Multi-beam sonar efficient filtering and distortion eliminating method on unmanned surface vehicle, program, equipment and storage medium

Abstract

The invention provides a multi-beam sonar efficient filtering and distortion eliminating method, program and device on an unmanned ship and a storage medium. Acquiring multi-beam original data of each measuring point on the unmanned ship; resolving the multi-beam original data, considering the heterogeneity of a water body, performing sound ray tracking and compensation, and converting the multi-beam original data into an unmanned ship coordinate system to obtain compensated and converted multi-beam data; and clustering the multi-beam data subjected to compensation and conversion of all the measuring points, and eliminating a distortion cluster by adopting a filtering algorithm based on Alpha-Shape to obtain the multi-beam data subjected to filtering and distortion elimination so as to achieve the purpose of eliminating counterfeit and preserving true. According to the method, more than 90% of distorted measuring points can be effectively eliminated, and a relatively high operation speed is provided on the premise of ensuring the precision, so that an accurate data basis is provided for subsequent processing such as landform or structure reconstruction, gridding and data association.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing and filtering, and in particular relates to a method, program, device and storage medium for efficient filtering and distortion removal of multi-beam sonar on an unmanned boat. Background Art

[0002] With the continuous development of unmanned boat technology, it is now possible to carry multi-beam sonar to perform various tasks such as underwater terrain mapping, underwater structure inspection, and waterway monitoring.

[0003] Multibeam sonar uses a transducer array consisting of multiple transmitters and receivers to detect underwater terrain and structures, efficiently and accurately acquiring large amounts of underwater point cloud data. However, in actual unmanned aerial vehicle applications, multibeam sonar is often affected by factors such as carrier disturbance caused by waves, interference from underwater clutter, and bending of sound lines due to medium inhomogeneity. This results in a large amount of noise and data distortion in the original multibeam survey line data, which negatively impacts subsequent topographic or structural map construction, gridding, and data association.

[0004] Therefore, in order to deal with the invalid noise and data distortion in the multi-beam survey line data, it is necessary to design an efficient and accurate filtering and distortion removal method to improve the quality and processing efficiency of the multi-beam data on the unmanned boat, and provide a guarantee for the subsequent application of algorithms based on multi-beam data. Summary of the Invention

[0005] The purpose of the present invention is to provide a method, program, device and storage medium for efficient filtering and distortion removal of multi-beam sonar on an unmanned boat, which can effectively filter noise and remove distortion of multi-beam data in real time, achieve "eliminating the false and retaining the true", thereby improving data quality and providing an accurate data basis for subsequent processing such as terrain or structure reconstruction, gridding and data association.

[0006] A method for efficiently filtering and removing distortion from multi-beam sonar on an unmanned boat, comprising:

[0007] Obtain multi-beam raw data from each measurement point on the unmanned vehicle, including the sound speed and incident angle measured at a certain echo time;

[0008] The multi-beam raw data is solved, the non-uniformity of the water body is considered, and sound ray tracking and compensation are performed. The multi-beam raw data is converted to the unmanned vehicle coordinate system to obtain the compensated and converted multi-beam data, including the relative water depth of each measuring point and the transducer, and the horizontal distance between each measuring point and the center point of the transducer;

[0009] The multi-beam data after compensation and conversion of all measurement points are clustered, and the distorted clusters are eliminated using the Alpha-Shape-based filtering algorithm to obtain the filtered and distortion-eliminated multi-beam data.

[0010] Furthermore, the multi-beam raw data of each measuring point on the unmanned boat is obtained as (C i (t),β i (t)), C i (t) is the sound velocity measured at the i-th measuring point at time t, β i (t) is the incident angle of the acoustic pulse measured at the i-th measuring point at time t.

[0011] Furthermore, the multi-beam data after compensation and conversion is (x i ,y i );

[0012]

[0013] Among them, x i Indicates the relative water depth between the i-th measuring point and the transducer; y i Indicates the horizontal distance between the i-th measuring point and the center point of the transducer; ΔD d Indicates the water depth correction value of the transducer; ΔD t Indicates the tide level correction value of the transducer.

[0014] Furthermore, the multi-beam data after compensation and conversion at all measurement points are clustered using a hierarchical clustering algorithm, specifically:

[0015] Step 3.1: Compensate and convert the multi-beam data (x i ,y i ) for normalization;

[0016] Step 3.2: Initialize the compensation of each measurement point with the converted multi-beam data (x i ,y i ) as a separate cluster;

[0017] Step 3.3: Calculate the Euclidean distance D between clusters ij ;

[0018]

[0019] Step 3.4: Merge the two clusters with the closest Euclidean distance into one cluster;

[0020] Step 3.5: Repeat steps 3.3 and 3.4 until the number of clusters reaches the specified threshold.

