Seabed obstacle identification method based on multi-beam point cloud data and related equipment

Abstract

The invention discloses a seabed obstacle identification method based on multi-beam point cloud data and related equipment. The method comprises the following steps: acquiring the multi-beam point cloud data of a target area; performing statistics on the basis of the elevation difference between the adjacent sounding point clouds to obtain a frequency-based elevation difference histogram; fitting an attenuation model according to the elevation difference histogram, determining a noise threshold value based on the attenuation model, and dividing the multi-beam point cloud data into a real terrain point cloud and a noise point cloud by using the noise threshold value; a multi-beam real terrain point cloud is obtained through iterative denoising division; and based on the multi-beam real terrain point cloud, obstacle identification is carried out by using the elevation slope, and obstacle information of the target area is obtained. According to the method, through the automatic processes of determining the noise threshold value through the fitting attenuation model and iterative denoising, a traditional interactive filtering method depending on experience of technicians is replaced, the data processing efficiency is greatly improved, and the method can be widely applied to the technical field of data processing.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and related equipment for identifying seabed obstacles based on multibeam point cloud data. Background Technology

[0002] Seabed topographic surveying is a fundamental marine mapping task aimed at obtaining the three-dimensional coordinates of seabed topographic points. Multibeam bathymetry systems are the mainstream equipment for depth measurement, widely used in seabed topographic surveying due to their advantages such as high measurement accuracy, large measurement area, fast measurement speed, and real-time high-density three-dimensional depth point output. Currently, seabed topographic surveying mainly utilizes shipborne multibeam bathymetry systems to conduct depth measurements, simultaneously performing tidal level, positioning, and sound velocity measurements. This technology features high precision, high resolution, and high efficiency in depth measurement.

[0003] The principle of shipborne multibeam echo sounding: It integrates multibeam echo sounding technology, GNSS RTK / PPK / PPP high-precision positioning technology, POS technology, and in-flight sound velocity measurement technology. Utilizing high-precision GNSS 3D solutions, combined with ship attitude and bearing, and the coordinates of the transducer and GNSS antenna in the ship's coordinate system, it obtains the 3D coordinates of the echo sounding transducer in real time, providing a high-precision instantaneous 3D reference for echo sounding. Combined with high-precision water depth obtained from in-flight sound velocity ray tracking, it ultimately obtains high-precision 3D coordinates of the underwater measurement point. However, due to the complex dynamic marine environment, multibeam transducer sonar parameter settings, and other factors, the raw multibeam water depth point cloud inevitably contains anomalous data. If this anomalous data is directly fused to obtain the target water depth information without processing, it will directly affect the accurate representation of the seabed micro-topography. Therefore, it is necessary to judge and remove point cloud noise.

[0004] Traditional multibeam point cloud noise processing mainly relies on interactive filtering based on the experience of technicians to remove gross errors. While this method is highly reliable, it is labor-intensive and has low filtering efficiency. Furthermore, automatic filtering of multibeam bathymetry data using denoising algorithms can also remove non-terrain point cloud information. However, these algorithms also suffer from drawbacks such as low automation in human-computer interaction, incomplete filtering of sparse edge beam point clouds, accidental deletion of real terrain points, and significant loss of terrain details. Summary of the Invention

[0005] The main objective of this invention is to propose a method, apparatus, electronic device, storage medium, and program product for identifying seabed obstacles based on multibeam point cloud data, aiming to solve at least one problem in the prior art.

[0006] To achieve the above objectives, one aspect of this invention proposes a method for identifying seabed obstacles based on multibeam point cloud data, the method comprising: Acquire multibeam point cloud data of the target area; wherein, the multibeam point cloud data includes the bathymetric point cloud at various locations in the target area; A frequency-based elevation difference histogram is obtained based on the statistical analysis of elevation differences between adjacent sounding point clouds. The attenuation model is fitted based on the elevation difference histogram, and the noise threshold is determined based on the attenuation model. The multibeam point cloud data is then divided into real terrain point cloud and noise point cloud using the noise threshold. Increment the denoising count by 1, update the multibeam point cloud data based on the real terrain point cloud, return to execute the step of obtaining a frequency-based elevation difference histogram based on the elevation difference statistics between adjacent sounding point clouds, until the denoising count reaches the preset number, and obtain the multibeam real terrain point cloud; Based on multibeam real terrain point clouds, obstacle identification is performed using elevation slope to obtain obstacle information in the target area.

[0007] In some embodiments, acquiring multibeam point cloud data of a target area includes the following steps: Acquire raw multibeam data of the target area, preprocess the raw multibeam data to obtain multibeam point cloud data in geographic coordinate system; The preprocessing includes surge correction, sound velocity correction, tide level correction, merging survey lines, and calculating TPU.

[0008] In some embodiments, a frequency-based elevation difference histogram is obtained based on the elevation difference statistics between adjacent sounding point clouds, including the following steps: Iterate through the multibeam point cloud data to obtain the elevation difference between all adjacent bathymetric point clouds; An initial elevation difference histogram is obtained based on the elevation difference; in the initial elevation difference histogram, the horizontal axis corresponds to the value of the elevation difference, and the vertical axis corresponds to the probability density of each value of the elevation difference. The vertical axis is converted from probability density to frequency to construct the target elevation difference histogram.

[0009] In some embodiments, converting the vertical axis from probability density to frequency includes the following steps: The frequency corresponding to each elevation difference value is obtained by multiplying the interval width corresponding to the horizontal axis with the probability density corresponding to each elevation difference value. Among them, frequency represents the ratio of the corresponding elevation difference count to the total number of sounding point clouds.

