Three-dimensional laser point cloud noise reduction method, device, equipment and storage medium

Abstract

The present invention discloses a method, apparatus, device, and storage medium for denoising three-dimensional laser point clouds. The method comprises acquiring point cloud data of a target study surface, determining a target fitting surface based on the point cloud data, obtaining nearby point information corresponding to each point in the point cloud data based on the distance from the target fitting surface and preset boundary conditions, and removing noise points from the point cloud data based on the nearby point information. By optimizing an improved least squares algorithm and automatically setting a threshold using a Poisson distribution, which changes with adjustments to boundary conditions, the present invention reduces the effort and time required for manual adjustments compared to previous improved least squares algorithms, and exhibits greater universality.

Description

Technical Field

[0001] The present invention relates to the field of digital processing technology, and in particular to a three-dimensional laser point cloud noise reduction method, device, equipment and storage medium. Background Art

[0002] Currently, 3D machine vision inspection technology has been widely used in the field of industrial intelligent manufacturing. In actual inspection projects, 3D machine vision inspection technology typically uses 3D laser scanning equipment to obtain 3D point cloud data of object surfaces. Due to factors such as ambient light noise, inherent nonlinear errors in the equipment, and object surface color, the acquired 3D point cloud data contains outlier noise, resulting in low processing accuracy of the 3D point cloud data. Therefore, a point cloud denoising algorithm is needed to remove outliers from the 3D point cloud data and perform smoothing correction on the 3D point cloud data, which is called noise reduction.

[0003] The least squares denoising theory compares the threshold set by the least squares fitting with the distance d between the point and the fitting surface, and deletes points that are larger than the threshold to achieve the purpose of noise reduction. The size of the threshold setting will directly affect the noise reduction effect. If the threshold is too large, the noise reduction effect will not be obvious. If the threshold is set too small, effective points will be lost, so it is difficult to select a suitable threshold. In addition, the least squares fitting method is based on the assumption that x and y are independent variables without errors, and z is a dependent variable with errors. When solving the plane parameters, the plane parameters are solved. However, in fact, the point cloud obtained has errors in the three directions of x, y, and z. Therefore, the least squares method cannot achieve a good noise reduction effect in the solution of the plane fitting of point cloud data.

[0004] The main idea of ​​an improved least squares algorithm is to substitute the measured point cloud data into the established mathematical model for each research surface, so as to obtain the distance between each measured point and the fitted structural surface, and record this value as D a Search for all measured points b within a certain range r near the point. If b is not less than a positive number B, it means that the point is not an isolated point outside the body. Otherwise, delete it. Then calculate the distance from each of the N points to the fitting plane and record the value as D ab , statistics in |D a -D ab |When the number n of b points that fall within this interval is less than a positive integer N, the point is considered a noise point and is deleted. Otherwise, it is retained. The above process is repeated for each measured point in the study unit until all points have been traversed. This improved algorithm, by assigning different weights to the spatial domain and the influence domain, can more accurately identify noise points. Even when many noise points are clustered together, it can be effectively identified, achieving optimal noise reduction.

[0005] However, the boundary conditions r, B, and N in this method all require manual input and adjustment to achieve optimal results. Furthermore, the mathematical relationship between the three factors makes manual adjustment time-consuming and labor-intensive. Therefore, improving the universality of 3D laser point cloud noise reduction and reducing the effort and time required for manual adjustment is a pressing technical challenge.

[0006] The above content is only used to assist in understanding the technical solution of the present invention and does not constitute an admission that the above content is prior art. Summary of the Invention

[0007] The main purpose of the present invention is to provide a three-dimensional laser point cloud denoising method, device, equipment and storage medium, aiming to solve the technical problem that the improved least squares method currently used for three-dimensional laser point cloud denoising requires manual input of relevant boundary conditions and has low universality.

[0008] To achieve the above object, the present invention provides a three-dimensional laser point cloud noise reduction method, which includes the following steps:

[0009] Acquiring point cloud data of a target research surface, and determining a target fitting surface based on the point cloud data;

[0010] Obtaining nearby point information corresponding to each point based on the distance from each point in the point cloud data to the target fitting surface and preset boundary conditions;

[0011] Based on the nearby point information, noise points in the point cloud data are removed.

