CN109035224A - A kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud - Google Patents
A kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud Download PDFInfo
- Publication number
- CN109035224A CN109035224A CN201810758534.9A CN201810758534A CN109035224A CN 109035224 A CN109035224 A CN 109035224A CN 201810758534 A CN201810758534 A CN 201810758534A CN 109035224 A CN109035224 A CN 109035224A
- Authority
- CN
- China
- Prior art keywords
- pipeline
- point cloud
- dimensional
- point
- sonar
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 69
- 238000007689 inspection Methods 0.000 title claims abstract description 12
- 238000001914 filtration Methods 0.000 claims abstract description 20
- 238000001514 detection method Methods 0.000 claims abstract description 13
- 238000004458 analytical method Methods 0.000 claims abstract description 8
- 238000003708 edge detection Methods 0.000 claims description 8
- 229910052705 radium Inorganic materials 0.000 claims description 6
- HCWPIIXVSYCSAN-UHFFFAOYSA-N radium atom Chemical compound [Ra] HCWPIIXVSYCSAN-UHFFFAOYSA-N 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims 1
- 239000000284 extract Substances 0.000 abstract description 5
- 238000005259 measurement Methods 0.000 abstract description 3
- 230000006378 damage Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
- G06T7/0006—Industrial image inspection using a design-rule based approach
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G06T5/70—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
- G06T2207/10012—Stereo images
Abstract
The present invention relates to multibeam sonar underwater target detections and point cloud data to model field, and in particular to a kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud.The underwater sonar image obtained according to multibeam echosounding sonar contact pipeline is classified and is extracted to image pixel point using threshold method, and three dimensional point cloud is obtained；Then the point cloud noise-removed filtering method based on density analysis, the three dimensional point cloud of the pipeline after obtaining filtering and noise reduction are used；Then round fitting is carried out to the point cloud data in each section of pipeline using linear fit method, the centre point of the radius for obtaining fitting circle and linear change is subjected to three-dimensional reconstruction, obtains the three-dimensional figure of the pipeline；Point cloud data is obtained relative to by depth measurement point, the present invention directly extracts point cloud data from sonar image, can still obtain more accurate point cloud model, and calculation amount is small, detection and three-dimensional reconstruction suitable for underwater various pipes.
Description
Technical field
The present invention relates to multibeam sonar underwater target detections and point cloud data to model field, and in particular to one kind is based on more
The Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method of beam spot cloud.
Background technique
In recent decades, global ocean petrol resources exploitation is swift and violent, and in petrol resources exploitation, sea-bottom oil-gas pipeline is defeated
Send petroleum resources most quick and safe and most economical mode, referred to as " lifeline " of marine oil and gas engineering.With marine oil and gas
Development of resources deepens continuously, and the laying scale of submarine pipeline is increasing, but applies in seabed complex environment and the mankind
Under the influence of the industry that works, sea-bottom oil-gas pipeline is easy to appear destruction and damage, and oil gas can occur to a certain extent and let out for damage accumulation
Dew, this will lead to the wasting of resources, destroy ecological environment, or even can cause to explode because of oil and gas leakage, cause casualties with
Bigger property loss, therefore, the Daily Round Check of submarine pipeline are particularly important.The operation conditions of submarine pipeline is inspected periodically, and
When grasp submarine pipeline safe condition, become offshore oil and gas production important leverage measure, can both prevent corrosive pipeline in this way,
It can guarantee that pipe safety is run again, extend pipeline service life.
Since the detection of submarine pipeline is mostly using internal detection and external detection, and outside existing pipe line under the ocean deferent
In detection technique, underwater robot carries this mode of multiple-beam system using more and more extensive.In order to preferably observe pipeline
State, need further to extract the three-dimensional information of pipeline, point cloud is to be widely used in target surface at present
Cloud is applied to detection and the three-dimensional reconstruction of submarine pipeline, can accurately extract submarine pipeline by three-dimensional information extraction
Three-dimensional information.