[0010] In some embodiments, fitting an attenuation model based on an elevation difference histogram and determining a noise threshold based on the attenuation model includes the following steps: The decay model of the elevation difference histogram is obtained by fitting the e-exponential decay formula. In this model, the dependent variable corresponds to frequency, and the independent variable corresponds to elevation difference. Obtain the constant term of the decay model; The target frequency is obtained by searching when the difference between the dependent variable and the constant term is less than a preset threshold. Based on the attenuation model, the target elevation difference corresponding to the target frequency is used as the noise threshold.

[0011] In some embodiments, multibeam point cloud data is divided into real terrain point clouds and noisy point clouds using a noise threshold, including the following steps: Traverse the elevation differences between all adjacent bathymetric point clouds in the multibeam point cloud data; When the elevation difference is greater than the noise threshold, the corresponding sounding point cloud is classified as a noise point cloud; otherwise, the corresponding sounding point cloud is classified as a real terrain point cloud.

[0012] In some embodiments, the multibeam real terrain point cloud includes real terrain point clouds at various locations in the target area. Based on the multibeam real terrain point cloud, obstacle identification is performed using elevation slope to obtain obstacle information of the target area, including the following steps: Based on the elevation and lateral position of the real terrain point cloud, the elevation slope between all adjacent real terrain point clouds in the target area is statistically obtained. When the elevation slope between the target real terrain point cloud and any of its adjacent real terrain point clouds is greater than the preset slope change threshold, the target real terrain point cloud is marked as a slope change point. Based on the slope change points, obstacle segments are marked in the target area, and the spatial location and height change range of the obstacle segments are used as obstacle information; The range of height variation is determined based on the elevation of each real terrain point cloud in the obstacle section.

[0013] To achieve the above objectives, another aspect of the present invention provides a seabed obstacle identification device based on multibeam point cloud data, the device comprising: The data acquisition module is used to acquire multibeam point cloud data of the target area; the multibeam point cloud data includes the bathymetric point cloud at various locations in the target area. The statistics module is used to obtain a frequency-based elevation difference histogram based on the elevation difference between adjacent sounding point clouds. The noise reduction module is used to fit an attenuation model based on the elevation difference histogram, determine a noise threshold based on the attenuation model, and use the noise threshold to divide the multibeam point cloud data into real terrain point cloud and noise point cloud. The iteration module is used to increment the denoising count by 1, update the multibeam point cloud data based on the real terrain point cloud, and return to execute the operation based on the statistics module until the denoising count reaches the preset count, and obtain the multibeam real terrain point cloud. The obstacle recognition module is used to identify obstacles based on multibeam real terrain point clouds and utilize elevation slope to obtain obstacle information in the target area.

[0014] To achieve the above objectives, another aspect of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method.

[0015] To achieve the above objectives, another aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method.

[0016] To achieve the above objectives, another aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0017] The embodiments of the present invention include at least the following beneficial effects: The present invention provides a method, apparatus, electronic device, storage medium, and program product for identifying seabed obstacles based on multibeam point cloud data. This solution acquires multibeam point cloud data of a target area; wherein the multibeam point cloud data includes bathymetric point clouds at various locations within the target area; a frequency-based elevation difference histogram is obtained based on the elevation difference between adjacent bathymetric point clouds; an attenuation model is fitted according to the elevation difference histogram, a noise threshold is determined based on the attenuation model, and the multibeam point cloud data is divided into real terrain point clouds and noise point clouds using the noise threshold; the denoising count is incremented by 1, the multibeam point cloud data is updated based on the real terrain point clouds, and the process of obtaining a frequency-based elevation difference histogram based on the elevation difference between adjacent bathymetric point clouds is repeated until the denoising count reaches a preset number, resulting in multibeam real terrain point clouds; obstacle identification is performed using the elevation slope based on the multibeam real terrain point clouds to obtain obstacle information for the target area. This invention significantly improves data processing efficiency by replacing traditional interactive filtering methods that rely on the experience of technicians with an automated process of "fitting an attenuation model to determine the noise threshold" and "iterative denoising." Specifically, this invention uses an attenuation model based on the statistical characteristics of the data itself (elevation difference histogram) to dynamically determine the threshold, rather than using a fixed threshold or a simple algorithm, making noise judgment more objective and accurate. This method is more adaptable and can effectively reduce the phenomena of "false deletion of real terrain points" and "severe loss of terrain details," especially for data processing in sparse areas such as edge beams, solving the problem of "incomplete filtering." This invention realizes an integrated process from denoising to recognition: using the high-precision denoising results as input for obstacle recognition, and using the sensitive indicator of "elevation slope" for obstacle judgment, ensuring the reliability of the final recognition result. This allows this invention not only to efficiently clean data but also to directly serve the key application of seabed obstacle detection, improving the practical value of the entire technical solution and the quality of the output. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of an implementation environment for the seabed obstacle recognition method based on multibeam point cloud data provided in this embodiment of the invention; Figure 2 This is a flowchart illustrating a method for identifying seabed obstacles based on multibeam point cloud data, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram illustrating the unfolded process of step S200 provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the expansion process for converting the vertical axis from probability density to frequency, provided in an embodiment of the present invention. Figure 5This is a schematic diagram of the expansion process of the fitting attenuation model to determine the noise threshold provided in the embodiments of the present invention; Figure 6 This is a schematic diagram of the unfolding process for dividing real terrain point clouds and noise point clouds according to an embodiment of the present invention; Figure 7 This is a schematic diagram illustrating the unfolded process of step S500 provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the overall process of the seabed obstacle recognition method based on multibeam point cloud data provided in the embodiments of the present invention; Figure 9 This is a schematic diagram of the structure of the seabed obstacle recognition device based on multibeam point cloud data provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0020] It is understood that the terms “first,” “second,” etc., used in this invention may be used herein to describe various concepts, but unless specifically stated otherwise, these concepts are not limited by these terms. These terms are used only to distinguish one concept from another. For example, first information may also be referred to as second information without departing from the scope of embodiments of the invention, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to determination” as used herein may be interpreted as “when…” or “when…” or “in response to determination.”

