Method for correcting submarine topography data measured by AUV (Autonomous Underwater Vehicle) through shipborne sounding data

Abstract

The invention provides a method for correcting submarine topography data measured by an AUV (Autonomous Underwater Vehicle) through shipborne sounding data. The method comprises the following steps of S1, acquiring shipborne reference data and AUV measurement data; s2, selecting target reference data from the shipborne reference data according to a preset water depth judgment condition; s3, performing matching processing on the AUV measurement data and the target reference data to obtain a position deviation vector; and S4, correcting the calculated position information in the AUV measurement data according to the position deviation vector, and generating corrected AUV topographic data. On the premise that the original working mode of the AUV is not interfered completely, reliable and high-precision position correction is carried out on the AUV topographic data recovered afterwards in different water depth environments.

Description

Technical Field

[0001] This invention relates to the field of underwater surveying and navigation technology, specifically to a method for correcting AUV seabed topography data using shipborne depth sounding data. Background Technology

[0002] With the increasing demand for marine resource development and scientific surveys, autonomous underwater vehicles (AUVs) are being used more and more widely in seabed topographic mapping, and their operating range has expanded from nearshore shallow waters to deep ocean areas. The multibeam echo sounder system carried by AUVs can efficiently acquire high-density seabed topographic point cloud data, providing important basic data support for marine engineering, resource exploration, and scientific research.

[0003] To address the issue of accumulated navigation errors in AUVs, current technologies typically employ post-hoc data correction methods. In shallow water areas, due to the shallow depth and relatively good acoustic propagation conditions, the survey vessel can use an onboard multibeam echo sounder to perform a full-coverage scan of the measurement area, acquiring a high-precision reference digital terrain model. The terrain data measured by the AUV is then matched with this reference model to correct the AUV's positional errors. This method has demonstrated good correction effectiveness and high operational efficiency in shallow water areas.

[0004] Different processing procedures and software systems are often used for AUV data correction in shallow water and deep water. There is a lack of a method that can flexibly integrate two different reference data sources, multibeam and singlebeam, adaptively select the optimal reference data according to the operating water depth, and uniformly process and correct the AUV data recovered afterward. This technological gap limits the high-quality mapping capabilities of AUVs across the entire sea area and also increases the complexity and workload of data processing.

[0005] Therefore, there is an urgent need to develop an AUV topographic data post-processing method that is applicable to different water depth environments, can intelligently select the optimal reference data source, and achieve high-precision position correction, so as to meet the needs of seabed topographic mapping in the entire sea area from shallow water to deep water. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for correcting AUV-measured seabed topography data using shipborne bathymetry data, comprising the following steps: Step S1: Acquire shipboard reference data and AUV measurement data; Step S2: Select target reference data from the shipborne reference data according to the preset water depth judgment conditions; Step S3: Match the AUV measurement data with the target reference data to obtain the position deviation vector; Step S4: Correct the estimated position information in the AUV measurement data according to the position deviation vector to generate corrected AUV terrain data.

[0007] Preferably, the method for obtaining shipboard reference data in S1 includes: The shallow water area reference data is obtained synchronously by the shipborne multibeam echo sounder and the integrated navigation system. The shallow water area reference data is a reference digital terrain model. The deep-water reference data is obtained synchronously by the shipborne single-beam echo sounder and the integrated navigation system. The deep-water reference data is a sequence of reference depth points.

[0008] Preferably, the method for obtaining AUV measurement data in step S1 includes: Acquire raw water depth point cloud data collected by the AUV multibeam echo sounder system; Obtain the estimated position and attitude information recorded by the AUV navigation system.

[0009] Preferably, the method in step S3 includes the following steps: Step S31: For each data point in the AUV measurement data, search for the nearest corresponding point in the target reference data and establish a point-to-point correspondence. Step S32: Based on the point pair correspondence, calculate the rigid body transformation matrix that minimizes the root mean square error between all corresponding point pairs; Step S33: Apply the rigid body transformation matrix to the AUV measurement data to update the position of the data points; Repeat steps S31 to S33 until the convergence condition is met, and output the final position deviation vector.

[0010] Preferably, the convergence condition is: the root mean square error between all corresponding point pairs is less than a preset error threshold, or the number of iterations performed in the loop reaches a preset iteration limit.