It is tested that current multibeam sounding system can once provide tens vertical with course made good even a seabeds up to a hundred
The water depth value of point accurately and fast show that the size, shape of submarine target and height change along course made good one fixed width, into
And good point cloud data is obtained, therefore have the characteristics that measurement range is big, measuring speed is fast, precision and high-efficient.But it is right
For the lesser pipeline of radius, the point cloud number of the pipe surface obtained by multibeam sounding system is few, is unable to satisfy pipe
The detection in road and three-dimensional reconstruction.
Summary of the invention
The purpose of the present invention is to provide a kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud, with
Improve submarine pipeline point cloud data three-dimensional reconstruction efficiency.
The embodiment of the present invention provides a kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud, comprising:
Step 1: the underwater sonar image obtained according to multibeam echosounding sonar contact pipeline using threshold method and
Canny edge detection method is classified and is extracted to image pixel point, and the three dimensional point cloud of the pipeline is obtained；
Step 2: using the point cloud noise-removed filtering method based on density analysis according to the three dimensional point cloud of the pipeline,
Different initial radium R and minimum Neighborhood Number k, the three dimensional point cloud of the pipeline after obtaining filtering and noise reduction are set；
Step 3: according to the three dimensional point cloud of the pipeline after the filtering and noise reduction use based on statistics with histogram method with
And spatial linear approximating method carries out round fitting to the point cloud data in each section of pipeline, obtains the radius of fitting circle and linear
The centre point of variation；
Step 4: alpha Shape algorithm is used according to the radius of the fitting circle and the centre point of linear change
Three-dimensional reconstruction is carried out to the pipeline, obtains the three-dimensional figure of the pipeline；
The step 1, comprising:
It is examined according to the underwater sonar image that multibeam echosounding sonar contact pipeline obtains using threshold method and the edge canny
Survey method is classified and is extracted to image pixel point；Wherein, the underwateracoustic that the multibeam echosounding sonar contact pipeline obtains
Image does not use through the methods of bottom detection but directly extracts pixel；The threshold method carries out at binaryzation pixel
Reason obtains the different two class pixels for setting value；The canny edge detection method carries out at edge detection all pixels point
Reason extracts the boundary point of pipeline in sonar image；Space coordinate is converted by the boundary point of the pipeline, obtains the three-dimensional of pipeline
Point cloud data；
Wherein, in the space coordinate, X-axis positive direction is multibeam sonar direction of advance:
X=t*v
Wherein, x is the abscissa of point cloud, and v is sonar movement speed, and t is indicated between multibeam sonar acquisition piece image
Every the time；
The Y-axis and Z axis coordinate of point cloud rectangular coordinate system in space, corresponding is the transverse and longitudinal coordinate of single width sonar image, Y-axis table
Show the vertical direction of sonar track, Z axis indicates the normal vector direction on ground；
The step 2, comprising:
The point cloud noise-removed filtering method based on density analysis is used according to the three dimensional point cloud of the pipeline, setting is different
Initial radium R and minimum Neighborhood Number k；Wherein, the density analysis method by some point in cloud using the point as ball
The heart defines the spherical area that a radius is R, and the point cloud number that inquiry ball inner region includes defines a cloud neighbour number k, by ball
Point cloud of the point cloud number less than k that inner region includes is considered as noise and is deleted, otherwise retains；Described cloud noise-removed filtering method
Twice, adjust initial radium R and minimum Neighborhood Number k, make an uproar respectively to the noise cluster far from pipeline and close to the sparse of pipeline
Sound point is filtered；
The step 3, comprising:
It is used according to the three dimensional point cloud of the pipeline after the filtering and noise reduction and is based on statistics with histogram method and space
Linear fit method carries out round fitting to the point cloud data in each section of pipeline；Wherein, the form in each section of the pipeline
For priori knowledge；The fitting circle obtained after the round fitting based on point cloud data of the statistics with histogram method to each section
Radius is averaged, and the mean radius of fitting circle is obtained；Point cloud of the spatial linear approximating method to each section
The center of circle point set of the fitting circle obtained after data circle fitting is fitted, and obtains the centre point of linear change and the direction of pipeline
Information；
The beneficial effects of the present invention are:
1. for pipeline lesser for radius, passing through the point cloud number pole for the pipe surface that multibeam sounding system obtains
It is few, it is unable to satisfy detection and the three-dimensional reconstruction of pipeline.Relative to using for depth measurement point, this method is directly mentioned from sonar image
It takes pipeline pixel to carry out processing and extracts point cloud data, more point cloud datas can be obtained, mentioned for lower step Accurate Curve-fitting pipeline
For sufficient data basis；
2. by the way that noise is divided into two classes: nearly pipe surface noise and the noise that peels off, first to all point clouds of extra large pipe into
The filtering of capable different scale twice, large scale filtering is main to remove the noise that peels off, and small scale filter mainly removes nearly pipe surface
Noise, round fitting then is carried out to corresponding of each section of pipeline cloud again, can effectively reduce noise in this way to pipe
The influence of road fitting more accurately carries out the circle fitting of pipeline, obtains more accurate pipeline fit radius and center location；
3. seeking the mean value of fit radius by statistics with histogram method, ignores the less radius value of the frequency of occurrences, effectively avoid
Influence of the radius extreme value to mean radius, since submarine pipeline material is not generally pliable, so using linear fit pair
Center of circle point set is fitted, and be can achieve and is met actual fitting result, and the pipeline three-dimensional reconstruction of next step is facilitated.
Detailed description of the invention
Fig. 1 is a kind of overall flow figure of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud；
Fig. 2 is that multibeam sonar of the present invention carries out submarine pipeline detection schematic diagram；
Fig. 3 is the present invention to the pipeline point cloud chart after the progress denoising of original point cloud；
Fig. 4 is the present invention to the pipeline point cloud chart put after cloud carries out round fitting after denoising；
Fig. 5 is the schematic diagram that the present invention carries out linear fit to the center of circle of fitting circle；
Fig. 6 is the pipeline point cloud chart that final process of the present invention obtains；
Fig. 7 (a) is the side view of pipeline three-dimensional reconstruction result of the present invention；
Fig. 7 (b) is the sectional view of pipeline three-dimensional reconstruction result of the present invention；
Specific embodiment
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing to the present invention
It is described further:
Fig. 1 is a kind of overall flow figure of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud.
The technical scheme of the present invention is realized as follows:
Step (1): obtaining underwater sonar image by multibeam sonar acquisition process, carries out two to image using threshold method
Value processing, the boundary point of pipeline in sonar image is extracted using canny edge detection method, converts space for boundary point
Coordinate, obtains the three dimensional point cloud of pipeline, and point cloud data is in using the normal vector on ground as the rectangular coordinate system in space of Z axis
In, X-axis positive direction is multibeam sonar direction of advance:
X=t*v
Wherein x is the abscissa of point cloud, and v is sonar movement speed, and t indicates the interval of multibeam sonar acquisition piece image
Time；
The Y-axis and Z axis coordinate of point cloud rectangular coordinate system in space, corresponding is the transverse and longitudinal coordinate of sonar image, Y-axis expression sound
The vertical direction of track, Z axis indicate the normal vector direction on ground；
Step (2): being filtered pipeline point cloud data using density analysis method, for some point in cloud, with
The point is the spherical area that the centre of sphere defines that a radius is R, and the point cloud number that inquiry ball inner region includes defines a cloud neighbour
The cloud is considered as noise and deleted by number k if the point cloud number that ball inner region includes is less than k, otherwise is retained, to a cloud
In each point carry out aforesaid operations, complete primary filtering processing.The present invention adjusts initial radium using filtering mode twice
And minimum neighbour's number, it is filtered respectively to the noise group far from pipeline and close to the sparse noise spot of pipeline, Fig. 3 is to pass through
Point cloud data after filtering processing, from figure it will be seen that by filtering processing after, good pipeline point can be obtained
Cloud data；
Step (3): using the circular cross-section of pipeline as priori conditions, it is quasi- that circle is carried out to the point cloud data in each section
It closes, the radius of digital simulation circle and the center of circle, is the pipeline point cloud obtained after being fitted shown in Fig. 4, sees that each is quasi- from figure
The radius for closing circle is not quite similar, and the center of circle is not on an axis, and there are situations not to be consistent with actual extra large pipe for this, therefore
Need to carry out it processing of step (4).
