Submarine topography surveying and mapping method and system based on underwater robot

Abstract

The invention discloses a submarine topography surveying and mapping method and system based on an underwater robot, relates to the technical field of underwater robots, and provides reliable basic data for measurement based on an original measurement point sound wave data set S, and extracts a multipath disturbance feature set F of each sonar scanning point; the multi-path interference in the underwater environment is effectively identified and represented, data are clustered through density clustering analysis, dense clusters and discrete points are successfully identified, then a disturbance residual Cc is obtained and compared with a preset interference judgment threshold Omap, each sonar scanning point is endowed with a multi-path label, and the multi-path interference in the underwater environment is effectively identified and represented. According to the method, the time sequence and the amplitude sequence of sonar scanning points are combined, the main path echo time of each measuring point is accurately obtained, the continuity of landforms is effectively judged, and high precision and stability of underwater topographic survey are ensured, especially in the aspects of multi-path interference elimination, main path reconstruction judgment and final accurate topographic reflection.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater robots, and specifically to a method and system for mapping submarine topography and geomorphology based on an underwater robot. Background Art

[0002] In the broad field of marine information perception and intelligent detection, underwater mapping, as one of the core technical branches, has long been responsible for the task of understanding the spatial structure and geomorphological form of the seabed. With the increasing demands in scenarios such as deep-sea resource development, submarine communication layout, and marine ecological monitoring, the requirements for the automation level and data accuracy of underwater spatial mapping are showing a rapid growth trend. At the same time, it has gradually become difficult to meet the task objectives of high efficiency and high precision using manual operation or towed sonar.

[0003] To adapt to the challenges of three-dimensional mapping in complex seabed environments, underwater robots have gradually become the main operation platforms. Especially in geomorphological areas such as submarine canyons, fault zones, coral reefs, and sunken shipwrecks, such systems with autonomous movement capabilities and high-precision sensor integration capabilities can achieve large-scale and fine-grained acquisition of geomorphological data.

[0004] Although current seabed mapping systems can construct point cloud images and complete three-dimensional modeling in underwater environments through equipment such as high-resolution multibeam sonars, there are still important technical bottlenecks in the goal of "true geomorphological reconstruction". Among them, in complex terrain conditions, the "multipath interference" phenomenon caused by the diversity of acoustic wave propagation paths is more obvious in areas with drastic changes in geometric features such as steep cliffs, overhanging structures, and cracks. In these scenarios, in addition to the "main path" where the acoustic wave travels directly from the robot to the seabed target point, the acoustic wave will also be reflected by different underwater interfaces or structures, forming a series of "secondary path" echoes that arrive with a delay. These multipath echoes will be misjudged as actual target structures, resulting in forged highlands, faults, and even continuous ridges or troughs in the mapping data, thus causing structural false content in the final three-dimensional geomorphological atlas. Especially in the automatic processing and modeling process, since the data filtering strategy relies on time windows and signal amplitude discrimination, the current methods are still difficult to effectively remove the pseudo-geomorphological information brought by multipath noise. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a method and system for mapping submarine topography and geomorphology based on an underwater robot, which solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for mapping submarine topography and geomorphology based on an underwater robot, including the following steps: S1. Based on the underwater robot, sonar scans are performed at fixed intervals along a preset survey path. Time series reflection data is obtained at each sonar scan point, and an original measurement point acoustic wave dataset S is formed. S2. Feature extraction is performed on the original measurement point acoustic wave dataset S to obtain multi-path perturbation height-related feature parameters for each sonar scan point, and a multi-path perturbation feature set F is formed. S3. The multi-path perturbation feature set F is mapped into a three-dimensional feature space, and density clustering algorithm is used for clustering analysis to identify dense clusters and discrete points, obtaining perturbation residuals Cc. Then, it is compared with a preset interference determination threshold Omap to assign multi-path labels to each sonar scan point, obtaining a residual set A and a multi-path label set Lmp. S4. The time series and amplitude series in the original measurement point acoustic wave dataset S of each sonar scan point in the multi-path label set Lmp are extracted, and the main path echo time Tmain is recalculated to form a main path time set Hmain. S5. Based on the acoustic wave propagation speed v, the main path time set Hmain is converted into depth coordinates Z to obtain a height difference sequence △Z and a horizontal distance difference △D, and a consistency index G for evaluating the geomorphology is calculated. Then, based on the consistency index G, the reconstruction of the main path is judged to be triggered.

[0007] Preferably, the S1 includes S11 and S12; S11. Based on the underwater robot, sonar scans are performed at fixed intervals along a preset survey path. Sonar scan points i are determined in sequence, and a sonar scan point set P = {1, 2, ……, i | i ∈ N} is obtained, where N represents the total number of sonar scan points. S12. Based on the sonar scan point set P, high-resolution directional scans are performed on each sonar scan point i to obtain complete time series echo data, forming the original acoustic wave data S(i) of each sonar scan point i. The original acoustic wave data of all sonar scan points in the sonar scan point set P is integrated to obtain the original measurement point acoustic wave dataset S = {S(1), S(2), ……, S(i) | i ∈ N}.

