Terrain surveying and mapping image restoration method and system based on laser radar

Abstract

The invention relates to the field of topographic surveying and mapping image processing, in particular to a topographic surveying and mapping image restoration method and system based on a laser radar. The method comprises the following steps: obtaining a local tightness degree according to data distribution and reflected signal intensity around a topographic surveying and mapping image, and further screening out all density sparse data; according to the density change stability degree of density sparse data distribution around the density sparse data; obtaining a data coplane degree according to point cloud data space distribution around the density sparse data, and obtaining a density uniformity index through combination so as to obtain all noise data; and removing the noise data to obtain a denoised topographic image, and repairing the denoised topographic image. According to the invention, all noise data can be removed to obtain an accurate topographic surveying and mapping image, so that the repairing effect of the topographic surveying and mapping image is improved.

Description

Technical Field

[0001] The present invention relates to the field of terrain mapping image processing, and in particular to a terrain mapping image restoration method and system based on laser radar. Background Art

[0002] LiDAR is a radar system that uses laser beams to detect target characteristics such as position and velocity. It operates by transmitting a detection signal (a laser beam) toward the target and then comparing the received signal reflected from the target (the target echo) with the transmitted signal to obtain relevant information about the target. LiDAR is widely used in topographic mapping scenarios, offering numerous advantages over traditional methods, including high precision and low cost. The resulting topographic image is a collection of discrete points based on a three-dimensional point cloud, accurately representing the shape and position of objects and providing detailed physical information about the environment.

[0003] During terrain mapping, LiDAR may generate noise points due to various reasons, such as environmental interference, system errors, and multipath reflections. These noise points can reduce the overall accuracy of the point cloud data. Existing techniques often use clustering algorithms to analyze all point cloud data in terrain mapping images, identify all noise data, remove it, and then repair the denoised terrain mapping images. However, in reality, there are areas where the LiDAR signal reflection intensity or return time is uneven, such as forested areas and urban areas with abundant greenery. This results in sparse point cloud data in such areas, making it difficult to distinguish between sparse point cloud data and noise data. This can easily lead to loss of detail in the point cloud data during denoising, which in turn affects the restoration of terrain mapping images. Summary of the Invention

[0004] In order to solve the problem that in actual situations, there are certain areas where the signal reflection intensity or return time of the laser radar is uneven, resulting in sparse real point cloud data in such areas, and the sparse point cloud data is difficult to distinguish from the noise data. It is easy to cause the loss of details of the point cloud data during denoising, which in turn affects the technical problem of repairing the topographic mapping image, the purpose of the present invention is to provide a topographic mapping image repair method and system based on laser radar, and the technical scheme adopted is as follows: a topographic mapping image repair method based on laser radar, the method comprises: obtaining a topographic mapping image; obtaining all point cloud data in the topographic mapping image and the reflection signal intensity of the laser radar corresponding to each point cloud data; selecting any point cloud data in the topographic mapping image as the reference point cloud data; and according to the spatial coordinates of the point cloud data in the preset space of the reference point cloud data The local compactness of the reference point cloud data is obtained by combining the spatial distribution of the point cloud data and the intensity of the reflected signal; all density sparse data are screened in all point cloud data according to the local compactness; any one density sparse data is selected as the reference sparse data; the density change stability of the reference sparse data is obtained according to the distribution characteristics of other density sparse data in the preset space of the reference sparse data; the data coplanarity of the reference sparse data is obtained according to the spatial distribution of the point cloud data in the preset space of the reference sparse data; the density uniformity index of the reference sparse data is obtained according to the density change stability and the data coplanarity of the reference sparse data; all noise data are 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; and the denoised topographic image is repaired.

[0005] Furthermore, the method for obtaining the local tightness includes: obtaining the local tightness according to a local tightness calculation formula, and the local tightness calculation formula is as follows: Where, Indicates the serial number of other point cloud data in the preset space except the reference point cloud data; Indicates the local tightness of the reference point cloud data; Indicates the volume of the preset space; Indicates the number of other point cloud data in the preset space; Indicates the reference point cloud data and the first The distance between other point cloud data; Indicates the first The reflected signal strength of other point cloud data; Indicates the reflected signal strength of the reference point cloud data.