Step (4): using the mean radius based on statistics with histogram method digital simulation circle, using spatial linear fitting side
Method is fitted center of circle point set, obtains the centre point of linear change, Fig. 5 is the fitting result in the center of circle, due to submarine pipeline material
Matter is not generally pliable, so meeting actual fitting result using what linear fit can achieve, Fig. 6 is to handle it
Afterwards, the result figure of the pipeline point cloud obtained.
Step (5): Fig. 7 is referred to, according to the mean radius and the center of circle being calculated, using alphaShape algorithm to a cloud
The three-dimensional reconstruction for carrying out pipeline, obtains the three-dimensional figure of submarine pipeline.Fig. 7 is pipeline three-dimensional reconstruction as a result, wherein Fig. 7 (a) is
The side visual angle of pipeline, Fig. 7 (b) are the section visual angle of pipeline.Finally, it using processing method of the invention, can obtain
To good pipeline construction effect.
Claims (4)
1. a kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud characterized by comprising
Step 1: the underwater sonar image obtained according to multibeam echosounding sonar contact pipeline is using threshold method and the side canny
Edge detection method is classified and is extracted to image pixel point, and the three dimensional point cloud of the pipeline is obtained；
Step 2: the point cloud noise-removed filtering method based on density analysis, setting are used according to the three dimensional point cloud of the pipeline
Different initial radium R and minimum Neighborhood Number k, the three dimensional point cloud of the pipeline after obtaining filtering and noise reduction；
Step 3: it is used according to the three dimensional point cloud of the pipeline after the filtering and noise reduction and is based on statistics with histogram method and sky
Between linear fit method round fitting is carried out to the point cloud data in each section of pipeline, obtain the radius and linear change of fitting circle
Centre point；
Step 4: according to the radius of the fitting circle and the centre point of linear change using alpha Shape algorithm to institute
It states pipeline and carries out three-dimensional reconstruction, obtain the three-dimensional figure of the pipeline.
2. a kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud according to claim 1, special
Sign is, the step 1, comprising:
The underwater sonar image obtained according to multibeam echosounding sonar contact pipeline is using threshold method and canny side edge detection
Method is classified and is extracted to image pixel point；Wherein, the underwater sonar figure that the multibeam echosounding sonar contact pipeline obtains
Pixel is directly extracted as not using through the methods of bottom detection；The threshold method carries out binary conversion treatment to pixel,
Obtain the different two class pixels for setting value；The canny edge detection method carries out edge detection process to all pixels point, mentions
Take the boundary point of pipeline in sonar image；Space coordinate is converted by the boundary point of the pipeline, obtains the three-dimensional point cloud of pipeline
Data；
Wherein, in the space coordinate, X-axis positive direction is multibeam sonar direction of advance:
X=t*v
Wherein, x is the abscissa of point cloud, and v is sonar movement speed, when t indicates the interval of multibeam sonar acquisition piece image
Between；
The Y-axis and Z axis coordinate of point cloud rectangular coordinate system in space, corresponding is the transverse and longitudinal coordinate of single width sonar image, Y-axis expression sound
The vertical direction of track, Z axis indicate the normal vector direction on ground.
3. a kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud according to claim 1, special
Sign is: the step 2, comprising:
Use the point cloud noise-removed filtering method based on density analysis according to the three dimensional point cloud of the pipeline, be arranged it is different just
Beginning radius R and minimum Neighborhood Number k；Wherein, the density analysis method passes through fixed by the centre of sphere of the point to some point in cloud
The spherical area that one radius of justice is R, the point cloud number that inquiry ball inner region includes, defines a cloud neighbour number k, by ball inner region
Point cloud of the point cloud number less than k that domain includes is considered as noise and is deleted, otherwise retains；Described cloud noise-removed filtering method carries out
Twice, initial radium R and minimum Neighborhood Number k is adjusted, respectively to the noise cluster far from pipeline and close to the sparse noise spot of pipeline
It is filtered.
