Coastal mud flat topographic survey platform and method based on air cushion unmanned surface vehicle

Abstract

The invention discloses a coastal mud flat topographic measurement platform and method based on an air cushion unmanned ship, and relates to the technical field of computer processing, and the method comprises the following steps: S1, a ground control station generates a self-adaptive measurement line according to the tidal data of a target area and the historical topography, the self-adaptive measuring line comprises a land area laser scanning line spacing setting rule and a water area acoustic measuring line density; s2, after the air cushion unmanned ship receives the self-adaptive measuring line, executing initialization setting on multiple sensors, enabling the air cushion unmanned ship to start a multi-sensor initialization GNSS-RTK module to enter a fixed solution state, enabling the attitude sensor to complete zero offset calibration, enabling the laser radar and the depth finder to realize timestamp alignment through a hardware synchronization line, and enabling the laser radar and the depth finder to complete zero offset calibration; therefore, the air cushion unmanned ship starts multi-sensor initialization. According to the invention, the centimeter-level precision continuous measurement of the mud flat-water area is realized, and the problems of data splitting and passing in a complex intertidal zone by a traditional means are solved.

Description

Technical Field

[0001] This invention relates to the field of computer processing technology, and in particular to a coastal mudflat topography measurement platform and method based on an air-cushioned unmanned surface vessel. Background Technology

[0002] The marine administrative authorities define tidal flats as sea areas below the mean high tide line and above the low tide line, while the land and resources management departments define coastal tidal flats as tidal zones between high and low tide levels. The geographical location of tidal flats determines their dual nature as both water and land. Current technical solutions for coastal tidal flat topographic surveying mainly involve obtaining spatial location information of the land portion of the tidal flat's topography through manual wading surveying or airborne lidar technology, and obtaining underwater topographic information of the sea portion through tide-gauging / non-tide-gauging underwater topographic surveying. Manual wading surveying requires surveyors to carry GNSS positioning receivers and traverse silty and sandy tidal flats on foot, resulting in harsh working conditions and low efficiency. Airborne lidar technology uses small aircraft or drones equipped with lidar equipment, which can acquire point cloud information of the land portion of the tidal flat's spatial location over a large area, but suffers from high equipment costs and the inability of lidar to penetrate tidal flat vegetation. Furthermore, using different platforms and technical solutions for topographic surveying of the land and sea portions can lead to data omissions in the boundary areas and difficulties in finding suitable locations. Summary of the Invention

[0003] To address the aforementioned technical problems, the present invention provides a method for measuring coastal mudflat topography based on an air-cushioned unmanned surface vessel, the method comprising the following steps: S1. The ground control station generates adaptive survey lines based on the tidal data and historical topography of the target area. The adaptive survey lines include the land area laser scanning line spacing setting rules and the water area acoustic measurement line density. S2. After receiving the adaptive survey line, the air-cushioned unmanned surface vessel (USV) performs initialization settings for multiple sensors, enabling the USV to start the multi-sensor initialization GNSS-RTK module to enter the fixed solution state, enabling the attitude sensor to complete zero-bias calibration, and the lidar and depth sounder to achieve timestamp alignment through a hardware synchronization line, so that the USV can start multi-sensor initialization. S3, The density of data collected by the lidar is ≥200 points / m 2 The surface point cloud data is obtained, and the roll / pitch angle data is output in real time using the attitude sensor. Motion compensation is performed in combination with the positioning coordinates given by the GNSS-RTK module. S4. Measure the water topography of the current positioning coordinates of the air-cushioned unmanned surface vessel. When the water depth is ≥0.3m, the single-beam echo sounder emits acoustic pulses at a frequency of 200kHz and simultaneously records the water level elevation provided by the GNSS-RTK module to generate three-dimensional seabed coordinates. S5. When the reflection intensity of the surface point cloud data drops sharply by more than 50%, the depth sounder is automatically triggered to work, and at the same time the navigation controller of the air cushion unmanned surface vessel adjusts its course according to the real-time attitude data. S6. Acquire real-time data collected by the binocular vision camera and the millimeter-wave radar on the air cushion unmanned surface vessel to identify obstacles in the direction of travel of the air cushion unmanned surface vessel, classify the obstacles according to height thresholds, and generate detour paths within 0.1s for obstacles that need to be avoided using the dynamic window method. S7. The ground control station performs point cloud denoising, tide level correction, and land-sea data stitching based on the received detour path to generate a digital elevation model of the tidal flats.

