A method and apparatus for equal-density processing of two-dimensional laser point clouds

Abstract

The present invention belongs to the technical field of point cloud data processing, and discloses a method for equal density processing of two-dimensional laser point cloud, including: S1: reading a point in the image coordinate system, calculating the Euclidean distance between it and the next adjacent point, and judging whether the two points are continuous; S2: calculating the equal density threshold of the point cloud image in the world coordinate system, and generating a distance lookup table according to the equal density threshold; S3: converting the point in the image coordinate system into a point in the world coordinate system, and calculating its Euclidean distance, and calculating the number of points to be inserted between them according to the distance lookup table; calculating the coordinates of the new inserted point according to the world coordinates of the two points and the number of points to be inserted; S4: reading the next point in the image coordinate system, repeating the above steps until all points are interpolated; S5: performing equal point number sampling on the point cloud data after the insertion point, and generating equal density point cloud data. The present invention has obvious equal density effect, good quality, simple and efficient algorithm, and can improve computing performance.

Description

Technical Field

[0001] The present invention relates to the technical field of point cloud data processing, and in particular to a method and device for iso-densification processing of two-dimensional laser point clouds. Background Art

[0002] The image coordinate points are converted to world coordinates by table lookup. Due to projection and other relationships, the world distance represented by a pixel is not equal at different positions in the image. The rigid body transformation is used from the image coordinate system to the world coordinate system, that is, in the coordinate conversion process, only a simple rotation and translation transformation is experienced, and the shape and volume of the original object will not change. Such a uniform image coordinate system becomes a non-uniform discrete point cloud after being converted to the world coordinate system. This creates an obstacle for the back-end recognition algorithm, and it is impossible to determine whether there is a point loss in the middle. At the same time, the uneven density of the point cloud may cause the established model to fail to accurately reflect the morphological changes of the entity, seriously affecting the subsequent feature extraction. Unprocessed point clouds usually show characteristics such as irregular distribution and uneven density. Rearranging the messy and irregularly distributed laser point clouds in a regular manner is a necessary means for point cloud processing in certain detection and recognition work.

[0003] The interpolation algorithm can effectively solve the unevenness of point clouds. The core of the interpolation algorithm is to accurately predict the information of unknown points based on the information of known points. Currently, the common point cloud interpolation methods are as follows:

[0004] Nearest neighbor interpolation algorithm: The operation is simple and efficient, but it is highly dependent on point cloud data. If there is a lot of noise, there may be more noise points in the interpolation result. In addition, the nearest neighbor interpolation algorithm ignores the geometric information of the point cloud data. When the data is unevenly distributed, it cannot reflect the changes in the attribute values ​​of the blank area; the boundary is prone to jagged effects.

[0005] Inverse distance weighted interpolation algorithm: The principle is to use the weighted average of the elevation values ​​of a set of points in a neighborhood to estimate the estimated value of the point to be interpolated. The weight of the points in the neighborhood is determined by the distance from the point to the point to be interpolated. The weight is the inverse of the kth power of the distance, so the closer the distance, the greater the weight. The inverse distance weighted interpolation algorithm is more accurate than the nearest neighbor interpolation algorithm because it effectively utilizes the location information of the point set in the neighborhood. The interpolation effect is poor in the missing value area, the error cannot be estimated, and it is easy to produce deviations.

[0006] Irregular triangulated network interpolation method: a method based on sampling points and broken lines to approximate the surface of an object. The shape of the surface is a triangle, and the size depends on the density and specific location of the sampling points. The principle of this interpolation method is: by constructing triangles, a grid that can cover the entire area is formed, and the constructed triangles cannot intersect. The surface of each node covered by the triangle is defined as known information, and all points of the constructed triangle are restricted by the surface of the triangle. The interpolation effect is relatively stable, but the workload of preliminary data processing is large. The nearest neighbor interpolation method and the irregular triangulated network interpolation method can both interpolate and fill in missing points well, and the overall effect is basically the same. However, from the perspective of local magnification, the nearest neighbor interpolation method is not as good as the irregular triangulated network interpolation method, and it has obvious edge jagged effects.