[0021] Furthermore, the alpha-shape based filtering algorithm is used to remove the clusters where the distortion occurs, specifically:

[0022] Step 4.1: According to y i Renumber the measuring points from small to large, from 0 to N;

[0023] Step 4.2: Calculate the average Euclidean distance between adjacent measurement points as the radius α of the search circle;

[0024]

[0025] Step 4.3: According to the altimeter reading on the unmanned boat, take x i The smallest sounding point whose difference with the altimeter reading is less than a given threshold is taken as p0, and the points whose Euclidean distance to p0 is less than or equal to 2α are searched to construct the set P;

[0026] Step 4.4: Take the point p with the largest number in the set P n , calculate the passing points p0 and p n And the center of the search circle with radius α is (x,y):

[0027]

[0028] Among them, (x0,y0) and (x n ,y n ) are p0 and p n The coordinates of s 2 =(x0-x n ) 2 +(y0-y n ) 2 :

[0029] Step 4.5: Eliminate the clusters where the measurement points are less than α from the center of the circle (x, y);

[0030] Step 4.6: If the measurement point number i corresponding to p0 is not the largest number, then take the measurement point with a number greater than i that is adjacent to i among the measurement points that have not been eliminated as p0, search for points with a Euclidean distance less than or equal to 2α from p0, construct the set P, and return to step 4.4;

[0031] If the measurement point number i corresponding to p0 is the largest number, the loop ends and the multi-beam data after compensation and conversion of the remaining measurement points are output as the multi-beam data after filtering and distortion elimination.

[0032] A computer device / equipment / system includes a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of the above-mentioned multi-beam sonar efficient filtering and distortion removal method on the unmanned boat.

[0033] A computer-readable storage medium stores a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned multi-beam sonar efficient filtering and distortion elimination method on an unmanned boat.

[0034] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned multi-beam sonar efficient filtering and distortion removal method on an unmanned boat.

[0035] The beneficial effects of the present invention are:

[0036] This method uses a hierarchical clustering algorithm to cluster the compensated and converted multibeam data from all measurement points. It then uses an Alpha-Shape-based filtering algorithm to eliminate clusters containing distortion, resulting in filtered and distortion-free multibeam data. This approach effectively eliminates over 90% of distorted measurement points while maintaining high computational speed while ensuring accuracy. This provides an accurate data foundation for subsequent processing, such as terrain or structure reconstruction, gridding, and data association. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the overall process of the present invention.

[0038] Figure 2 It is a flowchart of the hierarchical clustering algorithm in the present invention.

[0039] Figure 3 It is a flowchart of the filtering algorithm based on Alpha-Shape in the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further described below with reference to the accompanying drawings.

[0041] Assuming that the sound pulse emitted by the transducer reaches the bottom of the water after one refraction to form a sounding point, if the sound velocity on the transducer surface is C0, the incident angle of the sound pulse is β0, the refraction angle is β1, and the sound velocity at the sounding point is C1, according to Snell's law, we can get:

[0042]

[0043] If the acoustic pulse echo time is t, considering the inhomogeneous nature of the water medium, taking into account the water depth correction value ΔD of the transducer d and tide level correction value ΔD t , the relative water depth x of the sounding point relative to the transducer i and lateral center distance y i It can be expressed as:

[0044]

[0045] Among them, x i Indicates the relative water depth between the i-th measuring point and the transducer; y i Indicates the horizontal distance between the i-th measuring point and the center point of the transducer.

[0046] A method for efficiently filtering and eliminating distortion of a multi-beam sonar on an unmanned boat comprises the following steps:

[0047] Step 1: Obtain the multi-beam raw data of each measurement point on the unmanned vehicle (C i (t),β i (t)), C i (t) is the sound velocity measured at the i-th measuring point at time t, β i (t) is the incident angle of the acoustic pulse measured at the i-th measuring point at time t

[0048] Step 2: Solve the multi-beam original data, consider the non-uniformity of the water body, and perform sound ray tracking and compensation. Convert the multi-beam original data to the unmanned boat coordinate system to obtain the compensated and converted multi-beam data (x i ,y i );

[0049] Step 3: Cluster the multi-beam data after compensation and conversion at all measurement points;

[0050] Step 3.1: Compensate and convert the multi-beam data (x i ,y i ) for normalization;

[0051] Step 3.2: Initialize the compensation of each measurement point with the converted multi-beam data (x i ,y i ) as a separate cluster;

[0052] Step 3.3: Calculate the Euclidean distance D between clusters ij ;

[0053]

[0054] Step 3.4: Merge the two clusters with the closest Euclidean distance into one cluster;

[0055] Step 3.5: Repeat steps 3.3 and 3.4 until the number of clusters reaches the specified threshold;

[0056] Step 4: Use the Alpha-Shape-based filtering algorithm to remove the clusters where the distortion occurs, and obtain the multi-beam data after filtering and distortion removal;

[0057] Step 4.1: According to y i Renumber the measuring points from small to large, from 0 to N;

[0058] Step 4.2: Calculate the average Euclidean distance between adjacent measurement points as the radius α of the search circle;

[0059]

