Acousto-optic combined guidance information processing method for ice well recovery AUV (Autonomous Underwater Vehicle)

Abstract

The invention provides an acousto-optic combined guidance information processing method for recovering an AUV (Autonomous Underwater Vehicle) in an ice well, belongs to the technical field of underwater vehicle recovery, solves the problem of low accuracy of guidance information of the AUV under the condition of ice layer coverage, and comprises the following steps: step 1, acquiring acoustic guidance information based on an acoustic positioning system; 2, acquiring optical guide information based on an optical positioning system; 3, the acoustic guiding information and the optical guiding information are processed respectively to obtain an acoustic guiding information processing result and an optical guiding information processing result, the acoustic guiding information processing result and the optical guiding information processing result are transmitted to the AUV, accurate estimation of the center position of the ice well is obtained, and AUV recycling is completed.

Description

Technical Field

[0001] The invention relates to an acoustic-optical combined guidance information processing method for recovering an AUV in an ice well, belonging to the technical field of underwater vehicle recovery. Background Art

[0002] With the deepening of ocean exploration in the North and South Poles in recent years, the requirements for the operational capabilities of AUVs in scientific survey missions are also increasing. Research on the safe recovery technology of AUVs under ice cover has become a key issue that needs to be solved for the widespread application of AUVs in the polar regions. At present, acoustic guidance and optical guidance are usually used to recover AUVs in ice areas. Among them, the effective positioning range of acoustic guidance positioning is larger, but the close positioning accuracy is not as good as optical guidance; and optical guidance is affected by seawater and light in the underwater environment, and the working distance is short. In addition, the operating environment of polar AUVs is covered by ice, and the AUVs themselves do not have the ability to break ice. It is usually necessary to deploy and recover AUVs in open water areas between ice or to dig ice wells on the ice surface. The recovery of AUVs in ice wells requires high-precision guidance information to ensure the smooth recovery of AUVs under ice. Summary of the invention

[0003] The present invention solves the problem of low accuracy of guidance information of AUV under ice cover, and further proposes an acoustic-optical combined guidance information processing method for ice well recovery AUV, the steps comprising:

[0004] Step 1: Acquire acoustic guidance information based on the acoustic positioning system;

[0005] Step 2: Acquire optical guidance information based on the optical positioning system;

[0006] Step 3: Process the acoustic guidance information and the optical guidance information respectively to obtain the acoustic guidance information processing results and the optical guidance information processing results, transmit the acoustic guidance information processing results and the optical guidance information processing results to the AUV, obtain an accurate estimate of the center position of the ice well and complete the AUV recovery.

[0007] Preferably, step 1 specifically includes:

[0008] Step 1.1: Establish an acoustic guidance model, based on the acoustic positioning system to provide three-dimensional distance information of the AUV relative to the ice-based acoustic positioning device, where the ice-based acoustic positioning device is an ultra-short baseline positioning device installed near the ice well;

[0009] Step 1.2: According to the heading information of the AUV bow and the ice-based acoustic positioning device, determine the coordinate transformation relationship between the AUV hull coordinate system and the ice-based acoustic positioning device coordinate system, and combine the AUV position, depth, speed information and the relative position information of the ice-based acoustic positioning device and the center of the ice well, and calculate the acoustically guided ice well position information through coordinate transformation.

[0010] Preferably, step 2 specifically includes:

[0011] An optical guidance model is established. Based on the optical system, the uncalibrated pixel coordinates of the center of the ice well in the image coordinate system of the camera carried by the AUV, the AUV bow direction, the position and the distance to the water surface are obtained. Combined with the installation position information of the camera, the position information of the center of the ice well in the longitude and latitude coordinate system of optical guidance is calculated through coordinate conversion.

[0012] Preferably, obtaining an accurate estimate of the ice well center position and completing AUV recovery in step 3 specifically includes:

[0013] When the AUV approaches the ice well at a long distance, an N-round clustering analysis algorithm is used to remove false values ​​in the acoustic guidance information, and the processed acoustically guided ice well position information is obtained based on mathematical statistics. The AUV approaches the ice well position based on the processed acoustically guided ice well position information.

[0014] When the AUV approaches the ice well and aligns with the ice well, the Kalman filter and the nonlinear weighted average method based on data quality are used to process the optical guidance information to obtain an accurate estimate of the center position of the ice well and complete the AUV recovery.