[0011] Preferably, the method for correction based on the position deviation vector in step S4 includes: The estimated position information in the AUV measurement data is batch corrected using the position deviation vector to generate corrected position information. The corrected location information is associated with the corresponding depth information to output the corrected AUV terrain data.

[0012] Preferably, step S2 includes: When the average water depth of the measurement area is less than the preset water depth threshold, the shallow water area reference data is selected as the target reference data. When the average water depth of the measurement area is greater than or equal to a preset water depth threshold, the deep water reference data is selected as the target reference data.

[0013] Preferably, when selecting the deep-water reference data as the target reference data, the method further includes the following step before step S3: Interpolation processing is performed on the reference depth point sequence in the deep water reference data to generate a reference depth contour line, and the reference depth contour line is used as the target reference data for matching processing.

[0014] Preferably, the rigid body transformation matrix includes translation parameters in the X direction, translation parameters in the Y direction, and rotation angle parameters.

[0015] Preferably, the integrated navigation system is a GNSS / INS integrated navigation system.

[0016] Compared with the prior art, the beneficial effects of the present invention include at least the following: 1) This invention proposes a dual-mode adaptive selection mechanism that intelligently switches between shipborne multibeam and single-beam reference data sources based on the water depth conditions of the operating area. This fully leverages the high efficiency of multibeams in shallow water and the high reliability of single-beams in deep water, achieving full sea area coverage from nearshore shallow water to deep ocean water, and breaking through the limitation of existing technologies that are only applicable to a single water depth environment.

[0017] 2) The method of this invention is a non-invasive post-processing method that does not require any special consideration from the AUV itself. When the AUV performs underwater measurement tasks, it maintains its original data acquisition and storage mode without requiring any hardware or software modifications to the AUV. It does not affect the normal operation of the AUV, has strong applicability, low implementation risk, and can be directly applied to various types of AUV systems.

[0018] 3) The method of the present invention provides a unified data processing flow. Whether it is multi-beam reference data in shallow water or single-beam reference data in deep water after interpolation, it can be corrected under the same processing flow, avoiding the complexity of using different processing software for different water depth environments, simplifying the operation process and improving work efficiency. Attached Figure Description

[0019] Figure 1 This is an overall flowchart of the method of the present invention; Figure 2 This is a schematic diagram illustrating the principle of terrain matching based on multibeam reference data in shallow water areas. Figure 3 This is a schematic diagram illustrating the principle of terrain matching based on single-beam reference data in deep water areas. Detailed Implementation

[0020] 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 protection scope of the present invention.

[0021] like Figure 1 As shown, this embodiment of the invention provides a method for correcting AUV-measured seabed topography data using shipborne bathymetry data, comprising the following steps: Step S1: Obtain shipboard reference data and AUV measurement data.

[0022] Specifically, shipborne reference data acquisition methods include two modes: The first mode involves the shipborne multibeam echo sounder and integrated navigation system working synchronously to conduct a full-coverage scan of the measurement area in shallow water, generating a reference digital terrain model containing precise geographical location information, which serves as the shallow water reference data; the second mode involves the shipborne single-beam echo sounder and integrated navigation system working synchronously to navigate along the planned survey line in deep water, acquiring a series of discrete water depth points with precise geographical locations, forming a reference depth point sequence, which serves as the deep water reference data. AUV measurement data acquisition involves the AUV retrieving and downloading its internally stored measurement data after completing its autonomous underwater measurement mission, according to its original operating mode. This data includes the raw water depth point cloud data collected by the AUV multibeam echo sounder, as well as the estimated position and attitude information recorded by the AUV navigation system.

[0023] In this embodiment of the invention, the integrated navigation system can adopt a GNSS / INS integrated navigation system, which can provide centimeter-level real-time positioning accuracy during ship navigation, providing reliable assurance for the geolocation of reference data.

[0024] Step S2: Select the target reference data from the shipborne reference data according to the preset water depth judgment conditions.

[0025] Specifically, when the average water depth of the measurement area is less than a preset water depth threshold, it is determined to be a shallow water operating environment, and shallow water reference data (i.e., a reference digital terrain model) is selected as the target reference data; when the average water depth of the measurement area is greater than or equal to the preset water depth threshold, it is determined to be a deep water operating environment, and deep water reference data (i.e., a reference depth point sequence) is selected as the target reference data. This dual-mode adaptive selection mechanism fully leverages the high coverage efficiency of multibeam arrays in shallow water areas and the high reliability of single-beam arrays in deep water areas.