4. a kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud according to claim 1, special
Sign is: the step 3, comprising:
It is used according to the three dimensional point cloud of the pipeline after the filtering and noise reduction and is based on statistics with histogram method and spatial linear
Approximating method carries out round fitting to the point cloud data in each section of pipeline；Wherein, the form in each section of the pipeline is first
Test knowledge；The radius based on statistics with histogram method to the fitting circle obtained after the point cloud data circle fitting in each section
It averages, obtains the mean radius of fitting circle；Point cloud data of the spatial linear approximating method to each section
The center of circle point set of the fitting circle obtained after circle fitting is fitted, and obtains the centre point of linear change and the direction letter of pipeline
Breath.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810758534.9A CN109035224B (en) | 2018-07-11 | 2018-07-11 | Submarine pipeline detection and three-dimensional reconstruction method based on multi-beam point cloud |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810758534.9A CN109035224B (en) | 2018-07-11 | 2018-07-11 | Submarine pipeline detection and three-dimensional reconstruction method based on multi-beam point cloud |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109035224A true CN109035224A (en) | 2018-12-18 |
| CN109035224B CN109035224B (en) | 2021-11-09 |
Family
ID=64640869
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810758534.9A Active CN109035224B (en) | 2018-07-11 | 2018-07-11 | Submarine pipeline detection and three-dimensional reconstruction method based on multi-beam point cloud |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109035224B (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110109142A (en) * | 2019-04-04 | 2019-08-09 | 深圳市速腾聚创科技有限公司 | Point cloud filtering method, device, computer equipment and storage medium |
| CN110533650A (en) * | 2019-08-28 | 2019-12-03 | 哈尔滨工程大学 | A kind of AUV submarine pipeline detecting and tracking method of view-based access control model |
| CN111144318A (en) * | 2019-12-27 | 2020-05-12 | 苏州联视泰电子信息技术有限公司 | Point cloud data noise reduction method for underwater sonar system |
| CN111238386A (en) * | 2019-11-26 | 2020-06-05 | 中国航空工业集团公司北京长城计量测试技术研究所 | Method for measuring minimum spacing of engine pipelines |
| CN111429563A (en) * | 2020-03-10 | 2020-07-17 | 山东大学 | Pipeline three-dimensional reconstruction method, system, medium and equipment based on deep learning |
| CN111899326A (en) * | 2020-06-18 | 2020-11-06 | 苏州小优智能科技有限公司 | Three-dimensional reconstruction method based on GPU parallel acceleration |
| 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 |
| CN113256697A (en) * | 2021-04-27 | 2021-08-13 | 武汉理工大学 | Three-dimensional reconstruction method, system and device of underwater scene and storage medium |
| CN113567968A (en) * | 2021-05-25 | 2021-10-29 | 自然资源部第一海洋研究所 | Underwater target real-time segmentation method based on shallow water multi-beam water depth data and application |
| CN114800520A (en) * | 2022-05-23 | 2022-07-29 | 北京迁移科技有限公司 | High-precision hand-eye calibration method |
| CN115880189A (en) * | 2023-02-22 | 2023-03-31 | 山东科技大学 | Submarine topography multi-beam point cloud filtering method |
| CN115932864A (en) * | 2023-02-24 | 2023-04-07 | 深圳市博铭维技术股份有限公司 | Pipeline defect detection method and pipeline defect detection device |
| WO2023238846A1 (en) * | 2022-06-08 | 2023-12-14 | 川崎重工業株式会社 | Image generating device, object recognition device, and image generating method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001094827A2 (en) * | 2000-06-07 | 2001-12-13 | Coflexip | Subsea pipeline touchdown monitoring |