[0015] Preferably, the step of processing the acoustic guidance information to obtain the acoustic guidance information processing result in step 3 includes:

[0016] Step 3.1.1: Perform N rounds of cluster analysis on the acoustically guided ice well location information, remove false value data in the cluster analysis results, and narrow the scatter range;

[0017] Step 3.1.2: Calculate the average value of the acoustically guided ice well position information after cluster analysis, and use the calculation result as the acoustically guided information processing result.

[0018] Preferably, the step of performing N rounds of cluster analysis on the acoustically guided ice well location information in step 3.1.1 comprises:

[0019] Step 3.1.1.1: Get the clustering data set X = {x1, x2, ···, x M},x n ∈R d ,(n=1,2,···,M)where x n =(x n1 ,y n1 ),(n=1,2,…,M);

[0020] Step 3.1.1.2: Cluster the acoustically guided ice well location information into two categories, namely true value data and false value data, and determine the maximum number of iterations N;

[0021] Step 3.1.1.3: Use the mean method to select the initial cluster center of the true value data, and randomly select a point in the ice well location area D as the initial cluster center of the false value data;

[0022] Step 3.1.1.4: Use the Euclidean distance metric to calculate the distance between each data and the initial cluster center of the true value class data and the distance between each data and the initial cluster center of the false value class data, and divide the corresponding data into the cluster with the closest distance;

[0023] Step 3.1.1.5: Use the mean method to recalculate the cluster centers of the two new clusters after division, and recalculate the distance between each data and the two cluster centers, and divide the data into the new cluster with the closest distance;

[0024] Step 3.1.1.6: Repeat steps 3.1.1.4 to 3.1.1.5 until the position of the cluster center does not change after iteration, and output the results of this round of cluster analysis;

[0025] Step 3.1.1.7: Use the true value class data of the cluster analysis results in step 3.1.1.6 as the cluster data set for the next round of cluster analysis, and repeat steps 3.1.1.1 to 3.1.1.6 until the maximum number of iterations N is reached, and the cluster analysis is completed;

[0026] The expression of the ice well location area D is:

[0027]

[0028] In formula (1), (x, y) is the coordinate of the initial cluster center of the false value class data.

[0029] Preferably, the step of processing the optical guidance information in step 3 to obtain the optical guidance information processing result includes:

[0030] Step 3.2.1: Combine the AUV speed information and inertial navigation information to construct a Kalman filter to perform fusion filtering on the optically guided uncalibrated ice well center position information;

[0031] Step 3.2.2: Sort the distance of the ice well center position information after fusion filtering and take the average. Starting from the first group of data of the n groups of ice well center position information after fusion filtering, for each group of data currently being processed, take the pixel coordinate distance between the center of the ice well and the center point of the image in the image coordinate system of the processed data in the data set, and use bubble sorting to arrange them in descending order. Take the average value of the m groups of data closest to the image center as the processing result of the current data group, traverse all data, and obtain the latest n groups of ice well center position information;

[0032] Step 3.2.3: Perform weighted averaging on the latest n groups of ice well center position information to complete the processing of the optical guidance information, and use a nonlinear mapping relationship to provide the weighted average ice well center position as the optical guidance information processing result to the AUV control system;

[0033] The expression of the weighting function of the nonlinear mapping used in weighted averaging is:

[0034]

[0035] In formula (2), s is the pixel coordinate distance from the center of the ice well to the center of the image in the image coordinate system.

[0036] Preferably, the step of fusing and filtering the optically guided uncalibrated ice well center position information in step 3.2.1 comprises:

[0037] Step 3.2.1.1: Define state parameter X and observation parameter Z;

[0038] Step 3.2.1.2: Import the uncalibrated ice well center position information;

[0039] Step 3.2.1.3: Set the initial value of the state parameter X0 and the initial value of the state variance P0, and set the covariance matrix Q of the system noise k and the covariance matrix R of the observation noise k ;

[0040] Step 3.2.1.4: Establish the recursive equation of Kalman filter, recursively update the position state information and velocity state information of the system, and obtain the optimal estimate of the filter Among them, the recursive equation of Kalman filtering includes a time update equation and a measurement update equation;

[0041] Step 3.2.1.5: Perform coordinate transformation on the optimal position information of the filtered ice well center, and calculate the position information of the filtered fused ice well center.