[0004] Preferably, the motion compensation calculation in step S3 includes: S31. Bind the heading angle output by the attitude sensor to the scanning angle of the lidar; S32. Eliminate point cloud distortion caused by ship hull rolling based on the following formula. ,in: For roll data from attitude sensor Pitch angle data , To determine the translation vector coordinates for GNSS-RTK positioning, The coordinates of the surface point cloud data are three-dimensional.

[0005] Preferably, the calculation of the reflection intensity of the surface point cloud data in step S5 includes the following formula: ; in, The average intensity of point clouds over land area. The point cloud intensity of the current frame.

[0006] Preferably, step S4, which involves classifying the obstacles according to a height threshold, includes: If the height of the obstacle is less than 15cm, it is considered an obstacle that can be crossed. If the obstacle height is ≥15cm, the obstacle avoidance path is planned using the dynamic window method with a minimum turning radius of 2m.

[0007] Preferably, the dynamic window method formula in step S6 includes: ,in, , , , For speed, Angular velocity, For the heading alignment function, Let the distance to the obstacle be a function. This is the velocity evaluation function.

[0008] Preferably, the point cloud denoising, tide level correction, and land and sea data processing in step S8 include: Point cloud denoising: A radius filtering algorithm is used to remove discrete points with fewer than 20 points within a neighborhood radius of 0.2m; Tide level correction: Constructing a water level surface model by integrating tide gauge observations. ,in, To correct the seabed elevation, The distance from the transducer to the seabed, measured by the depth sounder. The tide height is the real-time tidal height observed at a nearby tide gauge station. For RTK instantaneous sea level height; Land-sea stitching: Based on the CGCS2000 coordinate system and millisecond-level timestamps, the laser point cloud and depth sounding data are registered using the ICP algorithm.

[0009] A coastal mudflat topography measurement platform based on an air-cushioned unmanned surface vessel (USV) is used to implement the coastal mudflat topography measurement method based on an air-cushioned USV described in the above scheme. The USV is a carbon fiber disc-shaped hull with a ring-shaped flexible skirt and a dual-duct centrifugal fan at its bottom. The dual-duct centrifugal fan generates an air cushion pressure of ≥500Pa. The multi-sensor system includes a GNSS-RTK module, an attitude sensor, a lidar, a depth sounder, and a forward-looking binocular vision camera, wherein: The forward-looking binocular vision camera is installed at the forward end of the air-cushioned unmanned surface vessel, and the GNSS-RTK module, attitude sensor, lidar and depth sounder are respectively fixedly installed on the air-cushioned unmanned surface vessel.

[0010] The present invention has at least the following beneficial effects: The multi-sensor spatiotemporal synchronization scheme controls the time alignment error between GNSS, LiDAR and depth sounder to ≤1ms. Combined with attitude motion compensation algorithm, it eliminates the influence of ship swaying, thereby improving the elevation accuracy of land point clouds and the depth sounding accuracy of water bodies.

[0011] Furthermore, the solution integrates vision and millimeter-wave radar, enabling 0.1s-level path replanning for obstacles ≥15cm in height, supporting maneuverable avoidance with a minimum turning radius of 2m, and ensuring safe operation in complex areas with a tidal ditch density of 2.3 ditches / km.

[0012] The dynamic water level correction model integrates RTK elevation and tide gauge data to eliminate ±15cm water level errors caused by tides; the radius filtering noise reduction algorithm effectively eliminates 92% of vegetation interference points. The emergency braking mechanism automatically stops the equipment when the pitch angle is >15°, reducing the failure rate of the equipment in high-salt and high-humidity environments and significantly reducing the risks of field operations. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart of a method for measuring coastal mudflat topography based on an air-cushioned unmanned surface vessel, provided in Embodiment 1 of the present invention. Figure 2 This is a flowchart provided in Embodiment 1 of the present invention; Figure 3 This is a structural diagram provided for Embodiment 2 of the present invention.