[0008] Preferably, the time series echo data includes the echo time series T(i) of the sonar scan point i, the amplitude series A(i) of the sonar scan point i, the effective signal duration D(i) of the sonar scan point i, the main echo peak time Tpeak, and the waveform oscillation coefficient γ(i) of the sonar scan point i; The original acoustic wave data S(i) of the sonar scan point i = {T(i), A(i), D(i), Tpeak(i), γ(i)}; The echo time series T(i) of the sonar scan point i specifically represents the time axis of the entire echo reception window, constituting the abscissa of the time domain. The echo time series T(i) of the sonar scan point i = {t1, t2, ……, tj|j ∈ k}, where tj represents the echo time series at the jth sampling time point, and k represents the total number of time points; The amplitude sequence A(i) of the sonar scan point i specifically represents the acoustic wave echo amplitude matching the echo time series T(i) of the sonar scan point i. The amplitude sequence A(i) of the sonar scan point i = {a1, a2, ……, aj|j ∈ k}, where aj represents the amplitude sequence at the jth sampling time point; The effective signal duration D(i) of the sonar scan point i specifically represents the time duration length of the echo signal. The effective signal duration D(i) of the sonar scan point i is obtained through the calculation formula of tk - t1; The main echo peak time Tpeak specifically represents the time point of the maximum amplitude of the main path reflection signal, which is used for multipath reflection comparison and is obtained by extracting the maximum value in the amplitude sequence A(i) of the sonar scan point i; The waveform oscillation coefficient γ(i) of the sonar scan point i specifically represents the degree of waveform mutation. The waveform oscillation coefficient γ(i) of the sonar scan point i is obtained through the following calculation formula: ; In the formula, A(i, j + 1) and A(i, j) respectively represent the amplitude sequence at the (j + 1)th sampling point and the amplitude sequence at the jth sampling point of the sonar scan point i. δ represents the preset amplitude mutation judgment threshold. When A(i, j + 1) - A(i, j) > the amplitude mutation judgment threshold δ holds, return 1; otherwise, return 0.

[0009] Preferably, the S2 includes S21; S21. Extract features from the original measured point acoustic wave data set S to obtain the multipath perturbation height - related characteristic parameters of each sonar scan point i in the original measured point acoustic wave data set S = {S(1), S(2), ……, S(i)|i ∈ N}, and form a multipath perturbation feature set F = {F(1), F(2), ……, F(i)|i ∈ N}; The multipath perturbation height - related characteristic parameters include the maximum echo delay time offset δT(i) of the sonar scan point i, the amplitude standard deviation σA(i) of the sonar scan point i, and the waveform jump density η(i) per unit time of the sonar scan point i; The maximum echo delay time offset δT(i) of the sonar scan point i is obtained through the following calculation formula: ; Wherein, max(T(i)) represents the maximum value in the echo time series T of sonar scan point i, specifically representing the last digit of the series, reflecting the arrival time of the residual reflection at the end; The amplitude standard deviation σA(i) of sonar scan point i is obtained through the following calculation formula: ; Wherein, std represents the standard deviation function; The waveform jump density η(i) per unit time of sonar scan point i is obtained through the calculation formula.

[0010] Preferably, the S3 includes S31 and S32; S31: Map the multipath perturbation feature set F into a three-dimensional feature space, and use the density clustering algorithm for clustering analysis to identify dense clusters and discrete points; Among them, the dense clusters and discrete points are obtained by using the DBSCAN density clustering algorithm to divide the density of the multipath perturbation highly correlated feature parameters of each sonar scan point in the multipath perturbation feature set F into clusters. Sonar scan points close to each other are clustered into one cluster, while discrete sonar scan points are marked as noise, and the sonar scan points marked as noise are synchronously removed from the three-dimensional feature space and do not participate in the calculation of the perturbation residual; S32: Calculate the perturbation residual of the sonar scan point clusters in the three-dimensional feature space after the removal process, obtain the perturbation residual Cc(i) of each sonar scan point i, and compare it with the preset interference determination threshold Omap to assign a multipath label to each sonar scan point, and obtain the residual set A and the multipath label set Lmp; Among them, the residual set A = {Cc(1), Cc(2), ……, Cc(i)|i ∈ N}; The multipath label set Lmp = {L(1), L(2), ……, L(i)|i ∈ N}.

[0011] Preferably, the calculation of the perturbation residual specifically calculates the Euclidean distance from each sonar scan point to the center of the sonar scan point cluster. The Euclidean distance is marked as the perturbation residual Cc, reflecting the deviation degree of each sonar scan point i from the center of the normal sonar scan point cluster; The multipath label is assigned through the following comparison method: When the perturbation residual Cc(i) of sonar scan point i > the interference determination threshold Omap, assign a multipath interference point label to sonar scan point i, mark sonar scan point i as multipath interference, and generate the multipath label L(i) = 1 of sonar scan point i; When the perturbation residual Cc(i) of the sonar scan point i < the interference determination threshold Omap, no multi-path interference point label assignment is performed for the sonar scan point i, the sonar scan point i is marked as a non-interference point, and the multi-path label L(i) of the sonar scan point i is generated as L(i) = 0.

[0012] Preferably, the S4 includes S41; S41. Extract the time series T and amplitude sequence A of each sonar scan point in the original measured point acoustic wave data set S of each sonar scan point in the multi-path label set Lmp, recalculate the main path echo time Tmain(i) of the sonar scan point i, and form the main path time set Hmain = {Tmain(1), Tmain(2), ……, Tmain(i)|i ∈ N}; The main path echo time Tmain(i) of the sonar scan point i is obtained through the following calculation formula: ; In the formula, argmax(A(i,j)) represents taking the amplitude sequence of the maximum sampling time point j of the sonar scan point i, and the sampling time point j of the obtained amplitude sequence is used as the sampling time point of the echo time sequence of the sonar scan point i; The sampling time point j of the obtained echo time sequence of the sonar scan point i is determined as the sampling time point corresponding to the maximum amplitude, and is marked as the main path echo time Tmain(i) of the sonar scan point i.

[0013] Preferably, the S5 includes S51 and S52; S51. Based on the acoustic wave propagation speed v, each sonar scan point in the main path time set Hmain is converted to obtain the depth coordinate Z(i) of the sonar scan point i, and based on all the converted depth coordinates Z, the height difference sequence △Z and the horizontal distance difference △D between each pair of adjacent sonar scan points are calculated; The depth coordinate Z(i) of the sonar scan point i is obtained through the following calculation formula: ; In the formula, Tmain(i) represents the main path echo time of the i-th sonar scan point; Among them, the height difference sequence △Z is obtained through the calculation formula △Z = Z(i + 1) - Z(i); The horizontal distance difference △D is obtained by extracting the interval between the (i + 1)-th sonar scan point and the i-th sonar scan point at a fixed interval on the preset survey path.