[0007] Kriging interpolation algorithm: The Kriging interpolation algorithm first needs to calculate a variation distribution of the point cloud data, then determine the point cloud range that affects the interpolation point based on the variation distribution, and then use the point set within the range to estimate the interpolation point. This algorithm takes into account the connection between point cloud data, but the calculation is more complicated, and the determination of the variation function requires human intervention. Summary of the invention

[0008] In order to adapt to the actual needs in the field of point cloud data processing technology, the present invention overcomes the shortcomings of the prior art and the technical problem to be solved is: to provide a method and device for iso-densification processing of two-dimensional laser point clouds, to iso-densify the messy two-dimensional laser point clouds so that the points are evenly arranged, thereby laying the foundation for subsequent point cloud feature recognition and other work, improving the efficiency of the back-end algorithm for point cloud feature recognition, and achieving a reasonable detection effect.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for iso-densification processing of two-dimensional laser point clouds, which comprises the following steps:

[0010] S1: Read a point P1 in the image coordinate system, calculate the Euclidean distance D between it and the next adjacent point P2, and determine whether point P1 and point P2 are continuous based on the Euclidean distance. If they are continuous, proceed to step S2; if not, mark P1 and P2 as endpoints and proceed to step S4;

[0011] S2: Calculate the equal density threshold b of the point cloud image in the world coordinate system, and generate a distance lookup table DST according to the equal density threshold b;

[0012] S3: Convert the points P1 and P2 in the image coordinate system into the points S1(y1,z1) and S2(y2,z2) in the world coordinate system, calculate the Euclidean distance Dis between the points S1 and S2, and calculate the number of points IC to be inserted between S1 and S2 based on Dis and the distance lookup table DST; calculate the new insertion points I1, I2...I according to the world coordinates of the two points S1(y1,z1), S2(y2,z2) and the number of points to be inserted IC n (1≤n≤IC) coordinates;

[0013] S4, read the next point P2 in the image coordinate system, and repeat steps S1 and S3 until all points are interpolated;

[0014] S5. Perform equal point number sampling on the point cloud data after the insertion point, while protecting the marked endpoints to generate equal-density and sparse point cloud data.

[0015] In the step S1, a threshold determination method is used to determine whether point P1 is continuous with point P2;

[0016] If the Euclidean distance D is greater than the image distance threshold, it is judged to be discontinuous;

[0017] If the Euclidean distance D is less than or equal to the image distance threshold, it is determined to be continuous.

[0018] In step S1, the calculation formula of the Euclidean distance D is:

[0019]

[0020] Among them, (r1, c1) and (r2, c2) represent the coordinates of point P1 and point P2 in the image coordinate system respectively.

[0021] In step S2, the calculation formula of the equal density threshold b is:

[0022] b=ω0(μ-μ0) 2 +ω1(μ-μ1) 2 ;

[0023] Among them, μ represents the average distance between all neighboring points, μ0 represents the average distance between all points whose distance is less than or equal to the variance, μ1 represents the average distance between all points whose distance is greater than the variance; ω0 represents the ratio of the number of points whose distance to the next nearest point is less than or equal to the variance to the total number of points M, and ω1 represents the ratio of the number of points whose distance to the next nearest point is greater than the variance to the total number of points M.

[0024] In step S3, if the Euclidean distance Dis is less than or equal to the equal density threshold b, no point insertion is performed, that is, the number of inserted points is zero; if the Euclidean distance Dis is greater than the equal density threshold b, point insertion is performed and the inserted point value is calculated.

[0025] In step S3, the calculation formula of the insertion points IC is:

[0026]

[0027] Or the calculation method of the number of insertion points IC is: look up the distance lookup table DST, if the Euclidean distance Dis is between nb and (n+1)b, ​​then:

[0028]

[0029] In step S2, the distance lookup table DST is specifically:

[0030] DST=[b, 2b, 3b, ..., 30b].