[0021] The terms “at least one,” “multiple,” “each,” “any,” etc., used in this invention, “at least one” includes one, two, or more than two; “multiple” includes two or more than two; “each” refers to each of the corresponding multiple; and “any” refers to any one of the multiple.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0023] In related technologies, traditional multibeam point cloud noise processing mainly relies on the experience of technicians to interactively filter and remove gross errors. This method is highly reliable but labor-intensive and has low filtering efficiency. Furthermore, automatic filtering of multibeam bathymetry data can remove non-terrain point cloud information through denoising algorithms. However, these algorithms also suffer from drawbacks such as low automation in human-computer interaction, incomplete filtering of sparse edge beam point clouds, easy deletion of real terrain points, and significant loss of terrain details.

[0024] In view of this, this invention provides a method and related equipment for identifying seabed obstacles based on multibeam point cloud data. This method acquires multibeam point cloud data of a target area; the multibeam point cloud data includes bathymetry point clouds at various locations within the target area; a frequency-based elevation difference histogram is obtained based on the elevation difference between adjacent bathymetry point clouds; an attenuation model is fitted according to the elevation difference histogram, a noise threshold is determined based on the attenuation model, and the multibeam point cloud data is divided into real terrain point clouds and noise point clouds using the noise threshold; the denoising count is incremented by 1, the multibeam point cloud data is updated based on the real terrain point clouds, and the process of obtaining the frequency-based elevation difference histogram based on the elevation difference between adjacent bathymetry point clouds is repeated until a preset number of denoising counts is reached, resulting in multibeam real terrain point clouds; obstacle identification is performed using the elevation slope based on the multibeam real terrain point clouds to obtain obstacle information for the target area. This invention, through an automated process of "fitting an attenuation model to determine the noise threshold" and "iterative denoising," replaces the traditional interactive filtering method that relies on the experience of technical personnel, greatly improving data processing efficiency. Specifically, this invention employs an attenuation model based on the statistical characteristics of the data itself (elevation difference histogram) to dynamically determine the threshold, rather than using a fixed threshold or a simple algorithm, making noise assessment more objective and accurate. This method is more adaptable and can effectively reduce the phenomena of "falsely deleting real terrain points" and "severe loss of terrain details," especially for data processing in sparse areas such as edge beams, solving the problem of "incomplete filtering." This invention achieves an integrated process from denoising to recognition: using high-precision denoising results as input for obstacle recognition, and utilizing the sensitive indicator of "elevation slope" for obstacle judgment, ensuring the reliability of the final recognition result. This allows this invention not only to efficiently clean data but also to directly serve the key application of seabed obstacle detection, improving the practical value of the entire technical solution and the quality of the output.

[0025] It is understood that the seabed obstacle recognition method based on multi-beam point cloud data provided by this invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal can be a smartphone, tablet, laptop, or desktop computer, but it is not limited to these.

[0026] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided by an embodiment of the present invention. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0027] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0028] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0029] Terminal 102 can be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the invention does not impose any limitations.

[0030] For example, based on Figure 1The implementation environment shown in this embodiment of the invention provides a method for identifying seabed obstacles based on multibeam point cloud data. The following description uses the application of this method for identifying seabed obstacles based on multibeam point cloud data in server 101 as an example. It can be understood that this method for identifying seabed obstacles based on multibeam point cloud data can also be applied in terminal 102.

[0031] Reference Figure 2 , Figure 2 This is an optional flowchart of a seabed obstacle recognition method based on multibeam point cloud data provided in an embodiment of the present invention. The execution subject of this seabed obstacle recognition method based on multibeam point cloud data can be any of the aforementioned computer devices (including servers or terminals). Figure 2 The method may include, but is not limited to, steps S100 to S500.

[0032] Step S100: Obtain multibeam point cloud data of the target area; Among them, the multibeam point cloud data includes the bathymetric point cloud at various locations in the target area; It should be noted that in some embodiments, step S100 may include the following steps: Acquire raw multibeam data of the target area, preprocess the raw multibeam data to obtain multibeam point cloud data in geographic coordinate system; the preprocessing includes surge correction, sound speed correction, tide level correction, merging survey lines and calculating TPU (total propagation uncertainty).

[0033] For example, in some specific implementations, the original multibeam point cloud data is first preprocessed, including surge correction, tide level correction, sound speed correction, merging survey lines, and calculating TPU, etc., to convert the collected original multibeam bathymetry point data into a bathymetry point cloud (X, Y, Z) in a geographic coordinate system.

[0034] Specifically, this embodiment of the invention ensures that the input "multibeam point cloud data" has extremely high initial accuracy and reliability through a series of standardized preprocessing steps, including surge correction and sound velocity correction. This lays a solid data foundation for subsequent automated denoising and obstacle recognition, reducing system errors at the source and making the final obstacle information location more accurate and reliable. Calculating the TPU (measurement uncertainty) provides a quantitative basis for subsequent data processing and quality assessment.

