CN115880189A - Submarine topography multi-beam point cloud filtering method - Google Patents
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Abstract
The application belongs to the technical field of ocean mapping and exploration and underwater acoustic signal processing, and provides a submarine topography multi-beam point cloud filtering method which comprises the following steps: s1, constructing an original multi-beam point cloud set based on original multi-beam sounding data; s2, performing trend surface filtering on the original multi-beam point cloud set to obtain a first multi-beam point cloud set; s3, performing combined filtering based on dynamic radius filtering and density clustering on the first multi-beam point cloud set to obtain a submarine topography point cloud set; and S4, generating a submarine three-dimensional terrain model based on the submarine terrain point cloud set. According to the submarine topography multi-beam point cloud filtering method, the edge beam filtering effect is improved according to specific data characteristics of multi-beam sounding data, meanwhile, the retention of edge topography details and the processing speed of mass point cloud data are considered, and the advantage complementation and organic combination of multiple algorithms are achieved.
Description
Technical Field
The application belongs to the technical field of marine surveying and mapping and underwater acoustic signal processing, and particularly provides a submarine topography multi-beam point cloud filtering method.
Background
The multi-beam sounding system is one of the most advanced ocean exploration measuring instruments at present. The system expands the traditional depth measurement technology from the original point and line to the plane, and further develops the three-dimensional depth measurement and automatic mapping. Its appearance has profoundly changed the research mode and final result quality in the field of oceanographic disciplines. The method has the advantages of large coverage area, high speed, high precision and efficiency, digital recording and the like, and is widely applied to the field of marine exploration surveying and mapping.
In the multi-beam measuring process, due to the influence of self-noise of instruments, unreasonable sea condition factors and equipment parameter setting and marine organisms, the multi-beam sounding data has more noise points, and the authenticity expression of the submarine topography is seriously influenced. In order to acquire real submarine topography, multi-beam data must be filtered, false signals are eliminated, and real information is restored and reserved. The traditional manual filtering process is used for eliminating gross error data according to the experience of operators, has high reliability, large workload and low filtering efficiency, and is gradually replaced by automatic filtering based on a filtering algorithm at present.
Common algorithms for filtering multi-beam sounding data at present comprise a trend surface filtering method and a point cloud denoising method based on radius filtering. The trend surface filtering method is simple to operate and very suitable for flat terrain, but edge beam point clouds are sparse, so that filtering is not thorough; the point cloud denoising method based on the radius filtering has the advantages of high processing speed, strong universality and stable denoising effect, but due to the characteristics of the multi-beam sounding system, the difference between the point cloud density of the central beam and the point cloud density of the edge beam is large, so that the method has the defect that the real topographic point is deleted by mistake to cause serious detail loss, and the precision of the method is not high when the method is used for processing the complex submarine topography.
Disclosure of Invention
The application aims to solve the problems in the prior art and provides the submarine topography multi-beam point cloud filtering method considering both the filtering precision and the processing speed.
The embodiment of the application can be realized by the following technical scheme:
a submarine topography multi-beam point cloud filtering method comprises the following steps:
s1, constructing an original multi-beam point cloud set based on original multi-beam sounding data;
s2, performing trend surface filtering on the original multi-beam point cloud set to obtain a first multi-beam point cloud set;
s3, performing combined filtering based on dynamic radius filtering and density clustering on the first multi-beam point cloud set to obtain a submarine topography point cloud set;
and S4, generating a submarine three-dimensional terrain model based on the submarine terrain point cloud set.
Specifically, step S2 further includes the steps of:
s21, establishing a trend surface model of the submarine topography based on the formula (1):
wherein,
is a trend surface model function of the seafloor topography, < > is selected>
Is a measured point plane coordinate, is based on the measured point plane coordinate>
For measuring the point depth>
Is a polynomial surface coefficient; />
S22, changing the formula (1) into a matrix form of the formula (2):
wherein,
，
is a model coefficient matrix, is based on>
Is an observation depth matrix;
s23, solving the formula (3) by using a least square method based on the three-dimensional coordinates of each discrete measuring point in the original multi-beam point cloud set:
s24, traversing each discrete measuring point of the original multi-beam point cloud set, and determining the discrete measuring points belonging to the first multi-beam point cloud set based on the formula (4) to obtain the first multi-beam point cloud set:
wherein,
respectively the ^ th or fourth in the original multi-beam point cloud set>
Three-dimensional coordinates of discrete measuring points->
Based on a fifth->
Mean square deviation of a plurality of discrete measuring point depths in the neighborhood of the discrete measuring points, < > 4>
Are trend surface filter coefficients.