[0060] Step 4.3: According to the altimeter reading on the unmanned boat, take x i The smallest sounding point whose difference with the altimeter reading is less than a given threshold is taken as p0, and the points whose Euclidean distance to p0 is less than or equal to 2α are searched to construct the set P;

[0061] Step 4.4: Take the point p with the largest number in the set P n , calculate the passing points p0 and p n And the center of the search circle with radius α is (x,y):

[0062]

[0063] Among them, (x0,y0) and (x n ,y n ) are p0 and p n The coordinates of s 2 =(x0-x n ) 2 +(y0-y n ) 2 :

[0064] Step 4.5: Eliminate the clusters where the measurement points are less than α from the center of the circle (x, y);

[0065] Step 4.6: If the measurement point number i corresponding to p0 is not the largest number, then take the measurement point with a number greater than i that is adjacent to i among the measurement points that have not been eliminated as p0, search for points with a Euclidean distance less than or equal to 2α from p0, construct the set P, and return to step 4.4;

[0066] If the measurement point number i corresponding to p0 is the largest number, the loop ends and the multi-beam data after compensation and conversion of the remaining measurement points are output as the multi-beam data after filtering and distortion elimination.

[0067] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for efficient filtering and distortion removal of multi-beam sonar on an unmanned boat, characterized by: Obtain multi-beam raw data from each measurement point on the unmanned vehicle, including the sound speed and incident angle measured at a certain echo time; The multi-beam raw data is solved, the non-uniformity of the water body is considered, and sound ray tracking and compensation are performed. The multi-beam raw data is converted to the unmanned vehicle coordinate system to obtain the compensated and converted multi-beam data, including the relative water depth of each measuring point and the transducer, and the horizontal distance between each measuring point and the center point of the transducer; The multi-beam data after compensation and conversion of all measurement points are clustered, and the distorted clusters are eliminated using the Alpha-Shape-based filtering algorithm to obtain the filtered and distortion-eliminated multi-beam data.

2. The method for efficient filtering and distortion removal of multi-beam sonar on an unmanned boat according to claim 1, characterized in that: The multi-beam raw data of each measuring point on the unmanned boat is obtained as (C i (t),β i (t)), C i (t) is the sound velocity measured at the i-th measuring point at time t, β i (t) is the incident angle of the acoustic pulse measured at the i-th measuring point at time t.

3. The method for efficient filtering and distortion removal of multi-beam sonar on an unmanned boat according to claim 2, characterized in that: The multi-beam data after compensation and conversion is (x i ,y i ); Among them, x i Indicates the relative water depth between the i-th measuring point and the transducer; y i Indicates the horizontal distance between the i-th measuring point and the center point of the transducer; ΔD d Indicates the water depth correction value of the transducer; ΔD t Indicates the tide level correction value of the transducer.

4. The method for efficient filtering and distortion removal of multi-beam sonar on an unmanned boat according to claim 3, characterized in that: The multi-beam data after compensation and conversion at all measurement points are clustered using a hierarchical clustering algorithm, specifically: Step 3.1: Compensate and convert the multi-beam data (x i ,y i ) for normalization; Step 3.2: Initialize the compensation of each measurement point with the converted multi-beam data (x i ,y i ) as a separate cluster; Step 3.3: Calculate the Euclidean distance D between clusters ij ; Step 3.4: Merge the two clusters with the closest Euclidean distance into one cluster; Step 3.5: Repeat steps 3.3 and 3.4 until the number of clusters reaches the specified threshold.

5. The method for efficient filtering and distortion removal of multi-beam sonar on an unmanned boat according to claim 3, characterized in that: The Alpha-Shape-based filtering algorithm is used to remove the distorted clusters, specifically: Step 4.1: According to y i Renumber the measuring points from small to large, from 0 to N; Step 4.2: Calculate the average Euclidean distance between adjacent measurement points as the radius α of the search circle; Step 4.3: According to the altimeter reading on the unmanned boat, take x i The smallest sounding point whose difference with the altimeter reading is less than a given threshold is taken as p0, and the points whose Euclidean distance to p0 is less than or equal to 2α are searched to construct the set P; Step 4.4: Take the point p with the largest number in the set P n , calculate the passing points p0 and p n And the center of the search circle with radius α is (x,y): Among them, (x0,y0) and (x n ,y n ) are p0 and p n The coordinates of s 2 =(x0-x n ) 2 +(y0-y n ) 2 : Step 4.5: Eliminate the clusters where the measurement points are less than α from the center of the circle (x, y); Step 4.6: If the measurement point number i corresponding to p0 is not the largest number, then take the measurement point with a number greater than i that is adjacent to i among the measurement points that have not been eliminated as p0, search for points with a Euclidean distance less than or equal to 2α from p0, construct the set P, and return to step 4.4; If the measurement point number i corresponding to p0 is the largest number, the loop ends and the multi-beam data after compensation and conversion of the remaining measurement points are output as the multi-beam data after filtering and distortion elimination.

6. A computer device / apparatus / system comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer program product comprising a computer program / instructions, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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