[0035] Step S200: Obtain a frequency-based elevation difference histogram based on the elevation difference statistics between adjacent sounding point clouds; It should be noted that in some embodiments, such as Figure 3As shown, step S200 may include the following steps: S210, traversing and obtaining the elevation difference between all adjacent bathymetric point clouds in the multibeam point cloud data; S220, organizing the elevation difference to obtain an initial elevation difference histogram; wherein, in the initial elevation difference histogram, the horizontal axis corresponds to the value of the elevation difference, and the vertical axis corresponds to the probability density of each elevation difference value; S230, converting the vertical axis from probability density to frequency, and constructing the target elevation difference histogram.

[0036] For example, in some specific implementations, a histogram of elevation differences of the preprocessed multibeam bathymetry point cloud data is plotted. The horizontal axis of the histogram represents the elevation difference between all adjacent underwater point clouds, and the vertical axis represents the frequency of all elevation differences. Specifically, the elevation difference between adjacent multibeam bathymetry point cloud data can be calculated according to Formula 1, and a histogram of elevation differences can be plotted.

[0037] , =1,2,…, N (Formula 1) in, It is the elevation difference between two adjacent point clouds. It is the first Elevation of a point cloud N It represents the total number of all point clouds.

[0038] It should be noted that in some embodiments, such as Figure 4 As shown, the vertical axis is converted from probability density to frequency, including the following steps: S231, based on the product of the interval width corresponding to the horizontal axis and the probability density corresponding to each value of elevation difference, the frequency corresponding to each value of elevation difference is obtained; wherein, the frequency represents the ratio of the count of the elevation difference of the corresponding value to the total number of sounding point clouds.

[0039] For example, in some specific implementations, the histogram of the elevation difference between two adjacent underwater point clouds is analyzed, which exhibits an exponentially decaying distribution. The y-axis of the histogram can be converted from probability density to frequency according to formulas 2 and 3, which are the conversion relationships between frequency and probability: (Formula 2) (Formula 3) in, It is a count of each elevation difference. It is the width of the interval in the elevation difference histogram. It is the original vertical axis (i.e., probability density) of the elevation difference histogram. It is the frequency of elevation differences.

[0040] Specifically, this embodiment of the invention defines a specific method for constructing an elevation difference histogram, using "frequency" rather than simply "probability density" as the vertical axis. Frequency is a direct reflection of the count, allowing the histogram to characterize the actual statistical regularity of the elevation difference distribution, rather than a smoothed estimate. This makes the subsequently fitted attenuation model more accurately reflect the distribution of noise and signal in the data, thereby making the noise threshold determined based on this model more accurate and reliable, further enhancing the scientific rigor and effectiveness of the denoising step.

[0041] Step S300: Fit an attenuation model based on the elevation difference histogram, determine a noise threshold based on the attenuation model, and use the noise threshold to divide the multibeam point cloud data into real terrain point cloud and noise point cloud. It should be noted that in some embodiments, such as Figure 5 As shown, fitting an attenuation model based on an elevation difference histogram and determining a noise threshold based on the attenuation model can include the following steps: S310, fitting an attenuation model of the elevation difference histogram based on the e-exponential attenuation formula; wherein, the dependent variable of the attenuation model corresponds to the frequency, and the independent variable of the attenuation model corresponds to the elevation difference; S320, obtaining the constant term of the attenuation model; S330, searching for the target frequency by using the difference between the dependent variable and the constant term being less than a preset threshold; S340, using the target elevation difference corresponding to the target frequency as the noise threshold based on the attenuation model.

[0042] For example, in some specific implementations, the abscissa of the elevation difference is used as the x-value of the attenuation model, and the frequency of the elevation difference is used as the y-value to fit the attenuation model. To remove noisy point clouds with large but few adjacent point clouds with significant elevation differences, the problem is transformed into making the constant term c in the fitted attenuation model close to 0, selecting a term that satisfies (yc-0) < 10. -3 The elevation difference corresponding to the y-value of the condition is used as the current denoising threshold, and based on this threshold, the multibeam bathymetry point cloud data is distinguished into noisy point clouds and true terrain point clouds. Under the assumption of continuous underwater terrain change, there are more point clouds with smaller elevation differences. The elevation difference histogram can be fitted using attenuation model formula 4. Formula 4 is the e-exponential attenuation formula used to fit the elevation difference histogram: (Formula 4) in, It is the amplitude of the fitted decay model. It is the decay rate of the decay model. It is a constant term in the decay model.

[0043] Specifically, this invention clarifies the mathematical methods for model fitting and threshold determination. The e-exponential decay formula can well fit the tailing phenomenon of noise in the elevation difference distribution. The threshold is determined by searching for points whose frequencies are close to the model constant term (i.e., background noise level), which is a data-driven adaptive threshold determination method. This method does not rely on manually set fixed values ​​and can dynamically adjust the noise judgment criteria according to different sea areas and data quality, exhibiting stronger robustness and adaptability. It effectively avoids the problems of filtered waves or under-filtering, ensuring optimal performance in the denoising process.