[0031] In step S4, the nth point I inserted between the two coordinate points S1 (y1, z1) and S2 (y2, z2) n (y n ,z n ) is calculated by:

[0032]

[0033] The step S4 also includes calculating the equalization index τ y and τ z The calculation formula is:

[0034] The equalization index τ y and τ z To determine y n and z n The rounding method is: if the equalization index is greater than or equal to 0.5, the corresponding coordinate is rounded up, otherwise, it is rounded down.

[0035] In addition, the present invention also provides a device for iso-densification of a two-dimensional laser point cloud, comprising:

[0036] Point cloud acquisition module: used to convert the point cloud coordinates in the image coordinate system into the point cloud coordinates in the world coordinate system;

[0037] Threshold generation module: used to generate the corresponding dynamic threshold value according to the specific coordinate data of the current point cloud image to generate the equal density distance threshold value;

[0038] Distance calculation module: used to calculate the Euclidean distance D between point clouds in the image coordinate system, and also used to calculate the Euclidean distance between point clouds in the world coordinate system;

[0039] Distance judgment module: used to judge whether two adjacent points are continuous points, and also used to judge whether it is necessary to interpolate between two non-continuous points according to the equal density threshold;

[0040] Isodensification module: used to perform isodensity interpolation between two non-continuous points according to the isodensification threshold;

[0041] Point cloud sampling module: used to perform equal point number sampling on the point cloud after equal density to generate equal-density and sparse point cloud data.

[0042] In addition, the present invention also provides a point cloud densification device, including a readable storage medium and a processor; wherein the readable storage medium is used to store machine-executable instructions; the processor is used to read the machine-executable instructions on the readable storage medium and execute the instructions to implement the steps of any of the above-mentioned methods.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The present invention provides a method and device for iso-densification processing of two-dimensional laser point clouds, which enables the point clouds to be arranged regularly and the points to be arranged at equal distances through iso-densification of two-dimensional laser point clouds. This processing method improves the quality of point clouds and the efficiency of subsequent point cloud feature recognition.

[0045] 2. High efficiency and excellent performance. The method for equal-density processing of two-dimensional point clouds provided by the present invention performs equal-point number sampling on the point cloud data after equal-density interpolation, so that the point cloud data can be streamlined under the premise of equal-density distribution, which greatly reduces the amount of calculation for the later point cloud feature recognition work and improves the processing efficiency; it does not require complex formula iterative calculations, takes into account both processing efficiency and effect, has obvious equal-density effect, and has good interpolation quality; the interpolation algorithm is simple and efficient, which greatly improves the computing performance.

[0046] 3. Threshold adaptation. The method for iso-densification processing of two-dimensional point clouds provided by the present invention determines whether a point needs to be inserted by calculating an adaptive threshold parameter and comparing it with the distance between two points. The threshold is dynamically generated and can be applied to various types of two-dimensional laser point clouds. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A schematic flow chart of a method for iso-densification processing of a two-dimensional laser point cloud provided in the first embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of a process for sampling an equal-density point cloud in the first embodiment of the present invention;

[0049] Figure 3A schematic diagram of a structure of a two-dimensional laser point cloud isodensification device provided in Embodiment 2 of the present invention;

[0050] Figure 4 A schematic diagram of the hardware structure of a point cloud iso-densification device provided in Embodiment 3 of the present invention;

[0051] Figure 5 This is a schematic diagram of reading original point cloud data in Embodiment 1 of the present invention, with 764 points;

[0052] Figure 6 This is a schematic diagram of point cloud data after medium-densification processing in Example 1 of the present invention, with 1349 points;

[0053] Figure 7 This is a schematic diagram of a sampling point cloud data after iso-densification processing according to an embodiment of the present invention, and the number of points is 135;

[0054] Figure 8 A partial schematic diagram of reading original point cloud data in Embodiment 1 of the present invention;

[0055] Fig. 9 This is a partial schematic diagram of point cloud data after medium-densification processing in Example 1 of the present invention;

[0056] Fig.10 It is a local schematic diagram of point cloud data after iso-densification processing and sampling in the first embodiment of the present invention.