[0014] Preferably, based on the linear relationship between the height difference sequence △Z(i) and the horizontal distance difference △D(i) obtained by analyzing adjacent sonar scan points i, a consistency index G for evaluating the landform is constructed to measure the continuity and stability of the landform. Based on the comparison between the consistency index G and the preset reconstruction threshold Gthe, the reconstruction of the main path is determined according to the comparison result. The consistency index G is obtained through the calculation formula. The reconstruction of the main path is determined by the following comparison method: When the consistency index G > the reconstruction threshold Gthe, it is determined that the landform is discontinuous, the path is re-evaluated, and the main path reconstruction of the main path time set Hmain is triggered. When the consistency index G ≤ the reconstruction threshold Gthe, it is determined that the landform is continuous, and the main path time set Hmain is applied.

[0015] The seabed terrain and landform mapping system based on an underwater robot includes a scan data acquisition module, a scan feature extraction module, a feature mapping and marking module, a main path acquisition module, and a decision optimization module. The scan data acquisition module performs sonar scans at fixed intervals on the preset mapping path based on the underwater robot, obtains time series reflection data at each sonar scan point, and forms the original measurement point acoustic wave data set S. The scan feature extraction module extracts features from the original measurement point acoustic wave data set S, obtains the multi-path perturbation height-related feature parameters of each sonar scan point, and forms the multi-path perturbation feature set F. The feature mapping and marking module maps the multi-path perturbation feature set F into a three-dimensional feature space, performs clustering analysis using the density clustering algorithm, identifies dense clusters and discrete points, obtains the perturbation residual Cc, compares it with the preset interference determination threshold Omap, assigns multi-path labels to each sonar scan point, and obtains the residual set A and the multi-path label set Lmp. The main path acquisition module extracts the time series and amplitude series in the original measurement point acoustic wave data set S of each sonar scan point in the multi-path label set Lmp, recalculates the main path echo time Tmain, and forms the main path time set Hmain. The decision optimization module converts the main path time set Hmain into the depth coordinate Z based on the acoustic wave propagation speed v, obtains the height difference sequence △Z and the horizontal distance difference △D, evaluates the consistency index G of the landform, and determines the reconstruction of the main path based on the consistency index G.

[0016] The present invention provides a seabed terrain and landform mapping method and system based on an underwater robot, having the following beneficial effects: (1) Based on the original measured point acoustic wave dataset S, it provides reliable basic data for measurement. The multi-path perturbation feature set F of each sonar scan point is extracted, effectively identifying and characterizing the multi-path interference in the underwater environment. The data is clustered through density clustering analysis, successfully identifying dense clusters and discrete points, and then obtaining the perturbation residual Cc. By comparing it with the preset interference determination threshold Omap, a multi-path label is assigned to each sonar scan point. Finally, the residual set A and the multi-path label set Lmp are generated. In the calculation of the main path echo time Tmain, the time series and amplitude series of the sonar scan points are combined to accurately obtain the main path echo time of each measurement point, effectively judging the continuity of the landform, and then based on the consistency index G to judge whether to trigger the reconstruction of the main path, ensuring the high precision and stability of underwater terrain survey. Especially in the elimination of multi-path interference, the reconstruction judgment of the main path, and the accurate reflection of the final terrain, it provides effective technical support.

[0017] (2) Complete echo data is obtained through high-resolution directional scanning and integrated to form the original measured point acoustic wave dataset S, laying a solid foundation for the precise processing of the data. Especially the calculation of the waveform oscillation coefficient γ(i) can capture the waveform mutations caused by multi-path interference in the acoustic wave signal, thus effectively identifying potential multi-path interference areas. Through this series of precise data acquisition and processing, it provides a comprehensive and accurate description of the seabed terrain, effectively avoiding the possible errors in traditional methods, improving the accuracy of the measurement results, and ensuring the efficiency and high quality of underwater mapping. This method not only improves the integrity and detail capture ability of the data, but also optimizes the detection and processing of multi-path interference, reducing the errors caused by signal noise, thus ensuring the reliability and stability of the final result.

[0018] (3) Through the density clustering algorithm DBSCAN, core points, boundary points, and noise points are effectively distinguished. Using its efficient clustering ability, potential multi-path interference can be identified in a complex underwater environment, and noise points are removed. This measure improves the accuracy of subsequent perturbation residual calculation. By calculating the perturbation residual of the sonar scan point clusters after the removal process and further combining the preset interference determination threshold Omap, an accurate multi-path label is assigned to each sonar scan point, ensuring the precise distinction between multi-path interference points and non-interference points. Description of the Drawings

[0019] Figure 1 It is a schematic diagram of the steps of the seabed terrain and landform mapping method based on an underwater robot according to the present invention; Figure 2 It is a schematic diagram of the block diagram of the seabed terrain and landform mapping system based on an underwater robot according to the present invention; Figure 3Schematic diagram of the relationship between the depth coordinate (Z(i)) and the sonar scanning points. Specific implementation mode

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] Embodiment 1 The present invention provides a method for mapping submarine topography and geomorphology based on an underwater robot. Please refer to Figure 1 and includes the following steps: S1. Based on the underwater robot, sonar scanning is performed at fixed intervals on a preset mapping path, and time series reflection data is obtained at each sonar scanning point to form an original measurement point acoustic wave dataset S. S2. Feature extraction is performed on the original measurement point acoustic wave dataset S to obtain multi-path disturbance height-related feature parameters for each sonar scanning point, and a multi-path disturbance feature set F is formed. S3. Map the multi-path disturbance feature set F into a three-dimensional feature space, perform clustering analysis using a density clustering algorithm, identify dense clusters and discrete points, obtain a disturbance residual Cc, compare it with a preset interference determination threshold Omap, assign multi-path labels to each sonar scanning point, and obtain a residual set A and a multi-path label set Lmp. S4. Extract the time series and amplitude series in the original measurement point acoustic wave dataset S for each sonar scanning point in the multi-path label set Lmp, recalculate the main path echo time Tmain, and form a main path time set Hmain. S5. Based on the acoustic wave propagation speed v, convert the main path time set Hmain into a depth coordinate Z, obtain a height difference series △Z and a horizontal distance difference △D, evaluate a consistency index G of the geomorphology, and judge the trigger for the reconstruction of the main path based on the consistency index G.

[0022] In this embodiment, based on the sonar scanning of the underwater robot at fixed intervals along the preset survey path, the system can obtain time-series reflection data at each sonar scanning point, forming the original measured-point acoustic wave dataset S, which provides reliable basic data for the measurement. The multipath perturbation feature set F of each sonar scanning point is extracted, effectively identifying and characterizing the multipath interference in the underwater environment. By performing density clustering analysis on the data, dense clusters and discrete points are successfully identified, and then the perturbation residual Cc is obtained and compared with the preset interference determination threshold Omap to assign a multipath label to each sonar scanning point. Finally, the residual set A and the multipath label set Lmp are generated. In the calculation of the main path echo time Tmain, the time series and amplitude series of the sonar scanning points are combined to accurately obtain the main path echo time of each measured point. The main path time set Hmain is converted into depth coordinates Z through the acoustic wave propagation speed v, and then the height difference sequence ΔZ and the horizontal distance difference ΔD are calculated. By evaluating the geomorphic consistency index G, the continuity of the geomorphology is effectively judged, and then based on the consistency index G, it is determined whether to trigger the reconstruction of the main path, ensuring the high precision and stability of the underwater topographic survey. Especially in the elimination of multipath interference, the reconstruction judgment of the main path, and the accurate reflection of the final terrain, effective technical guarantees are provided. Through this method, not only the accuracy and efficiency of underwater measurement are improved, but also the measurement error problem caused by multipath interference is solved, ensuring the reliability and integrity of the seabed terrain data.

[0023] Embodiment 2 This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically: The S1 includes S11 and S12; S11. Based on the sonar scanning of the underwater robot at fixed intervals along the preset survey path, the sonar scanning point i is determined in sequence, and the sonar scanning point set P = {1, 2,..., i|i ∈ N} is obtained, where N represents the total number of sonar scanning points; S12. Based on the sonar scanning point set P, high-resolution directional scanning is performed on each sonar scanning point i to obtain complete time-series echo data, forming the original acoustic wave data S(i) of each sonar scanning point i. The original acoustic wave data S of all sonar scanning points in the sonar scanning point set P is integrated to obtain the original measured-point acoustic wave dataset S = {S(1), S(2),..., S(i)|i ∈ N}.

[0024] Among them, the time-series echo data includes the echo time series T(i) of the sonar scanning point i, the amplitude series A(i) of the sonar scanning point i, the effective signal duration D(i) of the sonar scanning point i, the main echo peak time Tpeak, and the waveform oscillation coefficient γ(i) of the sonar scanning point i; The original acoustic wave data S(i) of sonar scan point i = {T(i), A(i), D(i), Tpeak(i), γ(i)}; The echo time series T(i) of sonar scan point i specifically represents the time axis of the entire echo reception window, constituting the abscissa of the time domain. The echo time series T(i) of sonar scan point i = {t1, t2, ……, tj|j ∈ k}, where tj represents the echo time series at the j-th sampling time point, and k represents the total number of time points; The amplitude sequence A(i) of sonar scan point i specifically represents the acoustic wave echo amplitude matching the echo time series T(i) of sonar scan point i. The amplitude sequence A(i) of sonar scan point i = {a1, a2, ……, aj|j ∈ k}, where aj represents the amplitude sequence at the j-th sampling time point; The effective signal duration D(i) of sonar scan point i specifically represents the time duration length of the echo signal. The effective signal duration D(i) of sonar scan point i is obtained through the calculation formula tk - t1; The main echo peak time Tpeak specifically represents the time point of the maximum amplitude of the main path reflection signal, which is used for multipath reflection comparison and is obtained by extracting the maximum value in the amplitude sequence A(i) of sonar scan point i; The waveform oscillation coefficient γ(i) of sonar scan point i specifically represents the degree of waveform mutation. The more unstable the waveform and the more jumps, the more likely there is multipath interference. The waveform oscillation coefficient γ(i) of sonar scan point i is obtained through the following calculation formula: ; In the formula, A(i, j + 1) and A(i, j) respectively represent the amplitude sequence at the (j + 1)-th sampling point and the j-th sampling point of sonar scan point i, and δ represents the preset amplitude mutation judgment threshold, usually taking 0.1 * max(A(i)). When A(i, j + 1) - A(i, j) > the amplitude mutation judgment threshold δ holds, return 1, otherwise return 0.

[0025] In this embodiment, by setting a predetermined survey path and performing sonar scans at fixed intervals, a set of sonar scan points P is obtained in sequence, ensuring the uniform distribution of measurement points and providing a basis for subsequent detailed data analysis. In step S12, by combining the echo time series T(i), amplitude sequence A(i), effective signal duration D(i), main echo peak time Tpeak, and waveform oscillation coefficient γ(i) of each sonar scan point, complete echo data is obtained through high-resolution directional scanning and integrated to form the original measured point acoustic wave data set S, laying a solid foundation for the precise processing of data. In particular, the calculation of the waveform oscillation coefficient γ(i) can capture the waveform mutations caused by multipath interference in the acoustic wave signal, thereby effectively identifying potential multipath interference regions. Through this series of precise data acquisitions and processes, a comprehensive and accurate description of the seabed topography is provided, while effectively avoiding the errors that may occur in traditional methods, improving the accuracy of measurement results, and ensuring the efficiency and high quality of underwater mapping. This method not only improves the integrity and detail capture ability of data, but also optimizes the detection and processing of multipath interference, reduces errors caused by signal noise, and thus ensures the reliability and stability of the final result.

[0026] Embodiment 3 This embodiment is an explanatory description carried out in Embodiment 2. Please refer to Figure 1 , specifically: The S2 includes S21; S21. Extract features from the original measured point acoustic wave data set S to obtain the multipath perturbation height-related feature parameters of each sonar scan point i in the original measured point acoustic wave data set S = {S(1), S(2),..., S(i)|i ∈ N}, and form a multipath perturbation feature set F = {F(1), F(2),..., F(i)|i ∈ N}; The multipath perturbation height-related feature parameters include the maximum echo delay time offset δT(i) of sonar scan point i, the amplitude standard deviation σA(i) of sonar scan point i, and the waveform jump density η(i) per unit time of sonar scan point i; The maximum echo delay time offset δT(i) of sonar scan point i is obtained through the following calculation formula: ; In the formula, max(T(i)) represents the maximum value in the echo time series T of sonar scan point i, specifically representing the last digit of the sequence, reflecting the arrival time of the end residual reflection; The amplitude standard deviation σA(i) of sonar scan point i is obtained through the following calculation formula: ; In the formula, std represents the standard deviation function; The waveform jump density η(i) of the sonar scan point i per unit time is obtained through the calculation formula.

[0027] The said S3 includes S31 and S32; S31 maps the multipath perturbation feature set F into a three-dimensional feature space, and uses the density clustering algorithm for clustering analysis to identify dense clusters and discrete points; Among them, the dense clusters and discrete points divide the density of the multipath perturbation highly correlated feature parameters of each sonar scan point in the multipath perturbation feature set F through the DBSCAN density clustering algorithm. The sonar scan points close to each other are clustered into one cluster, while the discrete sonar scan points are marked as noise, and the sonar scan points marked as noise are synchronously removed from the three-dimensional feature space and do not participate in the calculation of the perturbation residual; The DBSCAN density clustering algorithm will distinguish core points, boundary points and noise points according to the neighborhood radius bj and the minimum number of points minPts; Core point: If a sonar scan point has at least minPtsd points within the radius bj, the sonar scan point is marked as a core point; Boundary point: The number of sonar scan points in the neighborhood is less than minPtsd, but it is within the neighborhood of the core point; Noise point: The sonar scan point that does not belong to the neighborhood of any core point is marked as noise by the DBSCAN density clustering algorithm; S32 calculates the perturbation residual of the sonar scan point clusters in the three-dimensional feature space after the removal process, obtains the perturbation residual Cc(i) of each sonar scan point i, and compares it with the preset interference determination threshold Omap to assign a multipath label to each sonar scan point, and obtains the residual set A and the multipath label set Lmp; Among them, the residual set A = {Cc(1), Cc(2), ……, Cc(i)|i ∈ N}; The multipath label set Lmp = {L(1), L(2), ……, L(i)|i ∈ N}.

[0028] Among them, the calculation of the perturbation residual specifically calculates the Euclidean distance from each sonar scan point to the center of the sonar scan point cluster. The Euclidean distance is marked as the perturbation residual Cc, which reflects the deviation degree of each sonar scan point i from the center of the normal sonar scan point cluster; The multipath label is assigned through the following comparison method: When the perturbation residual Cc(i) of the sonar scan point i > the interference determination threshold Omap, assign a multipath interference point label to the sonar scan point i, mark the sonar scan point i as multipath interference, and generate the multipath label L(i) = 1 of the sonar scan point i; When the disturbance residual Cc(i) of the sonar scan point i < the interference determination threshold Omap, no multi-path interference point label assignment is performed for the sonar scan point i, the sonar scan point i is marked as a non-interference point, and the multi-path label L(i) of the sonar scan point i is generated as L(i)=0.

[0029] In this embodiment, through feature extraction of the original measured point acoustic wave dataset S, multi-path disturbance height-related feature parameters of each sonar scan point i are extracted, including the maximum echo delay time offset δT(i), the amplitude standard deviation σA(i), and the waveform jump density η(i), and a multi-path disturbance feature set F is formed. This process can effectively identify and characterize multi-path interference in the underwater environment by accurately capturing the waveform features of each measured point. In particular, the change of the signal is measured by the waveform jump density η(i), so as to detect the interference source more accurately. Through the density clustering algorithm DBSCAN, core points, boundary points and noise points are effectively distinguished. Using its efficient clustering ability, potential multi-path interference can be identified in a complex underwater environment, and noise points are removed. This measure improves the accuracy of subsequent disturbance residual calculation. By calculating the disturbance residual of the sonar scan point cluster after the removal process, and further combining the preset interference determination threshold Omap, an accurate multi-path label is assigned to each sonar scan point, ensuring the accurate distinction between multi-path interference points and non-interference points. Finally, the whole process ensures the accuracy of the measurement results, and maintains high stability and credibility under the influence of multi-path interference, provides effective means for path reconstruction and interference removal, thus improving the overall quality and efficiency of seabed topography mapping.

[0030] Example 4 This embodiment is an explanatory description based on Embodiment 3. Please refer to Figure 1 , specifically: The S4 includes S41; S41. Extract the time series T and amplitude series A of each sonar scan point in the original measured point acoustic wave dataset S of each sonar scan point in the multi-path label set Lmp, recalculate the main path echo time Tmain(i) of the sonar scan point i, and form a main path time set Hmain={Tmain(1), Tmain(2), ……, Tmain(i)|i∈N}; The main path echo time Tmain(i) of the sonar scan point i is obtained through the following calculation formula: ; In the formula, argmax(A(i,j)) represents taking the amplitude series of the maximum sampling time point j of the sonar scan point i, and the obtained sampling time point j of the amplitude series is used as the sampling time point of the echo time series of the sonar scan point i; The sampling time point j of the echo time series for obtaining the sonar scan point i is determined as the sampling time point corresponding to the maximum amplitude, and is marked as the main path echo time Tmain(i) of the sonar scan point i.

[0031] The said S5 includes S51 and S52; S51. Based on the acoustic wave propagation speed v, each sonar scan point in the main path time set Hmain is converted to obtain the depth coordinate Z(i) of the sonar scan point i. Based on all the converted depth coordinates Z, the height difference sequence △Z and the horizontal distance difference △D between each pair of adjacent sonar scan points are calculated; The depth coordinate Z(i) of the sonar scan point i is obtained through the following calculation formula: ; In the formula, Tmain(i) represents the main path echo time of the i-th sonar scan point; Among them, the height difference sequence △Z is obtained through the calculation formula △Z = Z(i + 1) - Z(i); The horizontal distance difference △D is obtained by extracting the interval between the (i + 1)-th sonar scan point and the i-th sonar scan point at a fixed interval on the preset mapping path.

[0032] S52. Based on analyzing the linear relationship between the height difference sequence △Z(i) and the horizontal distance difference △D(i) obtained for adjacent sonar scan points i, a consistency index G for evaluating the landform is constructed to measure the continuity and stability of the landform, and based on the comparison between the consistency index G and the preset reconstruction threshold Gthe, and then according to the comparison result, the reconstruction of the main path is judged; The consistency index G is obtained through The calculation formula; The reconstruction of the main path is judged through the following comparison method: When the consistency index G > the reconstruction threshold Gthe, it is determined that the landform is discontinuous, the path is re-evaluated, and the main path reconstruction of the main path time set Hmain is triggered; When the consistency index G ≤ the reconstruction threshold Gthe, it is determined that the landform is continuous, and the main path time set Hmain is applied.

[0033] In this embodiment, by extracting the time series T and amplitude series A in the original measurement point dataset S of each sonar scan point in the multi-path label set Lmp, the main path echo time Tmain(i) is recalculated, and the main path time set Hmain is generated. This calculation process extracts the maximum value of the amplitude series, ensuring the accuracy of the main path echo time of each sonar scan point, providing accurate time data for subsequent depth coordinate conversion and path analysis. Combining with the sound wave propagation speed v, the depth coordinate Z(i) of each sonar scan point is calculated based on the main path time set Hmain, and the height difference series ΔZ and horizontal distance difference ΔD between adjacent sonar scan points are further calculated, providing important data for landform consistency evaluation. By analyzing the linear relationship between the height difference and horizontal distance difference between adjacent sonar scan points, a landform consistency index G is constructed, effectively measuring the continuity and stability of the landform, and by comparing with the preset reconstruction threshold Gthresh, it is determined whether to trigger the reconstruction of the main path. The greatest advantage of this method is that it can dynamically judge the changes in the underwater terrain, ensure the timing and accuracy of path reconstruction, making the finally generated main path time set Hmain have extremely high terrain consistency and reliability, effectively avoiding path errors caused by irregular terrain, and significantly improving the measurement accuracy and stability.

[0034] Embodiment 5 For the seabed terrain and landform mapping system based on an underwater robot, please refer to Figure 2 , specifically: it includes a scan data acquisition module, a scan feature extraction module, a feature mapping and marking module, a main path acquisition module, and a decision optimization module; The scan data acquisition module performs sonar scans at fixed intervals based on the underwater robot on the preset mapping path, obtains time series reflection data at each sonar scan point, and forms the original measurement point acoustic wave dataset S; The scan feature extraction module extracts features from the original measurement point acoustic wave dataset S, obtains the multi-path perturbation height-related feature parameters of each sonar scan point, and forms the multi-path perturbation feature set F; The feature mapping and marking module maps the multi-path perturbation feature set F into a three-dimensional feature space, performs clustering analysis using the density clustering algorithm, identifies dense clusters and discrete points, obtains the perturbation residual Cc, and compares it with the preset interference determination threshold Omap to assign multi-path labels to each sonar scan point, obtaining the residual set A and the multi-path label set Lmp; The main path acquisition module extracts the time series and amplitude series in the original measured point acoustic wave dataset S of each sonar scan point in the multi-path label set Lmp, recalculates the main path echo time Tmain, and forms the main path time set Hmain; the decision optimization module converts the main path time set Hmain into depth coordinates Z based on the acoustic wave propagation speed v, obtains the height difference series △Z and the horizontal distance difference △D, evaluates the consistency index G of the terrain, and judges whether to trigger the reconstruction of the main path based on the consistency index G.

[0035] Specific example illustration of calculating the depth coordinate Z(i) from the main path echo time Tmain(i): Suppose the underwater robot performs sonar scanning on a preset path, and the data is as follows: The acoustic wave propagation speed v = 1500; The echo time series T(i) of the echo time data for the i-th sonar scan point is as follows: Sonar scan point i: i = 1: 1; Echo time series T(i): 0.3; Amplitude series A(i): 0.8; i = 2: 2; Echo time series T(i): 0.32; Amplitude series A(i): 0.75; i = 3: 3; Echo time series T(i): 0.31; Amplitude series A(i): 0.85; i = 4: 4; Echo time series T(i): 0.29; Amplitude series A(i): 0.82; Calculate the main path echo time Tmain(i): Sonar scan point i: i = 1: 1; Main path echo time Tmain(i): 0.31; i = 2: 2; Main path echo time Tmain(i): 0.32; i = 3: 3; Main path echo time Tmain(i): 0.31; i = 4: 4; Main path echo time Tmain(i): 0.29; Calculate the depth coordinate Z(i): Sonar scan point i: i = 1: 1; Main path echo time Tmain(i): 0.31; Depth coordinate Z(i): 232.5; i = 2: 2; Main path echo time Tmain(i): 0.32; Depth coordinate Z(i): 240; i = 3: 3; Main path echo time Tmain(i): 0.31; Depth coordinate Z(i): 232.5; i = 4:4; Main path echo time Tmain(i): 0.29; Depth coordinate Z(i): 217.5; Calculate the height difference sequence ΔZ, horizontal distance difference ΔD = 5: Sonar scan point i: i = 1 - 2: 1 → 2; Height difference ΔZi: 7.5; i = 2 - 3: 2 → 3; Height difference ΔZi: -7.5; i = 3 - 4: 3 → 4; Height difference ΔZi: 15.0; Calculate the geomorphic consistency index G = [7.5 + (-7.5) + 15.0] / [5 + 5 + 5] = 1.0.

[0036] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for mapping submarine topography and geomorphology based on an underwater robot, characterized in that: It includes the following steps: S1. Based on the underwater robot, sonar scans are performed at fixed intervals along a preset survey path. At each sonar scan point, time series reflection data is obtained to form the original measured point acoustic wave dataset S; S2. Feature extraction is performed on the original measured point acoustic wave dataset S to obtain the multipath perturbation height correlation feature parameters for each sonar scan point, forming the multipath perturbation feature set F; S3. The multipath perturbation feature set F is mapped into a three-dimensional feature space, and density clustering algorithm is used for clustering analysis to identify dense clusters and discrete points, obtaining the perturbation residual Cc, which is compared with the preset interference determination threshold Omap. Multipath labels are assigned to each sonar scan point to obtain the residual set A and the multipath label set Lmp; S4. The time series and amplitude series in the original measured point acoustic wave dataset S of each sonar scan point in the multipath label set Lmp are extracted, and the main path echo time Tmain is recalculated to form the main path time set Hmain; S5. Based on the acoustic wave propagation velocity v, the main path time set Hmain is converted into depth coordinates Z to obtain the height difference series △Z and the horizontal distance difference △D, and the consistency index G of the evaluated landform is obtained. Based on the consistency index G, the reconstruction of the main path is judged to be triggered.

2. The method for mapping submarine topography and geomorphology based on an underwater robot according to claim 1, wherein: The said S1 includes S11 and S12; S11. Based on the underwater robot, sonar scans are performed at fixed intervals along a preset survey path. The sonar scan point i is determined in sequence to obtain the sonar scan point set P = {1, 2, ……, i|i ∈ N}, where N represents the total number of sonar scan points; S12. Based on the sonar scan point set P, high-resolution directional scans are performed on each sonar scan point i to obtain complete time series echo data, forming the original acoustic wave data S(i) of each sonar scan point i. The original acoustic wave data of all sonar scan points in the sonar scan point set P is integrated to obtain the original measured point acoustic wave dataset S = {S(1), S(2), ……, S(i)|i ∈ N}.

3. The method for mapping submarine topography and geomorphology based on an underwater robot according to claim 2, wherein: Among them, The time series echo data includes the echo time series T(i) of the sonar scan point i, the amplitude series A(i) of the sonar scan point i, the effective signal duration D(i) of the sonar scan point i, the main echo peak time Tpeak, and the waveform oscillation coefficient γ(i) of the sonar scan point i; The original acoustic wave data S(i) of the sonar scan point i = {T(i), A(i), D(i), Tpeak(i), γ(i)}; The echo time series T(i) of the sonar scan point i specifically represents the time axis of the entire echo reception window, constituting the abscissa of the time domain. The echo time series T(i) of the sonar scan point i = {t1, t2, ……, tj|j ∈ k}, where tj represents the echo time series at the jth sampling time point, and k represents the total number of time points; The amplitude series A(i) of the sonar scan point i specifically represents the acoustic wave echo amplitude matching the echo time series T(i) of the sonar scan point i. The amplitude series A(i) of the sonar scan point i = {a1, a2, ……, aj|j ∈ k}, where aj represents the amplitude series at the jth sampling time point; The effective signal duration D(i) of the sonar scan point i specifically represents the time duration length of the echo signal, and the effective signal duration D(i) of the sonar scan point i is obtained through the calculation formula of tk - t1; The main echo peak time Tpeak specifically represents the maximum amplitude time point of the main path reflection signal, which is used for multipath reflection comparison and is obtained by extracting the maximum value in the amplitude sequence A(i) of the sonar scan point i; The waveform oscillation coefficient γ(i) of the sonar scan point i specifically represents the degree of waveform mutation, and the waveform oscillation coefficient γ(i) of the sonar scan point i is obtained through the following calculation formula: ； In the formula, A(i, j + 1) and A(i, j) respectively represent the amplitude sequence of the sonar scan point i at the (j + 1)-th sampling point and the amplitude sequence at the j-th sampling point, δ represents a preset amplitude mutation judgment threshold. When A(i, j + 1) - A(i, j) > the amplitude mutation judgment threshold δ holds, return 1, otherwise return 0.

4. The seabed topographic and geomorphic mapping method based on an underwater robot according to claim 3, characterized in that: The said S2 includes S21; S21. Extract features from the original measured point acoustic wave dataset S to obtain the multi-path perturbation height-related feature parameters of each sonar scan point i in the original measured point acoustic wave dataset S = {S(1), S(2), ……, S(i)|i ∈ N}, and form a multi-path perturbation feature set F = {F(1), F(2), ……, F(i)|i ∈ N}; The multi-path perturbation height-related feature parameters include the maximum echo delay time offset δT(i) of the sonar scan point i, the amplitude standard deviation σA(i) of the sonar scan point i, and the waveform jump density η(i) per unit time of the sonar scan point i; The maximum echo delay time offset δT(i) of the sonar scan point i is obtained through the following calculation formula: ； In the formula, max(T(i)) represents the maximum value in the echo time series T of the sonar scan point i, specifically representing the last digit of the sequence, which reflects the arrival time of the end residual reflection; The amplitude standard deviation σA(i) of the sonar scan point i is obtained through the following calculation formula: ； In the formula, std represents the standard deviation function; The waveform jump density η(i) of the sonar scan point i per unit time is obtained through the calculation formula.

5. The seabed topography mapping method based on an underwater robot according to claim 4, characterized in that: The said S3 includes S31 and S32; S31. Map the multi-path perturbation feature set F into a three-dimensional feature space, and use the density clustering algorithm for clustering analysis to identify dense clusters and discrete points; Among them, the dense clusters and discrete points are obtained by dividing the density of the multi-path perturbation height-related feature parameters of each sonar scan point in the multi-path perturbation feature set F through the DBSCAN density clustering algorithm. Sonar scan points close to each other are clustered into a cluster, and discrete sonar scan points are marked as noise. At the same time, the sonar scan points marked as noise are removed from the three-dimensional feature space and do not participate in the calculation of the perturbation residual; S32. Calculate the perturbation residual of the sonar scan point clusters in the three-dimensional feature space after the removal process, obtain the perturbation residual Cc(i) of each sonar scan point i, and compare it with the preset interference determination threshold Omap to assign multi-path labels to each sonar scan point, and obtain the residual set A and the multi-path label set Lmp; Among them, the residual set A = {Cc(1), Cc(2), ……, Cc(i)|i ∈ N}; The multipath label set $L_{mp}=\{L^{(1)},L^{(2)},\cdots,L^{(i)}|i\in N\}$.