[0044] It should be noted that in some embodiments, such as Figure 6 As shown, dividing multibeam point cloud data into real terrain point clouds and noisy point clouds using a noise threshold can include the following steps: S350, traversing the elevation differences between all adjacent bathymetric point clouds in the multibeam point cloud data; S360, when the elevation difference is greater than the noise threshold, classifying the corresponding bathymetric point cloud as a noisy point cloud; otherwise, classifying the corresponding bathymetric point cloud as a real terrain point cloud.

[0045] For example, in some specific implementations, when the elevation difference between two adjacent point clouds is greater than this threshold, the two adjacent point clouds are marked as noise point clouds; otherwise, they are marked as real terrain point clouds.

[0046] Specifically, this embodiment of the invention clarifies the specific logic for point cloud segmentation based on a threshold. This judgment criterion is clear, explicit, and easy to implement in a computer program, ensuring the smooth execution of the automated filtering process. Its core benefit lies in its effective application of an adaptive noise threshold to each data point, completing a crucial step from theoretical model to practical classification, and ensuring that the denoising algorithm's performance matches its design intent.

[0047] Step S400: Increment the denoising count by 1, update the multibeam point cloud data based on the real terrain point cloud, and return to execute the step of obtaining a frequency-based elevation difference histogram based on the elevation difference statistics between adjacent sounding point clouds, until the denoising count reaches the preset number of times, and obtain the multibeam real terrain point cloud. For example, in some specific implementations, based on the first denoising, a second denoising operation is repeated. The data marked as real terrain point clouds in the first denoising is used for the second denoising, and the data marked as real terrain point clouds in the second denoising is used for the third denoising. The above process is repeated until the final multibeam bathymetry point cloud denoising result is obtained.

[0048] Step S500: Based on the multibeam real terrain point cloud, obstacle identification is performed using elevation slope to obtain obstacle information of the target area; It should be noted that the multibeam real terrain point cloud includes real terrain point clouds at various locations within the target area. In some embodiments, such as... Figure 7 As shown, step S500 may include the following steps: S510, based on the elevation and lateral position corresponding to the real terrain point cloud, calculate the elevation slope between all adjacent real terrain point clouds in the target area; S520, when the elevation slope between the target real terrain point cloud and any of its adjacent real terrain point clouds is greater than a preset slope change threshold, mark the target real terrain point cloud as a slope change point; S530, mark obstacle segments in the target area based on the slope change points, and use the spatial position and height change range corresponding to the obstacle segment as obstacle information; wherein, the height change range is determined based on the elevation corresponding to each real terrain point cloud in the obstacle segment.

[0049] For example, in some specific implementations, the elevation slope of adjacent points is calculated based on real terrain points. A slope change threshold θ is set according to the seabed topographic features of the survey area. When the slope between a point and its preceding / following adjacent points is greater than θ, the point is considered a slope abrupt change point. Multiple consecutive slope abrupt change points and their abnormal ranges are marked as potential obstacle areas, thereby achieving automatic identification of seabed obstacles. Finally, all obstacle sections that meet the conditions are recorded, and their spatial location, elevation abrupt change range, and other information are output for subsequent geological interpretation, seabed engineering planning, or navigation obstacle avoidance.

[0050] Specifically, this embodiment of the invention performs calculations based on denoised "real terrain point clouds," avoiding noise interference with slope calculations and improving the signal-to-noise ratio of recognition. Using "elevation slope" as a criterion, it can keenly capture abrupt changes in seabed topography, a typical characteristic of obstacles. This method not only determines the presence of obstacles but also outputs their "spatial location and range of height changes," providing rich and quantitative obstacle information far exceeding the simple judgment of "presence" or "absence." It has extremely high practical value for maritime safety assurance and marine engineering construction.

[0051] To explain in detail the principle of the technical solution of the present invention, the overall process of the present invention will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0052] First, it should be noted that related technologies achieve gross error removal through processing multibeam bathymetry data, amplifying multibeam bathymetry gross error samples, training an automatic multibeam bathymetry gross error detection model, and using the model to detect gross errors in multibeam bathymetry point cloud data. However, this method requires manual selection of thresholds to remove bathymetry points with gross error probabilities exceeding the threshold, thus completing the removal of gross errors from the multibeam point cloud. While other related technologies apply trend surface filtering and a combination of dynamic radius filtering and density clustering to the original multibeam point cloud set to obtain a seabed topographic point cloud set, trend surface filtering can mistakenly delete multibeam point clouds in dense noise areas, affecting the overall point cloud denoising accuracy. Furthermore, this method uses multiple algorithms, making the point cloud denoising calculation process complex. Another related technology involves constructing a gridded water depth surface based on multibeam bathymetry data, then building a first water depth point grid in flat areas and a second water depth point grid in areas with abrupt topographic changes. The beam points of the original multibeam bathymetry data are then filtered based on the first and second water depth point grids to obtain the filtering results. However, this method is only applicable to areas with large topographic differences and has a specific scope of use.

[0053] In view of the shortcomings of existing technologies, in order to accurately remove noise from multibeam bathymetry point clouds and identify and determine seabed obstacle information, this invention proposes a seabed obstacle identification method based on multibeam bathymetry point cloud data. This method adaptively determines the denoising threshold based on the elevation difference distribution of multibeam bathymetry point cloud data, which has the advantage of automatically adjusting parameters without manual intervention. It achieves efficient and accurate removal of bathymetry noise information, improves the quality of multibeam bathymetry results, and further calculates the elevation slope of adjacent points based on the denoised point cloud results. Seabed obstacles are identified and determined based on the continuous abrupt changes in elevation slope and their corresponding anomaly ranges.