[0012] Optionally, the nearby point information is a two-dimensional array consisting of the number of points near the boundary condition and the number of points near the average distance.

[0013] Optionally, obtain the number of points near the boundary condition corresponding to each point, including:

[0014] Determining whether the distance from each point in the point cloud data to the target fitting surface is within the preset boundary condition range;

[0015] If so, mark the point as a point near the boundary condition, and count the number of points near the boundary condition corresponding to each point.

[0016] Optionally, get the average distance to each point and the number of nearby points, including:

[0017] Obtain the average distance from each point in the point cloud data to the target fitting surface, where the distance from the point satisfies the preset boundary conditions;

[0018] Determine an average distance condition based on the distance average corresponding to each point and the distance from each point to the target fitting surface;

[0019] The points within the average distance condition corresponding to each point in the point cloud data are marked as average distance nearby points, and the number of average distance nearby points corresponding to each point is obtained by counting.

[0020] Optionally, the average distance condition is the absolute value of the difference between the distance from each point to the target fitting surface and the average value of the distance corresponding to each point.

[0021] Optionally, based on the number of points near the boundary condition, noise points in the point cloud data are removed, specifically including:

[0022] According to the number of points near the boundary conditions of each point in the point cloud data, the mean value, variance and standard deviation of the number of points near the boundary conditions of all points are obtained;

[0023] Based on the mean, variance, and standard deviation, a first threshold corresponding to the number of points near the boundary condition is determined, and points in the point cloud data that do not meet the first threshold are removed as noise points.

[0024] Optionally, noise points in the point cloud data are removed based on the number of points near the average distance, specifically including:

[0025] According to the number of points near the average distance of each point in the point cloud data, the mean value, variance and standard deviation of all points with respect to the number of points near the average distance are obtained;

[0026] Based on the mean value, variance, and standard deviation, a second threshold corresponding to the number of points near the mean distance is determined, and points in the point cloud data that do not meet the second threshold are removed as noise points.

[0027] In addition, in order to achieve the above-mentioned object, the present invention further provides a three-dimensional laser point cloud noise reduction device, the three-dimensional laser point cloud noise reduction device comprising:

[0028] A determination module, configured to obtain point cloud data of a target research surface and determine a target fitting surface based on the point cloud data;

[0029] An acquisition module, configured to obtain nearby point information corresponding to each point based on the distance from each point in the point cloud data to the target fitting surface and preset boundary conditions;

[0030] A removal module is used to remove noise points in the point cloud data based on the nearby point information.

[0031] In addition, in order to achieve the above-mentioned purpose, the present invention also provides a three-dimensional laser point cloud denoising device, which includes: a memory, a processor, and a three-dimensional laser point cloud denoising method program stored in the memory and runnable on the processor. When the three-dimensional laser point cloud denoising method program is executed by the processor, the steps of the three-dimensional laser point cloud denoising method described above are implemented.

[0032] In addition, in order to achieve the above-mentioned purpose, the present invention also provides a storage medium, on which a three-dimensional laser point cloud denoising method program is stored. When the three-dimensional laser point cloud denoising method program is executed by a processor, the steps of the three-dimensional laser point cloud denoising method as described above are implemented.

[0033] Embodiments of the present invention propose a three-dimensional laser point cloud denoising method, apparatus, device, and storage medium. The method comprises acquiring point cloud data of a target study surface, determining a target fitting surface based on the point cloud data, obtaining nearby point information corresponding to each point in the point cloud data based on the distance from the target fitting surface and preset boundary conditions, and removing noise points from the point cloud data based on the nearby point information. This method optimizes an improved least squares algorithm, automatically setting a threshold using a Poisson distribution that changes with adjustments to boundary conditions. Compared to previous improved least squares algorithms, this method reduces the effort and time required for manual adjustments and offers greater universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Schematic diagram of the structure of a three-dimensional laser point cloud noise reduction device in an embodiment of the present invention;

[0035] Figure 2 Schematic diagram of a process for denoising a three-dimensional laser point cloud according to an embodiment of the present invention;

[0036] Figure 3 Schematic diagram of the principle of a three-dimensional laser point cloud denoising method according to an embodiment of the present invention;

[0037] Figure 4 A schematic diagram of the principle of threshold setting in an embodiment of the present invention;

[0038] Figure 5 This is a structural block diagram of a three-dimensional laser point cloud noise reduction device in an embodiment of the present invention.