[0006] Furthermore, the method for acquiring all density sparse data includes: taking point cloud data whose local density is not greater than a preset first threshold as density sparse data.

[0007] Furthermore, the method for obtaining the stability of density variation includes: establishing a spatial rectangular coordinate system with the reference sparse data as the center; taking a plane formed by every two coordinate axes as an initial plane to obtain all initial planes; selecting any one of the initial planes as a target plane, and continuously rotating the target plane by a preset angle along any one of the coordinate axes forming the plane 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 variation based on the distribution of density sparse data in the equal-volume spaces, and the calculation formula for the stability of density variation is as follows: Where, Indicates the stability of density changes of reference sparse data; Indicates the number of initial planes; Represents a preset number of equal-volume spaces; Indicates the The first rotation of the initial plane The local compactness of the reference sparse data in equal volume spaces; Indicates the The first rotation of the initial plane The local compactness of the reference sparse data in equal volume spaces; Represents the variance of the local compactness of the reference sparse data in all equal-volume spaces formed by all initial plane rotations.

[0008] Furthermore, the method for obtaining the data coplanarity includes: determining a reference plane with reference sparse data and any two other density sparse data to obtain all reference planes where the reference sparse data is located; using density sparse data other than the density sparse data that determines each reference plane in a preset space as sparse points to be compared on each reference plane; and obtaining the data coplanarity according to the data coplanarity calculation formula, which is as follows: Where, Indicates the degree of data coplanarity with reference to sparse data; Indicates the number of datum planes; Indicates the The number of sparse points to be compared outside the reference plane; Indicates the The first datum plane outside The sparse points to be compared with the The vertical distance between the two reference planes; Indicates the The first datum plane outside The local compactness of the sparse points to be compared; Indicates the local tightness of the reference sparse data; Indicates the The first datum plane outside The distance between the sparse points to be compared and the reference sparse data; Indicates confirmation The sequence number of the first other density sparse data of the reference plane; Indicates confirmation The sequence number of the second other density sparse data of the reference plane; Indicates confirmation The first datum plane Other density-sparse data and The distance between other density-sparse data; Indicates confirmation The first datum plane The reflected signal intensity of other density-sparse data; Indicates confirmation The first datum plane The reflected signal intensity of other density-sparse data; represents the absolute value function.

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

[0010] Furthermore, the method for acquiring noise data includes: taking density sparse data whose density uniformity index is not less than a preset second threshold as noise data.

[0011] A laser radar-based terrain surveying and mapping image restoration system, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the laser radar-based terrain surveying and mapping image restoration method described above are implemented.

[0012] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for repairing terrain surveying and mapping images based on laser radar.

[0013] A computer device 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, the steps of the above-mentioned method for repairing topographic mapping images based on laser radar are implemented.

[0014] The present invention has the following beneficial effects: the present invention obtains a topographic mapping image; in order to analyze the point cloud data in the topographic mapping image to obtain all noise data, first all point cloud data in the topographic mapping image and the reflected signal intensity of the laser radar corresponding to each point cloud data are obtained; because the reflected signal intensity of the point cloud data in a vegetation-rich area is significantly different from that in other areas, and the point cloud data is relatively sparse, the local density of each point cloud data is obtained through combined analysis, and all density sparse data are screened from all point cloud data based on the local density; because the distribution characteristics and density variation characteristics of real density sparse data and noise data are different, the density variation stability and data coplanarity of each density sparse data in a preset space are analyzed; based on the density variation stability and data coplanarity of each density sparse data, a density uniformity index of the reference sparse data is obtained, and all noise data are found using the density uniformity index. The noise data in the topographic mapping image is removed to obtain a denoised topographic image; and the denoised topographic image is restored. The present invention can remove all noise data, obtain an accurate topographic mapping image, and thereby improve the restoration effect of the topographic mapping image. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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.

[0016] Figure 1 A flowchart of a method for restoring terrain mapping images based on lidar is provided in accordance with one embodiment of the present invention. DETAILED DESCRIPTION

[0017] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a LiDAR-based terrain mapping image restoration method and system proposed in accordance with the present invention. In the following description, references to different "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.