[0054] Specifically, under the assumption that the seabed topography changes relatively continuously, the embodiments of the present invention preprocess the raw multibeam measurement data and convert it into point cloud data in a geographic coordinate system; adaptively determine the denoising threshold based on the elevation difference distribution of the multibeam point cloud data to distinguish between noisy point clouds and real topographic point clouds; and automatically identify seabed obstacles based on the elevation slope of adjacent points calculated from the real topographic point cloud data and their corresponding abnormal ranges.

[0055] The technical solutions implemented in the embodiments of the present invention include: First, the original multibeam point cloud data is preprocessed, including surge correction, tide level correction, sound speed correction, merging survey lines, and TPU calculation, to convert the collected original multibeam bathymetry point data into a bathymetry point cloud (X, Y, Z) in a geographic coordinate system.

[0056] Secondly, the elevation difference histogram of the preprocessed multibeam point cloud data is statistically analyzed. The horizontal axis of the elevation difference histogram represents the elevation difference between all two adjacent underwater point clouds, and the vertical axis represents the frequency of all elevation differences.

[0057] Using the x-coordinate of the elevation difference as the x-value of the attenuation model and the frequency of the elevation difference as the y-value, the attenuation model is fitted. To remove noisy point clouds with large but few adjacent point clouds, the problem is transformed into making the constant term c in the fitted attenuation model close to 0, choosing a value that satisfies (yc-0) < 10. -3 The elevation difference corresponding to the y-value of the condition is used as the threshold for the first denoising step. Based on this threshold, the multibeam bathymetry point cloud data is distinguished into noisy point clouds and true terrain point clouds. When the elevation difference between two adjacent point clouds is greater than this threshold, these two adjacent point clouds are marked as noisy point clouds; otherwise, they are marked as true terrain point clouds. The second denoising operation is repeated, using the data marked as true terrain point clouds in the first denoising step for the second denoising step, and using the data marked as true terrain point clouds in the second denoising step for the third denoising step. This process is repeated until the final multibeam bathymetry point cloud denoising result is obtained.

[0058] Then, based on the real terrain points, the elevation slope of adjacent points is calculated. According to the seabed topography characteristics of the survey area, the slope change threshold θ is set. When the slope between a certain point and its preceding / following adjacent points is greater than θ, the point is considered to be a slope change point. Multiple consecutive slope change points and their abnormal ranges are marked as potential obstacle areas, thereby realizing automatic identification of seabed obstacles.

[0059] like Figure 8 As shown, the specific process of implementing the seabed obstacle recognition method based on multibeam point cloud data in this embodiment of the invention is as follows: (1) Perform preprocessing on the original multibeam bathymetry data, such as surge correction, sound velocity correction, tide level correction, merging survey lines, and calculating TPU, to obtain multibeam bathymetry point cloud data in geographic coordinate system.

[0060] (2) Calculate the elevation difference of adjacent multibeam bathymetry point cloud data according to Formula 1, and compile a histogram of elevation differences.

[0061] , =1,2,…, N (Formula 1) (3) Analyze the histogram of elevation difference between two adjacent underwater point clouds, which exhibits an exponential decay distribution. The y-axis of the histogram needs to be converted from probability density to frequency according to formulas 2 and 3. Noise point clouds are randomly distributed, so their elevation difference distribution is not concentrated. However, the distribution of real terrain point clouds satisfies the continuity assumption, so their elevation difference distribution is concentrated. Under the assumption of continuous underwater terrain change, there are more point clouds with smaller elevation differences. The attenuation model formula 4 is used to fit the elevation difference histogram. Formulas 2 and 3 are the conversion relationship between frequency and probability, and formula 4 is the exponential decay formula used to fit the elevation difference histogram.

[0062] (Formula 2) (Formula 3) (Formula 4) (4) The goal is to mark point clouds with elevation differences greater than a certain threshold as noise and remove them. Therefore, this problem can be transformed into making the constant term c in the fitted decay model as close to zero as possible. This will satisfy (yc-0) < 10 -3 The elevation difference corresponding to the maximum value of the conditional frequency y-value is used as the elevation difference threshold for the initial denoising. Then, based on this threshold, the point cloud is divided into real terrain point cloud and noisy point cloud.

[0063] (5) To avoid several adjacent point clouds being considered noise, a histogram of elevation differences is plotted on the real terrain point clouds obtained from the first denoising. If the elevation difference between two adjacent point clouds is too large during the second denoising, it can also be considered noise and removed. The above operation is repeated on the result of the second denoising to perform the Nth denoising. The value of the constant term c becomes smaller and smaller, while the amplitude term becomes larger and larger. Thus, the purpose of denoising is achieved.

[0064] (6) After completing the point cloud denoising process, to further enhance the terrain recognition capability of the point cloud data, an automatic obstacle recognition module based on elevation slope is added. The seabed terrain is relatively flat and continuous, and obstacles such as reefs, shipwrecks, and artificial facilities usually exhibit abrupt elevation changes. The specific implementation process is as follows: 1) Based on the denoised real terrain point cloud data, calculate the elevation slope between adjacent points using Formula 5. The slope is calculated as the ratio of the horizontal distance to the elevation difference between adjacent points, specifically: (Formula 5) in: Let be the elevation slope between the i-th point and the (i+1)-th point; , Let i be the lateral position and elevation of the i-th point; , Let represent the horizontal position and elevation of the (i+1)th point.

[0065] 2) Based on the actual topographic features of the seabed in the survey area, a slope change threshold θ is set. When the slope between a certain point and its adjacent points before / after it is greater than θ, the point is considered to be a slope change point.