[0039] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0040] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0041] At present, in the relevant technical field, the improved least squares method used for three-dimensional laser point cloud denoising requires manual input of relevant boundary conditions and has low universality.

[0042] To address this issue, various embodiments of the 3D laser point cloud denoising method of the present invention are proposed. This 3D laser point cloud denoising method optimizes an improved least squares algorithm, automatically sets a threshold using a Poisson distribution, and adjusts the threshold as boundary conditions are adjusted. Compared to previous improved least squares algorithms, this method reduces the effort and time required for manual adjustments and offers greater universality.

[0043] Reference Figure 1 , Figure 1 This is a schematic structural diagram of a three-dimensional laser point cloud noise reduction device according to an embodiment of the present invention.

[0044] The device can be a user equipment (UE) such as a mobile phone, smart phone, laptop computer, digital broadcast receiver, personal digital assistant (PDA), tablet computer (PAD), handheld device, vehicle-mounted device, wearable device, computing device or other processing device connected to a wireless modem, mobile station (MS), etc. The device may be called a user terminal, portable terminal, desktop terminal, etc.

[0045] Typically, the device includes: at least one processor 301, a memory 302, and a three-dimensional laser point cloud denoising method program stored on the memory and executable on the processor, wherein the three-dimensional laser point cloud denoising method program is configured to implement the steps of the three-dimensional laser point cloud denoising method as described above.

[0046] The processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. The processor 301 may also include an AI (Artificial Intelligence) processor, which is used to process operations related to the three-dimensional laser point cloud denoising method, so that the three-dimensional laser point cloud denoising method model can be trained and learned autonomously to improve efficiency and accuracy.

[0047] Memory 302 may include one or more computer-readable storage media, which may be non-transitory. Memory 302 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in memory 302 is used to store at least one instruction, which is executed by processor 301 to implement the three-dimensional laser point cloud denoising method provided in the method embodiment of the present application.

[0048] In some embodiments, the terminal may optionally include a communication interface 303 and at least one peripheral device. The processor 301, memory 302, and communication interface 303 may be connected via a bus or signal lines. Each peripheral device may be connected to the communication interface 303 via a bus, signal lines, or circuit boards. Specifically, the peripheral device may include at least one of a radio frequency circuit 304, a display screen 305, and a power supply 306.

[0049] The communication interface 303 can be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 301 and the memory 302. The communication interface 303 is used to receive the movement trajectories and other data of multiple mobile terminals uploaded by users through the peripheral device. In some embodiments, the processor 301, the memory 302, and the communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302, and the communication interface 303 can be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0050] The RF circuit 304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 304 communicates with communication networks and other communication devices via electromagnetic signals, thereby acquiring the movement trajectories and other data of multiple mobile terminals. The RF circuit 304 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the RF circuit 304 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The RF circuit 304 can communicate with other terminals via at least one wireless communication protocol. Such wireless communication protocols include, but are not limited to, metropolitan area networks, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 304 may also include circuits related to NFC (Near Field Communication), which is not limited in this application.

[0051] Display screen 305 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, or any combination thereof. When display screen 305 is a touch screen display, it is also capable of collecting touch signals on or above the surface of display screen 305. This touch signal can be input as a control signal to processor 301 for processing. In this case, display screen 305 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, display screen 305 can be a single display screen, the front panel of the electronic device; in other embodiments, display screen 305 can be at least two, each disposed on different surfaces of the electronic device or in a foldable design; in still other embodiments, display screen 305 can be a flexible display screen, disposed on a curved or foldable surface of the electronic device. Display screen 305 can also be configured as a non-rectangular irregular shape, i.e., a special-shaped screen. Display screen 305 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0052] Power supply 306 is used to power various components in the electronic device. Power supply 306 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 306 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0053] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the three-dimensional laser point cloud noise reduction device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0054] The embodiment of the present invention provides a three-dimensional laser point cloud noise reduction method, referring to Figure 2 , Figure 2 Schematic diagram of the process of an embodiment of the three-dimensional laser point cloud denoising method of the present invention.