[0018] 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.

[0019] The following describes in detail a method and system for restoring terrain mapping images based on laser radar provided by the present invention with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a method for repairing a terrain mapping image based on laser radar provided by an embodiment of the present invention, the method comprising: step S1: obtaining a terrain mapping image; obtaining all point cloud data in the terrain mapping image and the reflected signal intensity of the laser radar corresponding to each point cloud data.

[0021] The embodiment of the present invention is primarily used in the restoration of topographic mapping images, so the topographic mapping image is first acquired for subsequent analysis. Since noise data exists within the three-dimensional point cloud data in the topographic mapping image and may be difficult to distinguish from actual sparse data points, and since sparse data points are caused by uneven LiDAR signal reflection intensity or return time in certain areas, it is necessary to analyze the radar reflection signal corresponding to each point cloud data. Therefore, the embodiment of the present invention obtains all point cloud data in the topographic mapping image and the LiDAR reflection signal intensity corresponding to all point cloud data.

[0022] In one embodiment of the present invention, a lidar system carried by a drone acquires terrain data of an area to be mapped and generates a corresponding terrain mapping image. It should be noted that the method for acquiring the terrain mapping image can be set by the implementer and is not limited here.

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

[0024] In practice, certain areas within topographic mapping images can cause uneven LiDAR signal reflection intensity or return times, such as forested areas and urban areas with abundant greenery. This results in sparse point cloud data in these areas, making it difficult to distinguish between sparse point cloud data and noise data. Because noise data in topographic mapping images manifests as a significant and prominent difference in distance between the noise data and surrounding point cloud data, embodiments of the present invention determine the local density of the reference point cloud data based on the spatial distribution and reflection signal strength of the point cloud data within a preset space of the reference point cloud data. This local density is then used to filter the sparse data to facilitate subsequent identification of noise data.

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

[0026] In the local tightness calculation formula, the distance between the reference point cloud data and each other point cloud data in the preset space is The larger the value, the less continuous the reference point cloud data is, that is, the more isolated the reference point cloud data is; and the difference in reflected signal strength between the reference point cloud data and each other point cloud data in the preset space is The larger the value is, the more prominent the reference point cloud data is in the preset space. Perform cumulative summation and sum value The larger the value, the greater the difference between the reference point cloud data and other point cloud data in the preset space, and the smaller the local density of the reference point cloud data. The density of point cloud data in the preset space of the reference point cloud data is The smaller it is, the less local density the reference point cloud data has within the preset space.

[0027] In one embodiment of the present invention, a spherical space with a radius of 10 meters and a reference point cloud data as the center 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 acquiring all density-sparse data includes: treating point cloud data whose local density is no 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 herein.

[0029] At this point, all density sparse data in the topographic mapping image are obtained.

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

[0031] In actual situations, when the laser radar is used for terrain mapping, the reflected signal intensity of the laser radar will be uneven due to vegetation cover. Therefore, there will be some sparse point cloud data similar to noise data in the terrain mapping image, and conventional methods cannot distinguish the real sparse point cloud data from the noise data. In areas with rich vegetation, most of the laser radar's signal beams are blocked by leaves, and only a few signal beams can directly reach the ground or branches. The reflected signal intensities of these unblocked signal beams are similar and differ greatly from the surrounding signal beams, thus forming density sparse data. However, the surface of the ground and branches is usually relatively flat, so the density sparse data with similar reflected signal intensities are mostly in similar planes. Therefore, the density distribution of the density sparse data around the density sparse data caused by vegetation cover is uneven. Therefore, in one embodiment of the present invention, the density change and density sparse data distribution around each density sparse data are analyzed.

[0032] Preferably, in one embodiment of the present invention, the density change stability of the reference sparse data is obtained based on the distribution characteristics of other density sparse data in the preset space of the reference sparse data, specifically including: establishing a spatial rectangular coordinate system with the reference sparse data as the center; taking the plane composed of every two coordinate axes as the initial plane, and obtaining all 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] Select any initial plane as the target plane, and continuously rotate the target plane by a preset angle along any coordinate axis constituting the plane 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, wherein the angle of each rotation of each initial plane is set to 30°. At this time, 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 by yourself and are not limited here.