[0066] 3) When multiple slope abrupt changes (i.e. steep boundaries) occur consecutively during the scanning process, these slope abrupt changes and their respective regions are marked as potential obstacle areas.

[0067] 4) Record all obstacle sections that meet the conditions, and output their spatial location, height change range and other information for subsequent geological interpretation, seabed engineering planning or navigation obstacle avoidance.

[0068] This invention provides a method for identifying seabed obstacles based on multibeam point cloud data. Specifically, a Gaussian function is obtained by fitting the elevation histogram of the multibeam depth point cloud data to extract the true seabed topography point cloud. The elevation slope of adjacent points is further calculated, and seabed obstacles are identified and determined based on continuous abrupt changes in elevation slope and their corresponding anomaly ranges. Compared to the existing technologies mentioned in the background section (i.e., interactive filtering relying on human experience and automatic filtering algorithms with many defects), the core beneficial effect of this invention is: 1. Enhanced automation and intelligence: The automatic threshold determination method based on statistics and model fitting significantly reduces manual intervention, improves filtering efficiency, and solves the problems of high workload and low efficiency of traditional methods.

[0069] 2. Enhanced denoising accuracy and reliability: By employing an iterative denoising strategy and an attenuation model based on the statistical distribution of elevation differences to dynamically determine the noise threshold, the system can more accurately distinguish between real terrain points and noise points. This effectively overcomes the shortcomings of existing automatic filtering algorithms, such as "real terrain points are easily mistakenly deleted" and "incomplete filtering of sparse edge beam point clouds," thus better preserving the detailed features of the terrain.

[0070] 3. Accuracy of obstacle recognition: After obtaining high-fidelity real terrain point clouds, obstacle recognition is performed by using changes in elevation slope. This can sensitively and accurately capture abrupt changes in seabed topography (such as obstacles), providing more reliable data support for maritime safety.

[0071] like Figure 9 As shown, this embodiment of the invention also provides a seabed obstacle recognition device 900 based on multibeam point cloud data, which can implement the above-described method. This device may include: The data acquisition module 910 is used to acquire multibeam point cloud data of the target area; wherein, the multibeam point cloud data includes the bathymetric point cloud at various locations in the target area; The statistics module 920 is used to obtain a frequency-based elevation difference histogram based on the elevation difference between adjacent sounding point clouds. The noise reduction module 930 is used to fit an attenuation model based on the elevation difference histogram, determine a noise threshold based on the attenuation model, and use the noise threshold to divide the multibeam point cloud data into real terrain point cloud and noise point cloud. The iteration module 940 is used to increment the denoising count by 1, update the multibeam point cloud data according to the real terrain point cloud, and return to execute the operation based on the statistics module until the denoising count reaches the preset count to obtain the multibeam real terrain point cloud. The obstacle recognition module 950 is used to identify obstacles based on multibeam real terrain point clouds and utilize elevation slope to obtain obstacle information of the target area.

[0072] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0073] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0074] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0075] like Figure 10 As shown, Figure 10 The hardware structure of an electronic device 1000 according to another embodiment is illustrated. The electronic device 1000 includes: The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (aSIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention. The memory 1002 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RaM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001. Input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004); The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0076] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0078] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0079] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0080] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0081] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0082] The present invention provides a method, apparatus, electronic device, storage medium, and program product for seabed obstacle identification based on multibeam point cloud data. This method acquires multibeam point cloud data of a target area, including bathymetric point clouds at various locations within the target area. A frequency-based elevation difference histogram is obtained based on the elevation difference between adjacent bathymetric point clouds. An attenuation model is fitted to the elevation difference histogram, and a noise threshold is determined based on the attenuation model. The noise threshold is then used to divide the multibeam point cloud data into real terrain point clouds and noise point clouds. The denoising count is incremented by 1, and the multibeam point cloud data is updated based on the real terrain point clouds. The process of obtaining the frequency-based elevation difference histogram based on the elevation difference between adjacent bathymetric point clouds is repeated until a preset number of denoising counts is reached, resulting in a multibeam real terrain point cloud. Obstacles are identified using the elevation slope based on the multibeam real terrain point cloud, yielding obstacle information for the target area. The present invention, through an automated process of "fitting an attenuation model to determine the noise threshold" and "iterative denoising," replaces the traditional interactive filtering method that relies on the experience of technical personnel, significantly improving data processing efficiency. Specifically, this invention employs an attenuation model based on the statistical characteristics of the data itself (elevation difference histogram) to dynamically determine the threshold, rather than using a fixed threshold or a simple algorithm, making noise assessment more objective and accurate. This method is more adaptable and can effectively reduce the phenomena of "falsely deleting real terrain points" and "severe loss of terrain details," especially for data processing in sparse areas such as edge beams, solving the problem of "incomplete filtering." This invention achieves an integrated process from denoising to recognition: using high-precision denoising results as input for obstacle recognition, and utilizing the sensitive indicator of "elevation slope" for obstacle judgment, ensuring the reliability of the final recognition result. This allows this invention not only to efficiently clean data but also to directly serve the key application of seabed obstacle detection, improving the practical value of the entire technical solution and the quality of the output.