[0015] Explanation of reference numerals in the attached figures: 1. Air-cushioned unmanned surface vessel; 2. GNSS-RTK module; 3. Attitude sensor; 4. LiDAR; 5. Forward-looking binocular vision camera; 6. Depth sounder; 7. Waterproof compartment. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0018] Example 1

[0019] This embodiment provides a coastal mudflat topography measurement platform and method based on an air-cushioned unmanned surface vessel. The method includes the following steps: Figures 1-2 As shown: S1. The ground control station generates adaptive survey lines based on the tidal data and historical topography of the target area. The adaptive survey lines include the rules for setting the spacing of laser scanning lines in the land area and the density of acoustic measurement lines in the water area. Specifically, the adaptive survey lines are generated by dynamically adjusting the laser scanning line spacing based on the tidal flat slope and vegetation density, and then setting the acoustic survey line spacing according to the measurement scale requirements. The adaptive survey lines also include the generation of spatiotemporally optimized track files (including velocity parameters and equipment start / stop commands). The purpose is to solve the problem of incomplete coverage caused by traditional fixed survey lines, while avoiding the risk of equipment grounding due to sudden tidal changes.

[0020] S2. After receiving the adaptive survey line, the air cushion unmanned surface vessel 1 performs initialization settings for multiple sensors, enabling the air cushion unmanned surface vessel 1 to start multi-sensor initialization GNS so that the attitude sensor 3 completes zero-bias calibration. The lidar 4 and the depth sounder 6 achieve timestamp alignment through a hardware synchronization line, so that the air cushion unmanned surface vessel 1 can start multi-sensor initialization. The aforementioned multi-sensor initialization also includes: GNSS-RTK fixed solution: achieving base station-free centimeter-level positioning (horizontal ±1cm) through SBAS satellite-based augmentation, with an initialization time ≤30s. IMU calibration: a three-axis MEMS gyroscope performs zero-bias compensation (Allan variance ≤0.1° / h). Hardware synchronization: the lidar 4 and depth sounder 6 align their timestamps via PPS pulse signals, with a synchronization error ≤1ms. Obstacle perception verification: cross-verification of the distance to a calibration object within 10m using a binocular camera and millimeter-wave radar (error <±2cm).

[0021] S3, Data collection density ≥200 points / m using lidar 4 2 The surface point cloud data is used, and the roll / pitch angle data is output in real time by the attitude sensor 3, and motion compensation is performed in combination with the positioning coordinates given by the GNSS-RTK module 2. Specifically, the motion compensation calculation in the above embodiments includes: S31. Bind the heading angle output by attitude sensor 3 to the scanning angle of lidar 4; S32. Eliminate point cloud distortion caused by ship hull rolling based on the following formula. ,in: Roll data from attitude sensor 3 Pitch angle data , To determine the translation vector coordinates for GNSS-RTK positioning, The coordinates of the surface point cloud data are three-dimensional.

[0022] S4. Measure the water topography of the current positioning coordinates of the air-cushioned unmanned surface vessel 1. When the water depth is ≥0.3m, the single-beam echo sounder 6 emits acoustic pulses at a frequency of 200kHz and simultaneously records the water level elevation provided by the GNSS-RTK module 2 to generate three-dimensional seabed coordinates. Specifically, the aforementioned classification of obstacles according to height thresholds (a result of the collaborative operation of the single-beam echo sounder 6 and the forward-looking binocular vision camera 5) includes: If the height of the obstacle is less than 15cm, it is considered an obstacle that can be crossed. If the obstacle height is ≥15cm, the dynamic window method is used to plan the obstacle avoidance path with a minimum turning radius of 2m.

[0023] The above-mentioned dynamic window method formula includes: ,in, , , , For speed, Angular velocity, For the heading alignment function, Let the distance to the obstacle be a function. This is the velocity evaluation function.

[0024] S5. When the reflection intensity of the surface point cloud data drops sharply by more than 50%, the depth sounder 6 is automatically triggered to work, and at the same time the navigation controller of the air cushion unmanned boat 1 adjusts the course according to the real-time attitude data. Specifically, the calculation of the reflection intensity of the above-mentioned surface point cloud data includes the following formula: ; in, The average intensity of point clouds over land area. The point cloud intensity of the current frame.