[0034] The stability of density change is obtained based on the density sparse data distribution in the equal volume space. The calculation formula for the stability of density change is as follows: Where, Indicates the stability of density changes of reference sparse data; Indicates the number of initial planes; Represents a preset number of equal-volume spaces; Indicates the The first rotation of the initial plane The local compactness of the reference sparse data in equal volume spaces; Indicates the The first rotation of the initial plane The local compactness of the reference sparse data in equal volume spaces; Represents the variance of the local compactness of the reference sparse data in all equal-volume spaces formed by all initial plane rotations.

[0035] In the density change stability calculation formula, the local compactness of each equal volume space divided by the reference sparse data on each initial plane is calculated; the local compactness difference of the reference sparse data in two adjacent equal volume spaces divided by each initial plane is calculated. The greater the difference, the greater the density difference of the sparse data in the two adjacent equal-volume spaces. The local density difference in each adjacent two equal-volume spaces among all equal-volume spaces divided by all initial planes is Get the average , which reflects the density dispersion degree in the preset space of reference sparse data; the larger the mean, the more discrete the density sparse data distribution in the preset space of reference sparse data, and the worse the density change stability of reference sparse data; and the local density variance in all equal volume spaces is The larger the value, the more discrete the density sparse data in different equal volume spaces, and the more drastic the density change of the secretary sparse data in the preset space; so The negative correlation mapping normalization process is performed to obtain the density change stability of the reference sparse data.

[0036] Since the density sparse data of the vegetation coverage area are all in similar planes, the distance between the plane formed by any few density sparse data and other density sparse data is usually not too large; while the noise data are irregularly randomly distributed, and the distance between the plane formed by it and the surrounding density sparse data and other density sparse data may be very large. In order to further distinguish the noise data from the real density sparse data, in an embodiment of the present invention, the data coplanarity of the reference sparse data is obtained according to the spatial distribution of the point cloud data in the preset space of the reference sparse data.

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

[0038] The density sparse data other than the density sparse data of each reference plane determined in the preset space is used as the sparse points to be compared on each reference plane; the data coplanarity is obtained according to the data coplanarity calculation formula, which is as follows: Where, Indicates the degree of data coplanarity with reference to sparse data; Indicates the number of datum planes; Indicates the The number of sparse points to be compared on the reference plane; Indicates the The first datum plane The sparse points to be compared with the The vertical distance between the two reference planes; Indicates the The first datum plane The local compactness of the sparse points to be compared; Indicates the local tightness of the reference sparse data; Indicates the The first datum plane The distance between the sparse points to be compared and the reference sparse data; Indicates confirmation The sequence number of the first other density sparse data of the reference plane; Indicates confirmation The sequence number of the second other density sparse data of the reference plane; Indicates confirmation The first datum plane Other density-sparse data and The distance between other density-sparse data; Indicates confirmation The first datum plane The reflected signal intensity of other density-sparse data; Indicates confirmation The first datum plane The reflected signal intensity of other density-sparse data; represents the absolute value function.

[0039] In the formula for calculating the degree of data coplanarity, The sparse points to be compared with the The vertical distance between the datum planes The farther away, the more the sparse point to be compared is from the reference sparse data. The more unlikely the first reference plane is to be coplanar, and because the difference in local tightness between the sparse point to be compared and the reference sparse data is greater, the greater the distance between the two points, indicating that the The sparse points to be compared with the The less likely the two datum planes are to be coplanar, the and The product of The weight of In the reference plane, the Other density-sparse data and The larger the distance between the other density sparse data, the Other density-sparse data and The greater the difference in reflected signal intensity between the other density sparse data, the greater the difference in the reflected signal intensity between the Other density-sparse data and The more likely other density sparse data are different types of density sparse data, the less representative the plane they form is. In this case, the first The weight of a datum plane among all datum planes, so Perform negative correlation mapping as The coplanarity of each reference plane is analyzed based on its weight to obtain the data coplanarity of the reference sparse data.