[0042] The beneficial effects of the present invention are:

[0043] The present invention provides different processing methods for the acoustic guidance information and the optical guidance information in the process of approaching the ice well and searching for the guidance light source during the recovery process of the AUV under ice and the process of aligning the AUV with the ice well. The present invention adopts a combined acoustic and optical guidance method. In the process of approaching the ice well at a long distance, an N-round clustering analysis algorithm is used to process the acoustic guidance information, which can effectively eliminate the wild values ​​in the obtained guidance information. A method based on mathematical statistics is used to overcome the problem that the acoustic guidance information cannot be processed in the time domain due to the long period and non-fixed period. In the process of aligning with the ice well at a close distance, a Kalman filter and a nonlinear weighted average method based on data quality are used to process the optical guidance information. When the distance of the AUV relative to the ice bottom is stable and the optical positioning information cannot provide an accurate position without calibration, the accuracy of the guidance information is improved, and a high-precision, high-efficiency, stable and reliable guidance information processing method is provided for the safe recovery of the AUV under ice. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic flow chart of a method for processing information of combined acoustic and optical guidance for ice well recovery AUV provided by the present invention;

[0045] Figure 2 A flow chart for processing acoustic guidance information provided by the present invention;

[0046] Figure 3 A flow chart for processing optical guidance information provided by the present invention;

[0047] Figure 4 This is a flowchart of the algorithm for N rounds of cluster analysis provided by the present invention. DETAILED DESCRIPTION

[0048] Combination Figure 1-4 This embodiment is described as follows. Figure 1 As shown, the steps of the acoustic-optical combined guidance information processing method for ice well recovery AUV described in this embodiment include:

[0049] S1: Acquiring acoustic guidance information based on the acoustic positioning system and processing the acoustic guidance information;

[0050] In this embodiment, when the AUV approaches the ice well at a long distance, N rounds of cluster analysis algorithm are used to remove false values ​​in the acoustic guidance information, and the processed acoustically guided ice well position information is obtained based on mathematical statistics. The AUV approaches the ice well position according to the processed acoustically guided ice well position information. Figure 2 As shown, the acoustic guidance process includes the following steps:

[0051] S101: Establish an acoustic guidance model and collect ice well location information;

[0052] The acoustic positioning system used in this embodiment is an ultra-short baseline positioning system. The array of ice-based acoustic positioning equipment, namely the USBL array, is installed near the ice well, and the acoustic transponder is installed on the target AUV.

[0053] When the AUV approaches the ice well at a long distance and searches for the guiding light source, this embodiment uses the acoustic positioning system carried to directly measure the three-dimensional distance of the target AUV relative to the USBL array, and then combines the AUV bow direction and the orientation information of the ice-based acoustic positioning device to determine the coordinate conversion relationship between the AUV hull coordinate system and the ice-based acoustic positioning device coordinate system. And combined with the position, depth, speed information of the AUV and the relative position information of the ice-based acoustic positioning device and the center of the ice well, after coordinate conversion, the ice well position information in the acoustically guided geodetic coordinate system is calculated.

[0054] S102: performing N rounds of clustering analysis on the collected ice well location information, and clustering them into two categories: true value category and false value category, wherein the false value category is the outlier to be removed due to the long and non-fixed period of the positioning information collected by the acoustic positioning system, as well as the AUV movement and ice layer reflection;

[0055] like Figure 4 As shown, the algorithm processing steps of N rounds of cluster analysis include:

[0056] S10201: Determine the clustered ice well location data set X = {x1, x2, ···, x M},x n ∈R d ,(n=1,2,···,M), where x n =(x n1 ,y n1 ),(n=1,2,···,M), determine the maximum number of iterations N;

[0057] The expression of the ice well location area D is:

[0058]

[0059] In formula (1), (x, y) is the coordinate of the initial cluster center of the false value data;

[0060] S10202: Selecting the initial cluster center: taking the mean of all cluster data as the initial cluster center of the true value class; randomly selecting a point within the coordinate range of the ice well position of all clusters, that is, area D, as the initial cluster center of the false value class;

[0061] S10203: For all data x n , using the Euclidean distance metric function, calculate the distance to each cluster center respectively. If the data x n With cluster center ci The distance between them is less than the distance to the cluster center c j , (i≠j), then the data x n Divide into C i In the cluster;

[0062] S10204: Recalculate each cluster C i The cluster center c i , using the mean method:

[0063]

[0064] In formula (2), n i C i The number of data in

[0065] S10205: Repeat S10203-S10204 until the iteration number n is reached or the data in each cluster no longer changes, then the iteration ends and the clustering result C is output. i If there is a cluster, it is considered as a clustering, otherwise continue to the next iteration;

[0066] S10206: After each clustering is completed, the true value class data in the output result of the previous S10205 is used to update to X, and the true and false value clustering of S10201-S10205 is continued in a cycle, and it is iterated until the maximum number of iterations N is reached, and the N-round clustering analysis algorithm ends.