| CN102915561A (en) * | 2012-09-27 | 2013-02-06 | 清华大学 | Method of three-dimensional reconstruction for pipeline structures |
| US20130226344A1 (en) * | 2012-02-29 | 2013-08-29 | Irobot Corporation | Mobile Robot |
| CN104075072A (en) * | 2014-07-17 | 2014-10-01 | 国家海洋技术中心 | Submarine pipeline detection device based on ROV platform |
| CN105096268A (en) * | 2015-07-13 | 2015-11-25 | 西北农林科技大学 | Denoising smoothing method of point cloud |
| CN105182350A (en) * | 2015-09-26 | 2015-12-23 | 哈尔滨工程大学 | Multi-beam sonar target detection method by applying feature tracking |
-
2018
- 2018-07-11 CN CN201810758534.9A patent/CN109035224B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001094827A2 (en) * | 2000-06-07 | 2001-12-13 | Coflexip | Subsea pipeline touchdown monitoring |
| US20130226344A1 (en) * | 2012-02-29 | 2013-08-29 | Irobot Corporation | Mobile Robot |
| CN102915561A (en) * | 2012-09-27 | 2013-02-06 | 清华大学 | Method of three-dimensional reconstruction for pipeline structures |
| CN104075072A (en) * | 2014-07-17 | 2014-10-01 | 国家海洋技术中心 | Submarine pipeline detection device based on ROV platform |
| CN105096268A (en) * | 2015-07-13 | 2015-11-25 | 西北农林科技大学 | Denoising smoothing method of point cloud |
| CN105182350A (en) * | 2015-09-26 | 2015-12-23 | 哈尔滨工程大学 | Multi-beam sonar target detection method by applying feature tracking |
Non-Patent Citations (5)
| Title |
|---|
| MAHMOUD AHMED ET AL.: "AUTONOMOUS MODELING OF PIPES WITHIN POINT CLOUDS", 《ISARC 2013》 * |
| THOMAS GUERNEVE ET AL.: "Three-dimensional reconstruction of underwater objects using wide-aperture imaging SONAR", 《J FIELD ROBOTICS》 * |
| 冯驰等: "双光斑中心识别算法比较", 《应用科技》 * |
| 朱俊锋等: "多尺度点云噪声检测的密度分析法", 《测绘学报》 * |
| 蒲定等: "水中气体目标的多波束声呐成像与检测算法", 《应用科技》 * |
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110109142A (en) * | 2019-04-04 | 2019-08-09 | 深圳市速腾聚创科技有限公司 | Point cloud filtering method, device, computer equipment and storage medium |
| CN110109142B (en) * | 2019-04-04 | 2021-04-02 | 深圳市速腾聚创科技有限公司 | Point cloud filtering method and device, computer equipment and storage medium |
| CN110533650A (en) * | 2019-08-28 | 2019-12-03 | 哈尔滨工程大学 | A kind of AUV submarine pipeline detecting and tracking method of view-based access control model |
| CN110533650B (en) * | 2019-08-28 | 2022-12-13 | 哈尔滨工程大学 | AUV underwater pipeline detection tracking method based on vision |
| CN111238386A (en) * | 2019-11-26 | 2020-06-05 | 中国航空工业集团公司北京长城计量测试技术研究所 | Method for measuring minimum spacing of engine pipelines |
| CN111144318A (en) * | 2019-12-27 | 2020-05-12 | 苏州联视泰电子信息技术有限公司 | Point cloud data noise reduction method for underwater sonar system |
| CN111144318B (en) * | 2019-12-27 | 2023-08-29 | 苏州联视泰电子信息技术有限公司 | Noise reduction method for point cloud data of underwater sonar system |
| CN111429563A (en) * | 2020-03-10 | 2020-07-17 | 山东大学 | Pipeline three-dimensional reconstruction method, system, medium and equipment based on deep learning |
| CN111899326A (en) * | 2020-06-18 | 2020-11-06 | 苏州小优智能科技有限公司 | Three-dimensional reconstruction method based on GPU parallel acceleration |
| 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 |
| CN112529841B (en) * | 2020-11-16 | 2023-01-31 | 中国海洋大学 | Method and system for processing seabed gas plume in multi-beam water column data and application |
| CN113256697A (en) * | 2021-04-27 | 2021-08-13 | 武汉理工大学 | Three-dimensional reconstruction method, system and device of underwater scene and storage medium |
| CN113567968A (en) * | 2021-05-25 | 2021-10-29 | 自然资源部第一海洋研究所 | Underwater target real-time segmentation method based on shallow water multi-beam water depth data and application |
| CN113567968B (en) * | 2021-05-25 | 2024-04-16 | 自然资源部第一海洋研究所 | Underwater target real-time segmentation method based on shallow water multi-beam water depth data and application thereof |