[0083] Specifically, the downstream product of the technical solution of this invention can be a program or software. Users can import datasets, classification parameters, and plotting parameters in the corresponding format through the user interface of the program or software. The program or software will automatically calculate and draw benthic habitat maps and provide interactive functions, enabling users to explore different habitat suitability and biodiversity distribution results and parameter settings. Based on this information, combined with the distribution characteristics of mineral resources, corresponding deep-sea mining habitat management strategies can be formulated, providing important scientific support for the protection and management of deep-sea ecosystems and promoting sustainable resource utilization and habitat protection.

[0084] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0085] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present invention, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0086] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0087] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0088] The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the present invention should be within the scope of the claims of the present invention.

Claims

1. A method for identifying seabed obstacles based on multibeam point cloud data, characterized in that, The method includes the following steps: Acquire multibeam point cloud data of the target area; wherein, the multibeam point cloud data includes bathymetric point clouds at various locations in the target area; A frequency-based elevation difference histogram is obtained based on the elevation difference statistics between adjacent sounding point clouds. Based on the attenuation model fitted by the elevation difference histogram, a noise threshold is determined based on the attenuation model, and the multibeam point cloud data is divided into real terrain point cloud and noise point cloud using the noise threshold. Increment the denoising count by 1, update the multibeam point cloud data according to the real terrain point cloud, and return to the step of obtaining a frequency-based elevation difference histogram based on the elevation difference statistics between adjacent sounding point clouds, until the denoising count reaches the preset number, and obtain the multibeam real terrain point cloud. Based on the multibeam real terrain point cloud, obstacle identification is performed using elevation slope to obtain obstacle information for the target area.

2. The method according to claim 1, characterized in that, The acquisition of multibeam point cloud data of the target area includes the following steps: Acquire the raw multibeam data of the target area, preprocess the raw multibeam data to obtain the multibeam point cloud data in the geographic coordinate system; The preprocessing includes surge correction, sound velocity correction, tide level correction, merging survey lines, and calculating TPU.

3. The method according to claim 1, characterized in that, The process of obtaining a frequency-based elevation difference histogram based on the elevation difference statistics between adjacent sounding point clouds includes the following steps: The elevation difference between all adjacent sounding point clouds in the multibeam point cloud data is obtained by traversing through the data. An initial elevation difference histogram is obtained based on the elevation difference; wherein, in the initial elevation difference histogram, the horizontal axis corresponds to the value of the elevation difference, and the vertical axis corresponds to the probability density of each value of the elevation difference. The vertical axis is converted from the probability density to the frequency to construct the target elevation difference histogram.

4. The method according to claim 3, characterized in that, The process of converting the vertical axis from the probability density to the frequency includes the following steps: The frequency corresponding to each elevation difference value is obtained by multiplying the interval width corresponding to the horizontal axis with the probability density corresponding to each elevation difference value. The frequency represents the ratio of the count of the elevation difference corresponding to the total number of the sounding point clouds.

5. The method according to claim 1, characterized in that, The step of fitting an attenuation model based on the elevation difference histogram and determining a noise threshold based on the attenuation model includes the following steps: The decay model of the elevation difference histogram is obtained by fitting the e-exponential decay formula. Wherein, the dependent variable of the attenuation model corresponds to the frequency, and the independent variable of the attenuation model corresponds to the elevation difference; Obtain the constant term of the attenuation model; The target frequency is obtained by searching when the difference between the dependent variable and the constant term is less than a preset threshold. Based on the attenuation model, the target elevation difference corresponding to the target frequency is used as the noise threshold.

6. The method according to claim 1, characterized in that, The process of dividing the multibeam point cloud data into real terrain point clouds and noisy point clouds using the noise threshold includes the following steps: Traverse the elevation difference between all adjacent sounding point clouds in the multibeam point cloud data; When the elevation difference is greater than the noise threshold, the corresponding sounding point cloud is classified as the noise point cloud; otherwise, the corresponding sounding point cloud is classified as the real terrain point cloud.

7. The method according to any one of claims 1 to 6, characterized in that, The multibeam real terrain point cloud includes the real terrain point cloud at various locations in the target area. The step of using the elevation slope to identify obstacles based on the multibeam real terrain point cloud to obtain obstacle information for the target area includes the following steps: Based on the elevation and lateral position corresponding to the real terrain point cloud, the elevation slope between all adjacent real terrain point clouds in the target area is statistically obtained. When the elevation slope between the target real terrain point cloud and any adjacent real terrain point cloud is greater than a preset slope change threshold, the target real terrain point cloud is marked as a slope change point. Based on the slope abrupt change point, obstacle segments are marked in the target area, and the spatial location and height abrupt change range corresponding to the obstacle segment are used as the obstacle information; The range of height variation is determined based on the elevation corresponding to each of the real terrain point clouds in the obstacle section.

8. A device for identifying seabed obstacles based on multibeam point cloud data, characterized in that, The device includes: The data acquisition module is used to acquire multibeam point cloud data of the target area; wherein, the multibeam point cloud data includes depth sounding point clouds at various locations in the target area; The statistics module is used to obtain a frequency-based elevation difference histogram based on the elevation difference between adjacent sounding point clouds. The noise reduction module is used to fit an attenuation model according to the elevation difference histogram, determine a noise threshold based on the attenuation model, and use the noise threshold to divide the multibeam point cloud data into real terrain point cloud and noise point cloud. The iteration module is used to increment the denoising count by 1, update the multibeam point cloud data according to the real terrain point cloud, and return to execute the operation based on the statistics module until the denoising count reaches the preset number to obtain the multibeam real terrain point cloud. The obstacle recognition module is used to identify obstacles based on the multibeam real terrain point cloud and by using elevation slope to obtain obstacle information of the target area.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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