[0067] S103: Calculate the average value of the acoustically guided ice well location information after cluster analysis, and use the calculation result as the acoustically guided information processing result.

[0068] S2: acquiring optical guidance information based on the optical positioning system and processing the optical guidance information;

[0069] In this embodiment, when the AUV approaches the ice well and aligns with the ice well, a Kalman filter and a nonlinear weighted average method based on data quality are used to process the optical guidance information, obtain an accurate estimate of the center position of the ice well and complete the AUV recovery. Figure 3 As shown, the processing steps of the optical guidance information include:

[0070] S201: Establishing optical guidance model:

[0071] The optical positioning system used in this embodiment uses a camera as a basic tool to collect optical guidance information. When the distance between the AUV and the ice bottom is stable, a close-range alignment process with the ice well is performed, and the image of the guiding light source arranged at the ice well is captured by the onboard camera, thereby obtaining the pixel coordinates of the center of the ice well in the image coordinate system of the camera carried by the AUV, and combining the installation position information of the camera to calculate the relative position of the AUV and the center of the ice well. Then, based on the AUV's bow direction, position, and distance to the water surface, after coordinate conversion, the position information of the center of the ice well in the optically guided geodetic coordinate system is calculated.

[0072] S202: combining the speed information of the AUV, constructing a Kalman filter to perform fusion filtering on the uncalibrated ice well center position information;

[0073] In this embodiment, the relative position between the AUV and the center of the ice well and the speed of the AUV are taken as state variables and a state equation is established. The target AUV can be considered to be moving in a uniform straight line in a short time, and the state transfer matrix Φ in the state equation is constructed;

[0074] The expression of the state variable is:

[0075]

[0076] In formula (3), x is the relative position deviation between the AUV and the center of the ice well in the x direction, and y is the relative position deviation between the AUV and the center of the ice well in the y direction. is the AUV speed, and T is the matrix transpose.

[0077] The expression of the state equation is:

[0078] X k =Φ k,k-1 X k-1 +W k-1 (4);

[0079] In formula (4), X k is the state of the system at time k, W k is the system process noise, which is Gaussian white noise, Φ k,k-1 is the state transfer matrix from time k-1 to time k, expressed as:

[0080]

[0081] The relative position of the AUV and the center of the ice well obtained by coordinate conversion of the image taken by the camera and the speed of the AUV measured by the inertial navigation are taken as the observation quantities and the observation equation is established.

[0082] The expression of the observed quantity is:

[0083]

[0084] The expression of the observation equation is:

[0085] Z k =HX k +V k (7);

[0086] In formula (7), Z k is the observed value at time k, X k is the state of the system, H is the observation matrix, V k is the observation noise, which is Gaussian white noise;

[0087] The expression of the observation matrix is:

[0088]

[0089] The covariance matrices of system noise W and observation noise V are Q k and R k , the initial state value is the first set of observed data values, and the processing steps of fusion filtering include:

[0090] S20201: define state parameter X and observation parameter Z;

[0091] S20202: Import observation data: Ice well center coordinates in AUV hull coordinate system and the speed of the AUV measured by the inertial navigation (u, v) T ;

[0092] S20203: Set the initial value of the state parameter X0 to the first set of observed data values, set the initial value of the state variance P0, and set the covariance matrix Q of the system noise k and the covariance matrix R of the observation noise k ;

[0093] S20204: Establish the recursive equation of Kalman filter, including the time update equation and the measurement update equation, recursively update the position state information and velocity state information of the system, and obtain the optimal estimate of the filter

[0094] S20205: Perform coordinate transformation on the filtered optimal position information to calculate the longitude and latitude coordinate position information of the optimal estimated ice well center.