6. The seabed topographic and geomorphic mapping method based on an underwater robot according to claim 5, wherein: Among them, The calculation of the perturbation residual specifically calculates the Euclidean distance from each sonar scan point to the center of the sonar scan point cluster. The Euclidean distance is marked as the perturbation residual $C_c$, which reflects the deviation degree of each sonar scan point $i$ from the center of the normal sonar scan point cluster. The multipath labels are assigned through the following comparison method: When the perturbation residual $C_c(i)$ of the sonar scan point $i$ is greater than the interference determination threshold $O_{map}$, the multipath interference point label of the sonar scan point $i$ is assigned, marking the sonar scan point $i$ as a multipath interference, and generating the multipath label $L(i)=1$ of the sonar scan point $i$. When the perturbation residual $C_c(i)$ of the sonar scan point $i$ is less than the interference determination threshold $O_{map}$, the multipath interference point label of the sonar scan point $i$ is not assigned, marking the sonar scan point $i$ as a non-interference point, and generating the multipath label $L(i)=0$ of the sonar scan point $i$.

7. The method for mapping submarine topography and geomorphology based on an underwater robot according to claim 6, wherein: The said $S4$ includes $S41$; $S41$. Extract the time series $T$ and amplitude series $A$ of each sonar scan point in the original measured point acoustic wave dataset $S$ of each sonar scan point in the multipath label set $L_{mp}$, and recalculate the main path echo time $T_{main}(i)$ of the sonar scan point $i$, forming the main path time set $H_{main}=\{T_{main}(1),T_{main}(2),\cdots,T_{main}(i)|i\in N\}$. The main path echo time $T_{main}(i)$ of the sonar scan point $i$ is obtained through the following calculation formula: ； In the formula, $\argmax(A(i,j))$ represents taking the amplitude series at the maximum sampling time point $j$ of the sonar scan point $i$, and the sampling time point $j$ of the obtained amplitude series is used as the sampling time point of the echo time series of the sonar scan point $i$. The sampling time point $j$ of the obtained echo time series of the sonar scan point $i$ is determined as the sampling time point corresponding to the maximum amplitude, and is marked as the main path echo time $T_{main}(i)$ of the sonar scan point $i$.

8. The seabed topographic and geomorphic mapping method based on an underwater robot according to claim 7, wherein: The said $S5$ includes $S51$ and $S52$; $S51$. Based on the sound wave propagation speed $v$, each sonar scan point in the main path time set $H_{main}$ is transformed to obtain the depth coordinate $Z(i)$ of the sonar scan point $i$. Based on all the transformed depth coordinates $Z$, the height difference series $\Delta Z$ and the horizontal distance difference $\Delta D$ between each pair of adjacent sonar scan points are calculated. The depth coordinate $Z(i)$ of the sonar scan point $i$ is obtained through the following calculation formula: ； In the formula, $T_{main}(i)$ represents the main path echo time of the $i$-th sonar scan point. Among them, the height difference series $\Delta Z$ is obtained through the calculation formula $\Delta Z = Z(i + 1)-Z(i)$. The horizontal distance difference $\Delta D$ is obtained by extracting the interval between the $(i + 1)$-th sonar scan point and the $i$-th sonar scan point at a fixed interval on the preset survey path.

9. The method for mapping submarine topography and geomorphology based on an underwater robot according to claim 8, characterized in that: Based on the linear relationship analysis of the height difference sequence △Z(i) and the horizontal distance difference △D(i) obtained from adjacent sonar scan points i, construct a consistency index G for evaluating the geomorphology to measure the continuity and stability of the geomorphology, and compare the consistency index G with the preset reconstruction threshold Gthe, and then judge whether to trigger the reconstruction of the main path according to the comparison result; The consistency index G is obtained by the calculation formula; The reconstruction of the main path is judged by the following comparison method: When the consistency index G > the reconstruction threshold Gthe, it is determined that the geomorphology is discontinuous, re-evaluate the path, and trigger the reconstruction of the main path in the main path time set Hmain; When the consistency index G ≤ the reconstruction threshold Gthe, it is determined that the geomorphology is continuous, and the main path time set Hmain is applied.

10. The seabed topography and geomorphology mapping system based on an underwater robot is applied to the seabed topography and geomorphology mapping method based on an underwater robot according to any one of claims 1 to 9, and is characterized in that: It includes a scan data acquisition module, a scan feature extraction module, a feature mapping and marking module, a main path acquisition module, and a decision optimization module; The scan data acquisition module performs sonar scans at fixed intervals based on the underwater robot on the preset survey path, and obtains time series reflection data at each sonar scan point to form the original measurement point acoustic wave data set S; The scan feature extraction module extracts features from the original measurement point acoustic wave data set S, obtains the multi-path perturbation height-related feature parameters of each sonar scan point, and forms the multi-path perturbation feature set F; The feature mapping and marking module maps the multi-path perturbation feature set F into a three-dimensional feature space, performs clustering analysis using the density clustering algorithm, identifies dense clusters and discrete points, obtains the perturbation residual Cc, and compares it with the preset interference determination threshold Omap, assigns multi-path labels to each sonar scan point, and obtains the residual set A and the multi-path label set Lmp; The main path acquisition module extracts the time series and amplitude series in the original measurement point acoustic wave data set S of each sonar scan point in the multi-path label set Lmp, recalculates the main path echo time Tmain, and forms the main path time set Hmain; the decision optimization module converts the main path time set Hmain into the depth coordinate Z based on the acoustic wave propagation speed v, obtains the height difference sequence △Z and the horizontal distance difference △D, evaluates the consistency index G of the geomorphology, and judges whether to trigger the reconstruction of the main path based on the consistency index G.

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