[0026] When deep-water baseline data is selected as the target baseline data, since single-beam bathymetry acquires discrete depth points distributed along the survey line, preprocessing is required before step S3: interpolation is performed on the baseline depth point sequence to generate a continuous baseline depth profile, which is then used as the target baseline data for subsequent matching processing. Interpolation expands the discrete depth points into a continuous terrain profile, providing sufficient matching baselines for the iterative nearest-point algorithm.

[0027] Step S3: Match the AUV measurement data with the target reference data to obtain the position deviation vector.

[0028] In this embodiment of the invention, the Iterative Closest Point (ICP) algorithm is used to achieve terrain matching. Specifically, the ICP algorithm iteratively executes the following three sub-steps until the convergence condition is met: Step S31: For each data point in the AUV measurement data, search for the nearest corresponding point in the target reference data to establish a point-to-point correspondence. In shallow water mode, the search range is the entire reference digital terrain model; in deep water mode, the search range is the interpolated reference depth contour line. Establishing a one-to-one point-to-point correspondence provides the foundation for subsequent transformation matrix calculations.

[0029] Step S32: Based on the point-to-point correspondence established in Step S31, calculate the rigid body transformation matrix that minimizes the root mean square error between all corresponding point pairs. The rigid body transformation matrix includes translation parameters in the X and Y directions and rotation angle parameters, which can describe the translation and rotation relationship between the measured AUV point cloud and the reference data. The optimal transformation parameters are solved using mathematical methods such as least squares or singular value decomposition, minimizing the fitting error between the transformed AUV point cloud and the reference data.

[0030] Step S33: Apply the calculated rigid body transformation matrix to the AUV measurement data to update the positions of the data points. This step translates and rotates the entire AUV point cloud to bring it closer to the reference data.

[0031] The above three sub-steps constitute one iterative loop. After each iteration, the alignment between the AUV point cloud and the reference data gradually improves, and the root mean square error gradually decreases. The algorithm continues to iterate until the convergence condition is met. The convergence condition includes two cases: the first is that the root mean square error between all corresponding point pairs is less than a preset error threshold, indicating that the data is sufficiently aligned; the second is that the number of iterations executed in the loop reaches a preset iteration limit to prevent the algorithm from falling into an infinite loop. When the convergence condition is met, the algorithm terminates the iteration and outputs the final position deviation vector, which comprehensively reflects the cumulative error of the AUV navigation system.

[0032] Step S4: Correct the estimated position information in the AUV measurement data according to the position deviation vector to generate corrected AUV terrain data.

[0033] Specifically, using the position deviation vector obtained in step S3, the estimated position information of all data points in the AUV measurement data is batch corrected to eliminate the influence of accumulated navigation errors and generate corrected position information. Then, the corrected position information is correlated with the corresponding depth information to form a geo-accurate seabed topographic point cloud, outputting the corrected AUV topographic data. The corrected data can be used to generate high-quality seabed topographic maps that meet accuracy requirements.

[0034] The method of this invention, when used in specific applications, comprises the following steps.

[0035] Operating in shallow water: During the preprocessing stage, the survey mother ship uses an onboard multibeam echo sounder to conduct full-coverage measurements of the survey area, generating a high-precision benchmark digital terrain model, which is then stored in the shallow water area of ​​the dual-mode benchmark data storage module.

[0036] After the AUV mission is completed, it is retrieved and the original measurement data stored internally is downloaded to the AUV data storage module according to its normal procedure.

[0037] The integrated processing terminal operation mode judgment and data selection program allows users to specify or the system to determine shallow water mode based on average water depth, and then calls up the multi-beam reference terrain.

[0038] The terminal runs a terrain matching and correction program, using an iterative nearest-point algorithm to match the AUV point cloud with a multibeam reference terrain. This algorithm iteratively executes three steps: searching for the nearest point, calculating the transformation matrix, and applying the transformation, until the AUV point cloud is aligned with the reference terrain. Finally, it outputs a high-precision position deviation vector. The principle is as follows: Figure 2 As shown.