Preferably, the
。
Specifically, step S3 further includes the steps of:
s31, classifying the discrete measuring points in the first multi-beam point cloud set into receiving measuring points and calibrating measuring points based on dynamic radius filtering, and classifying the receiving measuring points into a submarine topography point cloud set;
and S32, performing density clustering on the calibration measuring points, and classifying the calibration measuring points with non-noise density clustering results into a submarine topography point cloud set.
Preferably, the dynamic radius filtering is performed based on equation (5):
wherein,
is the number of discrete measurement points in the first multi-beam point cloud set, <' >>
For the sequence number of each discrete measurement point in the first multi-beam point cloud set, <' >>
Filtering a number of thresholds for an initial radius->
At a ^ th greater than or equal to the first multi-beam point cloud set>
The number of discrete measuring points contained in the filter radius of the discrete measuring points->
Is->
The threshold transform coefficient of (2).
Preferably, the
Determining based on equation (6):
wherein,
for the ^ th or greater in the first multi-beam point cloud set>
Neighborhood distance of discrete test points, < >>
Is the mean of the neighborhood distances of all discrete survey points in the first multi-beam point cloud set, < >>
A neighborhood distance of less than ≦ for any one of the discrete side points in the first multi-beam point cloud set>
The probability of (c).
Specifically, the neighborhood distance for each discrete measurement point is based on the closest discrete measurement point to the discrete measurement pointKThe mean value of the distances of the discrete measuring points is determined.
Preferably, the
Determining based on equation (7):
wherein,
is an error function>
Determined by the formulae (8) and (9), respectively:
preferably, the filtering radius is
Wherein->
Is an integer of 1 or more.
The submarine topography multi-beam point cloud filtering method provided by the embodiment of the application has the following beneficial effects:
the filtering method improves various existing filtering algorithms by organically combining specific data characteristics of the multi-beam sounding data, improves the edge beam filtering effect, simultaneously considers the retention of edge terrain details and the processing speed of mass point cloud data, and realizes the advantage complementation and organic combination of various algorithms;
according to the filtering method, firstly, a trend surface method is adopted for preprocessing multi-beam point clouds aiming at dense noise, then, an efficient and simple radius filtering mode is adopted for solving the problem that the filtering effect of trend surface edge beam point clouds is poor, the filtering effect of the edge beam point clouds is improved, meanwhile, the problem that the traditional radius filtering mistakenly deletes edge topographic points in the filtering process of the multi-beam point clouds is improved, and edge topographic details are well reserved through density clustering and dynamic radius threshold setting;
according to the filtering method, the density clustering mode of the questioning data is adopted to replace the whole point cloud data to carry out density clustering, the clustering convergence time is shortened, the integral efficiency of the algorithm is ensured, and the method is suitable for processing massive multi-beam point cloud data.
Drawings
Fig. 1 is a flow chart of a seafloor terrain multi-beam point cloud filtering method according to an embodiment of the application;
FIG. 2 is any one obtained from multi-beam sounding data statistics and from lognormal distributionNeighborhood distance of discrete measurement point is less thanLProbability of (2)pRelative toL(ii) a change in (c);
FIG. 3 is a flowchart of an embodiment of example 1 of the present application;
FIG. 4 is a schematic diagram of a three-dimensional submarine terrain model obtained according to embodiment 1 of the present application;
FIG. 5 is a schematic diagram of a three-dimensional terrain model of the seafloor obtained using trend surface filtering alone;
fig. 6 is a schematic diagram of a three-dimensional submarine terrain model obtained by using the radius filtering method alone.
Detailed Description
Hereinafter, the present application will be further described based on preferred embodiments with reference to the accompanying drawings.
In addition, various components on the drawings are enlarged or reduced for convenience of understanding, but this is not intended to limit the scope of the present application.
Singular references also include plural references and vice versa.