[0055] In this embodiment, the three-dimensional laser point cloud noise reduction method includes the following steps:

[0056] Step S100: acquiring point cloud data of a target research surface, and determining a target fitting surface based on the point cloud data.

[0057] Specifically, after obtaining the point cloud data of the target research surface, fitting can be performed based on the point cloud data to obtain the target fitting surface. Then, based on the relationship between the obtained target fitting surface and the point cloud data, the noise points in the point cloud data can be removed to achieve noise reduction processing of the three-dimensional laser point cloud.

[0058] Step S200 , obtaining nearby point information corresponding to each point based on the distance from each point in the point cloud data to the target fitting surface and preset boundary conditions.

[0059] Specifically, after obtaining the target fitting surface, the distance from each point in the point cloud data to the target fitting surface and the preset boundary conditions can be used to obtain the nearby point information corresponding to each point, so as to determine whether each point cloud data is a noise point.

[0060] In this application, the number of points near the obtained boundary conditions and the number of points near the average distance are put into a two-dimensional array so as to perform two normal distribution calculations and overall noise reduction, rather than the original noise reduction one by one, thereby improving the overall noise reduction effect.

[0061] It should be noted that the nearby point information is a two-dimensional array consisting of the number of points near the boundary condition and the number of points near the average distance.

[0062] Specifically, the number of points near the boundary condition corresponding to each point can be obtained by: judging whether the distance from each point in the point cloud data to the target fitting surface is within the preset boundary condition range; if so, marking the point as a point near the boundary condition, and statistically obtaining the number of points near the boundary condition corresponding to each point.

[0063] Specifically, the number of points near the average distance corresponding to each point can be obtained by: obtaining the average distance from the points in the point cloud data whose distance from each point meets the preset boundary conditions to the target fitting surface, determining the average distance condition based on the average distance corresponding to each point and the distance from each point to the target fitting surface, marking the points within the average distance condition corresponding to each point in the point cloud data as points near the average distance, and statistically obtaining the number of points near the average distance corresponding to each point.

[0064] In a preferred embodiment, the average distance condition is the absolute value of the difference between the distance of each point to the target fitting surface and the average value of the distance corresponding to each point.

[0065] On this basis, this application changes the original two point calculations from serial to parallel, which improves the computational efficiency of noise reduction to a certain extent.

[0066] Step S300: removing noise points in the point cloud data based on the nearby point information.

[0067] Specifically, after obtaining the nearby point information, the noise data in the point cloud data can be used based on the nearby point information.

[0068] In practical applications, noise points in the point cloud data are removed based on the number of points near the boundary conditions. This can be done by: obtaining the average value, variance, and standard deviation of all points with respect to the number of points near the boundary conditions based on the number of points near the boundary conditions for each point in the point cloud data; determining a first threshold corresponding to the number of points near the boundary conditions based on the average value, variance, and standard deviation; and removing points in the point cloud data that do not meet the first threshold as noise points.

[0069] In practical applications, noise points in the point cloud data are removed based on the number of points near the average distance. This can be done by: obtaining the average value, variance, and standard deviation of all points with respect to the number of points near the average distance based on the number of points near the average distance for each point in the point cloud data; determining a second threshold corresponding to the number of points near the average distance based on the average value, variance, and standard deviation; and removing points in the point cloud data that do not meet the second threshold as noise points.

[0070] In this application, the normal distribution is used to automatically set the threshold without manual experience and trial, which improves the universality of 3D laser point cloud noise reduction and reduces the energy and time spent on manual adjustment.

[0071] In this embodiment, a three-dimensional laser point cloud denoising method is provided. By optimizing the improved least squares algorithm, the threshold is automatically set using the Poisson distribution, and the threshold changes with the adjustment of the boundary conditions. Compared with the previous improved least squares algorithm, the effort and time spent on manual adjustment are reduced, and the method has higher universality.

[0072] In order to explain the present application more clearly, a specific example of the 3D laser point cloud denoising method of the present application is provided below. Figure 3 As shown, the following steps are included:

[0073] S1: Noise reduction processing starts.

[0074] S2: Obtain point cloud data of a research surface.

[0075] S3: Fit the research surface to obtain a fitting surface.