[0039] Finally, the batch correction of AUV data is completed through the data fusion output program.

[0040] In deep-water operation mode: During the preprocessing stage, the survey mother ship sails along the planned survey line and uses the shipborne single-beam echo sounder to acquire a series of water depth points with precise geographical locations, forming a reference water depth point sequence, which is then stored in the deep-water area of ​​the dual-mode reference data storage module.

[0041] The AUV data preparation stage is the same as above, without changing any AUV settings.

[0042] After the integrated processing terminal determines that it is in deep water mode, it calls up the single-beam reference data.

[0043] The terminal first interpolates discrete single-beam depth points to generate a continuous depth profile. Then, the terrain matching and correction procedure is initiated. Based on the three core steps of the iterative nearest-point algorithm, it matches the entire point cloud data measured by the AUV with this depth profile to calculate the positional deviation. The principle is as follows: Figure 3 As shown.

[0044] The subsequent data fusion and output process is the same as in the shallow water mode.

[0045] Through the aforementioned hardware system and dual-mode workflow, this invention achieves reliable and high-precision position correction of AUV topographic data recovered after the event in different water depth environments without interfering with the original working mode of the AUV.

[0046] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; only preferred embodiments of the present invention are illustrated. The descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. As long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0047] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this invention should be determined by the appended claims.

Claims

1. A method for correcting AUV-measured seabed topography data using shipborne bathymetry data, characterized in that: Includes the following steps: Step S1: Acquire shipboard reference data and AUV measurement data; Step S2: Select target reference data from the shipborne reference data according to the preset water depth judgment conditions; Step S3: Match the AUV measurement data with the target reference data to obtain the position deviation vector; Step S4: Correct the estimated position information in the AUV measurement data according to the position deviation vector to generate corrected AUV terrain data.

2. The method according to claim 1, characterized in that: The method for obtaining shipboard reference data in step S1 includes: The shallow water area reference data is obtained synchronously by the shipborne multibeam echo sounder and the integrated navigation system. The shallow water area reference data is a reference digital terrain model. The deep-water reference data is obtained synchronously by the shipborne single-beam echo sounder and the integrated navigation system. The deep-water reference data is a sequence of reference depth points.

3. The method according to claim 1, characterized in that: The method for obtaining AUV measurement data in step S1 includes: Acquire raw water depth point cloud data collected by the AUV multibeam echo sounder system; Obtain the estimated position and attitude information recorded by the AUV navigation system.

4. The method according to claim 1, characterized in that: The method in step S3 includes the following steps: Step S31: For each data point in the AUV measurement data, search for the nearest corresponding point in the target reference data and establish a point-to-point correspondence. Step S32: Based on the point pair correspondence, calculate the rigid body transformation matrix that minimizes the root mean square error between all corresponding point pairs; Step S33: Apply the rigid body transformation matrix to the AUV measurement data to update the position of the data points; Repeat steps S31 to S33 until the convergence condition is met, and output the final position deviation vector.

5. The method according to claim 4, characterized in that: The convergence condition is: the root mean square error between all corresponding point pairs is less than a preset error threshold, or the number of iterations performed in the loop reaches a preset iteration limit.

6. The method according to claim 1, characterized in that: The method for correction based on the position deviation vector in step S4 includes: The estimated position information in the AUV measurement data is batch corrected using the position deviation vector to generate corrected position information. The corrected location information is associated with the corresponding depth information to output the corrected AUV terrain data.

7. The method according to claim 2, characterized in that: Step S2 includes: When the average water depth of the measurement area is less than the preset water depth threshold, the shallow water area reference data is selected as the target reference data. When the average water depth of the measurement area is greater than or equal to a preset water depth threshold, the deep water reference data is selected as the target reference data.

8. The method according to claim 7, characterized in that: When selecting the deep-water reference data as the target reference data, the method further includes the following steps before step S3: Interpolation processing is performed on the reference depth point sequence in the deep water reference data to generate a reference depth contour line, and the reference depth contour line is used as the target reference data for matching processing.

9. The method according to claim 4, characterized in that: The rigid body transformation matrix includes translation parameters in the X direction, translation parameters in the Y direction, and rotation angle parameters.

10. The method according to claim 2, characterized in that: The integrated navigation system is a GNSS / INS integrated navigation system.

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