In the description of the embodiments of the present application, it should be noted that if the terms "upper", "lower", "inner", "outer", etc. are used for indicating the orientation or positional relationship based on the orientation or positional relationship shown in the drawings or the orientation or positional relationship which is usually arranged when the products of the embodiments of the present application are used, the description is only for convenience and simplicity, but the indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation and be operated, and thus, the application cannot be construed as being limited. Furthermore, the terms first, second, etc. may be used herein to distinguish between various elements, but these should not be limited by the order of manufacture or by importance to indicate or imply relative importance, and their names may differ from the descriptions and claims provided herein.
The terminology used in the description is for the purpose of describing the embodiments of the application and is not intended to be limiting of the application. It is also to be understood that, unless otherwise expressly stated or limited, the terms "disposed," "connected," and "connected" are intended to be open-ended, i.e., may be fixedly connected, detachably connected, or integrally connected; they may be mechanically coupled, directly coupled, indirectly coupled through intervening media, or may be interconnected between two elements. The specific meaning of the above terms in the present application will be specifically understood by those skilled in the art.
The application provides a multi-beam point cloud filtering method for submarine topography through an embodiment, and the filtering method filters multi-beam sounding data to obtain a point cloud set capable of accurately describing the submarine topography. Fig. 1 is a flowchart of a submarine topography multi-beam point cloud filtering method according to an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
s1, constructing an original multi-beam point cloud set based on original multi-beam sounding data;
s2, performing trend surface filtering on the original multi-beam point cloud set to obtain a first multi-beam point cloud set;
s3, performing combined filtering based on dynamic radius filtering and density clustering on the first multi-beam point cloud set to obtain a submarine topography point cloud set;
and S4, generating a submarine three-dimensional terrain model based on the submarine terrain point cloud set.
The step S1 is used for reading original multi-beam sounding data and integrating the original multi-beam sounding data to construct an original multi-beam point cloud set, wherein the original multi-beam sounding data stores sounding data of a plurality of channels according to beam angles and transmitting and receiving sequences, and the sounding data of each measuring point is extracted from the sounding data
Plane coordinate and water depth>
The three-dimensional coordinates of each discrete measuring point can be determined. And summarizing the three-dimensional coordinates of all the discrete measuring points to construct an original multi-beam point cloud set. The above-described embodiments for extracting and constructing a set of raw multi-beam point clouds from raw multi-beam sounding data are known to those skilled in the art and are described hereinAnd will not be described in detail.
After the original multi-beam point cloud set is constructed, trend surface filtering is carried out on the original multi-beam point cloud data through the step S2, and large-scale dense noise is filtered out, so that the problem that traditional radius filtering is incomplete in filtering of the dense noise is solved; secondly, in step S4, firstly, dynamic radius filtering is utilized, edge beam noise points are filtered, meanwhile, multi-beam point cloud edge topographical points can be well reserved, the problem of mistaken deletion of the edge topographical points of the traditional radius filtering is solved, and edge topographical details are well reserved; furthermore, density clustering is carried out on the suspected points of dynamic radius filtering in step S3 to further separate real submarine topography points from noise points, so that the problem of long convergence time of a clustering algorithm used alone is solved, and the algorithm has high efficiency.
Specifically, in the embodiment of the present application, the step S2 further includes the steps of:
s21, establishing a trend surface model of the submarine topography based on the formula (1):
wherein,
is a trend surface model function of the seafloor topography, < > is selected>
Is a measured point plane coordinate, is based on the measured point plane coordinate>
For a measuring point depth,>
is a polynomial surface coefficient;
s22, changing the formula (1) into a matrix form of the formula (2):
wherein,
，
is a model coefficient matrix, is based on>
Is an observation depth matrix;
s23, solving the formula (3) by using a least square method based on the three-dimensional coordinates of each discrete measuring point in the original multi-beam point cloud set:
s24, traversing each discrete measuring point of the original multi-beam point cloud set, and determining the discrete measuring points belonging to the first multi-beam point cloud set based on the formula (4) to obtain the first multi-beam point cloud set:
wherein,
respectively a first ÷ in an original multi-beam point cloud set>
Three-dimensional coordinates of discrete measuring points->
Based on a fifth->
Mean square deviation of a plurality of discrete measuring point depths in the neighborhood of the discrete measuring points, < > 4>
Are trend surface filter coefficients.