| CN114800520A (en) * | 2022-05-23 | 2022-07-29 | 北京迁移科技有限公司 | High-precision hand-eye calibration method |
| CN114800520B (en) * | 2022-05-23 | 2024-01-23 | 北京迁移科技有限公司 | High-precision hand-eye calibration method |
| WO2023238846A1 (en) * | 2022-06-08 | 2023-12-14 | 川崎重工業株式会社 | Image generating device, object recognition device, and image generating method |
| CN115880189B (en) * | 2023-02-22 | 2023-05-30 | 山东科技大学 | Multi-beam point cloud filtering method for submarine topography |
| CN115880189A (en) * | 2023-02-22 | 2023-03-31 | 山东科技大学 | Submarine topography multi-beam point cloud filtering method |
| CN115932864A (en) * | 2023-02-24 | 2023-04-07 | 深圳市博铭维技术股份有限公司 | Pipeline defect detection method and pipeline defect detection device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109035224B (en) | 2021-11-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109035224A (en) | A kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud | |
| CN110849335B (en) | Remote sensing rapid determination method for reservoir capacity of dammed lake water of waterless underground form data | |
| CN201680157U (en) | Apparatus for checking underwater pipeline based on light vision | |
| CN101832470A (en) | Method and device for polling underwater lines based on light vision sensing | |
| CN103364408A (en) | Method for detecting underwater surface crack of hydraulic concrete structure by using underwater robot system | |
| CN104268848A (en) | Ocean internal wave velocity monitoring method | |
| CN105787886A (en) | Multi-beam image sonar-based real-time image processing method | |
| CN110533650B (en) | AUV underwater pipeline detection tracking method based on vision | |
| CN104297790B (en) | Quantitative evaluation method for offset distribution in fluctuating submarine earthquake observation system | |
| CN108320291A (en) | Submarine geomorphy boundary extraction method based on Otsu algorithm | |
| CN105136126B (en) | The method that tsunami ripple detecting is carried out using deep seafloor pressure data | |
| Majcher et al. | Using difference modelling and computational fluid dynamics to investigate the evolution of complex, tidally influenced shipwreck sites | |
| CN108896997B (en) | Method for correcting side scan sonar detection result under complex terrain condition | |
| CN107942316A (en) | Concentrate suspension movement velocity method of estimation in a kind of water based on multibeam sonar beamformer output signal | |
| CN116523822A (en) | Submarine cable detection and identification method based on side-scan sonar | |
| JP2009229432A (en) | Seismic sea wave observation system using gps receiver | |
| Shao et al. | Verification of echosounder measurements of thickness and spatial distribution of kelp forests | |
| Shen et al. | Rapid Three-Dimensional Reconstruction of Underwater Defective Pile Based on Two-Dimensional Images Obtained Using Mechanically Scanned Imaging Sonar | |
| Bayram et al. | Shoreline extraction and change detection using 1: 5000 scale orthophoto maps: A case study of Latvia-Riga | |
| CN113848209A (en) | Dam crack detection method based on unmanned aerial vehicle and laser ranging | |
| Zhang et al. | Application of airborne LiDAR measurements to the topographic survey of the tidal flats of the Northern Jiangsu radial sand ridges in the Southern Yellow Sea | |
| CN113009575A (en) | Method and system for monitoring pollutants in underground water | |
| CN109342568B (en) | Full-period voiceprint recognition method for instability and collapse of muddy submarine channel | |
| CN109342570B (en) | Silt submarine channel instability and slump prediction and early warning method | |
| Zhu et al. | Wave force measurement on a large-scale vertical cylinder using a visual technique |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |