Slope-adaptive single-photon laser radar point cloud denoising method and system

Abstract

The invention discloses a slope-adaptive single-photon laser radar point cloud denoising method and system, and relates to the technical field of photon point cloud denoising, and the method comprises the steps: calculating a self-adaptive slope value of point cloud data according to the elevation and along-rail distance of each point cloud in two rectangular regions with the same size at two sides of the point cloud data; constructing a quadrilateral denoising kernel with a preset size by taking the point cloud data as a center, deforming the quadrilateral denoising kernel according to the adaptive slope value, scaling the quadrilateral denoising kernel according to an equal proportion, and determining an along-track vertex distance value of each piece of point cloud data; after zooming is completed, taking the point cloud data with the minimum along-rail vertex distance value as new to-be-accessed point cloud data, and repeating the steps until all the point cloud data become accessed point cloud data; and according to the reachable distance of the cloud data of each accessed point, an OTSU algorithm is adopted to determine a reachable distance threshold value, and noise points are determined to be removed. The de-noising kernel constructed by the invention can better complete de-noising 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] LiDAR is a remote sensing technology that uses laser pulses to obtain target distance and other information. LiDAR calculates the distance between the target object and the LiDAR device by measuring the time interval between the emission and reception of the laser pulse. The receiver determines the target position and generates point cloud data in three-dimensional space. Single-photon LiDAR uses pulsed lasers. Since each pulse contains only a few photons, the number of received photons can be accurately counted, so that single-photon LiDAR can not only provide target distance information, but also provide laser pulse time information, photon quantity information, etc., thereby achieving more accurate and sensitive measurements. Satellite-borne single-photon LiDAR cleverly combines the functions of single-photon radar, global positioning system and inertial navigation system, thereby achieving accurate acquisition of three-dimensional surface information. 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 final data having noise points that are far larger than the available signal points. Therefore, it is necessary to denoise the obtained photon point cloud and 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 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.

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

[0011] The deformed quadrilateral denoising kernel of the point cloud data to be accessed is scaled proportionally, 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 touches the point cloud data when the deformed quadrilateral denoising kernel of the point cloud data to be accessed is scaled proportionally.

[0012] The reachable distance of the point cloud data to be accessed is calculated, and the point cloud data to be accessed is used as the accessed point cloud data.

[0013] The point cloud data with the smallest vertex distance along the track is taken as the new point cloud data to be accessed, and the process is skipped to step: 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; until all point cloud data become accessed point cloud data.

[0014] The accessible distances of each visited point cloud data are sorted, and the accessible distance threshold is determined using the OTSU algorithm. The visited point cloud data greater than the accessible distance threshold are removed as noise points.

[0015] In the second aspect, corresponding to the aforementioned adaptive slope single-photon laser radar point cloud denoising method, the present invention also provides an adaptive slope single-photon laser radar point cloud denoising system, which, when run by a computer, executes the adaptive slope single-photon laser radar point cloud denoising method as described above.

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

[0017] The present application provides a single-photon laser radar point cloud denoising method and system with adaptive slope, the method comprising: for any point cloud data in a point cloud data set, 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, calculating the adaptive slope value of the point cloud data; 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, deforming it according to the adaptive slope value, scaling it in proportion, and determining the along-track vertex distance value of each point cloud data therein; after completing the scaling, calculating the reachable distance of the point cloud data to be accessed, and taking the point cloud data with the smallest along-track vertex distance value as the new point cloud data to be accessed, repeating the above steps until all point cloud data become accessed point cloud data, sorting the reachable distances of each accessed point cloud data, using the OTSU algorithm to determine the reachable distance threshold, and removing the accessed point cloud data greater than the reachable distance threshold as noise points. Based on the complex terrain, this application adds a large number of factors affecting slope denoising, and improves the denoising algorithm in combination with the characteristics of different photon densities at different slopes, so that denoising has better resistance to different slopes and different densities in 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 embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 This is a diagram of the application environment of an adaptive slope single-photon lidar point cloud denoising method in one embodiment of the present application.

[0020] Figure 2 A flowchart of a slope-adaptive single-photon lidar point cloud denoising method provided in one embodiment of the present application.

[0021] Figure 3 A detailed flowchart of step S204 in a slope-adaptive single-photon lidar point cloud denoising method provided in one embodiment of the present application.

[0022] Figure 4 A schematic flow chart of a slope-adaptive single-photon lidar point cloud denoising method provided in accordance with another embodiment of the present application.

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

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

[0025] Figure 7 For this application Figure 4 Schematic diagram of deformation of the initial radius h0 in a slope-adaptive single-photon lidar point cloud denoising method provided.

[0026] Figure 8 For this application Figure 4 Schematic diagram of the proportional scaling of quadrilateral denoising kernels in a slope-adaptive single-photon lidar point cloud denoising method.

[0027] Fig. 9 A schematic diagram of the functional modules of a slope-adaptive single-photon lidar point cloud denoising system provided in one embodiment of the present application.

[0028] Fig.10 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0031] The slope-adaptive single-photon laser radar point cloud denoising method provided in the embodiment of the present application can be applied to 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 up separately, integrated on the server 104, or 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 the server 104 receives the point cloud data set to be processed, the server 104 executes the single-photon laser radar point cloud denoising method with adaptive slope mentioned in any of the following embodiments for the point cloud data set to be processed. The server 104 can feed back the obtained denoising results to the terminal 102. In addition, in some embodiments, the single-photon laser radar point cloud denoising method with adaptive slope can also be implemented separately by the server 104 or the terminal 102, such as the terminal 102 can directly perform denoising 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 on the point cloud data set to be processed.

[0032] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0033] In an exemplary embodiment, Figure 2 As shown, a method for denoising a single-photon laser radar point cloud with an adaptive slope is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 Taking the server 104 in the example as an example, the following steps 201 to 208 are included:

[0034] S201, for any point cloud data in the point cloud data set, 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, calculate the adaptive slope value 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, construct two rectangular areas of the same size 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.

[0036] S2012, according to the elevation and the distance along the track of each point cloud data in the two rectangular areas, calculate the adaptive slope value of the point cloud data. The adaptive slope value of the point cloud data is calculated according to the following formula:

[0037] .

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

[0039] .

[0040] 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.

[0041] S202. Select any point cloud data in the point cloud data set as the point cloud data to be accessed.

[0042] S203, for the point cloud data to be accessed, construct a quadrilateral denoising kernel of a preset size with the point cloud data to be accessed as the center; in this embodiment, step S203 specifically includes the following steps:

[0043] S2031. For the point cloud data set, establish a plane rectangular coordinate system 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.

[0044] S2032. Taking the point cloud data to be accessed as the center point O, establish a quadrilateral denoising kernel of a preset size and a slope of 0 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.

[0045] S204, deforming 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 a deformed quadrilateral denoising kernel of the point cloud data to be accessed; in this embodiment, Figure 3 In the flowchart shown, step S204 specifically includes the following steps:

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

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

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

[0049] S2044, rotating the quadrilateral denoising kernel of the point cloud data to be accessed by 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.

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

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

[0052] S205, scaling the deformed quadrilateral denoising kernel of the point cloud data to be accessed in proportion, and determining the along-track vertex distance value of each point cloud data; 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; in this embodiment, step S205 specifically includes the following steps:

[0053] S2051, scaling the deformed quadrilateral denoising kernel of the point cloud data to be accessed in equal proportion, and when any side touches any point cloud data, taking the point cloud data to be accessed as the center point, generating a quadrilateral denoising kernel of the point cloud data through the point cloud data; until only the point cloud data to be accessed remains in the quadrilateral denoising kernel.

[0054] S2052, for the quadrilateral denoising kernel of each point cloud generated when the deformed quadrilateral denoising kernel of the point cloud data to be accessed is scaled in proportion, calculate the vertex distance value along the track of each point cloud data. In this embodiment, the vertex distance value along the track of the point cloud data is calculated according to the following formula:

[0055] .

[0056] .

[0057] .

[0058] Where W is the vertex distance value along the track of the point cloud data, h0 is the initial 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.

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

[0060] .

[0061] .

[0062] 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 ).

[0063] S207 , taking the point cloud data with the smallest vertex distance along the track as new point cloud data to be accessed, and jumping to step S203 ; until all point cloud data become accessed point cloud data.

[0064] S208, sorting the reachable distances of each visited point cloud data, using the OTSU algorithm to determine the reachable distance threshold, and removing the visited point cloud data 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]

[0067]

[0068]

[0069]

[0070] .