[0025] S6. Acquire real-time data from the binocular vision camera and millimeter-wave radar on the air-cushion unmanned surface vessel 1 to identify obstacles in the forward direction of the air-cushion unmanned surface vessel 1, classify the obstacles according to height thresholds, and generate detour paths within 0.1s for obstacles that need to be avoided using the dynamic window method. The above-mentioned dynamic window method formula includes: ,in, , , , For speed, Angular velocity, For the heading alignment function, Let the distance to the obstacle be a function. This is the velocity evaluation function.

[0026] S7. The ground control station performs point cloud denoising, tide level correction, and land-sea data stitching based on the received detour path to generate a digital elevation model of the tidal flats.

[0027] Specifically, the point cloud denoising, tide level correction, and land and sea data processing mentioned above include: Point cloud denoising: A radius filtering algorithm is used to remove discrete points with fewer than 20 points within a neighborhood radius of 0.2m; Tide level correction: Constructing a water level surface model by integrating tide gauge observations. ,in, To correct the seabed elevation, The distance from the transducer to the seabed measured by depth sounder 6. The tide height is the real-time tidal height observed at a nearby tide gauge station. For RTK instantaneous sea level height; Land-sea stitching: Based on the CGCS2000 coordinate system and millisecond-level timestamps, the laser point cloud and depth sounding data are registered using the ICP algorithm.

[0028] In summary, the multi-sensor spatiotemporal synchronization scheme controls the time alignment error of GNSS, LiDAR, and depth sounder 6 to ≤1ms. Combined with an attitude motion compensation algorithm to eliminate the effects of ship swaying, it improves the accuracy of land point cloud elevation and water depth sounding. Furthermore, this scheme integrates visual and millimeter-wave radar, achieving 0.1s-level path replanning for obstacles ≥15cm in height, supporting maneuvering avoidance with a minimum turning radius of 2m, ensuring safe operation in complex areas with a tidal channel density of 2.3 channels / km. Secondly, the dynamic water level correction model integrates RTK elevation and tide gauge data to eliminate ±15cm water level errors caused by tides, and the radius filtering noise reduction algorithm effectively removes 92% of vegetation interference points. The emergency braking mechanism automatically stops the equipment when the pitch angle is >15°, reducing the failure rate of the equipment in high-salt and high-humidity environments and significantly reducing the risks of field operations.

[0029] Example 2

[0030] Combination Figure 3 As shown, based on the above embodiment 1, this embodiment aims to provide a coastal mudflat topographic measurement platform based on the air cushion unmanned surface vessel 1. The air cushion unmanned surface vessel 1 is a carbon fiber disc-shaped hull, and its bottom is equipped with an annular flexible skirt and a double-duct centrifugal fan. The double-duct centrifugal fan generates an air cushion pressure of ≥500Pa. The multi-sensor system includes a GNSS-RTK module 2, an attitude sensor 3, a lidar 4, a depth sounder 6, and a forward-looking binocular vision camera 5, wherein: A forward-looking binocular vision camera 5 is installed at the forward end of the hovercraft 1, and a GNSS-RTK module 2, an attitude sensor 3, a lidar 4, and a depth sounder 6 are fixedly installed on the hovercraft 1.

[0031] Furthermore, a fin-shaped waterproof compartment 7 is fixedly installed on the bottom of the hovercraft 1, and the depth sounder 6 is located inside the waterproof compartment 7.