[0040] According to the above process, the density change stability and data coplanarity of the reference sparse data are obtained, and then 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 change stability degree and the data coplanarity degree of the reference sparse data to obtain the density uniformity index of the reference sparse data.

[0042] The density uniformity index is obtained according to the density uniformity index calculation formula. The density uniformity index calculation formula is as follows: Where, represents the density uniformity index of the reference sparse data; Indicates the stability of density changes of reference sparse data; Indicates the degree of data coplanarity with reference to sparse data; Represents the normalization function.

[0043] In the density uniformity index calculation formula, the greater the density change stability of the reference sparse data, the more uniform the distribution of the density sparse data around the reference sparse data, the more likely the reference sparse data is noise data, and the larger the density uniformity index; the greater the data coplanarity of the reference sparse data, the more likely the reference sparse data and the surrounding density sparse data are to be coplanar, and the more uneven the distribution of the density sparse data around the reference sparse data, the more likely the reference sparse data is real point cloud data in the vegetation coverage area, and the smaller the density uniformity index of the reference sparse data.

[0044] Since the distribution of the actual point cloud data of the vegetation coverage area in the terrain mapping image is mostly in a plane and is not uniform, in the embodiment of the present invention, all noise data are obtained based on the density uniformity index of each density sparse data.

[0045] Preferably, in one embodiment of the present invention, the method for obtaining noise data includes: treating density sparse data whose density uniformity index is 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 voluntarily and is not limited here.

[0046] Step S4: removing noise data from the topographic mapping image to obtain a denoised topographic image; and repairing the denoised topographic image.

[0047] In one embodiment of the present invention, all noise data in a topographic mapping image is removed to obtain a denoised topographic image. The point cloud data within the denoised topographic image is more realistic and accurate. A data fusion algorithm is then used to inpaint the denoised topographic image, 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 inpaint topographic mapping images. Both data fusion and interpolation algorithms are well known to those skilled in the art and are not limited or elaborated upon herein.

[0048] In summary, a topographic mapping image is obtained; all point cloud data in the topographic mapping image and the reflected signal intensity of the laser radar corresponding to each point cloud data are obtained; any point cloud data in the topographic mapping image is selected as the reference point cloud data; the local compactness of the reference point cloud data is obtained according to the spatial distribution of the point cloud data in the preset space of the reference point cloud data and the reflected signal intensity; all density sparse data are screened and obtained from all point cloud data according to the local compactness; any density sparse data is selected as the reference sparse data; the density change stability of the reference sparse data is obtained according to the distribution characteristics of other density sparse data in the preset space of the reference sparse data; the data coplanarity of the reference sparse data is obtained according to the spatial distribution of the point cloud data in the preset space of the reference sparse data; the density uniformity index of the reference sparse data is obtained according to the density change stability and data coplanarity of the reference sparse data; all noise data are 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; and the denoised topographic image is repaired.

[0049] The second purpose of an embodiment of the present invention is to provide a lidar-based terrain surveying and mapping image restoration system, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor, and is characterized in that when the processor executes the computer program, it implements the steps of the above-mentioned lidar-based terrain surveying and mapping image restoration method.

[0050] The third purpose of an embodiment of the present invention is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned method for repairing terrain surveying and mapping images based on lidar are implemented.

[0051] The fourth object of an embodiment of the present invention is to provide a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the above-mentioned method for repairing terrain surveying and mapping images based on lidar.

[0052] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain 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, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for restoring terrain mapping images based on laser radar, characterized in that: The method includes: obtaining a topographic mapping image; obtaining all point cloud data in the topographic mapping image and the reflected signal intensity of a laser radar corresponding to each point cloud data; selecting any point cloud data in the topographic mapping image as reference point cloud data; obtaining the local compactness of the reference point cloud data based on the spatial distribution of the point cloud data in a preset space of the reference point cloud data and the reflected signal intensity; screening all density sparse data from all point cloud data based on the local compactness; selecting any 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 the 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; obtaining all noise data based on the density uniformity index of each density sparse data; removing the noise data in the topographic mapping image to obtain a denoised topographic image; and repairing the denoised topographic image.