[0057] Specific embodiment

[0058] In order to make the technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in combination with specific embodiments and drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0059] Embodiment 1

[0060] like Figures 1-2 As shown, an embodiment of the present invention provides a method for iso-densification processing of a two-dimensional laser point cloud, which comprises the following steps:

[0061] S1: Read a point P1 in the image coordinate system, calculate the Euclidean distance D between it and the next adjacent point P2, and determine whether point P1 and point P2 are continuous based on the Euclidean distance. If they are continuous, go to step S2; if not, mark P1 and P2 as endpoints and go to step S4.

[0062] Specifically, in step S1, the threshold judgment method is used to determine whether P1 and point P2 are continuous: if the Euclidean distance is greater than the image distance threshold, it is determined to be discontinuous; if the Euclidean distance D is less than or equal to the image distance threshold, it is determined to be continuous and the interpolation judgment begins.

[0063] Among them, according to the distance formula:

[0064]

[0065] It can be seen that in the image coordinate system, let the coordinates of P1 be (r1, c1) and the coordinates of P2 be (r2, c2). Substituting points P1 and P2 into the above formula, the Euclidean distance D between the two points can be obtained as

[0066]

[0067] In this embodiment, the Euclidean distance D between the two points is compared with the image distance threshold a. If D>a, it is determined that P1 and P2 are discontinuous, and P1 and P2 are marked as endpoints; if D≤a, it is determined that P1 and P2 are continuous, and the insertion point judgment is started.

[0068] S2: Calculate the equal density threshold b of the point cloud image in the world coordinate system, and generate a distance lookup table DST according to the equal density threshold b.

[0069] In this embodiment, whether equal density (point insertion) is required between two points is determined by selecting a suitable equal density threshold. How to accurately select the threshold is the key to accurate equal density, because whether the threshold selection is appropriate has a significant impact on the accuracy of equal density. If the equal density threshold is too small, it will lead to too many points being inserted, making the point cloud data redundant and large, resulting in a decrease in computing performance, which is not conducive to the processing of the point cloud by the subsequent recognition algorithm. If it is too large, the number of inserted points is too small, and the effect of equal density processing on the point cloud will not be obvious. Step S2 specifically includes the following three steps.

[0070] (2.1) Calculate the variance σ of the distance between all points in the calibration table and their neighboring points 2 .

[0071] Suppose the 6 or 8 points p around a point p0(r0,c0) in the calibration table are i (r i ,c i ), (1≤i≤N) has a Euclidean distance of d i ,(1≤i≤N),N=6or8,then d i for:

[0072]

[0073] Then p0 to p iThe average distance for:

[0074]

[0075] The Euclidean distance d from p0 to the surrounding N points i and The difference is Δd i ,but:

[0076]

[0077] Compare p0 with the N adjacent points p in the horizontal, vertical, and diagonal directions. i The average distance difference is recorded as

[0078] but for:

[0079]

[0080] Calculate Δd i The variance σ 2 , then 2 for:

[0081]

[0082] (2.2) Obtain the equal density threshold b.

[0083] In the point cloud image, suppose there are a total of M points, where the distance between a point and the next nearest point is less than or equal to the variance σ 2 The number of points is recorded as N0, the ratio of N0 to the total number of points M is recorded as ω0, and all distances are less than or equal to the variance σ 2 The average distance between points is recorded as μ0; the distance between a point and the next closest point is greater than the variance σ 2 The number of points is recorded as N1, the ratio of N1 to the total number of points M is recorded as ω1, and all distances greater than the variance σ 2 The average distance between the points is recorded as μ1; then there is the following formula:

[0084]

[0085] From the above formula, we can get that if the average distance between all neighboring points is μ, then μ is:

[0086] μ=ω0μ0+ω1μ1; (9)

[0087] Finally, the calculation formula of the equal density threshold b is:

[0088] b=ω0(μ-μ0) 2 +ω1(μ-μ1) 2; (10)

[0089] The adaptability of the threshold in this embodiment is reflected in that its value is determined by the distance variance σ of all points in the calibration table. 2 The distance between adjacent points of the point cloud image is determined by the statistical characteristics of different images and calibration tables, so the equal density threshold b used is also different. According to the derivation, this threshold can take into account the characteristics of the mean of the calibration table and the minimum distance between adjacent points of the point cloud image under the operation of the formula. This reflects the two characteristics of adaptability: one is to take values ​​according to the characteristics of the image itself, and different images have different values; the other is that the values ​​can optimize the final processing results as much as possible.

[0090] (2.3) According to the equal density threshold b, obtain the distance lookup table DST of 1 to 30 times b:

[0091] DST=[b, 2b, 3b, ..., 30b]. (11)

[0092] S3: Convert the points P1 and P2 in the image coordinate system into the points S1(y1,z1) and S2(y2,z2) in the world coordinate system, calculate the Euclidean distance Dis between the points S1 and S2, and calculate the number of points IC to be inserted between S1 and S2 based on Dis and the distance lookup table DST; calculate the new insertion points I1, I2...I according to the world coordinates of the two points S1(y1,z1), S2(y2,z2) and the number of points to be inserted IC n (1≤n≤IC) coordinates.

[0093] The step S3 specifically comprises:

[0094] (3.1) Use the calibration table to convert P1(r1,c1) and P2(r2,c2) in the image coordinate system into points S1(y1,z1) and S2(y2,z2) in the world coordinate system.

[0095] The conversion formula is:

[0096]

[0097] K is the internal parameter: the transformation of the pixel coordinate system relative to the camera coordinate system (related to the camera and lens);

[0098] T is the external parameter: the transformation of the camera coordinate system relative to the world coordinate system;

[0099] According to the distance formula (1), the calculation formula of the Euclidean distance Dis between S1 and S2 can be obtained as follows:

[0100]

[0101] In order to improve performance, the square of the distance is used for judgment without taking the square root.

[0102] (3.2) Determine the relationship between the Euclidean distance Dis and the equal density threshold b.

[0103] If Dis is less than the equal density threshold parameter β, no point insertion is performed, and if it is greater than β, point insertion is performed. Calculate the number of points IC to be inserted:

[0104]

[0105] In order to improve performance and reduce division operations, you can also use the distance lookup table DST to find IC:

[0106] In the distance lookup table DST, by judging that Dis is between nb and (n+1)b, ​​we can get:

[0107]

[0108] (3.3) Calculate the coordinates of each insertion point.

[0109] According to Newton's first interpolation formula:

[0110] Node:x0,x1=x0+h,…,x n =x0+nh (h is the step length); (16)

[0111] Interpolation points:

[0112] Interpolation formula: f(x) = N1(x) + R n (x); (18)

[0113] in:

[0114]

[0115] We can get:

[0116]

[0117] in is the binomial coefficient.

[0118] When n=1,

[0119]

[0120] Among them, N1(x)=f(x0)+f[x0,x1](x-x0).

[0121] According to the above formula, the step lengths sy and sz in the x- and y-directions can be calculated by knowing the coordinates of two points S1(y1,z1) and S2(y2,z2) and the number of points IC to be inserted:

[0122]

[0123] Substituting into the above formula, we can get the inserted nth point I n (y n ,z n ) has coordinates of

[0124]

[0125] (3.4) The coordinates of the new insertion point are equalized and rounded.

[0126] Since the above formula (23) contains division and square root operations, the obtained coordinates are most likely non-integer types. Considering the accuracy requirements, the coordinates of the point cloud image must be integers, so the obtained coordinates need to be rounded. After the coordinates are rounded, the position of the newly inserted point will be offset. In order to achieve high-precision insertion, it is necessary to determine whether to round up or down. The judgment basis depends on the equalization index τ.