[0095] S203: sort the distance of the ice well center position information after fusion filtering and take the average value, starting from the first group of data of the n groups of ice well center position information after fusion filtering, for each group of data currently being processed, take the pixel coordinate distance between the ice well center and the image center point in the image coordinate system of the processed data in the data set, use bubble sorting to arrange in descending order, take the average value of the m groups of data closest to the image center as the processing result of the current data group, traverse all data, and obtain the latest n groups of ice well center position information;

[0096] S204: performing weighted averaging on the latest n groups of ice well center position information to complete the processing of the optical guidance information, and using a nonlinear mapping relationship to provide the weighted average ice well center position as the optical guidance information processing result to the AUV control system;

[0097] The expression of the weighting function of the nonlinear mapping used in weighted averaging is:

[0098]

[0099] In formula (9), s is the pixel coordinate distance from the center of the ice well to the center of the image in the image coordinate system.

[0100] S3: The acoustic guidance information processing results and the optical guidance information processing results are transmitted to the AUV to obtain an accurate estimate of the center position of the ice well and complete the AUV recovery.

[0101] In summary, this embodiment adopts a combined acoustic and optical guidance method. In the process of approaching the ice well at a long distance, N rounds of cluster analysis algorithms are used to process the acoustic guidance information, which can effectively eliminate wild values ​​in the obtained guidance information. A method based on mathematical statistics is used to overcome the problem that time domain filtering cannot be performed due to the long period and non-fixed period of the acoustic guidance information. In the process of aligning with the ice well at a close distance, a Kalman filter and a nonlinear weighted average method based on data quality are used to process the optical guidance information. When the distance of the AUV relative to the ice bottom is stable and the optical positioning information cannot provide an accurate position without calibration, the accuracy of the guidance information is improved, providing a high-precision, high-efficiency, stable and reliable guidance information processing method for the safe recovery of AUVs under the ice.

[0102] The above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement made to the above embodiments without departing from the content of the technical solution of the present invention, based on the technical essence of the present invention, within the spirit and principles of the present invention, still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for processing acoustic and optical combined guidance information for AUV recovery in ice wells, characterized in that: The steps of the method for processing acoustic and optical combined guidance information for ice well recovery AUV include: Step 1: Acquire acoustic guidance information based on the acoustic positioning system; Step 2: Acquire optical guidance information based on the optical positioning system; Step 3: Process the acoustic guidance information and the optical guidance information respectively to obtain the acoustic guidance information processing results and the optical guidance information processing results, transmit the acoustic guidance information processing results and the optical guidance information processing results to the AUV, obtain an accurate estimate of the center position of the ice well and complete the AUV recovery.

2. The method for processing acoustic and optical combined guidance information for ice well recovery AUV according to claim 1, characterized in that: Step 1 specifically includes: Step 1.1: Establish an acoustic guidance model, based on the acoustic positioning system to provide three-dimensional distance information of the AUV relative to the ice-based acoustic positioning device, where the ice-based acoustic positioning device is an ultra-short baseline positioning device installed near the ice well; Step 1.2: According to the heading information of the AUV bow and the ice-based acoustic positioning device, determine the coordinate transformation relationship between the AUV hull coordinate system and the ice-based acoustic positioning device coordinate system, and combine the AUV position, depth, speed information and the relative position information of the ice-based acoustic positioning device and the center of the ice well, and calculate the acoustically guided ice well position information through coordinate transformation.

3. The method for processing acoustic and optical combined guidance information for ice well recovery AUV according to claim 1, characterized in that: Step 2 specifically includes: An optical guidance model is established. Based on the optical system, the uncalibrated pixel coordinates of the center of the ice well in the image coordinate system of the camera carried by the AUV, the AUV bow direction, the position and the distance to the water surface are obtained. Combined with the installation position information of the camera, the position information of the center of the ice well in the longitude and latitude coordinate system of optical guidance is calculated through coordinate conversion.

4. The method for processing acoustic and optical combined guidance information for ice well recovery AUV according to claim 1, characterized in that: Obtaining an accurate estimate of the ice well center position and completing AUV recovery in step 3 specifically includes: When the AUV approaches the ice well at a long distance, an N-round clustering analysis algorithm is used to remove false values ​​in the acoustic guidance information, and the processed acoustically guided ice well position information is obtained based on mathematical statistics, and the AUV approaches the ice well position according to the processed acoustically guided ice well position information; When the AUV approaches the ice well and aligns with the ice well, a Kalman filter and a nonlinear weighted average method based on data quality are used to process the optical guidance information to obtain an accurate estimate of the center position of the ice well and complete the AUV recovery.