[0076] S4: Based on the point cloud data of the study surface and the fitting surface, calculate the distance Da from the a-th point on the study surface to the fitting surface.

[0077] S5: Get the boundary condition, i.e., the radius r.

[0078] S6: parallel calculation to obtain bi and ni;

[0079] Specifically, calculating and obtaining bi includes: calculating the number of nearby points bi that meet the boundary conditions of point a, wherein the distance between bi and point a is less than the radius r;

[0080] Specifically, the calculation to obtain ni includes: calculating the average distance Dab from the point r away from point a to the fitting surface; if there is no point at distance r, the farthest point in the range is used instead; and counting the number ni of points within the radius of |Da-Dab| near point a.

[0081] S7: Store bi and ni into the two-dimensional array Q(bi, ni).

[0082] S8: Determine whether point a is the last point. If so, execute step S9; if not, return to execute step S4.

[0083] S9: Calculate the mean, variance, and standard deviation of the number of points bi near all points to obtain the point threshold B. Remove the points with bi < B as isolated noise points, and retain the points with bi ≥ B as non-isolated noise points to obtain a two-dimensional array Q'(bi', ni').

[0084] S10: Calculate the mean, variance, and standard deviation of ni' to obtain the point threshold N. Remove points with ni' < N as noise points, and retain points with ni' ≥ N as non-noise points.

[0085] S11: The noise reduction process ends.

[0086] It is easy to understand the principle of setting the threshold value of this application. Figure 4 As shown, when one-dimensional measurement data follows a normal distribution (a histogram plotted using a frequency table of a general distribution has a peak in the center and is generally symmetrical. With sufficient data, the intervals between groups become denser, approaching a smooth curve). Letting μ represent the mean and σ the standard deviation, the statistical area within the horizontal interval (μ-σ, μ+σ) reaches 68.26%; the statistical area within the horizontal interval (μ-2σ, μ+2σ) reaches 95.44%; and the area within the horizontal interval (μ-3σ, μ+3σ) reaches 99.74%. Assuming that the median values ​​of the number of neighbors bi and ni of laser point a approximately follow a normal distribution, 99.74% of the points will fall within the region (μ-3σ, μ+3σ). Points within this range are considered valid points, and only a very small number of points that do not meet the criteria will fall outside the range and are removed as noise. Setting the threshold in this way automatically calculates the threshold based on the data size, eliminating the need for manual repetitive setting and improving efficiency and speed.

[0087] In this embodiment, optimization is performed based on an improved least squares algorithm, and the values ​​of B and N are automatically calculated using Poisson distribution, and change with the adjustment of r. Compared with the previous improved least squares algorithm, the energy and time spent on manual adjustment are reduced, and it has higher universality.

[0088] Reference Figure 5 , Figure 5This is a structural block diagram of an embodiment of a three-dimensional laser point cloud noise reduction device of the present invention.

[0089] like Figure 5 As shown, the three-dimensional laser point cloud noise reduction device proposed in the embodiment of the present invention includes:

[0090] A determination module 10 is configured to obtain point cloud data of a target research surface and determine a target fitting surface based on the point cloud data;

[0091] An acquisition module 20 is configured to obtain nearby point information corresponding to each point based on the distance from each point in the point cloud data to the target fitting surface and preset boundary conditions;

[0092] The removal module 30 is configured to remove noise points in the point cloud data based on the nearby point information.

[0093] Other embodiments or specific implementations of the three-dimensional laser point cloud noise reduction device of the present invention can refer to the above-mentioned method embodiments and will not be repeated here.

[0094] In addition, an embodiment of the present invention further proposes a storage medium, on which a three-dimensional laser point cloud denoising method program is stored, and when the three-dimensional laser point cloud denoising method program is executed by a processor, the steps of the three-dimensional laser point cloud denoising method as described above are implemented. Therefore, it will not be described in detail here. In addition, the description of the beneficial effects of adopting the same method will not be repeated. For technical details not disclosed in the computer-readable storage medium embodiment involved in this application, please refer to the description of the method embodiment of this application. As an example, the program instructions can be deployed to be executed on one computing device, or on multiple computing devices located at one location, or on multiple computing devices distributed at multiple locations and interconnected by a communication network.