In the above steps, firstly, a polynomial surface type submarine topography curved surface is established through step S21, then, in steps S22 and S23, the three-dimensional coordinates of each discrete measurement point in the original multi-beam point cloud set are used, each polynomial surface coefficient in the trend surface model is determined through the least square method, so as to determine a trend surface model for describing the submarine topography, and finally, in step S24, the measurement point far away from the trend surface is taken as noise elimination, so as to obtain the first multi-beam point cloud set filtered by the trend surface.
In some preferred embodiments of the present application,
。
in a specific implementation process, the filtering precision range of large-scale noise points and large-scale dense noise points can be controlled by adjusting the trend surface filter coefficient.
Although the trend surface filtering is simple to operate and has a good effect on a relatively flat terrain, a phenomenon of incomplete filtering is likely to occur for a measuring point corresponding to an edge beam with a large inclination angle because the point cloud is sparse, and therefore, in the embodiment of the application, the filtering processing is further performed on the first multi-beam point cloud set obtained through the trend surface filtering in the step S3.
In an embodiment of the present application, step S3 further comprises the steps of:
s31, classifying the discrete measuring points in the first multi-beam point cloud set into a receiving measuring point and a calibration measuring point based on dynamic radius filtering, and classifying the receiving measuring point into a submarine topography point cloud set.
And S32, performing density clustering on the calibration measuring points, and classifying the calibration measuring points with non-noise density clustering results into a submarine topography point cloud set.
Step S31, further classifying each discrete measuring point in the first multi-beam point cloud set through dynamic radius filtering, and directly returning the measuring points to be measured into the submarine topography point cloud set; then, in step S32, density clustering operation is performed on the calibrated measuring points with the doubtful classification result, and measuring points with clustering results not being noise are extracted from the calibrated measuring points and are classified into the submarine topography point cloud set. By combining the dynamic radius filtering with the processing of the density clustering algorithm, the problem of mistaken deletion of edge topographic points due to sparseness of edge beam measuring points can be avoided, and the problem of low calculation efficiency caused by directly using the density clustering algorithm to process mass point cloud data is also avoided.
The following describes in detail specific embodiments of step S31 and step S32.
The basic principle of radius filtering is to consider the number of adjacent points in a specified radius space range centered on a certain point in a point cloud as a basis for judging whether the point is an isolated point. If the number of the adjacent points is larger than or equal to the designated threshold value, the point is a non-isolated point and is reserved, otherwise, the point is an isolated point and is removed. When a traditional radius filtering algorithm is used for processing a point cloud set constructed by multi-beam sounding data, the density of discrete measuring points corresponding to edge beams is obviously lower than that of discrete measuring points corresponding to a central beam, and if a fixed threshold is used, the problem that the measuring points corresponding to the edge of a terrain are deleted by mistake is caused, so that the details of the edge terrain are lost in later-stage terrain modeling, and the submarine terrain is difficult to be described accurately.
In some preferred embodiments of the present application, the dynamic radius filtering is performed based on equation (5):
wherein,
is the number of discrete measurement points in the first multi-beam point cloud set, <' >>
For the sequence number of each discrete measurement point in the first multi-beam point cloud set, <' >>
Filtering a number of thresholds for an initial radius->
On a first multi-beam point cloud set>
The number of discrete measuring points contained in the filter radius of the discrete measuring points->
Is->
The threshold transform coefficients of (a) are, in some preferred embodiments,
determining based on equation (6):
wherein,
is a first multi-beam point cloud set>
The distance of the neighbourhood of a discrete measurement point, in particular on the basis of the discrete measurement point and the nearest theretoKDetermining the average value of the distances of the discrete measuring points;
Is the mean of the neighborhood distances of all discrete survey points in the first multi-beam point cloud set, < >>
A neighborhood distance of less than ≦ for any one of the discrete side points in the first multi-beam point cloud set>
The probability of (c).