[0127] Equalization index τ in the y direction y and the equalization index τ in the z direction z The value of the newly inserted point I n (y n ,z n ) and the coordinates of the two points S1(y1,z1) and S2(y2,z2) to find, τ y and τ z The evaluation formula is:

[0128]

[0129] According to τ y and τ z The size of y is used to determine n and z n Rounding method:

[0130]

[0131] S4: Read the next point P2 in the image coordinate system and repeat steps S1 and S3 until all points are interpolated.

[0132] S5: Figure 2 As shown, the point cloud data after the insertion is sampled with equal number of points, while the marked endpoints are protected to generate equal-density and sparse point cloud data.

[0133] The amount of point cloud data after equal density may increase exponentially compared to the original point cloud data, which greatly increases the computational cost of later point cloud data processing. Therefore, the point cloud data after insertion should be sampled with equal points, extracting one point every sn points, while protecting the marked endpoints, to generate equal-density and sparse point cloud data. The sampled point cloud data retains the characteristics of equal-density arrangement without reducing the computational performance of the later recognition algorithm.

[0134] like Figures 5 to 10 As shown, this is a schematic diagram of the point cloud data obtained in this embodiment.

[0135] Embodiment 2

[0136] like Figure 3 As shown, the second embodiment of the present invention provides a device for iso-densification of a two-dimensional laser point cloud, which is used to implement the iso-densification processing method described in the first embodiment, including:

[0137] Point cloud acquisition module: used to convert the point cloud coordinates in the image coordinate system into the point cloud coordinates in the world coordinate system;

[0138] Threshold generation module: used to generate the corresponding dynamic threshold value according to the specific coordinate data of the current point cloud image to generate the equal density distance threshold value;

[0139] Distance calculation module: used to calculate the Euclidean distance D between point clouds in the image coordinate system, and also used to calculate the Euclidean distance between point clouds in the world coordinate system;

[0140] Distance judgment module: used to judge whether two adjacent points are continuous points, and also used to judge whether it is necessary to interpolate between two non-continuous points according to the equal density threshold;

[0141] Isodensification module: used to perform isodensity interpolation between two non-continuous points according to the isodensification threshold;

[0142] Point cloud sampling module: used to perform equal point number sampling on the point cloud after equal density to generate equal-density and sparse point cloud data.

[0143] Embodiment 3

[0144] like Figure 4As shown, the third embodiment of the present invention provides a point cloud density device, the device includes: a communication interface, a processor, a machine-readable storage medium and a bus; wherein the communication interface, the processor and the machine-readable storage medium complete mutual communication through the bus. The processor can execute the two-dimensional laser point cloud density method described above by reading and executing the machine executable instructions corresponding to the control logic of the two-dimensional laser point cloud density method in the machine-readable storage medium. The specific content of the method is referred to the above embodiment, which is not repeated here. The machine-readable storage medium mentioned in this application can be any electronic, magnetic, optical or other physical storage device, which can contain or store information, such as executable instructions, data, etc. For example, the machine-readable storage medium can be: a volatile memory, a non-volatile memory or a similar storage medium. Specifically, the machine-readable storage medium can be a RAM (Radom Access Memory, random access memory), a flash memory, a storage drive (such as a hard disk drive), any type of storage disk (such as an optical disk, a DVD, etc.), or a similar storage medium, or a combination thereof. It includes a readable storage medium and a processor; wherein the readable storage medium is used to store machine executable instructions; the processor is used to read the machine executable instructions on the readable storage medium and execute the instructions to implement the steps of the equal density processing method described in Example 1.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for iso-densification processing of two-dimensional laser point clouds, characterized in that: It includes the following steps: S1: Read a point P1 in the image coordinate system, calculate the Euclidean distance D between it and the next adjacent point P2, and determine whether point P1 and point P2 are continuous based on the Euclidean distance. If so, proceed to step S2; if not, proceed to step S3. and Mark as endpoint and go to step S4; S2: Calculate the equal density threshold b of the point cloud image in the world coordinate system, and generate a distance lookup table DST according to the equal density threshold b; S3: Points in the image coordinate system and Convert to a point in the world coordinate system and Point , calculation point and The Euclidean distance Dis between them is calculated based on Dis and the distance lookup table DST. and The number of points that should be inserted between IC; according to the world coordinates of the two points , The IC calculates the new insertion point based on the number of points to be inserted. The coordinates of S4, read the next point P2 in the image coordinate system, and repeat steps S1 and S3 until all points are interpolated; S5. Perform equal point number sampling on the point cloud data after the insertion point, while protecting the marked endpoints to generate equal-density and sparse point cloud data.

