of the present invention, the initial plane xy is continuously rotated along the x-axis, the initial plane xz is continuously rotated along the x-axis, and the initial plane yz is continuously rotated along the y-axis. Each initial plane is rotated by an angle of 30° each time. In this case, each initial plane divides the preset space into 12 equal-volume spaces, which is the preset number of equal-volume spaces. It should be noted that in other embodiments of the present invention, the rotation direction and the angle of each rotation of each initial plane can be set independently and are not limited here.

[0034] The stability of density changes is obtained based on the sparse density data distribution within a space of equal volume. The formula for calculating the stability of density changes is shown below: In the formula, This indicates the stability of density changes in the reference sparse data; Indicates the number of initial planes; Indicates the preset quantity of spaces of equal volume; Indicates the first The first initial plane rotation constitutes the... The local compactness of reference sparse data in an equal-volume space; Indicates the first The first initial plane rotation constitutes the... The local compactness of reference sparse data in an equal-volume space; It represents the variance of the local compactness of the reference sparse data within all equal-volume spaces formed by all initial plane rotations.

[0035] In the formula for calculating the stability of density changes, the local compactness of each equal-volume space segmented by the reference sparse data on each initial plane is calculated; the difference in local compactness between two adjacent equal-volume spaces segmented by the reference sparse data on each initial plane is also calculated. The greater the difference, the greater the density difference of the sparse data in two adjacent equal-volume spaces, indicating the difference in local compactness between each pair of adjacent equal-volume spaces in all equal-volume spaces divided by the initial plane. Calculate the average This reflects the degree of density dispersion within the preset space of the reference sparse data; the larger the mean, the more dispersed the density distribution of the sparse data within the preset space, and the worse the stability of the density change of the reference sparse data; and the variance of the local compactness in all equal-volume spaces. The larger the value, the more dispersed the sparse data is within spaces of different volumes, and the more drastic the density variation of the sparse data within the preset space; therefore, [the value is missing here]. Negative correlation mapping normalization is performed to obtain the stability of density changes in the reference sparse data.

[0036] Since the density sparse data of the vegetation cover area are all located in similar planes, the plane formed by randomly selecting a few density sparse data points is usually not too far away from other density sparse data points. However, noise data is randomly distributed without any rules, and the plane formed by it and the surrounding density sparse data points may be far away from other density sparse data points. In order to further distinguish between noise data and real density sparse data, in this embodiment of the invention, the data coplanarity of the reference sparse data is obtained according to the spatial distribution of point cloud data within the preset space of the reference sparse data.

[0037] Preferably, in one embodiment of the present invention, the method for obtaining the coplanarity of data includes: determining a reference plane by using reference sparse data and any two other density sparse data, and obtaining all reference planes where the reference sparse data is located.

[0038] The sparse data outside the density-sparse data of each reference plane within the preset space are used as the sparse points to be compared for each reference plane; the coplanarity of the data is obtained according to the data coplanarity calculation formula, which is shown below: Where, Indicates the degree of data coplanarity in the reference sparse data; Indicates the number of reference planes; Indicates the first The number of sparse points to be compared on each reference plane; Indicates the first The first reference plane The sparse points to be compared with the first... Vertical distance between reference planes; Indicates the first The first reference plane The degree of local compactness of the sparse points to be compared; Indicates the local compactness of the reference sparse data; Indicates the first The first reference plane The distance between each sparse point to be compared and the reference sparse data; Indicates determining the first The sequence number of the first other density-sparse data in the reference plane; Indicates determining the first The index of the second other density-sparse data in the reference plane; Indicates determining the first The first reference plane The other density sparse data and the first The distance between other sparse data points; Indicates determining the first The first reference plane The intensity of reflected signals from other sparse data; Indicates determining the first The first reference plane The intensity of reflected signals from other sparse data; This represents the absolute value function.

[0039] In the formula for calculating the coplanarity of data, the first... The sparse points to be compared with the first... Vertical distance between reference planes The farther apart the points are, the further they are from the reference sparse data. The less likely the reference planes are to be coplanar, and because the greater the difference in local compactness between the sparse point to be compared and the reference sparse data, the greater the distance between the two points, indicating that the th... The sparse points to be compared with the first... The less likely two reference planes are to be coplanar, the more likely they are to be... and The product of as The weights; in the first In the reference plane, the first The other density sparse data and the first The greater the distance between the other sparse data points, the more likely the 1st sparse data point is to be found. The other density sparse data and the first The greater the difference in reflected signal intensity between other sparse data points, the more likely the first data point is to be affected. The other density sparse data and the first The more likely other density-sparse data points are of different types, the less representative the resulting plane will be. In this case, the density of the first sparse data point should be reduced. The weights of each reference plane across all reference planes, therefore... Perform negative correlation mapping as We analyze the coplanarity of each reference plane by assigning weights to it, and obtain the coplanarity of the reference sparse data.