In the implementation process of the dynamic radius filtering, before the filtering radius and the threshold value of the initial radius filtering point number are kept as fixed valuesUnder the lifting of
And adjusting the actual point threshold corresponding to each discrete measuring point. Wherein for any one discrete measuring point->
Its neighborhood distance ∑ when it is located in the central beam region>
Smaller, and greater density of discrete points near the location, at which time the corresponding dynamic point threshold remains ≥ based on>
(ii) a When it is located in the edge beam region, since the discrete points are more significant and the more off-center the beam, the greater the value of the beam>
The smaller the probability value, so that->
Correspondingly, the smaller, so that a dynamic point threshold value +>
And smaller, thereby avoiding the edge topographical points from being deleted by mistake.
In the calculation of
In general, each discrete survey point ≥ can be determined by ranking the neighborhood distances for all discrete survey points in the first set of multi-beam point clouds>
Corresponding>
However, when the number of discrete measuring points in the first multi-beam point cloud set is too large, the sorting of all the discrete measuring points greatly reduces the submarine topography generation efficiency, namelyThis is required to be ensured>
Calculation with further increased accuracy>
The speed of (2).
Fig. 2 shows that the neighborhood distance of any one discrete measurement point obtained by statistics of a large number of multibeam sounding data is less thanLProbability of (2)pRelative toLWhile fig. 2 also shows that when the discrete measuring points are distributed according to the log normal distributionp～LRelative change in the state of the art. As shown in FIG. 2, the statistical neighborhood distance of each discrete measurement point substantially conforms to the lognormal distribution, so in some preferred embodiments, the neighborhood distance can be determined based on the lognormal distribution function of equation (7)
To speed up the filtering:
wherein,
is an error function>
Determined by the formulae (8) and (9), respectively:
further, in some preferred embodiments, when performing dynamic radius filtering, the filtering radius is
In which>
Is an integer of 1 or more.
Because the ocean power can erode the irregular part of the ocean floor, the natural topography of the ocean floor is continuously changed, although the slope exists, the abrupt appearance can not happen, and the edge topography details which are deleted by mistake are also applicable. According to the characteristic that density clustering is sensitive to noise, in step S32, density clustering is performed on the in-doubt data (i.e., the calibration measuring points) after dynamic radius filtering, the calibration measuring points with the clustering result being noise are removed, and the other calibration measuring points with the clustering result being non-noise are classified into the submarine topography point cloud set.
Specifically, the density clustering algorithm is to cluster a plurality of d-dimensional data samples into a plurality of classes, so that the similarity of the samples in the same class is maximum, and the similarity of the samples in different classes is minimum. The clustering process comprises three main steps of core point search, initial core point selection and cluster diffusion. Searching all core points meeting conditions in a data set to be clustered; then starting from any core point cluster (core point and point in neighborhood radius), continuously expanding to unclassified points by using density connection relation until the points cannot be continued, thereby obtaining a point cluster in which all points are connected with each other in density, wherein the point cluster can be called a cluster, and all data points in the cluster are marked as the same class; and then continuing to inquire whether the core points which are not accessed exist, starting a new round of cluster expansion, and continuing to form new cluster clusters until all the core points are accessed, and marking the remaining points which are not absorbed by any cluster as noise. The algorithm can effectively identify a plurality of dense point clusters isolated from each other and mark discrete points that cannot constitute clusters as noise.
The above density clustering algorithm is known to those skilled in the art, but if the above density clustering algorithm is directly applied to denoising and filtering of an original multi-beam point cloud set, the problems of too large data volume, too long clustering process and the like are faced. Therefore, in the embodiment of the application, the characteristic that density clustering is sensitive to noise is utilized, only the in-doubt data obtained after dynamic radius filtering is subjected to density clustering to identify noise points, noise points calibrated by the density clustering are filtered, and the rest in-doubt data are stored as topographic points. Compared with the original point cloud data, the suspicious data has smaller data volume and short clustering convergence time, so that the algorithm has higher efficiency.
Further, after the submarine topography point cloud set is obtained in the step S3, the submarine topography modeling can be performed in the step S4. At present, there are many methods for three-dimensional modeling based on point cloud data, for example, a connection relationship between each two discrete measurement points in an ocean floor topography point cloud set can be established according to spatial position information of each discrete measurement point, so as to form an ocean floor topography three-dimensional mesh model in the form of a triangular/quadrangular patch and the like. The above-mentioned method for reconstructing a three-dimensional model based on a point cloud set is known to those skilled in the art, and is not described herein again.