5. The method for processing acoustic and optical combined guidance information for ice well recovery AUV according to claim 1, characterized in that: The step of processing the acoustic guidance information to obtain the acoustic guidance information processing result in step 3 includes: Step 3.1.1: Perform N rounds of cluster analysis on the acoustically guided ice well location information, remove false value data in the cluster analysis results, and narrow the scatter range; Step 3.1.2: Calculate the average value of the acoustically guided ice well position information after cluster analysis, and use the calculation result as the acoustically guided information processing result.

6. The method for processing acoustic and optical combined guidance information for ice well recovery AUV according to claim 5, characterized in that: The steps of performing N rounds of cluster analysis on acoustically guided ice well location information in step 3.1.1 include: Step 3.1.1.1: Get the clustering data set, X = {x1, x2, ···, x M },x n ∈R d ,(n=1,2,···,M), where x n =(x n1 ,y n1 ),(n=1,2,…,M); Step 3.1.1.2: Cluster the acoustically guided ice well location information into two categories, namely true value data and false value data, and determine the maximum number of iterations N; Step 3.1.1.3: Use the mean method to select the initial cluster center of the true value data, and randomly select a point in the ice well location area D as the initial cluster center of the false value data; Step 3.1.1.4: Use the Euclidean distance metric to calculate the distance between each data and the initial cluster center of the true value class data and the distance between each data and the initial cluster center of the false value class data, and divide the corresponding data into the cluster with the closest distance; Step 3.1.1.5: Use the mean method to recalculate the cluster centers of the two new clusters after division, and recalculate the distance between each data and the two cluster centers, and divide the data into the new cluster with the closest distance; Step 3.1.1.6: Repeat steps 3.1.1.4 to 3.1.1.5 until the position of the cluster center does not change after iteration, and output the results of this round of cluster analysis; Step 3.1.1.7: Use the true value class data of the cluster analysis results in step 3.1.1.6 as the cluster data set for the next round of cluster analysis, and repeat steps 3.1.1.1 to 3.1.1.6 until the maximum number of iterations N is reached, and the cluster analysis is completed; The expression of the ice well location area D is: In formula (1), (x, y) is the coordinate of the initial cluster center of the false value class data.

7. The method for processing acoustic and optical combined guidance information for ice well recovery AUV according to claim 1, characterized in that: The step of processing the optical guidance information to obtain the optical guidance information processing result in step 3 includes: Step 3.2.1: Combine the AUV speed information and inertial navigation information to construct a Kalman filter to perform fusion filtering on the optically guided uncalibrated ice well center position information; Step 3.2.2: Sort the distance of the ice well center position information after fusion filtering and take the average. Starting from the first group of data of the n groups of ice well center position information after fusion filtering, for each group of data currently being processed, take the pixel coordinate distance between the center of the ice well and the center point of the image in the image coordinate system of the processed data in the data set, and use bubble sorting to arrange them in descending order. Take the average value of the m groups of data closest to the image center as the processing result of the current data group, traverse all data, and obtain the latest n groups of ice well center position information; Step 3.2.3: Perform weighted averaging on the latest n groups of ice well center position information to complete the processing of the optical guidance information, and use a nonlinear mapping relationship to provide the weighted average ice well center position as the optical guidance information processing result to the AUV control system; The expression of the weighting function of the nonlinear mapping used in weighted averaging is: In formula (2), s is the pixel coordinate distance from the center of the ice well to the center of the image in the image coordinate system.

8. The method for processing acoustic and optical combined guidance information for ice well recovery AUV according to claim 7, characterized in that: The step of fusing and filtering the optically guided uncalibrated ice well center position information in step 3.2.1 includes: Step 3.2.1.1: Define state parameter X and observation parameter Z; Step 3.2.1.2: Import the uncalibrated ice well center position information; Step 3.2.1.3: Set the initial value of the state parameter X0 and the initial value of the state variance P0, and set the covariance matrix Q of the system noise k and the covariance matrix R of the observation noise k ; Step 3.2.1.4: Establish the recursive equation of Kalman filter, recursively update the position state information and velocity state information of the system, and obtain the optimal estimate of the filter Among them, the recursive equation of Kalman filtering includes a time update equation and a measurement update equation; Step 3.2.1.5: Perform coordinate transformation on the optimal position information of the filtered ice well center, and calculate the position information of the filtered fused ice well center.

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