CN110400252B - Material yard contour line digitalization method and system - Google Patents
Material yard contour line digitalization method and system Download PDFInfo
- Publication number
- CN110400252B CN110400252B CN201910576174.5A CN201910576174A CN110400252B CN 110400252 B CN110400252 B CN 110400252B CN 201910576174 A CN201910576174 A CN 201910576174A CN 110400252 B CN110400252 B CN 110400252B
- Authority
- CN
- China
- Prior art keywords
- point cloud
- data
- data point
- cloud template
- template
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 61
- 239000000463 material Substances 0.000 title description 2
- 230000009467 reduction Effects 0.000 claims abstract description 21
- 238000009499 grossing Methods 0.000 claims abstract description 13
- XQIMLPCOVYNASM-UHFFFAOYSA-N borole Chemical compound B1C=CC=C1 XQIMLPCOVYNASM-UHFFFAOYSA-N 0.000 claims abstract description 5
- 238000006243 chemical reaction Methods 0.000 claims abstract description 3
- 239000003086 colorant Substances 0.000 claims description 7
- 238000004040 coloring Methods 0.000 claims description 4
- 230000002146 bilateral effect Effects 0.000 claims description 3
- 230000008439 repair process Effects 0.000 claims description 2
- 230000000007 visual effect Effects 0.000 abstract description 4
- 239000003245 coal Substances 0.000 description 10
- 238000003860 storage Methods 0.000 description 6
- 230000001174 ascending effect Effects 0.000 description 5
- 238000013499 data model Methods 0.000 description 3
- 229910052717 sulfur Inorganic materials 0.000 description 3
- 239000011593 sulfur Substances 0.000 description 3
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 229910002651 NO3 Inorganic materials 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009194 climbing Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- FGIUAXJPYTZDNR-UHFFFAOYSA-N potassium nitrate Chemical compound [K+].[O-][N+]([O-])=O FGIUAXJPYTZDNR-UHFFFAOYSA-N 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
- 239000000725 suspension Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/06—Topological mapping of higher dimensional structures onto lower dimensional surfaces
- G06T3/067—Reshaping or unfolding 3D tree structures onto 2D planes
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Processing Or Creating Images (AREA)
Abstract
A stock ground contour line digitalization method and a system belong to the technical field of data processing. The invention comprises the following steps: acquiring three-dimensional point cloud data of a stock ground; generating a blank point cloud template according to the stock yard boundary; after the three-dimensional point cloud data is subjected to dimensionality reduction, drawing the data subjected to dimensionality reduction on the blank point cloud template to generate a data point cloud template; judging whether a blank area exists on the data point cloud template, repairing the blank area, and then smoothing the data point cloud template by adopting a fuzzy method; after the repairing is finished, generating a contour line borol graph according to the data point cloud template; and converting the color of the data point cloud template, covering the contour line bool image on the data point cloud template after the color conversion, adding external data for describing stock ground information to the data point cloud template, and generating a contour line two-dimensional stock ground image. The invention adopts a dimension reduction mode to convert a complex three-dimensional model into a simple two-dimensional model, and the operator is easy, convenient and visual to operate.
Description
Technical Field
The invention relates to a stockyard contour line digitalization method and a system, belonging to the technical field of data processing.
Background
In the current era, the heavy industry is rapidly developing the digital industry, and the collected 3-dimensional data is used for industrial operation and industrial management by means of scanners such as GPS and UWB and the like, so that clear and safe operation planning is provided for operators, and accurate management data is also provided for managers. The use of three-dimensional data has problems of multiple visual angles, operation depth and the like, in the actual operation and production process, not only certain 3d operation technical requirements are required for operators, but also misoperation caused by abnormal selection depth of a field operation area or selection of an error area is caused due to the problem of the visual angles, and therefore, multiple visual angles are required for confirming the actual situation of the field for each selection. And because the three-dimensional model is too big because the work area is too big, the too big three-dimensional model operation will occupy a large amount of hardware resources to phenomena such as the card pause easily appear, is very poor operation experience to the operator.