2. The method for restoring terrain mapping images based on laser radar according to claim 1, characterized in that: The method for obtaining the local tightness includes: obtaining the local tightness according to a local tightness calculation formula, and the local tightness calculation formula is as follows: Where, Indicates the serial number of other point cloud data in the preset space except the reference point cloud data; Indicates the local tightness of the reference point cloud data; Indicates the volume of the preset space; Indicates the number of other point cloud data in the preset space; Indicates the reference point cloud data and the first The distance between other point cloud data; Indicates the first The reflected signal strength of other point cloud data; Indicates the reflected signal strength of the reference point cloud data; Represents the normalization function.

3. The method for restoring terrain mapping images based on laser radar according to claim 1, characterized in that: The method for acquiring all density sparse data includes: taking point cloud data whose local density is not greater than a preset first threshold as density sparse data.

4. The method for restoring terrain mapping images based on laser radar according to claim 1, characterized in that: The method for obtaining the density change stability includes: establishing a spatial rectangular coordinate system with the reference sparse data as the center; taking a plane formed by every two coordinate axes as an initial plane to obtain all initial planes; selecting any one of the initial planes as a target plane, and continuously rotating the target plane by a preset angle along any coordinate axis forming the plane until the preset space of the reference sparse data is divided into a preset number of equal-volume spaces by the target plane; and obtaining the density change stability based on the distribution of the density sparse data in the equal-volume spaces. The density change stability calculation formula is as follows: Where, Indicates the stability of density changes of reference sparse data; Indicates the number of initial planes; Represents a preset number of equal-volume spaces; Indicates the The first rotation of the initial plane The local compactness of the reference sparse data in equal volume spaces; Indicates the The first rotation of the initial plane The local compactness of the reference sparse data in equal volume spaces; Represents the variance of the local compactness of the reference sparse data in all equal-volume spaces formed by all initial plane rotations.

5. The method for restoring terrain mapping images based on laser radar according to claim 1, characterized in that: The method for obtaining the data coplanarity includes: determining a reference plane with reference sparse data and any two other density sparse data to obtain all reference planes where the reference sparse data is located; using density sparse data other than the density sparse data that determines each reference plane in a preset space as sparse points to be compared on each reference plane; and obtaining the data coplanarity according to the data coplanarity calculation formula, wherein the data coplanarity calculation formula is as follows: Where, Indicates the degree of data coplanarity with reference to sparse data; Indicates the number of datum planes; Indicates the The number of sparse points to be compared outside the reference plane; Indicates the The first datum plane outside The sparse points to be compared with the The vertical distance between the two reference planes; Indicates the The first datum plane outside The local compactness of the sparse points to be compared; Indicates the local tightness of the reference sparse data; Indicates the The first datum plane outside The distance between the sparse points to be compared and the reference sparse data; Indicates confirmation The sequence number of the first other density sparse data of the reference plane; Indicates confirmation The sequence number of the second other density sparse data of the reference plane; Indicates confirmation The first datum plane Other density-sparse data and The distance between other density-sparse data; Indicates confirmation The first datum plane The reflected signal intensity of other density-sparse data; Indicates confirmation The first datum plane The reflected signal intensity of other density-sparse data; represents the absolute value function.

6. The method for restoring terrain mapping images based on laser radar according to claim 1, characterized in that: The method for obtaining the density uniformity index includes normalizing the ratio between the density change stability of the reference sparse data and the data coplanarity to obtain the density uniformity index of the reference sparse data.

7. The method for restoring terrain mapping images based on laser radar according to claim 1, characterized in that: The method for acquiring noise data includes: using density sparse data whose density uniformity index is not less than a preset second threshold as noise data.

8. A laser radar-based terrain mapping image restoration 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, the steps of the lidar-based terrain surveying and mapping image restoration method as described in any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a method for repairing topographic mapping images based on laser radar are implemented as described in any one of claims 1 to 7.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the processor implements the steps of a lidar-based terrain mapping image restoration method as described in any one of claims 1 to 7.

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