[0095] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The above-described program can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The above-described storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0096] It should also be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0097] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware, and of course can also be implemented by means of dedicated hardware including application-specific integrated circuits, dedicated CPUs, dedicated memories, dedicated components, etc. In general, all functions performed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structures used to implement the same function can also be diverse, such as analog circuits, digital circuits, or dedicated circuits. However, for the present invention, software program implementation is a better implementation method in most cases. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc., and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

Claims

1. A 3D laser point cloud denoising method, characterized in that: The method comprises the following steps: Acquiring point cloud data of a target research surface, and determining a target fitting surface based on the point cloud data; Obtaining nearby point information corresponding to each point based on the distance from each point in the point cloud data to the target fitting surface and preset boundary conditions; Based on the nearby point information, removing noise points in the point cloud data; The nearby point information is a two-dimensional array consisting of the number of points near the boundary condition and the number of points near the average distance; Obtaining the number of points near the boundary condition corresponding to each point, including: determining whether the distance from each point in the point cloud data to the target fitting surface is within the preset boundary condition range; if so, marking the point as a point near the boundary condition, and obtaining the number of points near the boundary condition corresponding to each point by counting; Obtaining the number of points near the average distance corresponding to each point, including: obtaining an average distance from points in the point cloud data whose distances from each point meet a preset boundary condition to a target fitting surface; determining an average distance condition based on the average distance corresponding to each point and the distance from each point to the target fitting surface; marking points within the average distance condition corresponding to each point in the point cloud data as points near the average distance, and obtaining the number of points near the average distance corresponding to each point by counting; Removing noise points from the point cloud data based on the number of points near the boundary condition, including: obtaining, based on the number of points near the boundary condition for each point in the point cloud data, an average value, a variance, and a standard deviation of all points with respect to the number of points near the boundary condition; determining a first threshold corresponding to the number of points near the boundary condition based on the average value, the variance, and the standard deviation, and removing points in the point cloud data that do not meet the first threshold as noise points; Based on the number of points near the average distance, noise points in the point cloud data are removed, including: obtaining the average value, variance and standard deviation of all points with respect to the number of points near the average distance according to the number of points near the average distance of each point in the point cloud data; determining a second threshold corresponding to the number of points near the average distance based on the average value, variance and standard deviation, and removing points in the point cloud data that do not meet the second threshold as noise points.

2. The 3D laser point cloud denoising method according to claim 1, wherein: The average distance condition is the absolute value of the difference between the distance from each point to the target fitting surface and the average value of the distance corresponding to each point.

3. A 3D laser point cloud denoising device, using the 3D laser point cloud denoising method according to claim 1, characterized in that: The three-dimensional laser point cloud noise reduction device comprises: A determination module, configured to obtain point cloud data of a target research surface and determine a target fitting surface based on the point cloud data; An acquisition module, configured to obtain nearby point information corresponding to each point based on the distance from each point in the point cloud data to the target fitting surface and preset boundary conditions; A removal module, configured to remove noise points in the point cloud data based on the nearby point information, comprising: Obtaining, based on the number of points near the boundary condition of each point in the point cloud data, an average value, variance, and standard deviation of all points with respect to the number of points near the boundary condition; determining a first threshold corresponding to the number of points near the boundary condition based on the average value, variance, and standard deviation, and removing points in the point cloud data that do not meet the first threshold as noise points; According to the number of points near the average distance of each point in the point cloud data, the mean, variance and standard deviation of all points with respect to the number of points near the average distance are obtained; based on the mean, variance and standard deviation, a second threshold corresponding to the number of points near the average distance is determined, and points in the point cloud data that do not meet the second threshold are removed as noise points.

4. A three-dimensional laser point cloud noise reduction device, characterized in that: The three-dimensional laser point cloud denoising device includes: a memory, a processor, and a three-dimensional laser point cloud denoising method program stored in the memory and runnable on the processor. When the three-dimensional laser point cloud denoising method program is executed by the processor, the steps of the three-dimensional laser point cloud denoising method according to claim 1 or 2 are implemented.

5. A storage medium, characterized in that The storage medium stores a three-dimensional laser point cloud denoising method program, which, when executed by a processor, implements the steps of the three-dimensional laser point cloud denoising method according to claim 1 or 2.

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