Therefore, the traditional method has the problems that the complexity and the error are easy to occur in the 3d model display operation, the field display is not intuitive and clear, and a large amount of resources are consumed in the model operation display. The description of the relationship between the points in the 3-dimensional point cloud model is complex, and the calculation amount is large. Using modeling as a scientific computation requires the use of a large number of spatial computations. And the original information carried by each point is too unique. The coupling between the points is strong, and after the model is formed, the positional relationship description of all three points can be influenced by single point modification. And when data of other dimensions are added, the display is complex, and the requirement on operators is high.
Disclosure of Invention
The technical problem solved by the invention is as follows: the method and the system overcome the defects of the prior art, and the 3d digital model is reduced into a pseudo color contour picture to be displayed in task release software. The invention is fixed on the view angle for the top view of the stock ground, and the actual steep and slow terrain trend on the spot is determined through the pseudo color and the density of contour lines. The colors on the graph are clear, so that an operator can visually see the actual situation on site, and the requirement of the operator on professional operation is also reduced. The pseudo-color ascending line graph can visually identify the height profile of each terrain and the volume of an object for an operator through graphic analysis. The maximum error is 0.1 m. However, the consumption of hardware resources and display operation is greatly reduced. The associated costs are saved. In the actual operation process, because the multi-view and depth reasons are not available, the wrong selection on the selected depth caused by the problem of the model view angle can be effectively avoided in the selected area. The reliability of user operation is ensured.
The technical solution of the invention is as follows: the method for digitizing contour lines of a stock ground comprises the following steps:
acquiring three-dimensional point cloud data of a stock ground;
generating a blank point cloud template according to the stock yard boundary;
after three-dimensional point cloud data are subjected to dimensionality reduction, drawing the data subjected to dimensionality reduction on the blank point cloud template to generate a data point cloud template;
judging whether a blank area exists on the data point cloud template; if the data point cloud template exists, repairing the blank area, and then smoothing the data point cloud template by adopting a fuzzy method; if not, smoothing the data point cloud template by adopting a fuzzy method; after the repairing is finished, generating a contour line borol graph according to the data point cloud template;
and converting the color of the data point cloud template, covering the contour line bool image on the data point cloud template after the color is converted, adding external data for describing stock ground information to the data point cloud template, and generating a contour line two-dimensional stock ground image.
Further, the data point cloud template is a gray scale map.
Furthermore, the method for reducing the three-dimensional point cloud data comprises the following steps: LDA dimension reduction method.
Further, the method for repairing the blank area comprises the following steps:
identifying a blank area, and marking a rectangular area of the blank area according to the maximum boundary of the blank area;
and setting the rectangular area as the attention area, and performing image patching on the attention area to finish patching.
Further, the image inpainting method is a PDE-based image inpainting algorithm, and the vector direction in the PDE-based image inpainting algorithm is opposite angle.
Further, the method for judging whether the blank area exists on the data point cloud template is a graph outline algorithm.
Furthermore, the fuzzy method is to perform bilateral fuzzy on the boundary of the data point cloud template and then perform Gaussian fuzzy on the region within the boundary of the data point cloud template.
Further, the method for converting the colors of the data point cloud template is a pseudo color JET template coloring method.
Further, the external data comprise stockpile labels of a stockyard, a stockpile track model and a stockpile equipment model.
A system for realizing the stock ground contour line digitalization method comprises
The first module is used for acquiring stock ground three-dimensional point cloud data;
the second module is used for generating a blank point cloud template according to the stock yard boundary;
the third module is used for drawing the data subjected to dimensionality reduction on the blank point cloud template after the dimensionality reduction of the three-dimensional point cloud data to generate a data point cloud template;
the fourth module is used for judging whether a blank area exists on the data point cloud template; if the data point cloud template exists, repairing the blank area, and then smoothing the data point cloud template by adopting a fuzzy method; if not, smoothing the data point cloud template by adopting a fuzzy method; after the repairing is finished, generating a contour line borol graph according to the data point cloud template;
and the fifth module is used for converting the color of the data point cloud template, covering the contour line bool map on the data point cloud template after the color is converted, adding external data used for describing stock ground information to the data point cloud template, and generating a contour line two-dimensional stock ground map.
Compared with the prior art, the invention has the advantages that:
(1) the invention adopts a dimension reduction mode to convert a complex three-dimensional model into a simple two-dimensional model, and operators can operate the model simply, conveniently and visually.
(2) The method has the advantages that the logical relationship between each 2-dimensional point is clear through the display of a two-position model, and the calculation amount of the position relationship of the whole model changed each time is obviously less than 3, so that the consumption of hardware resources is reduced for the model;
(3) the method effectively and quickly calculates the volume data position data of the operation area in the ascending line model for the operator through the algorithm identification outline of the gray model;
(4) the invention can intuitively show the steep and slow position relation of each position for an operator by drawing the climbing line, and the operator can conveniently and accurately analyze the actual situation of the site.
Drawings
FIG. 1 is a schematic view of a two-dimensional contour model of the present invention;
FIG. 2 is a schematic diagram of a two-dimensional display model according to the present invention.
Detailed Description
The method for digitizing contour lines of a stock ground comprises the following steps:
acquiring three-dimensional point cloud data of a stock ground;
generating a blank point cloud template according to the stock yard boundary;
after three-dimensional point cloud data are subjected to dimensionality reduction, drawing the data subjected to dimensionality reduction on the blank point cloud template to generate a data point cloud template;
judging whether a blank area exists on the data point cloud template; if the data point cloud template exists, repairing the blank area, and then smoothing the data point cloud template by adopting a fuzzy method; if not, smoothing the data point cloud template by adopting a fuzzy method; after the repairing is finished, generating a contour line borol graph according to the data point cloud template;
and converting the color of the data point cloud template, covering the contour line bool image on the data point cloud template after the color is converted, adding external data for describing stock ground information to the data point cloud template, and generating a contour line two-dimensional stock ground image.
The data point cloud template is a gray scale image.
The method for reducing the dimension of the three-dimensional point cloud data comprises the following steps: LDA dimension reduction method.
The method for repairing the blank area comprises the following steps:
identifying a blank area, and marking a rectangular area of the blank area according to the maximum boundary of the blank area;
and setting the rectangular area as a region of interest, and performing image inpainting on the region of interest to finish inpainting.
The image inpainting method is a PDE-based image inpainting algorithm, and the vector direction in the PDE-based image inpainting algorithm is opposite angle.
The method for judging whether a blank area exists on the data point cloud template is a graph outline algorithm.
The fuzzy method is that firstly, bilateral fuzzy is carried out on the boundary of the data point cloud template, and then Gaussian fuzzy is carried out on the area within the boundary of the data point cloud template.
The method for converting the colors of the data point cloud template is a pseudo color JET template coloring method.
The external data comprises stockyard stockpile labels, a stockyard track model and a stockyard equipment model.
The system for realizing the stock ground contour line digitalization method comprises
The first module is used for acquiring stock ground three-dimensional point cloud data;
the second module is used for generating a blank point cloud template according to the stock yard boundary;
the third module is used for drawing the data subjected to dimensionality reduction to the blank point cloud template after dimensionality reduction of the three-dimensional point cloud data to generate a data point cloud template;
the fourth module is used for judging whether a blank area exists on the data point cloud template; if the data point cloud template exists, repairing the blank area, and then smoothing the data point cloud template by adopting a fuzzy method; if not, smoothing the data point cloud template by adopting a fuzzy method; after the repair is finished, generating a contour line boul graph according to the data point cloud template;
and the fifth module is used for converting the color of the data point cloud template, covering the contour bool image on the data point cloud template after the color is converted, adding external data for describing stock ground information to the data point cloud template, and generating a contour two-dimensional stock ground image.