Example 1.
In this embodiment, the multi-beam sounding data obtained by actual measurement is processed by using the above method, and fig. 3 is a specific implementation flow of this embodiment.
In this embodiment, the original multi-beam sounding data includes 163227 discrete measurement points in total, the sea floor area is about 8542 square meters, the threshold of the initial radius filter point number is set to 100, and the log normal distribution fitting of the neighborhood distance is performed on the discrete measurement points to obtain the log normal distribution
And finally determining the filtering radius of the dynamic radius filtering to be 3.0717 meters.
Fig. 4 schematically shows a three-dimensional submarine topography model obtained by using the submarine topography multi-beam point cloud filtering method, and fig. 5 and 6 schematically show the three-dimensional submarine topography model obtained by using the trend surface filtering method and the radius filtering method separately, respectively, for comparison. Comparing fig. 4 with fig. 5 to fig. 6, it can be seen that by using the method for filtering the submarine topography multi-beam point cloud provided by the present application, the accuracy and precision of partial filtering of edge beams can be significantly improved, so that the edge topography details are better retained, meanwhile, the reduction of the operation speed caused by directly adopting a density clustering algorithm is avoided, and the advantage complementation and organic combination of various algorithms are realized.
While the present invention has been described in detail and with reference to specific embodiments thereof, it will be apparent to one skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope thereof as defined in the appended claims.
Claims (9)
1. The submarine topography multi-beam point cloud filtering method is characterized by comprising the following steps of:
s1, constructing an original multi-beam point cloud set based on original multi-beam sounding data;
s2, performing trend surface filtering on the original multi-beam point cloud set to obtain a first multi-beam point cloud set;
s3, performing combined filtering based on dynamic radius filtering and density clustering on the first multi-beam point cloud set to obtain a submarine topography point cloud set;
and S4, generating a submarine three-dimensional terrain model based on the submarine terrain point cloud set.
2. The seafloor terrain multi-beam point cloud filtering method of claim 1, wherein the step S2 further comprises the steps of:
s21, establishing a trend surface model of the submarine topography based on the formula (1):
wherein,
is a trend surface model function of the seafloor topography, < > is selected>
For measuring point plane coordinates, and>
in order to measure the depth of the point,
is a polynomial surface coefficient;
s22, changing the formula (1) into a matrix form of the formula (2):
wherein,
，
is a model coefficient matrix, is based on>
Is an observation depth matrix;
s23, solving the formula (3) by using a least square method based on the three-dimensional coordinates of each discrete measuring point in the original multi-beam point cloud set:
s24, traversing each discrete measuring point of the original multi-beam point cloud set, and determining the discrete measuring points belonging to the first multi-beam point cloud set based on the formula (4) to obtain the first multi-beam point cloud set:
wherein,
respectively a first ÷ in an original multi-beam point cloud set>
Three-dimensional coordinates of discrete measuring points->
Based on a fifth->
Mean square differences determined for a plurality of discrete station depths in the neighborhood of a discrete station, <' > based on>
Are trend surface filter coefficients.
3. The seafloor terrain multi-beam point cloud filtering method of claim 2, wherein:
the above-mentioned
。
4. The seafloor terrain multi-beam point cloud filtering method of claim 1, wherein the step S3 further comprises the steps of:
s31, classifying the discrete measuring points in the first multi-beam point cloud set into a receiving measuring point and a calibration measuring point based on dynamic radius filtering, and classifying the receiving measuring point into a submarine topography point cloud set;
and S32, performing density clustering on the calibration measuring points, and classifying the calibration measuring points with non-noise density clustering results into a submarine topography point cloud set.
5. The seafloor terrain multi-beam point cloud filtering method of claim 4, wherein the dynamic radius filtering is based on equation (5):
wherein,
is the number of discrete measurement points in the first multi-beam point cloud set, <' >>
For the sequence number of each discrete measurement point in the first multi-beam point cloud set, <' >>
Filtering a number of thresholds for an initial radius->
At a ^ th greater than or equal to the first multi-beam point cloud set>
The number of discrete measuring points contained in the filter radius of the discrete measuring points->
Is->
The threshold transform coefficient of (2).