[0040] Based on the above process, the stability of density changes and the coplanarity of the reference sparse data are obtained, and thus the density uniformity index around the reference sparse data is obtained.

[0041] Preferably, in one embodiment of the present invention, the method for obtaining the density uniformity index includes: normalizing the ratio between the density variation stability of the reference sparse data and the coplanarity of the data to obtain the density uniformity index of the reference sparse data.

[0042] The density uniformity index is obtained according to the formula for calculating the density uniformity index, which is shown below: Where, This indicates the density uniformity index of the reference sparse data. This indicates the stability of density changes in the reference sparse data; Indicates the degree of data coplanarity in the reference sparse data; This represents the normalization function.

[0043] In the formula for calculating the density uniformity index, the greater the stability of the density change of the reference sparse data, the more uniform the distribution of density sparse data around the reference sparse data is. In this case, the reference sparse data is more likely to be noisy data, and the density uniformity index is larger. The greater the coplanarity of the reference sparse data, the more likely the reference sparse data is to be coplanar with the surrounding density sparse data. In this case, the more uneven the distribution of density sparse data around the reference sparse data is. In this case, the reference sparse data is more likely to be real point cloud data of vegetation cover area, and the density uniformity index of the reference sparse data is smaller.

[0044] Since the distribution of real point cloud data in vegetation-covered areas in topographic mapping images is mostly on a plane and is not uniform, in this embodiment of the invention, all noise data are obtained based on the density uniformity index of each sparse data.

[0045] Preferably, in one embodiment of the present invention, the method for obtaining noise data includes: using sparse data with a density uniformity index not less than a preset second threshold as noise data. It should be noted that the preset second threshold is set to 0.6. In other embodiments of the present invention, the preset second threshold can be set arbitrarily, and is not limited here.

[0046] Step S4: Remove noise data from the topographic mapping image to obtain a denoised topographic image; repair the denoised topographic image.

[0047] In one embodiment of the present invention, all noise data in the topographic mapping image is removed to obtain a denoised topographic image. The point cloud data in the denoised topographic image is more realistic and accurate. A data fusion algorithm is used to repair the denoised topographic image, thus completing the restoration of the topographic mapping image. It should be noted that, in addition to the data fusion algorithm, an interpolation algorithm can also be used to repair the topographic mapping image. Both data fusion algorithms and interpolation algorithms are well known to those skilled in the art and will not be limited or described in detail here.

[0048] In summary, the process involves: acquiring topographic mapping images; acquiring all point cloud data and the reflected signal intensity of the corresponding lidar signal for each point cloud data; selecting one point cloud data from the topographic mapping images as a reference point cloud data; obtaining the local compactness of the reference point cloud data based on its spatial distribution and reflected signal intensity within a preset space; filtering all density-sparse data from all point cloud data based on the local compactness; selecting one density-sparse data as a reference sparse data; obtaining the density change stability of the reference sparse data based on the distribution characteristics of other density-sparse data within a preset space; obtaining the data coplanarity of the reference sparse data based on its spatial distribution within the preset space; obtaining the density uniformity index of the reference sparse data based on its density change stability and data coplanarity; acquiring all noise data based on the density uniformity index of each density-sparse data; removing noise data from the topographic mapping images to obtain denoised topographic images; and performing restoration on the denoised topographic images.

[0049] The second objective of this invention is to provide a topographic mapping image restoration system based on lidar. The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The feature is that when the processor executes the computer program, it implements the steps of the topographic mapping image restoration method based on lidar as described above.

[0050] A third objective of this invention is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the aforementioned lidar-based terrain mapping image restoration method.

[0051] A fourth objective of this invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned lidar-based terrain mapping image restoration method.

