A Single-Photon LiDAR Point Cloud Denoising Method and System with Adaptive Slope

Abstract

The present application discloses a method and system for denoising single-photon lidar point clouds with an adaptive slope, which relates to the technical field of photon point cloud denoising. The method includes: calculating an adaptive slope value of the point cloud data according to the elevation and along-track distance of each point cloud in two rectangular regions of the same size on both sides of the point cloud data; constructing a quadrilateral denoising kernel of a preset size centered on the point cloud data, deforming it according to the adaptive slope value and scaling it proportionally, and determining the along-track vertex distance value of each point cloud data; after completion of the scaling, taking the point cloud data with the minimum along-track vertex distance value as the new point cloud data to be accessed, and repeating the above steps until all point cloud data have become accessed point cloud data; determining a reachable distance threshold by using the OTSU algorithm according to the reachable distances of each accessed point cloud data, and determining and removing the noise points. The denoising kernel constructed in the present application can better complete the denoising work even in some extreme terrains.

Description

Technical Field

[0001] The present application relates to the technical field of photon point cloud denoising, and in particular to a slope-adaptive single-photon laser radar point cloud denoising method and system. Background Art

[0002] The satellite-borne single-photon lidar cleverly integrates the functions of single-photon radar, global positioning system and inertial navigation system, thus achieving accurate acquisition of three-dimensional information of the surface. However, since the laser pulse signal emitted by the single-photon lidar is relatively weak, a large number of photon events are generated due to atmospheric scattering, solar radiation, target object scattering, etc., and the instrument will record all photon events as photon point clouds, which results in the generation of noise points in the final data that are far larger than the available signal points. Therefore, the obtained photon point cloud needs to be denoised to extract the ground points mixed with vegetation points and noise points.

[0003] The current photon point cloud data denoising algorithms still have many shortcomings, including low accuracy, high computational complexity, limited adaptability to complex scenes, difficulty in parameter setting, and insufficient processing of small-scale noise. Photon counting lidar data usually contains a lot of noise. To eliminate these noises, it is necessary to design specific denoising algorithms that take into account the unique characteristics of photon counting. These algorithms must consider various noise sources and noise characteristics and be customized specifically for photon counting data. At present, for ICESat-2 point cloud data, whether it is improving the denoising method or the clustering algorithm, most experiments and analyses are conducted based on the sea surface and flat land, which can easily cause misjudgment of signal points and noise points when the slope is large. Therefore, how to provide a photon point cloud denoising method suitable for complex terrains such as large slopes is a technical problem that needs to be solved urgently in this field. Summary of the invention

[0004] The purpose of this application is to provide a slope-adaptive single-photon lidar point cloud denoising method and system, which can perform denoising work well in some extreme terrains.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a slope-adaptive single-photon laser radar point cloud denoising method, comprising the following steps:

[0007] For any point cloud data in the point cloud data set, the adaptive slope value of the point cloud data is calculated according to the elevation and along-track distance of each point cloud data in two rectangular areas of equal size on both sides of the point cloud data.

[0008] Select any point cloud data in the point cloud dataset as the point cloud data to be accessed.

[0009] For the to-be-accessed point cloud data, construct a quadrilateral denoising kernel with a preset size centered on the to-be-accessed point cloud data.

[0010] According to the adaptive slope value of the to-be-accessed point cloud data, deform the quadrilateral denoising kernel of the to-be-accessed point cloud data to obtain the deformed quadrilateral denoising kernel of the to-be-accessed point cloud data.

[0011] Scale the deformed quadrilateral denoising kernel of the to-be-accessed point cloud data proportionally, and determine the distance value of each point cloud data along the track vertex; the distance value of the point cloud data along the track vertex is the distance value from the vertex along the track to the center point of the new quadrilateral denoising kernel generated for the point cloud data when any side touches the point cloud data during the proportional scaling of the deformed quadrilateral denoising kernel of the to-be-accessed point cloud data.

[0012] Calculate the accessible distance of the to-be-accessed point cloud data, and regard the to-be-accessed point cloud data as the accessed point cloud data.

[0013] Regard the point cloud data with the minimum distance value along the track vertex as the new to-be-accessed point cloud data, and jump to the step: For the to-be-accessed point cloud data, construct a quadrilateral denoising kernel with a preset size centered on the to-be-accessed point cloud data; until all point cloud data becomes the accessed point cloud data.