[0032] Example 3

[0033] This invention provides a non-transitory computer-readable storage medium storing at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the following steps: The ground control station generates adaptive survey lines based on the tidal data and historical topography of the target area. The adaptive survey lines include the rules for setting the spacing of laser scanning lines in the land area and the density of acoustic measurement lines in the water area. After receiving the adaptive survey line, the air-cushioned unmanned surface vessel 1 performs initialization settings for multiple sensors, enabling the air-cushioned unmanned surface vessel 1 to start the multi-sensor initialization GNSS-RTK module 2 to enter the fixed solution state, enabling the attitude sensor 3 to complete zero-bias calibration, and the lidar 4 and the depth sounder 6 to achieve timestamp alignment through a hardware synchronization line, so that the air-cushioned unmanned surface vessel 1 can start the multi-sensor initialization. Data was collected using lidar 4 at a density ≥200 points / m. 2 The surface point cloud data is used, and the roll / pitch angle data is output in real time by the attitude sensor 3, and motion compensation is performed in combination with the positioning coordinates given by the GNSS-RTK module 2. For the current positioning coordinates of the air-cushioned unmanned surface vessel 1, when the water depth is ≥0.3m, the single-beam echo sounder 6 emits acoustic pulses at a frequency of 200kHz and simultaneously records the water level elevation provided by the GNSS-RTK module 2 to generate three-dimensional seabed coordinates. When the reflection intensity of the ground point cloud data drops by more than 50%, the depth sounder 6 is automatically triggered to work, and at the same time the navigation controller of the air cushion unmanned surface vessel 1 adjusts its course according to the real-time attitude data. The system acquires real-time data from both the binocular vision camera and the millimeter-wave radar on the hovercraft 1 to identify obstacles in the direction of travel of the hovercraft 1, and classifies the obstacles according to height thresholds. For obstacles that need to be avoided, a detour path is generated within 0.1s using the dynamic window method. The ground control station performs point cloud denoising, tide level correction, and land-sea data stitching based on the received detour path to generate a digital elevation model of the tidal flats.

[0034] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0035] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0036] Example 4

[0037] This invention provides an electronic device, including a processor and a memory, wherein the memory stores at least one instruction or at least one program segment, and the at least one instruction or the at least one program segment is loaded and executed by the processor to implement the following steps: The ground control station generates adaptive survey lines based on the tidal data and historical topography of the target area. The adaptive survey lines include the rules for setting the spacing of laser scanning lines in the land area and the density of acoustic measurement lines in the water area. After receiving the adaptive survey line, the air-cushioned unmanned surface vessel 1 performs initialization settings for multiple sensors, enabling the air-cushioned unmanned surface vessel 1 to start the multi-sensor initialization GNSS-RTK module 2 to enter the fixed solution state, enabling the attitude sensor 3 to complete zero-bias calibration, and the lidar 4 and the depth sounder 6 to achieve timestamp alignment through a hardware synchronization line, so that the air-cushioned unmanned surface vessel 1 can start the multi-sensor initialization. Data was collected using lidar 4 at a density ≥200 points / m. 2The surface point cloud data is used, and the roll / pitch angle data is output in real time by the attitude sensor 3, and motion compensation is performed in combination with the positioning coordinates given by the GNSS-RTK module 2. For the current positioning coordinates of the air-cushioned unmanned surface vessel 1, when the water depth is ≥0.3m, the single-beam echo sounder 6 emits acoustic pulses at a frequency of 200kHz and simultaneously records the water level elevation provided by the GNSS-RTK module 2 to generate three-dimensional seabed coordinates. When the reflection intensity of the ground point cloud data drops by more than 50%, the depth sounder 6 is automatically triggered to work, and at the same time the navigation controller of the air cushion unmanned surface vessel 1 adjusts its course according to the real-time attitude data. The system acquires real-time data from both the binocular vision camera and the millimeter-wave radar on the hovercraft 1 to identify obstacles in the direction of travel of the hovercraft 1, and classifies the obstacles according to height thresholds. For obstacles that need to be avoided, a detour path is generated within 0.1s using the dynamic window method. The ground control station performs point cloud denoising, tide level correction, and land-sea data stitching based on the received detour path to generate a digital elevation model of the tidal flats.

[0038] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for measuring coastal mudflat topography based on an air-cushioned unmanned surface vessel, characterized in that, The method includes the following steps: S1. The ground control station generates adaptive survey lines based on the tidal data and historical topography of the target area. The adaptive survey lines include the land area laser scanning line spacing setting rules and the water area acoustic measurement line density. S2. After receiving the adaptive survey line, the air-cushioned unmanned surface vessel (USV) performs initialization settings for multiple sensors, enabling the USV to start the multi-sensor initialization GNSS-RTK module to enter the fixed solution state, enabling the attitude sensor to complete zero-bias calibration, and the lidar and depth sounder to achieve timestamp alignment through a hardware synchronization line, so that the USV can start multi-sensor initialization. S3, The density of data collected by the lidar is ≥200 points / m 2 The surface point cloud data is obtained, and the roll / pitch angle data is output in real time using the attitude sensor. Motion compensation is performed in combination with the positioning coordinates given by the GNSS-RTK module. S4. Measure the water topography of the current positioning coordinates of the air-cushioned unmanned surface vessel. When the water depth is ≥0.3m, the single-beam echo sounder emits acoustic pulses at a frequency of 200kHz and simultaneously records the water level elevation provided by the GNSS-RTK module to generate three-dimensional seabed coordinates. S5. When the reflection intensity of the surface point cloud data drops sharply by more than 50%, the depth sounder is automatically triggered to work, and at the same time the navigation controller of the air cushion unmanned surface vessel adjusts its course according to the real-time attitude data. S6. Acquire real-time data collected by the binocular vision camera and the millimeter-wave radar on the air cushion unmanned surface vessel to identify obstacles in the direction of travel of the air cushion unmanned surface vessel, classify the obstacles according to height thresholds, and generate detour paths within 0.1s for obstacles that need to be avoided using the dynamic window method. S7. The ground control station performs point cloud denoising, tide level correction, and land-sea data stitching based on the received detour path to generate a digital elevation model of the tidal flats.