[0052] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0053] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for topographic mapping image restoration based on lidar, characterized in that, The method includes: acquiring topographic mapping images; acquiring all point cloud data in the topographic mapping images and the reflected signal intensity of the lidar corresponding to each point cloud data; randomly selecting one point cloud data in the topographic mapping images as reference point cloud data; obtaining the local compactness of the reference point cloud data based on the distance and reflected signal intensity difference between the reference point cloud data and each other point cloud data in the preset space; filtering all density-sparse data from all point cloud data based on the local compactness; randomly selecting one density-sparse data as reference sparse data; obtaining the density change stability of the reference sparse data based on the distribution characteristics of other density-sparse data in the preset space of the reference sparse data; obtaining the data coplanarity of the reference sparse data based on the spatial distribution of point cloud data in the preset space of the reference sparse data; obtaining the density uniformity index of the reference sparse data based on the density change stability and the data coplanarity of the reference sparse data; and determining the density uniformity index based on the density uniformity of each density-sparse data. The process involves: acquiring all noise data; removing the noise data from the topographic mapping image to obtain a denoised topographic image; repairing the denoised topographic image; establishing a spatial rectangular coordinate system centered on the reference sparse data; using the plane formed by every two coordinate axes as the initial plane to obtain all initial planes; selecting any initial plane as the target plane and continuously rotating the target plane along any coordinate axis that forms the plane by a preset angle until the preset space of the reference sparse data is divided into a preset number of equal-volume spaces by the target plane; obtaining the stability of density change based on the distribution of density sparse data within the equal-volume spaces; determining a reference plane by comparing the reference sparse data with any two other density sparse data to obtain all reference planes where the reference sparse data is located; using the density sparse data outside the density sparse data that determines each reference plane within the preset space as the sparse points to be compared for each reference plane; and obtaining the coplanarity of the data according to the data coplanarity calculation formula, which is shown below: Where, Indicates the degree of data coplanarity in the reference sparse data; Indicates the number of reference planes; Indicates the first The number of sparse points to be compared outside the reference plane; Indicates the first The first reference plane outside the first reference plane The sparse points to be compared with the first... Vertical distance between reference planes; Indicates the first The first reference plane outside the reference plane The degree of local compactness of the sparse points to be compared; Indicates the local compactness of the reference sparse data; Indicates the first The first reference plane outside the reference plane The distance between each sparse point to be compared and the reference sparse data; Indicates determining the first The sequence number of the first other density-sparse data in the reference plane; Indicates determining the first The index of the second other density-sparse data in the reference plane; Indicates determining the first The first reference plane The other density sparse data and the first The distance between other sparse data points; Indicates determining the first The first reference plane The intensity of reflected signals from other sparse data; Indicates determining the first The first reference plane The intensity of reflected signals from other sparse data; This represents the absolute value function.

2. The method for topographic mapping image restoration based on lidar according to claim 1, characterized in that, The method for obtaining the local compactness includes: obtaining the local compactness according to a local compactness calculation formula, the local compactness calculation formula being as follows: Where, Indicates the sequence number of other point cloud data within the preset space, excluding the reference point cloud data; Indicates the local compactness of the reference point cloud data; Indicates the volume of the preset space; Indicates the number of other point cloud data within the preset space; Indicates the reference point cloud data and the first point within the preset space. The distance between other point cloud data; Indicates the first [number] in the preset space The reflected signal strength of other point cloud data; Indicates the reflected signal strength of the reference point cloud data; This represents the normalization function.

3. The method for topographic mapping image restoration based on lidar according to claim 1, characterized in that, All methods for obtaining density-sparse data include: taking point cloud data with a local compactness not greater than a preset first threshold as density-sparse data.

4. The method for topographic mapping image restoration based on lidar according to claim 1, characterized in that, The formula for calculating the stability of the density change is as follows: In the formula, This indicates the stability of density changes in the reference sparse data; Indicates the number of initial planes; Indicates the preset quantity of spaces of equal volume; Indicates the first The first initial plane rotation constitutes the... The local compactness of reference sparse data in an equal-volume space; Indicates the first The first initial plane rotation constitutes the... The local compactness of reference sparse data in an equal-volume space; It represents the variance of the local compactness of the reference sparse data within all equal-volume spaces formed by all initial plane rotations.

5. The method for topographic mapping image restoration based on lidar according to claim 1, characterized in that, The method for obtaining the density uniformity index includes: normalizing the ratio between the stability of the density change of the reference sparse data and the coplanarity of the data to obtain the density uniformity index of the reference sparse data.

6. The method for topographic mapping image restoration based on lidar according to claim 1, characterized in that, The method for obtaining the noise data includes: using sparse data with a density uniformity index not less than a preset second threshold as noise data.

7. A topographic mapping image restoration system based on lidar, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the topographic mapping image restoration method based on lidar as described in any one of claims 1-6.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the lidar-based terrain mapping image restoration method as described in any one of claims 1 to 6.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the lidar-based terrain mapping image restoration method as described in any one of claims 1 to 6.

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