A prior map terrain adaptation region division method, program, device and storage medium considering navigation error trend

Abstract

The present application belongs to the technical field of underwater navigation and positioning, and particularly relates to a method, program, device and storage medium for dividing a terrain adaptation area in a prior sea chart considering navigation error trends. The present application studies the influence of various terrain characteristic parameters on the seabed terrain matching navigation method, selects seven terrain characteristic parameters in the grid map and the contour map as factors influencing the seabed terrain matching navigation performance, brings the terrain characteristic parameters and the seabed terrain matching navigation positioning error and the terrain matching navigation result change into the twin support vector machine as input data and output data respectively through terrain matching navigation, obtains the relationship between the terrain characteristic parameters and the seabed terrain matching navigation positioning error and the error change, and then completes the calculation of the seabed terrain matching navigation positioning error and the error change of the prior sea chart, thereby realizing the adaptation area division of the prior sea chart.

Description

Technical Field

[0001] This invention belongs to the field of underwater navigation and positioning technology, and specifically relates to a method, program, device and storage medium for dividing terrain adaptation areas in a priori nautical charts that takes into account navigation error trends. Background Technology

[0002] With the continuous improvement of AUV endurance, current underwater navigation technologies cannot provide feasible navigation methods for long-range AUV underwater navigation. However, terrain matching navigation methods using single-beam sonar and particle filtering can provide bounded navigation errors in long-term underwater navigation, showing the potential for achieving long-term underwater navigation. Furthermore, compared to multi-beam sonar, single-beam sonar can effectively reduce the energy consumption of terrain matching navigation systems, thus extending the underwater endurance of AUVs to a limited extent. However, due to the limited measurement information of single-beam sonar, its navigation results have poor robustness. Therefore, it is essential to improve the accuracy and robustness of terrain matching navigation results by finding terrain adaptation zones.

[0003] Current methods for terrain-matching navigation adaptation zone segmentation focus on evaluating terrain adaptability by using various terrain feature parameters and terrain-matching navigation results through methods such as neural networks or support vector machines. However, since particle filter-based terrain-matching navigation results involve Markov processes, using only navigation and positioning results for adaptation zone segmentation can lead to significant errors over time. Even if the terrain adaptation zone is reached, the navigation results may still contain large errors, causing existing methods to fail to identify the adaptation zone and rendering the adaptation zone segmentation ineffective.

[0004] A patent application published on August 4, 2020, with publication number CN109000656A and titled "Method for Selecting Adaptation Zones for Underwater Terrain Matching Navigation Based on Spatial Clustering," describes a method that selects six terrain points within a terrain area and divides the seabed terrain into six classification regions through spatial clustering, thereby selecting adaptation zones. However, this method does not take into account various terrain feature parameters in the adaptation zone division, resulting in insufficient robustness of the adaptation zone division results obtained when using single-beam sonar for terrain matching navigation.

[0005] The patent application, published on November 8, 2019, with publication number CN106767834A and titled "A Method for Delineating Adaptation Zones for AUV Underwater Terrain Matching Based on Fuzzy Entropy," uses membership functions and fuzzy rules to solve for the adaptation capability of sub-maps within a grid, thereby achieving adaptation zone delineation. However, this method does not consider the cumulative navigation error inherent in terrain matching navigation methods involving Markov processes, leading to inaccurate adaptation zone delineation when applied to particle filter-based terrain matching navigation. Summary of the Invention

[0006] The purpose of this invention is to address the poor navigation robustness of single-beam sonar seabed terrain matching navigation methods. It provides a method, program, device, and storage medium for terrain adaptation zone division based on prior nautical charts that considers navigation error trends. This invention effectively improves the accuracy of terrain adaptation zone division by using multiple terrain feature parameters as inputs to a twin support vector machine (SVM) and incorporating the terrain matching navigation results and navigation error divergence trends as the SVM outputs.

[0007] A method for dividing terrain adaptation zones in prior nautical charts that considers navigation error trends includes the following steps:

[0008] Step 1: Generate a depth contour map based on prior nautical chart data, and divide the depth contour map into depth contour sub-maps; generate a raster map based on prior nautical chart data, and use the same sub-map division operation as the depth contour map to divide the raster map into raster maps that are the same as the depth contour sub-maps.

[0009] Step 2: For each location point on the prior nautical chart, the feature parameters of the isobath submap and the feature parameters of the grid submap where it is located are used together as the terrain feature parameters of that location point;

[0010] Step 3: The AUV performs terrain matching navigation in the real sea area corresponding to the prior nautical chart. At each waypoint, it obtains water depth measurement values ​​based on single-beam sonar, position measurement values ​​based on inertial navigation system, and water depth estimation values ​​through single-beam sonar seabed terrain matching navigation algorithm.

[0011] Step 4: Calculate the horizontal Euclidean distance between the estimated water depth and the actual location at each path point, and calculate the average positioning error of the water depth data at each path point and the change in positioning error between the path point and the previous path point through Monte Carlo experiments to construct a training dataset.

[0012] Step 5: Train the twin support vector machine using the training dataset. Use the terrain feature parameters obtained from the prior nautical chart based on the actual location of the path points as the input of the twin support vector machine. Use the average positioning error and the change value of the positioning error of the water depth data of the path points as the output of the twin support vector machine.

[0013] Step 6: Input the terrain feature parameters corresponding to each location point in the prior nautical chart into the trained twin support vector machine to obtain the average positioning error and positioning error change value of the water depth data corresponding to each location point in the prior nautical chart.

[0014] The mean of the average positioning error of the water depth data and the mean of the positioning error variation are obtained. The smaller of the two means is used as the adaptation zone division standard. Location points whose average positioning error of the water depth data is less than the adaptation zone division standard are assigned to the adaptation zone set, and the remaining location points are assigned to the non-adaptation zone set.

[0015] Step 7: Cluster the dataset of positioning error changes corresponding to all locations on the prior nautical chart, dividing the data into three cluster regions. The boundary value of the first region is negative, and the boundary value of the second region is positive.

[0016] If the location error change value of a point in the adaptation area set is greater than the boundary value of the second area, then it is moved to the non-adaptation area set.

[0017] For a location point in the non-fitting area set, if its positioning error change value is less than the boundary value of the first area, then it is moved to the fitting area set.

[0018] Ultimately, the location points in all the fit areas form the terrain fit areas in the prior nautical chart, and the location points in all the non-fit areas form the terrain non-fit areas in the prior nautical chart.

[0019] Furthermore, the terrain feature parameters of the location point in step 2 are specifically as follows:

[0020] The isobath density δ and isobath smoothness ξ of the isobath submap where the location point is located in the prior nautical chart, as well as the topographic elevation standard deviation σ, topographic elevation entropy H, topographic correlation coefficient R, topographic slope S, and topographic roughness θ of the grid submap where the location point is located, are used as the topographic feature parameters [δ,ξ,σ,H,R,S,θ] of the location point; the seven topographic feature parameters are normalized respectively.

[0021] Furthermore, the method for calculating the isobath density δ of the isobath sub-map is as follows: the number of isobaths in the isobath sub-map is taken as the isobath density δ;

[0022] The method for calculating the contour smoothness ξ of the contour sub-map is as follows: each contour line in the contour sub-map is discretized to obtain contour points; the contour lines are smoothed, the smoothed contour lines are discretized to obtain smoothed contour points, the minimum distance of each contour point from the smoothed contour points is calculated, and the average value is taken as the contour smoothness ξ of the contour sub-map.

[0023]

[0024] Among them, (x c ,y c (x) represents the contour points obtained by discretizing the contour lines, where O is the number of contour points; o ,y o() represents the smoothed contour points obtained by discretizing the smoothed contour lines;

[0025] The method for calculating the standard deviation σ of the terrain elevation of the raster map is as follows:

[0026]

[0027] Where h(i,j) represents the water depth data at location (i,j) in the raster map; M and N represent the length and width of the raster map, respectively;

[0028] The method for calculating the terrain elevation entropy H of the raster map is as follows:

[0029]

[0030] The method for calculating the terrain correlation coefficient R of the raster map is as follows:

[0031]

[0032] R = (R x +R y ) / 2

[0033] The method for calculating the terrain slope S of the raster map is as follows:

[0034]

[0035] The method for calculating the terrain roughness θ of the raster map is as follows:

[0036]

[0037] Furthermore, in step 4, the average positioning error set {E} of the water depth data of W path points is obtained. w} and the set of positioning error change values ​​{Error w After}, for the set {E w} and {Error w Clustering is performed separately, and path points corresponding to outliers are removed. The data of the remaining path points constitute the training dataset.

[0038] Further, step 6 specifically involves: inputting the terrain feature parameters corresponding to each location point on the prior nautical chart into the trained twin support vector machine to obtain the set of average positioning errors of the water depth data {map}. error} and the set of positioning error change values ​​{map change};

[0039] The mean of the average positioning error of the water depth data. error median of the change in positioning errorerror The smaller of the two means is used as the division criterion for the adaptation region. error The average positioning error of the water depth data is less than the adaptation zone division standard. error The selected locations are assigned to the adaptive region set, while the remaining locations are assigned to the non-adaptive region set.

[0040]

[0041] Among them, suitable i =1 indicates that position point i is assigned to the suitable region set, suitable i =0 indicates that position point i is assigned to the set of unfit regions; This represents the average positioning error value of the water depth data corresponding to location point i.

[0042] Further, step 7 specifically involves: for the set of positioning error change values ​​{map} change The error change value can be either positive or negative. The K-Means clustering method is used to group {map}. change The cluster is divided into three cluster regions, with the first region's boundary value... Negative values ​​indicate the boundary value of the second region. It is a positive value; based on the change in positioning error corresponding to position point i. Perform adaptation area division:

[0043]

[0044] For position point i in the fit region set, if suitable i If the value is -1, then move it to the set of unfit regions;

[0045] For a location point in the set of unsuitable regions, if suitable i If the value is 1, then move it to the adaptation set.

[0046] An application of a terrain adaptation zone division method in a priori nautical chart that considers navigation error trends is proposed. The terrain adaptation zone in the priori nautical chart is used as a reward function and incorporated into the path planning. The path planning aims to make the planned path to the target point pass through the terrain adaptation zone as much as possible, thereby improving the accuracy and robustness of the navigation results during the path tracking process.

[0047] A computer device / apparatus / system includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described method for dividing terrain adaptation zones in a priori nautical charts that takes into account navigation error trends.

[0048] A computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the above-described method for dividing terrain adaptation zones in a priori nautical charts that takes into account navigation error trends.

[0049] A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of the above-described method for dividing terrain adaptation zones in a priori nautical charts that takes into account navigation error trends.

[0050] The beneficial effects of this invention are as follows:

[0051] This invention studies the influence of various terrain feature parameters on seabed terrain matching navigation methods. Seven terrain feature parameters from raster maps and isobath maps are selected as factors affecting the performance of seabed terrain matching navigation. By performing terrain matching navigation, the terrain feature parameters, the seabed terrain matching navigation positioning error, and the changes in the terrain matching navigation results are used as input and output data, respectively, and fed into a twin support vector machine to obtain the relationship between the terrain feature parameters, the seabed terrain matching navigation positioning error, and the error changes. This allows for the calculation of the seabed terrain matching navigation positioning error and error changes for prior nautical charts, thereby achieving the division of the adaptation zone for prior nautical charts. Attached Figure Description

[0052] Figure 1 This is a flowchart of the present invention.

[0053] Figure 2 This is a flowchart of the terrain adaptation zone division in this invention.

[0054] Figure 3 This is a flowchart of a density-based spatial clustering method for noisy data.

[0055] Figure 4 This is a flowchart of the coarse and fine division of terrain adaptation zones in this invention. Detailed Implementation

[0056] The present invention will now be further described with reference to the accompanying drawings.

[0057] This invention relates to a method for dividing terrain adaptation zones in a priori nautical charts, considering navigation error trends. The method includes calculating terrain feature parameters of the priori nautical chart, calculating terrain feature parameters of waypoints, calculating waypoint matching positioning errors, determining the relationship between terrain feature parameters and terrain matching positioning errors, calculating terrain matching positioning errors of the priori nautical chart, and dividing terrain adaptation zones. Based on the priori nautical chart sub-map grid division standard, the chart is divided into isobath sub-maps and raster sub-maps. The isobath density, isobath smoothness, terrain elevation standard deviation, terrain elevation entropy, terrain roughness, terrain slope, and terrain correlation coefficient are calculated as terrain feature parameters for each region. Terrain matching navigation is performed using single-beam sonar terrain data, establishing the relationship between terrain feature parameters and terrain matching positioning results, thereby obtaining the terrain matching positioning results for the entire priori nautical chart and completing the adaptation zone division.

[0058] A method for dividing terrain adaptation zones in prior nautical charts that considers navigation error trends includes the following steps:

[0059] Step 1: Generate a depth contour map based on prior nautical chart data, divide the depth contour map into depth contour sub-maps, and calculate the depth contour density and depth contour smoothness of each depth contour sub-map;

[0060] Step 2: Generate a raster map based on the prior nautical chart. Using the same sub-map division operation as the isobath map, divide the raster map into raster sub-maps that are the same as the isobath sub-maps. Calculate the topographic elevation standard deviation, topographic elevation entropy, topographic correlation coefficient, topographic slope, and topographic roughness for each raster sub-map.

[0061] Step 3: For each location point on the prior nautical chart, the isobath density δ and isobath smoothness ξ of the isobath submap, as well as the topographic elevation standard deviation σ, topographic elevation entropy H, topographic correlation coefficient R, topographic slope S, and topographic roughness θ of the grid submap, are used as the topographic feature parameters [δ,ξ,σ,H,R,S,θ] of that location point; and the seven topographic feature parameters are normalized respectively.

[0062] Step 4: The AUV performs terrain matching navigation in the real sea area corresponding to the prior nautical chart. At each waypoint, it obtains water depth measurement values ​​based on single-beam sonar, position measurement values ​​based on inertial navigation system, and water depth estimation values ​​through single-beam sonar seabed terrain matching navigation algorithm.

[0063] Step 5: Calculate the horizontal Euclidean distance between the estimated water depth and the actual location at each path point, and calculate the average positioning error of the water depth data at each path point and the change in positioning error between the path point and the previous path point through Monte Carlo experiments to construct a training dataset.

[0064] Step 6: Terrain matching navigation may result in mismatches, which could cause some navigation results to fail to accurately reflect the navigability of the terrain. Therefore, the obtained navigation and positioning errors are clustered.

[0065] The average positioning error set {E} of the water depth data of W path points obtained in step 5 is... w} and the set of positioning error change values ​​{Error w}, for set {E w} and {Error w Cluster them separately, remove the path points corresponding to outliers, and the data of the remaining path points constitute the training dataset.

[0066] Step 7: Train the twin support vector machine using the training dataset. Use the terrain feature parameters obtained from the prior nautical chart based on the actual location of the path points as the input of the twin support vector machine, and use the average positioning error and the change value of the positioning error of the water depth data of the path points as the output of the twin support vector machine.

[0067] Step 8: Input the terrain feature parameters corresponding to each location point on the prior nautical chart into the trained twin support vector machine to obtain the set of average positioning errors of the water depth data {map}. error} and the set of positioning error change values ​​{map change};

[0068] The mean of the average positioning error of the water depth data. error median of the change in positioning error error The smaller of the two means is used as the division criterion for the adaptation region. error The average positioning error of the water depth data is less than the adaptation zone division standard. error The selected locations are assigned to the adaptive region set, while the remaining locations are assigned to the non-adaptive region set.

[0069]

[0070] Among them, suitable i =1 indicates that position point i is assigned to the suitable region set, suitable i =0 indicates that position point i is assigned to the set of unfit regions; This represents the average positioning error value of the water depth data corresponding to location point i;

[0071] Step 9: For the set of positioning error change values ​​{map change The error change value can be either positive or negative. The K-Means clustering method is used to group {map}. change The cluster is divided into three cluster regions, with the first region's boundary value... Negative values ​​indicate the boundary value of the second region. It is a positive value; based on the change in positioning error corresponding to position point i. Perform adaptation area division:

[0072]

[0073] For position point i in the fit region set, if suitable i If the value is -1, then move it to the set of unfit regions;

[0074] For a location point in the set of unsuitable regions, if suitable i If the value is 1, then move it to the adaptation set.

[0075] Ultimately, the location points in all the fit areas form the terrain fit areas in the prior nautical chart, and the location points in all the non-fit areas form the terrain non-fit areas in the prior nautical chart.

[0076] By incorporating the terrain adaptation area within the prior nautical chart as a reward function into the path planning, the planned path to the target point will pass through the terrain adaptation area as much as possible, thereby improving the accuracy and robustness of the navigation results during path tracking.

[0077] The method for calculating the isobath density δ of the isobath sub-map is as follows: the number of isobaths in the isobath sub-map is taken as the isobath density δ.

[0078] The method for calculating the contour smoothness ξ of the contour sub-map is as follows: each contour line in the contour sub-map is discretized to obtain contour points; the contour lines are smoothed, the smoothed contour lines are discretized to obtain smoothed contour points, the minimum distance of each contour point from the smoothed contour points is calculated, and the average value is taken as the contour smoothness ξ of the contour sub-map.

[0079]

[0080] Among them, (x c ,y c (x) represents the contour points obtained by discretizing the contour lines, where O is the number of contour points; o ,y o () represents the smoothed contour points obtained by discretizing the smoothed contour lines;

[0081] The method for calculating the standard deviation σ of the terrain elevation of the raster map is as follows:

[0082]

[0083] Where h(i,j) represents the water depth data at location (i,j) in the raster map; M and N represent the length and width of the raster map, respectively;

[0084] The method for calculating the terrain elevation entropy H of the raster map is as follows:

[0085]

[0086] The method for calculating the terrain correlation coefficient R of the raster map is as follows:

[0087]

[0088] R = (R x +R y ) / 2

[0089] The method for calculating the terrain slope S of the raster map is as follows:

[0090]

[0091] The method for calculating the terrain roughness θ of the raster map is as follows:

[0092]

[0093] The clustering method can be a density-based spatial clustering method for noisy data. This method is suitable for noisy datasets and can discover clusters of arbitrary shapes, and can effectively handle outliers.

[0094] For a navigation and positioning error dataset containing W waypoints, Error = {Error1, ..., Error...} n First, input the data to calculate the Euclidean distance. Based on the calculated Euclidean distance, the cutoff distance d is obtained. c The parameter t is used to calculate the distance d. ij , and let d ij =d ji If i,j∈Error, then determine the cutoff distance d. c Calculate density based on cutoff distance. And generate descending index order Density The calculation formula is:

[0095] Calculate distance and number of categories Determine the final cluster centers and output the clustering results. (The distance is included in the calculation.) It is defined by calculating the minimum distance between point i and other high-density points, and is calculated as follows:

[0096] Among them, the indicator set It is represented as follows:

[0097]

[0098] In current single-beam sonar seabed terrain matching navigation methods, the positioning results are not robust due to the poor information measurement of single-beam sonar. There is currently little research on the division of adaptation zones for single-beam sonar seabed terrain matching. At the same time, the Markov process of terrain matching navigation error is not taken into account in the division of adaptation zones, making it difficult to complete the division of adaptation zones using multiple terrain feature parameters.

[0099] This invention studies the influence of various terrain feature parameters on seabed terrain matching navigation methods. Seven terrain feature parameters from raster maps and isobath maps are selected as factors affecting the performance of seabed terrain matching navigation. By performing terrain matching navigation, the terrain feature parameters, the seabed terrain matching navigation positioning error, and the changes in the terrain matching navigation results are used as input and output data, respectively, and fed into a twin support vector machine to obtain the relationship between the terrain feature parameters, the seabed terrain matching navigation positioning error, and the error changes. This allows for the calculation of the seabed terrain matching navigation positioning error and error changes for prior nautical charts, thereby achieving the division of the adaptation zone for prior nautical charts.

[0100] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for dividing a terrain adaptation zone in a priori chart considering a trend of navigation error, characterized by, The method comprises the following steps: Step 1: generating a contour map according to prior map data, dividing the contour map into contour sub-maps; generating a grid map according to the prior map, and dividing the grid map into grid sub-maps by using the same sub-map division operation as the contour map; Step 2: for each position point in the prior map, the feature parameters of the contour sub-map where the position point is located and the feature parameters of the grid sub-map where the position point is located are taken as the terrain feature parameters of the position point; Step 3: the AUV performs terrain matching navigation in the real sea area corresponding to the prior map, obtains a water depth measurement value at each path point according to a single-beam sonar, obtains a position measurement value according to an inertial navigation system, and obtains a water depth estimation value through a single-beam sonar seabed terrain matching navigation algorithm; Step 4: calculating the horizontal Euclidean distance between the water depth estimation value and the true position at each path point, and obtaining the average positioning error of the water depth data at each path point and the positioning error change value between the path point and the previous path point through a Monte Carlo test to construct a training data set; Step 5: training a twin support vector machine using the training data set, taking the terrain feature parameters of the path point obtained from the prior map according to the true position of the path point as the input of the twin support vector machine, and taking the average positioning error of the water depth data of the path point and the positioning error change value as the output of the twin support vector machine; Step 6: inputting the terrain feature parameters corresponding to each position point in the prior map into the trained twin support vector machine to obtain the average positioning error of the water depth data and the positioning error change value corresponding to each position point in the prior map; obtaining the mean value of the average positioning error of the water depth data and the mean value of the positioning error change value, and taking the smaller value of the two mean values as the division standard of the adaptation area; dividing the position points with the average positioning error of the water depth data less than the division standard of the adaptation area into the adaptation area set, and dividing the remaining position points into the non-adaptation area set; Step 7: clustering and dividing the positioning error change value data set corresponding to all position points in the prior map, and dividing the data into three clustering regions, the boundary value of the first region being negative, and the boundary value of the second region being positive; for the position points in the adaptation area set, if the positioning error change value is greater than the boundary value of the second region, the position points are moved to the non-adaptation area set; for the position points in the non-adaptation area set, if the positioning error change value is less than the boundary value of the first region, the position points are moved to the adaptation area set; finally, all the position points in the adaptation area set form the terrain adaptation area in the prior map, and all the position points in the non-adaptation area set form the terrain non-adaptation area in the prior map.

2. The method of claim 1, wherein the method further comprises: The terrain feature parameters of the position point in step 2 are specifically: The isobath density of the isobath sub-map in which the position point in the prior map is located and the isobath smoothness , and the terrain elevation standard deviation of the grid sub-map in which the position point is located , the terrain elevation entropy , the terrain correlation coefficient , the terrain slope and the terrain roughness are taken as the terrain feature parameters of the position point ; the seven terrain feature parameters are normalized respectively.

3. The method of claim 2, wherein the method further comprises: determining a trend of the navigation error; and determining the region of interest based on the trend of the navigation error. The isobath density of the isobath sub-map The calculation method is as follows: taking the number of isobaths in the isobath sub-map as the isobath density ​ The isobath smoothness of the isobath submap The calculation method is: discrete each isobath in the isobath submap to obtain isobath points; the isobaths are smoothed, the smoothed isobaths are discrete, the minimum value of the distance between each isobath point and the smoothed isobath point is calculated, and the average value is taken as the isobath smoothness of the isobath submap ; ； wherein, denotes the isobath points obtained by discretizing the isobath, is the number of isobath points; denotes the smoothed isobath points obtained by discretizing the smoothed isobath. a terrain elevation standard deviation of the grid submap The calculation method is as follows: ； in, Represents a location point in a grid map. The water depth data at the location; M and N represent the length and width of the raster map, respectively; a terrain elevation entropy of the grid submap The calculation method is: ； The terrain-related coefficient of the grid submap The calculation method is as follows: ； a terrain slope of the grid submap The calculation method is: ； ； ； ； a terrain roughness of the grid submap The calculation method is as follows: 。 4. The method of claim 1, wherein the method further comprises: determining a trend of the navigation error; and determining the region of interest based on the trend of the navigation error. The step 4 obtains a set of average positioning error of water depth data of the path points and a set of positioning error variation values After that, the sets and are respectively clustered, the path points corresponding to the outliers are removed, and the data of the remaining path points constitute a training data set.

5. The method of claim 1, wherein: The step 6 is specifically: inputting the terrain feature parameters corresponding to each position point in the prior chart into the trained twin support vector machine to obtain a set of water depth data average positioning errors{ } and a set of positioning error change values{ } ; The mean of the average positioning error of the water depth data is obtained. The mean of the position error variation The smaller of the two means is used as the criterion for dividing the adaptation region. The average positioning error of the water depth data is less than the adaptation zone division standard. The selected locations are assigned to the adaptive region set, while the remaining locations are assigned to the non-adaptive region set. ； wherein, representing a position point divided into a set of adapted regions, representing a position point divided into a set of adapted regions; representing a position point corresponding to the average positioning error value of the depth data.

6. The method of claim 5, wherein the method further comprises: The step 7 is specifically: for the positioning error change value set }, the error change value exists in both positive and negative cases, using the K-Means clustering method to divide into three clustering areas, the first area boundary value is negative, the second area boundary value is positive; according to the position point corresponding positioning error change value , the adaptive area is divided: ； For a position point in the set of adaptation regions , if , then move it to the set of non-adaptation regions; For a position point in the non-adaptation region set, if then it is moved to the adaptation region set.

7. The method of claim 1, wherein: The terrain adaptation area in the prior map is taken as a reward function and brought into path planning, so that as many paths as possible pass through the terrain adaptation area in the path planning to the target point, thereby improving the accuracy and robustness of the navigation result in the path tracking process.

8. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein: The processor executes the computer program to implement the steps of the method of any one of claims 1 to 7.

9. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that: The computer instructions, when executed by the processor, implement the steps of the method of any one of claims 1 to 7.

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