[0014] Sort the accessible distances of each accessed point cloud data, use the OTSU algorithm to determine the accessible distance threshold, and remove the accessed point cloud data with an accessible distance greater than the accessible distance threshold as noise points.

[0015] In a second aspect, corresponding to the single-photon lidar point cloud denoising method with the aforementioned adaptive slope, the present invention also provides a single-photon lidar point cloud denoising system with an adaptive slope. When the single-photon lidar point cloud denoising system with an adaptive slope is run by a computer, it executes the single-photon lidar point cloud denoising method with an adaptive slope as described above.

[0016] According to the specific embodiments provided in this application, the following technical effects are disclosed in this application:

[0017] The present application provides a method and system for denoising single-photon lidar point clouds with an adaptive slope. The method includes: for any point cloud data in the point cloud dataset, calculating the adaptive slope value of the point cloud data according to the elevation and along-track distance of each point cloud data in two rectangular regions of the same size on both sides of the point cloud data; for the to-be-accessed point cloud data, constructing a denoising kernel of a preset size centered on the to-be-accessed point cloud data, deforming it according to the adaptive slope value, scaling it proportionally, and determining the along-track vertex distance value of each point cloud data therein; after the scaling is completed, calculating the accessible distance of the to-be-accessed point cloud data, and taking the point cloud data with the smallest along-track vertex distance value as the new to-be-accessed point cloud data, repeating the above steps until all point cloud data have become accessed point cloud data, sorting the accessible distances of each accessed point cloud data, determining the accessible distance threshold by using the OTSU algorithm, and removing the accessed point cloud data greater than the accessible distance threshold as noise points. The present application adds a large number of influencing factors of slope to the denoising on the basis of complex terrain, and improves the denoising algorithm in combination with the characteristics of different photon densities at different slopes, so that the denoising has better resistance to different slopes and different densities in the actual terrain, and the constructed denoising kernel can better complete the denoising work even in some extreme terrains. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is an application environment diagram of a method for denoising single-photon lidar point clouds with an adaptive slope in an embodiment of the present application.

[0020] Figure 2 It is a flowchart of a method for denoising single-photon lidar point clouds with an adaptive slope provided by an embodiment of the present application.

[0021] Figure 3 It is a refined flowchart of step S204 in a method for denoising single-photon lidar point clouds with an adaptive slope provided by an embodiment of the present application.

[0022] Figure 4 It is a schematic flowchart of a method for denoising single-photon lidar point clouds with an adaptive slope provided by another embodiment of the present application.

[0023] Figure 5 For the present application Figure 4Schematic diagram of the deformation of the quadrilateral denoising kernel in an adaptive slope single-photon lidar point cloud denoising method provided.

[0024] Figure 6 For this application Figure 4 Schematic diagram of the deformation after rotation of the quadrilateral denoising kernel in an adaptive slope single-photon lidar point cloud denoising method provided.

[0025] Figure 7 For this application Figure 4 Initial radius h in an adaptive slope single-photon lidar point cloud denoising method provided 0 Schematic diagram of the deformation.

[0026] Figure 8 For this application Figure 4 Schematic diagram of the equal-proportion scaling of the quadrilateral denoising kernel in an adaptive slope single-photon lidar point cloud denoising method provided.

[0027] Figure 9 Schematic diagram of the functional modules of an adaptive slope single-photon lidar point cloud denoising system provided by an embodiment of this application.

[0028] Figure 10 Schematic diagram of the structure of a computer device provided by an embodiment of this application. Detailed implementation manners

[0029] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0030] To make the above objects, features, and advantages of this application more obvious and understandable, the following further detailed description of this application will be given in conjunction with the accompanying drawings and specific implementation manners.

[0031] The adaptive slope single-photon lidar point cloud denoising method provided by the embodiments of this application can be applied to, for example Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set separately, integrated on the server 104, placed on the cloud or other servers. The terminal 102 can send the point cloud data set to be processed to the server 104. After receiving the point cloud data set to be processed, for the point cloud data set to be processed, the server 104 executes the adaptive slope single-photon lidar point cloud denoising method mentioned in any of the following embodiments. The server 104 can feedback the obtained denoising result to the terminal 102. In addition, in some embodiments, the adaptive slope single-photon lidar point cloud denoising method can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly perform denoising processing on the point cloud data set to be processed, or the server 104 can obtain the point cloud data set to be processed from the data storage system and perform denoising processing on the point cloud data set to be processed.

[0032] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0033] In an exemplary embodiment, as Figure 2 shown, a single-photon lidar point cloud denoising method with an adaptive slope is provided. This method is executed by a computer device, and specifically can be executed separately by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of the present application, taking this method as applied to Figure 1 the server 104 in it as an example for illustration, it includes the following steps 201 to step 208:

[0034] S201. For any point cloud data in the point cloud data set, calculate the adaptive slope value of the point cloud data according to the elevation and along-track distance of each point cloud data in two rectangular regions of the same size on both sides of the point cloud data; in this embodiment, step S201 specifically includes the following steps:

[0035] S2011. For any point cloud data in the point cloud data set, with the point cloud data as the center point, construct two rectangular regions of the same size; the length of the rectangular region in the elevation direction is 2 times the length in the along-track direction.

[0036] S2012. Calculate the adaptive slope value of the point cloud data based on the elevation and along-track distance of each point cloud data within two rectangular regions. Calculate the adaptive slope value of the point cloud data according to the following formula:

[0037] θ = arctan(b).

[0038] Where, θ is the adaptive slope value of the point cloud data, and b is the slope value of the point cloud data.

[0039]

[0040] Where, h l and l l are respectively the length in the elevation direction and the length in the along-track direction of the rectangular region on the left side of the point cloud data, and h r and l r are respectively the length in the elevation direction and the length in the along-track direction of the rectangular region on the right side of the point cloud data.

[0041] S202. Select any point cloud data from the point cloud dataset as the point cloud data to be accessed.

[0042] S203. For the point cloud data to be accessed, construct a denoising kernel of a preset size in the shape of a quadrilateral centered on the point cloud data to be accessed; in this embodiment, step S203 specifically includes the following steps:

[0043] S2031. For the point cloud dataset, establish a plane rectangular coordinate system with the along-track direction of all point cloud data as the positive x-axis direction and the direction perpendicular to the sea level and upward as the positive y-axis direction.

[0044] S2032. With the point cloud data to be accessed as the center point O, establish a quadrilateral denoising kernel with a preset size and a slope of 0 in the plane rectangular coordinate system; the four vertices of the quadrilateral denoising kernel starting from the upper left corner in a clockwise direction are a, b, c, and d respectively, the length of the quadrilateral denoising kernel in the along-track direction is S, and the length in the elevation direction is h.

[0045] S204. Deform the quadrilateral denoising kernel of the point cloud data to be accessed according to the adaptive slope value of the point cloud data to be accessed to obtain the deformed quadrilateral denoising kernel of the point cloud data to be accessed; in this embodiment, as Figure 3 shown in the flowchart, step S204 specifically includes the following steps:

[0046] S2041. Judge whether the adaptive slope value of the point cloud data to be accessed is greater than 45°, and obtain the first judgment result. If the first judgment result is no, then execute step S2042; if the first judgment result is yes, then execute step S2044.

[0047] S2042. Move vertex a and vertex d of the quadrilateral denoising kernel of the to-be-accessed point cloud data downward by a first deformation distance.

[0048] S2043. Move vertex b and vertex c of the quadrilateral denoising kernel of the to-be-accessed point cloud data upward by a first deformation distance to obtain the deformed quadrilateral denoising kernel of the to-be-accessed point cloud data; the first deformation distance is (h × tanθ) / 2.

[0049] S2044. Rotate the quadrilateral denoising kernel of the to-be-accessed point cloud data clockwise by 90° to obtain the rotated quadrilateral denoising kernel of the to-be-accessed point cloud data; the four vertices of the rotated quadrilateral denoising kernel of the to-be-accessed point cloud data in counterclockwise order starting from the upper right corner are a', b', c' and d' respectively.

[0050] S2045. Move vertex a' and vertex b' of the rotated quadrilateral denoising kernel of the to-be-accessed point cloud data to the right by a second deformation distance.

[0051] S2046. Move vertex c' and vertex d' of the rotated quadrilateral denoising kernel of the to-be-accessed point cloud data to the left by a second deformation distance to obtain the deformed quadrilateral denoising kernel of the to-be-accessed point cloud data; the second deformation distance is h / 2tan|θ|.

[0052] S205. Scale the deformed quadrilateral denoising kernel of the to-be-accessed point cloud data proportionally and determine the distance value of the along-track vertex of each point cloud data; the distance value of the along-track vertex of the point cloud data is the distance value from the along-track vertex to the center point of the new quadrilateral denoising kernel generated for the point cloud data when any side touches the point cloud data during the proportional scaling of the deformed quadrilateral denoising kernel of the to-be-accessed point cloud data; in this embodiment, step S205 specifically includes the following steps:

[0053] S2051. Scale the deformed quadrilateral denoising kernel of the to-be-accessed point cloud data proportionally. When any side touches any point cloud data, use the to-be-accessed point cloud data as the center point to generate the quadrilateral denoising kernel of the point cloud data through the point cloud data; until only the to-be-accessed point cloud data remains in the quadrilateral denoising kernel.

[0054] S2052. For each quadrilateral denoising kernel of the point cloud generated during the proportional scaling of the deformed quadrilateral denoising kernel of the to-be-accessed point cloud data, calculate the distance value of the along-track vertex of each point cloud data. In this embodiment, the distance value of the along-track vertex of the point cloud data is calculated according to the following formula:

[0055]

[0056] Max(a 1 ,a 2 )=a.

[0057]

[0058] Among them, W is the along-track vertex distance value of the point cloud data, and h 0 is the initial length of the quadrilateral denoising kernel of the to-be-accessed point cloud data in the elevation direction, S is the length of the quadrilateral denoising kernel of the to-be-accessed point cloud data in the along-track direction, y a and y b represent the elevations of point a and point b respectively, x a and x b represent the along-track distances of point a and point b respectively, and θ is the adaptive slope value of the to-be-accessed point cloud data.

[0059] S206. Calculate the reachable distance of the to-be-accessed point cloud data, and use the to-be-accessed point cloud data as the accessed point cloud data; in this embodiment, the reachable distance of the to-be-accessed point cloud data is calculated according to the following formula:

[0060]

[0061]

[0062] Among them, MP is the denoising threshold, NR(O 0 ) is the number of points within the given radius R of the point cloud data O 0 , W is the along-track vertex distance value of the point cloud data O 0 , is an empty set, RD R (O 0 , O i ) is the reachable distance of the to-be-accessed point cloud data O i , RD(i) is the abbreviation of RD R (O 0 , O i ), W(O 0 , O i ) is to calculate the W value of all the surrounding point clouds with O 0 as the center point cloud, and W i is the abbreviation of W(O 0 , O i ).

[0063] S207. Use the point cloud data with the minimum along-track vertex distance value as the new to-be-accessed point cloud data, and jump to step S203; until all the point cloud data becomes the accessed point cloud data.

[0064] S208. Sort the reachable distances of each accessed point cloud data, use the OTSU algorithm to determine the reachable distance threshold, and remove the accessed point cloud data with a reachable distance greater than the reachable distance threshold as noise points. In this embodiment, the reachable distance threshold is determined according to the following formula:

[0065]

[0066] Wherein, P is the label of the currently to-be-accessed point cloud data, N is the total number of point cloud data, RD(i) is the reachable distance of the i-th point cloud data, and σ 2 (P) is the between-class variance of signal points and noise points. When σ 2 (P) is the largest, this RD value is the threshold for judging denoising.

[0067] In another exemplary embodiment of the present application, as Figure 4 shown, the steps of the above-mentioned single-photon lidar point cloud denoising method with an adaptive slope can be replaced by the following process:

[0068] Step 1) Construct two rectangular regions distributed on the left and right with the target point as the center to select point cloud data, and set the length of the selected rectangle in the elevation direction to be 2 times the length in the track direction; then calculate the elevation average values h l 、h r and the average values l l 、l r of the distances along the track of all point cloud data in the left and right rectangular regions respectively; then calculate the slope θ using the average values.

[0069]

[0070] θ = arctan(b).

[0071] In the formula, b represents the slope and θ is the slope. Performing this process for each point can obtain an adaptive slope.

[0072] In this embodiment, when the slope is greater than 45°, the entire image is rasterized in the direction perpendicular to the ground to obtain the lowest elevation point in each grid, and the following formula is used to calculate the slope between the central grid and its adjacent 8 grids, and finally the average value is calculated as the slope value of the central grid.

[0073]

[0074] In the formula, dz / dx and dz / dy respectively represent the change rates of the central pixel in the x and y directions.

[0075] Step 2) For the point cloud data set, establish a coordinate system with the track direction as the positive x-axis direction and vertically upward from the sea level as the positive y-axis direction. Select any point in the point cloud and label it as O, and establish a quadrilateral denoising kernel with a length of S, a width of H, and a slope of 0, composed of corner points a, b, c, and d, centered on the photon point O. The denoising kernel contains n + 1 photon points O and O i (i = 1, 2,..., n).

[0076] Step 3) Transform the parallelogram denoising kernel, such as Figure 5 the transformation form shown. Move the points a and b of the parallelogram denoising kernel downward by distances a' and b', and move the points c and d upward by distances c' and d'. While keeping the abscissas of a', b', c', and d' unchanged, the S and h of the parallelogram denoising kernel can be kept unchanged.

[0077] Step 4) If the slope is too large, such as Figure 6 the transformation form shown. When it is greater than 45°, rotate the denoising kernel 90° clockwise with point O as the center to generate a new denoising kernel composed of corner points a', b', c', and d'. Then move the vertices a' and b' to the right by a distance of h / 2tan|θ| to become a" and b", and move the vertices c' and d' to the left by a distance of h / 2tan|θ| to become c" and d", while keeping the ordinates unchanged.

[0078] Step 5) To further adapt to the possible situation of drastic slope changes, deform the initially set radius h 0 to a certain extent. As Figure 7 shown, when calculating the slope along the horizontal increment direction, detect the angle θ a and the angle θ a of point a near the a photon point coordinate (x a , y b ) at 20 meters. If |θ a -θ b |>45°, and if there are photon points that meet the conditions, select the nearest photon point b coordinate (x b , y b ) To avoid the situation of drastic slope changes, calculate the slope change deformation coefficient D for it. The closer the along-track distance between the two is, the greater the slope change, and the greater the value of D. Thus, the denoising kernel is shorter in the area closer to the large-scale slope change of the terrain, and at the same time, the denoising threshold is reduced by the corresponding multiple. This can effectively reduce the situation where the denoising kernel of the target point is too long and breaks away from the target area when the slope changes relatively drastically, making the denoising kernel more adaptable to the actual terrain changes, thereby reducing a large number of misclassifications and false classifications in the area of large-scale slope changes.

[0079] Step 6) Keep the horizontal and vertical ratios of the denoising kernel for contraction. When any edge of the denoising kernel (such as the a-b edge) coincides with a photon point, stop contracting the denoising kernel. As Figure 8 shown, the edge a-d passes through the photon point O 5 to generate a new parallelogram denoising kernel Q 1 , composed of four corner points a 1 , b 1 , c 1 , d 1Composition. Repeat the above steps so that c-d passes through the photon point O 6 Generate a new parallelogram denoising kernel Q 2 , which consists of four corner points a 2 , b 2 , c 2 , d 2 .

[0080] Step 7) Continue to shrink the denoising kernel. Similar to step 5, for the remaining n photon points O in the denoising kernel i (i = 1, 2,..., n), use the following recorded distance W i . Stop when the denoising kernel shrinks to only 1 photon point. W i is the distance from the vertex to the center point of the denoising kernel generated through this photon point along the track direction.

[0081] Step 8) Set appropriate denoising thresholds MP and reachable distances RD. According to the method described above, with the photon point a as the center, construct a parallelogram denoising kernel with the slope θ of the photon point, the track distance along the track as the height h, and the perpendicular distance as the base length s. Statistically analyze the distance W of the photon points around the photon point a i . Taking the photon point b as an example, calculate it using the following formula:

[0082]

[0083] Max(a 1 , a 2 ) = a.

[0084]

[0085] In the formula, y a , y b represent the elevations of points a and b, x a , x b represents the horizontal track distance, θ is the slope of the central photon point a. When a 1 , a 2 are both less than 1 at the same time, it is determined that the photon point b is within the parallelogram denoising kernel divided by the photon point a. Take the larger value of a 1 , a 2 to make the newly generated denoising kernel have the same aspect ratio as the set denoising kernel. The smaller the W of the photon point b, the smaller the reachable distance RD that meets the denoising requirements.

[0086] Since the number of photon signal points obtained by the lidar within a certain period of time is limited, and the signal points in the area with a larger slope are sparser than those in the area with a smaller slope. Therefore, in order to make up for the signal loss in the horizontal direction, a corresponding reduction is made to MP.

[0087]

[0088] When calculating CD, multiply MP by a coefficient to reduce the values of CD and RD by reducing the number of required core photon points.

[0089] Step 9) Mark O 0 The point cloud data is marked as visited point cloud data, and the obtained W i (i = 1, 2,..., n) are arranged in ascending order, and the O with the smallest W value is selected i point as the access core photon point O 1 , as the center point for the next denoising, that is, the new point cloud data to be visited.

[0090] Step 10) Calculate the point O according to the following formula 0 The number of points NR(O within the given radius R 0 ) is less than MP, then it is recorded as a noise point in advance.

[0091]

[0092]

[0093] Among them, W(O 0 , O i ) is W i , which is the W value of all the surrounding point clouds calculated with O 0 as the center point cloud, and W i is the abbreviation of W(O 0 , O i ); RD R (O 0 , O i ) is the reachable distance value RD(0) of the center point cloud of O 0 , and RD R (O i , O i ) is the RD(i) of the O i point. When calculating RD, the same formula is used to calculate W. The obtained W is arranged in ascending order and compared with CD, and the larger value is recorded as RD. The CD values of each photon and the smallest RD value are statistically calculated.

[0094] Step 11) Since the distribution of noise points is sparser than that of signal points, photons with large RD values are judged as noise photons, and vice versa as signal photons. When judging, first arrange the RD values of the photon points within the window in ascending order, and then use the OTSU method to set an adaptive threshold for classification.

[0095]

[0096] Where N is the total number of photons within the window, and P is the bit position for photon sorting. Assume that the first P photons are signal points and the subsequent photons are noise points. When the between-class variance σ 2 (P) is at its maximum, this RD value serves as the threshold for denoising judgment.

[0097] Repeat steps 5) to 11) until each photon point is marked as visited, obtaining the corresponding RD for all photon points. After sorting the RD in ascending order, continue to use the OTSU method to divide the photon points into noise photon points and signal photon points to obtain the denoising result.

[0098] In the above method embodiment provided by this application, a large number of influencing factors of slope on denoising are added on the basis of complex terrain. Combining the characteristics of different photon densities at different slopes, the denoising algorithm is improved, enabling the denoising to have better resistance to different slopes and densities in actual terrain, and enabling the constructed denoising kernel to better complete the denoising work even in some extreme terrains.

[0099] Based on the same inventive concept, the embodiment of this application also provides an adaptive-slope single-photon lidar point cloud denoising system for implementing the above-mentioned adaptive-slope single-photon lidar point cloud denoising method. The implementation solutions provided by this system to solve problems are similar to those recorded in the above method. Therefore, the specific limitations in one or more of the following embodiments of the adaptive-slope single-photon lidar point cloud denoising system can refer to the limitations on the adaptive-slope single-photon lidar point cloud denoising method in the above text, and will not be elaborated here.

[0100] In an exemplary embodiment, as Figure 9 shown, an adaptive-slope single-photon lidar point cloud denoising system is provided, including: as Figure 9 shown, this adaptive-slope single-photon lidar point cloud denoising system may include an adaptive slope value calculation module M1, a quadrilateral denoising kernel construction module M2, a denoising kernel deformation module M3, a denoising kernel scaling module M4, an along-track vertex distance determination module M5, an accessible distance calculation module M6, an accessible distance threshold determination module M7, and a noise point removal module M8; some modules may also have sub-units for implementing their functions. Of course, Figure 9 shown architecture is only exemplary. When implementing different functions, one or at least two components in the Figure 9 shown system can be omitted according to actual needs.

[0101] In an exemplary embodiment, a computer device is provided. This computer device can be a server or a terminal, and its internal structure diagram can be as Figure 10As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store point cloud dataset processing data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for denoising single-photon lidar point clouds with an adaptive slope.

[0102] Those skilled in the art can understand that Figure 10 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0103] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0104] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0105] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0106] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0107] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random-access memories (ReRAM), magnetoresistive random-access memories (MRAM), ferroelectric random-access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random-access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random-access memory (SRAM) or dynamic random-access memory (DRAM), etc.

[0108] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0109] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0110] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A slope-adaptive single-photon laser radar point cloud denoising method, characterized in that: The adaptive slope single-photon laser radar point cloud denoising method comprises: For any point cloud data in the point cloud data set, the adaptive slope value of the point cloud data is calculated according to the elevation and along-track distance of each point cloud data in two rectangular areas of equal size on both sides of the point cloud data; Selecting any point cloud data in the point cloud data set as the point cloud data to be accessed; For the point cloud data to be accessed, constructing a quadrilateral denoising kernel of a preset size with the point cloud data to be accessed as the center; According to the adaptive slope value of the point cloud data to be accessed, deforming the quadrilateral denoising kernel of the point cloud data to be accessed to obtain the deformed quadrilateral denoising kernel of the point cloud data to be accessed; The deformed quadrilateral denoising kernel of the point cloud data to be accessed is scaled in proportion, and the along-track vertex distance value of each point cloud data is determined; the along-track vertex distance value of the point cloud data is the distance value from the along-track vertex to the center point of the new quadrilateral denoising kernel generated for the point cloud data when any side of the deformed quadrilateral denoising kernel of the point cloud data to be accessed touches the point cloud data when the deformed quadrilateral denoising kernel of the point cloud data to be accessed is scaled in proportion; Calculate the reachable distance of the point cloud data to be accessed, and use the point cloud data to be accessed as the accessed point cloud data; The point cloud data with the smallest vertex distance along the track is taken as the new point cloud data to be visited, and the process is skipped to the step: for the point cloud data to be visited, a quadrilateral denoising kernel of a preset size is constructed with the point cloud data to be visited as the center; until all the point cloud data become the visited point cloud data; The accessible distances of each visited point cloud data are sorted, and the reachable distance threshold is determined using the OTSU algorithm. The visited point cloud data with a distance greater than the reachable distance threshold are removed as noise points.

2. The slope-adaptive single-photon laser radar point cloud denoising method according to claim 1, characterized in that: For any point cloud data in the point cloud data set, the adaptive slope value of the point cloud data is calculated according to the elevation and along-track distance of each point cloud data in two rectangular areas of equal size on both sides of the point cloud data, specifically including: For any point cloud data in the point cloud data set, two rectangular areas of the same size are constructed with the point cloud data as the center point; the length of the rectangular area in the elevation direction is twice the length in the along-track direction; According to the elevation and the distance along the track of each point cloud data in the two rectangular areas, the adaptive slope value of the point cloud data is calculated.

3. The slope-adaptive single-photon laser radar point cloud denoising method according to claim 1, characterized in that: The adaptive slope value of the point cloud data is calculated according to the following formula: θ = arctan(b); Among them, θ is the adaptive slope value of the point cloud data, and b is the slope value of the point cloud data; Among them, h l and l l are the length of the rectangular area on the left side of the point cloud data in the elevation direction and the length along the track direction, h r and l r They are respectively the length of the rectangular area on the right side of the point cloud data in the elevation direction and the length along the track direction.

4. The slope-adaptive single-photon laser radar point cloud denoising method according to claim 1, characterized in that: For the point cloud data to be accessed, a quadrilateral denoising kernel of a preset size is constructed with the point cloud data to be accessed as the center, specifically including: For the point cloud data set, a plane rectangular coordinate system is established with the along-track direction of all point cloud data as the positive direction of the x-axis and the upward direction perpendicular to the sea level as the positive direction of the y-axis; Taking the point cloud data to be accessed as the center point O, a quadrilateral denoising kernel of a preset size and a slope of 0 is established in the plane rectangular coordinate system; the four vertices of the quadrilateral denoising kernel clockwise from the upper left corner are a, b, c and d, and the length of the quadrilateral denoising kernel in the along-track direction is S, and the length in the elevation direction is h.

5. The slope-adaptive single-photon laser radar point cloud denoising method according to claim 4, characterized in that: According to the adaptive slope value of the point cloud data to be accessed, deforming the quadrilateral denoising kernel of the point cloud data to be accessed to obtain the deformed quadrilateral denoising kernel of the point cloud data to be accessed specifically includes: Determine whether the adaptive slope value of the point cloud data to be accessed is greater than 45°, and obtain a first determination result; If the first judgment result is no, then the vertices a and d of the quadrilateral denoising core of the point cloud data to be accessed are moved downward by a first deformation distance, and the vertices b and c of the quadrilateral denoising core of the point cloud data to be accessed are moved upward by the first deformation distance, so as to obtain a deformed quadrilateral denoising core of the point cloud data to be accessed; If the first judgment result is yes, the quadrilateral denoising kernel of the point cloud data to be accessed is rotated 90° clockwise to obtain a rotated quadrilateral denoising kernel of the point cloud data to be accessed; the four vertices of the rotated quadrilateral denoising kernel of the point cloud data to be accessed counterclockwise from the upper right corner are a', b', c' and d' respectively; The vertices a' and b' of the rotated quadrilateral denoising kernel of the point cloud data to be accessed are moved to the right by a second deformation distance, and the vertices c' and d' of the rotated quadrilateral denoising kernel of the point cloud data to be accessed are moved to the left by a second deformation distance to obtain the deformed quadrilateral denoising kernel of the point cloud data to be accessed.

6. The slope-adaptive single-photon laser radar point cloud denoising method according to claim 1, characterized in that: The deformed quadrilateral denoising kernel of the point cloud data to be accessed is scaled in equal proportion, and the vertex distance value along the track of each point cloud data is determined, specifically including: The deformed quadrilateral denoising core of the point cloud data to be accessed is scaled in equal proportion, and when any side touches any point cloud data, a quadrilateral denoising core of the point cloud data is generated through the point cloud data with the point cloud data to be accessed as the center point; until only the point cloud data to be accessed is left in the quadrilateral denoising core; When the deformed quadrilateral denoising kernel of the point cloud data to be accessed is scaled in equal proportion, the quadrilateral denoising kernel of each point cloud is generated, and the along-track vertex distance value of each point cloud data is calculated.

7. The slope-adaptive single-photon laser radar point cloud denoising method according to claim 6, characterized in that: The vertex distance along the track of the point cloud data is calculated according to the following formula: Max(a1,a2)=a; Where W is the vertex distance value along the track of the point cloud data, h0 is the length of the quadrilateral denoising kernel of the point cloud data to be accessed in the elevation direction, S is the length of the quadrilateral denoising kernel of the point cloud data to be accessed in the along-track direction, y a and b Represents the elevation of point a and point b respectively, x a and x b They represent the distances along the track of point a and point b respectively, and θ is the adaptive slope value of the point cloud data to be accessed.

8. The slope-adaptive single-photon laser radar point cloud denoising method according to claim 1, characterized in that: The reachable distance of the point cloud data to be accessed is calculated according to the following formula: Among them, MP is the denoising threshold, NR(O0) is the number of points within a given radius R of the point cloud data O0, W is the vertex distance value along the track of the point cloud data O0, is an empty set, RD R (O0,O i ) is the point cloud data to be accessed O i The reachable distance, RD(i) is RD R (O0,O i ), W(O0,O i ) is the W value of all the point clouds around the point cloud with O0 as the center, W i is W(O0,O i ).

9. The slope-adaptive single-photon laser radar point cloud denoising method according to claim 1, characterized in that: The reachable distance threshold is determined according to the following formula: μ(P)=ω(P)*μ(P)+ω1(P)*μ(P) s 2 (P)=ω0(P)*(μ0(P)-μ(P)) 2 +ω1(P)*(μ1(P)-μ(P)) 2 Where P is the number of the current point cloud data to be accessed, N is the total number of point cloud data, RD(i) is the reachable distance of the i-th point cloud data, σ 2 (P) is the inter-class variance of signal points and noise points, when σ 2 When (P) is the largest, the RD value is the threshold for judging denoising.

10. A slope-adaptive single-photon laser radar point cloud denoising system, characterized in that: The adaptive slope single-photon laser radar point cloud denoising system includes a processor, a memory and a computer program stored in the memory. When the computer program is executed by the processor, the adaptive slope single-photon laser radar point cloud denoising method as described in any one of claims 1 to 8 is executed.

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