6. The seafloor terrain multi-beam point cloud filtering method of claim 5, wherein the method comprises
Determining based on equation (6):
wherein,
for the ^ th or greater in the first multi-beam point cloud set>
Neighborhood distance of discrete test points, < >>
Is the mean of the neighborhood distances of all discrete survey points in the first multi-beam point cloud set, < >>
A neighborhood distance of less than ≦ for any one of the discrete side points in the first multi-beam point cloud set>
The probability of (c).
7. The seafloor terrain multi-beam point cloud filtering method of claim 6, wherein:
the neighborhood distance of each discrete measurement point is based on the discrete measurement point and its closest oneKThe mean value of the distances of the discrete measuring points is determined.
8. The seafloor terrain multi-beam point cloud filtering method of claim 6, wherein the method comprises
Determining based on equation (7):
wherein,
is an error function>
Determined by the formulae (8) and (9), respectively:
9. the seafloor terrain multi-beam point cloud filtering method of claim 8, wherein:
the filter radius is
Wherein->
Is an integer of 1 or more. />
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117269944A (en) * | 2023-11-20 | 2023-12-22 | 浙江华东岩土勘察设计研究院有限公司 | Time-varying gain control method for multi-beam terrain sonar echo |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106599129A (en) * | 2016-12-02 | 2017-04-26 | 山东科技大学 | Multi-beam point cloud data denoising method considering terrain characteristics |
| CN109035224A (en) * | 2018-07-11 | 2018-12-18 | 哈尔滨工程大学 | A kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud |
| CN110796741A (en) * | 2019-09-24 | 2020-02-14 | 山东科技大学 | Airborne laser sounding point cloud filtering method based on bidirectional cloth simulation |
| US10634793B1 (en) * | 2018-12-24 | 2020-04-28 | Automotive Research & Testing Center | Lidar detection device of detecting close-distance obstacle and method thereof |
| CN112508803A (en) * | 2020-11-03 | 2021-03-16 | 中山大学 | Denoising method and device for three-dimensional point cloud data and storage medium |
| CN112529841A (en) * | 2020-11-16 | 2021-03-19 | 中国海洋大学 | Method and system for processing seabed gas plume in multi-beam water column data and application |
-
2023
- 2023-02-22 CN CN202310147519.1A patent/CN115880189B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106599129A (en) * | 2016-12-02 | 2017-04-26 | 山东科技大学 | Multi-beam point cloud data denoising method considering terrain characteristics |
| CN109035224A (en) * | 2018-07-11 | 2018-12-18 | 哈尔滨工程大学 | A kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud |
| US10634793B1 (en) * | 2018-12-24 | 2020-04-28 | Automotive Research & Testing Center | Lidar detection device of detecting close-distance obstacle and method thereof |
| CN110796741A (en) * | 2019-09-24 | 2020-02-14 | 山东科技大学 | Airborne laser sounding point cloud filtering method based on bidirectional cloth simulation |
| CN112508803A (en) * | 2020-11-03 | 2021-03-16 | 中山大学 | Denoising method and device for three-dimensional point cloud data and storage medium |
| CN112529841A (en) * | 2020-11-16 | 2021-03-19 | 中国海洋大学 | Method and system for processing seabed gas plume in multi-beam water column data and application |
Non-Patent Citations (2)
| Title |
|---|
| LIANGCAI REN，AND ETC: "An Improved Cloth Simulation Filtering Algorithm Based on Mining Point Cloud", 《2021 INTERNATIONAL CONFERENCE ON CYBER-PHYSICAL SOCIAL INTELLIGENCE (ICCSI)》 * |
| 彭彤;邹亚未;周平;卢凯乐;: "基于趋势面的多波束测深粗差剔除研究", 江西测绘 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117269944A (en) * | 2023-11-20 | 2023-12-22 | 浙江华东岩土勘察设计研究院有限公司 | Time-varying gain control method for multi-beam terrain sonar echo |
| CN117269944B (en) * | 2023-11-20 | 2024-01-30 | 浙江华东岩土勘察设计研究院有限公司 | Time-varying gain control method for multi-beam terrain sonar echo |
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