[0071] 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.

[0072] In another exemplary embodiment of the present application, Figure 4 As shown, the steps of the above-mentioned adaptive slope single-photon lidar point cloud denoising method can be replaced by the following process:

[0073] Step 1) Construct two rectangular areas with the target point as the center point to select point cloud data. Set the length of the selected rectangle along the elevation direction to be twice the length along the track direction; then calculate the average elevation value h of all point cloud data in the left and right rectangular areas respectively. l 、h r and the average distance along the track l l , l r ; the average is then used to calculate the slope θ.

[0074] .

[0075] .

[0076] In the formula, b represents the slope and θ represents the gradient. This process can be performed for each point to obtain the adaptive gradient.

[0077] In this embodiment, when the slope is greater than 45°, the entire image is rasterized from a direction perpendicular to the ground to obtain the lowest elevation point in each grid. The following formula is used to calculate the slope of the central grid and its eight adjacent grids, and finally the average value is calculated as the slope value of the central grid.

[0078] .

[0079] Where dz / dx and dz / dy represent the rate of change of the central pixel in the x and y directions, respectively.

[0080] Step 2) For the point cloud dataset, establish a coordinate system with the along-track direction as the positive direction of the x-axis and the vertical direction upward from the sea level as the positive direction of the y-axis. Select any point in the point cloud and label it as O. Establish a quadrilateral denoising kernel with a length of S, a width of H, and a slope of 0, with the photon point O as the center and composed of corner points a, b, c, and d. The denoising kernel contains n+1 photon points O and O i (i=1,2,...,n).

[0081] Step 3) Change the parallelogram denoising kernel, such as Figure 5 In the variation shown, the distance that points a and b of the parallelogram denoising kernel move downward is changed to a' and b', and the distance that points c and d move upward is changed to c' and d', while the horizontal coordinates of a', b', c' and d' are kept unchanged, so that S and h of the parallelogram denoising kernel can be kept unchanged.

[0082] Step 4) If the slope is too large, Figure 6 As shown in the variation, when the angle is greater than 45°, the denoising kernel is rotated 90° clockwise with point O as the center to generate a new denoising kernel consisting of corner points a', b', c', and d'. Then, the vertices a' and b' are moved to the right by a distance of h / 2tan|θ| to become a'' and b'', and the vertices c' and d' are moved to the left by a distance of h / 2tan|θ| to become c'' and d'', while keeping the ordinate unchanged.

[0083] Step 5) In order to further adapt to the possible drastic change in slope, the initially set radius h0 is deformed to a certain extent. Figure 7 As shown, when calculating the slope, the coordinates of photon point a (x a ,y a ) The angle θ of point a near 20 meters a The angle θ with point b b Satisfy |θ a -θ b |>45°, if there is a photon point that meets the conditions, select the nearest photon point b coordinate (x b ,y b ) In order to avoid the situation of drastic changes in slope, the slope change deformation coefficient D is calculated. The closer the distance between the two along the track, the greater the slope change, and the greater the value of D, so that the denoising kernel is shorter in the area where the terrain slope changes drastically, and the denoising threshold is also reduced by a corresponding multiple. In this way, when the slope changes relatively drastically, it can effectively reduce the target point denoising kernel from being out of the target area due to being too long, making the denoising kernel more adaptable to the actual terrain changes, thereby reducing a large number of misclassifications in areas with drastic slope changes.

[0084] Step 6) Keep the horizontal and vertical ratios of the denoising kernel and shrink it. When any edge of the denoising kernel (such as the ab edge) coincides with the photon point, stop shrinking the denoising kernel. Figure 8 As shown, edge ad passes through photon point O5 to generate a new parallelogram denoising kernel Q1, which is composed of four corner points a1, b1, c1, and d1. Repeat the above steps and edge cd passes through photon point O6 to generate a new parallelogram denoising kernel Q2, which is composed of four corner points a2, b2, c2, and d2.

[0085] Step 7) Continue to reduce the denoising kernel, as in step 5, for the remaining n photon points O in the denoising kernel i (i=1,2,...,n) Use the following to record the distance W i , and stop when there is only one photon point in the denoising kernel. i The distance from the vertex to the center point of the denoising kernel generated by the photon point along the track direction.

[0086] Step 8) Set the appropriate denoising threshold MP and reachable distance RD. According to the method described above, with photon point a as the center, construct a parallelogram denoising kernel with the slope θ of the photon point, the height h of the track distance, and the base length s of the vertical distance. Count the distances W of the photon points around photon point a. i , taking photon point b as an example, use the following formula to calculate:

[0087] .

[0088] .

[0089] .

[0090] In the formula, y a ,y b represents the elevation of points a and b, x a , x b represents the horizontal distance along the track, θ is the slope of the central photon point a, when a1 and a2 are both less than 1, it is judged that the photon point b is in the parallelogram denoising kernel divided by the photon point a, and the larger value of a1 and a2 is taken 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 RD reachable distance that meets the denoising requirements.

[0091] Since the number of photon signal points acquired by the laser radar in 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, in order to compensate for the lack of signals in the horizontal direction, the MP is reduced accordingly.

[0092] .

[0093] Multiplying MP by a factor when calculating CD reduces the values ​​of CD and RD by reducing the number of core photon points required.

[0094] Step 9) Mark the O0 point cloud data as the accessed point cloud data, and obtain W i (i=1,2,...,n) are sorted in ascending order and the O with the smallest W value is selected. i The point is used as the access core photon point O1, which is the center point of the next denoising, that is, the new point cloud data to be accessed.

[0095] Step 10) Calculate the number of points NR(O0) within a given radius R of point O0 according to the following formula. If NR(O0) is less than MP, it will be recorded as a noise point in advance.

[0096] .

[0097] .

[0098] Among them, W(O0,O i ) is W 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 ) abbreviation; RD R (O0,O i ) is the reachable distance value RD(0) of the center point cloud of O0, RD R (O i ,O i ) is O i When calculating RD, use the same formula to calculate W. Arrange the obtained W in ascending order and compare it with CD. Record the larger value as RD. Count the CD value and the minimum RD value of each photon.

[0099] Step 11) Since noise points are more sparsely distributed than signal points, photons with large RD values ​​are identified as noise photons, and vice versa. When identifying, first sort the photon points in the window in ascending order according to their RD values, and then use the OTSU method to set an adaptive threshold for classification.

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] .

[0106] In the formula, N is the total number of photons in the window, and P is the number of photons in the sorting. Assuming that the first P photons are signal points and the following photons are noise points, when the inter-class variance σ of the signal point and the noise point 2 When (P) is the largest, the RD value is the threshold for judging denoising.

[0107] Repeat steps 5) to 11) until each photon point is marked as visited, and obtain the corresponding RD of all photon points. After arranging the RD in ascending order, continue to use the OSTU method to divide the photon points into noise photon points and signal photon points to obtain the denoising result.

[0108] The above method embodiment provided in the present application adds a large number of factors affecting denoising based on complex terrain, and improves the denoising algorithm in combination with the characteristics of different photon densities at different slopes, so that denoising has better resistance to different slopes and different densities in actual terrain, and the constructed denoising kernel can better complete the denoising work even in some extreme terrains.

[0109] Based on the same inventive concept, the embodiment of the present application also provides an adaptive slope single-photon laser radar point cloud denoising system for implementing the adaptive slope single-photon laser radar point cloud denoising method involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more adaptive slope single-photon laser radar point cloud denoising system embodiments provided below can refer to the limitations of the adaptive slope single-photon laser radar point cloud denoising method above, and will not be repeated here.

[0110] In an exemplary embodiment, Fig. 9 As shown, a single-photon laser radar point cloud denoising system with adaptive slope is provided, including: Fig. 9 As shown, the adaptive slope single-photon laser radar 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, a reachable distance calculation module M6, a reachable distance threshold determination module M7 and a noise point removal module M8; some modules may also have subunits for realizing their functions. Of course, Fig. 9 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different functions. Fig. 9 One or at least two components of the system shown.

[0111] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to 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 data set processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. 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, a single-photon laser radar point cloud denoising method with adaptive slope is implemented.

[0112] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0113] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0114] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0115] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0116] 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 used 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 must comply with relevant regulations.

[0117] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0118] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0119] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0120] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this 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 equal 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 equal 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: ； 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: ； ； ； 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: ； 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: When the slope-adaptive single-photon laser radar point cloud denoising system is run by a computer, it executes the slope-adaptive single-photon laser radar point cloud denoising method as described in any one of claims 1 to 8.

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