Specifically, the method comprises the steps of reducing the dimension of a three-dimensional data model into a two-dimensional gray scale model, wherein the two-dimensional data model is a color model, the external information is rich, and the man-machine interaction display is realized. The three-dimensional model is reduced into a two-dimensional gray scale model, the three-dimensional point cloud data is converted into two-point gray scale value information to realize dimension reduction operation, and the multi-view three-dimensional model is converted into a single-view two-dimensional gray scale model through the dimension reduction operation. And generating a two-dimensional model of pseudo colors by coloring the colors of the two-dimensional gray scale model. The gray model is realized and the point on the color model is in one-to-one correspondence. The boolean algorithm records by contour recognition on a gray scale model and draws the recorded data to generate contour lines on a color map as a model. The method comprises the steps of adding description of data in each outline in an external interface in a contour line two-dimensional color model and drawing a mark record of the description, converting the suspension position of an operating mouse on the two-dimensional contour line model into position information in a gray scale model through a scale, and directly converting the position information into information data (east coordinates, north coordinates and altitude coordinates) of a three-dimensional position which can be directly identified by a user. The circle selection operation of the mouse is also directly converted into position information on the gray scale model through the scale, and the position information mention information in the selection area can be analyzed on the gray scale model.
The pixels of the ascending line model and the gray scale model described above are in one-to-one correspondence. The gray model is used as a data source of the ascending line model. The mode is that the display graph is suitable for observation and use of human as much as possible, and the data model provides data support for the display model in the memory.
The method comprises the steps of converting original three-dimensional point cloud data into two-dimensional model data, performing point supplementing operation on the original data after the conversion is completed, correcting abnormal points caused by environment, equipment and the like through median operation, opening operation and Gaussian operation, and forming a complete and smooth gray scale model. The gray model is subjected to ascending line matrix, corresponding point information is recorded through boundary contour operation, and the model shown in the figure 1 is generated through drawing the pseudo colors of the gray graph again. And through accessing external interface data, scale marks are added, the position relation of the equipment is drawn, and the information of the model inner contour recognition is obtained. This information is plotted on fig. 1 to generate the model data of fig. 2. And because fig. 2 and the gray-scale map are in a one-to-one correspondence position relationship, the volume information of the data in each contour can be easily calculated. And outputs it to the outside.
Examples
The actual use condition on site, 25 ten thousand 3 kilotons of coal coexist on the whole site. The storage boundaries of different stocks of each coal quality divided into 12 coal qualities and divided into 12 IDs for identifying each coal quality are also different.
Actual storage state of the first coal yard.
No. 1 coal, the calorific value is 3200, the bottom sulfur and the high nitre are contained, the stacking height is 12 meters, the storage position is 0.6 ten thousand tons, the storage position is in a space of 145 meters to 160 meters, and the identification ID is 1.
No. 2 coal, the sulfur content exceeds the standard, the stock amount is 2.1 ten thousand tons, the heat value is 4100, the stacking height is 16.2 meters, the storage space is 90 to 140, and the ID is 2.
No. 3 coal, high-quality coal, a calorific value of 4900 low-sulfur low-nitrate, a stacking height of 14.3 meters, a stock of 3.3 ten thousand tons, a storage space of 3 to 90 meters, and an ID (identification) mark of 3.
And the intuitive display of the coal quality information by the user is enhanced in the actual use. The complexity of the user operation is reduced.
Those skilled in the art will appreciate that those matters not described in detail in the present specification are not particularly limited to the specific examples described herein.
Claims (7)
1. The method for digitizing the contour line of the stock ground is characterized by comprising the following steps of:
acquiring three-dimensional point cloud data of a stock ground;
generating a blank point cloud template according to the stock yard boundary;
after three-dimensional point cloud data are subjected to dimensionality reduction, drawing the data subjected to dimensionality reduction on the blank point cloud template to generate a data point cloud template;
judging whether a blank area exists on the data point cloud template; if the data point cloud template exists, repairing the blank area, and then smoothing the data point cloud template by adopting a fuzzy method; if not, smoothing the data point cloud template by adopting a fuzzy method; after the repairing is finished, generating a contour line borol graph according to the data point cloud template;
converting the color of the data point cloud template, covering the contour line bool image on the data point cloud template after the color conversion, adding external data for describing stock ground information to the data point cloud template, and generating a contour line two-dimensional stock ground image;
the data point cloud template is a gray scale image;
the fuzzy method comprises the steps of firstly carrying out bilateral fuzzy on the boundary of the data point cloud template, and then carrying out Gaussian fuzzy on the region within the boundary of the data point cloud template;
the method for converting the colors of the data point cloud template is a pseudo color JET template coloring method.