2. The method for iso-densification processing of a two-dimensional laser point cloud according to claim 1, characterized in that: In the step S1, a threshold determination method is used to determine whether point P1 is continuous with point P2; If the Euclidean distance D is greater than the image distance threshold, it is judged to be discontinuous; If the Euclidean distance D is less than or equal to the image distance threshold, it is determined to be continuous.

3. The method for iso-densification processing of two-dimensional laser point clouds according to claim 1, characterized in that: In step S1, the calculation formula of the Euclidean distance D is: ； in, and Represent the coordinates of point P1 and point P2 in the image coordinate system respectively.

4. The method for iso-densification processing of a two-dimensional laser point cloud according to claim 1, characterized in that: In step S2, the calculation formula of the equal density threshold b is: ； in, represents the average distance between all neighboring points. represents the average distance between all points whose distance is less than or equal to the variance, Represents the average distance between all points whose distance is greater than the variance; It represents the ratio of the number of points whose distance to the next nearest point is less than or equal to the variance to the total number of points M. It represents the ratio of the number of points whose distance to the next closest point is greater than the variance to the total number of points M.

5. The method for iso-densification processing of two-dimensional laser point clouds according to claim 1, characterized in that: In step S3, if the Euclidean distance Dis is less than or equal to the equal density threshold b, no point insertion is performed, that is, the number of inserted points is zero; if the Euclidean distance Dis is greater than the equal density threshold b, point insertion is performed and the inserted point value is calculated.

6. The method for iso-densification processing of two-dimensional laser point clouds according to claim 5, characterized in that: In step S3, the calculation method of the number of insertion points IC is as follows: search the distance lookup table DST, if the Euclidean distance Dis is in Between, then: ； In step S2, the distance lookup table DST is specifically: 。 7. The method for iso-densification processing of two-dimensional laser point clouds according to claim 1, characterized in that: In step S4, the two coordinate points and The nth point inserted in The coordinate calculation formula is: 。 8. The method for iso-densification processing of two-dimensional laser point clouds according to claim 7, characterized in that: The step S4 also includes calculating the equalization index and The calculation formula is: ； The equalization index Used to judge The rounding method is: if the equalization index is greater than or equal to 0.5, the corresponding coordinate is rounded up, otherwise, it is rounded down.

9. A device for iso-densification of two-dimensional laser point clouds, characterized in that: include: Point cloud acquisition module: used to convert the point cloud coordinates in the image coordinate system into the point cloud coordinates in the world coordinate system; Threshold generation module: used to generate the corresponding equal density threshold according to the specific coordinate data of the current point cloud image; Distance calculation module: used to calculate the Euclidean distance D between point clouds in the image coordinate system, and also used to calculate the Euclidean distance between point clouds in the world coordinate system; Distance judgment module: used to judge whether two adjacent points are continuous points, and also used to judge whether it is necessary to interpolate between two non-continuous points according to the equal density threshold; Isodensification module: used to perform isodensity interpolation between two non-continuous points according to the isodensity threshold; Point cloud sampling module: used to perform equal point number sampling on the point cloud after equal density to generate equal-density and sparse point cloud data.

10. A point cloud density device, characterized in that: It comprises a readable storage medium and a processor; wherein the readable storage medium is used to store machine-executable instructions; and the processor is used to read the machine-executable instructions on the readable storage medium and execute the instructions to implement the steps of any method described in claims 1-8.

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