2. The method for measuring coastal mudflat topography based on an air-cushioned unmanned surface vessel according to claim 1, characterized in that, The calculation of motion compensation in step S3 includes: S31. Bind the heading angle output by the attitude sensor to the scanning angle of the lidar; S32. Eliminate point cloud distortion caused by ship hull rolling based on the following formula. ,in: For roll data from attitude sensor Pitch angle data , To determine the translation vector coordinates for GNSS-RTK positioning, The coordinates of the surface point cloud data are three-dimensional.

3. The method for measuring coastal mudflat topography based on an air-cushioned unmanned surface vessel according to claim 1, characterized in that, The calculation of the reflection intensity of the surface point cloud data in step S5 includes the following formula: ； in, The average intensity of point clouds over land area. The point cloud intensity of the current frame.

4. The method for measuring coastal mudflat topography based on an air-cushioned unmanned surface vessel according to claim 1, characterized in that, The step S4 of classifying the obstacles according to height thresholds includes: If the height of the obstacle is less than 15cm, it is considered an obstacle that can be crossed. If the obstacle height is ≥15cm, the obstacle avoidance path is planned using the dynamic window method with a minimum turning radius of 2m.

5. A method for measuring coastal mudflat topography based on an air-cushioned unmanned surface vessel according to claim 4, characterized in that, The dynamic window method formula in step S6 includes: ,in, , , , For speed, Angular velocity, For the heading alignment function, Let the distance to the obstacle be a function. This is the velocity evaluation function.

6. The method for measuring coastal mudflat topography based on an air-cushioned unmanned surface vessel according to claim 1, characterized in that, The point cloud denoising, tide level correction, and land and sea data processing in step S8 include: Point cloud denoising: A radius filtering algorithm is used to remove discrete points with fewer than 20 points within a neighborhood radius of 0.2m; Tide level correction: Constructing a water level surface model by integrating tide gauge observations. ,in, To correct the seabed elevation, The distance from the transducer to the seabed, measured by the depth sounder. The tide height is the real-time tidal height observed at a nearby tide gauge station. For RTK instantaneous sea level height; Land-sea stitching: Based on the CGCS2000 coordinate system and millisecond-level timestamps, the laser point cloud and depth sounding data are registered using the ICP algorithm.

7. A coastal mudflat topographic surveying platform based on a hovercraft, used to implement the coastal mudflat topographic surveying method based on a hovercraft as described in any one of claims 1-6, characterized in that, The air-cushioned unmanned surface vessel has a carbon fiber disc-shaped hull, with a ring-shaped flexible apron and a dual-duct centrifugal fan at its bottom. The dual-duct centrifugal fan generates an air cushion pressure of ≥500Pa. The multi-sensor system includes a GNSS-RTK module, an attitude sensor, a lidar, a depth sounder, and a forward-looking binocular vision camera, wherein: The forward-looking binocular vision camera is installed at the forward end of the air-cushioned unmanned surface vessel, and the GNSS-RTK module, attitude sensor, lidar and depth sounder are respectively fixedly installed on the air-cushioned unmanned surface vessel.

8. A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores at least one instruction or at least one program segment, characterized in that, The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the steps of the coastal mudflat topography measurement method based on any one of claims 1-6.

9. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the steps of the coastal mudflat topography measurement method based on any one of claims 1-6.

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