2. The stock ground contour line digitizing method according to claim 1, characterized in that: the method for reducing the dimension of the three-dimensional point cloud data comprises the following steps: LDA dimension reduction method.
3. The stock ground contour line digitizing method according to claim 1, characterized in that: the method for repairing the blank area comprises the following steps:
identifying a blank area, and marking a rectangular area of the blank area according to the maximum boundary of the blank area;
and setting the rectangular area as the attention area, and performing image patching on the attention area to finish patching.
4. The stock ground contour line digitizing method according to claim 3, characterized in that: the image inpainting method is a PDE-based image inpainting algorithm, and the vector direction in the PDE-based image inpainting algorithm is opposite angle.
5. The stock ground contour line digitizing method according to claim 1, characterized in that: the method for judging whether the blank area exists on the data point cloud template is a graph outline algorithm.
6. The stock yard contour line digitizing method of claim 1, characterized in that: the external data comprises stockyard stockpile labels, a stockyard track model and a stockyard equipment model.
7. A system for implementing the stock yard contour digitization method of claim 1, wherein the method comprises the following steps: comprises that
The first module is used for acquiring stock ground three-dimensional point cloud data;
the second module is used for generating a blank point cloud template according to the stock yard boundary;
the third module is used for drawing the data subjected to dimensionality reduction to the blank point cloud template after dimensionality reduction of the three-dimensional point cloud data to generate a data point cloud template;
the fourth module is used for judging whether a blank area exists on the data point cloud template; if the data point cloud template exists, repairing the blank area, and then smoothing the data point cloud template by adopting a fuzzy method; if not, smoothing the data point cloud template by adopting a fuzzy method; after the repair is finished, generating a contour line boul graph according to the data point cloud template;
and the fifth module is used for converting the color of the data point cloud template, covering the contour line bool map on the data point cloud template after the color is converted, adding external data used for describing stock ground information to the data point cloud template, and generating a contour line two-dimensional stock ground map.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910576174.5A CN110400252B (en) | 2019-06-28 | 2019-06-28 | Material yard contour line digitalization method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910576174.5A CN110400252B (en) | 2019-06-28 | 2019-06-28 | Material yard contour line digitalization method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110400252A CN110400252A (en) | 2019-11-01 |
| CN110400252B true CN110400252B (en) | 2022-09-06 |
Family
ID=68323907
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910576174.5A Active CN110400252B (en) | 2019-06-28 | 2019-06-28 | Material yard contour line digitalization method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110400252B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113658339B (en) * | 2021-10-19 | 2022-01-21 | 长江水利委员会长江科学院 | Contour line-based three-dimensional entity generation method and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794753A (en) * | 2015-04-29 | 2015-07-22 | 武汉大学 | Earth rock cofferdam breach flood risk mapping method |
| CN107730585A (en) * | 2017-11-06 | 2018-02-23 | 济南市市政工程设计研究院（集团）有限责任公司 | A kind of landform threedimensional model generation method and system |
| CN108470139A (en) * | 2018-01-25 | 2018-08-31 | 天津大学 | A kind of small sample radar image human action sorting technique based on data enhancing |
| CN109242809A (en) * | 2018-10-31 | 2019-01-18 | 安徽中科智链信息科技有限公司 | A kind of point cloud filtering system and filtering method based on RGB-D information |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102214410B (en) * | 2002-11-05 | 2014-01-01 | 亚洲航测株式会社 | Slant red stereoscopic image generator |
| US10297074B2 (en) * | 2017-07-18 | 2019-05-21 | Fuscoe Engineering, Inc. | Three-dimensional modeling from optical capture |
-
2019
- 2019-06-28 CN CN201910576174.5A patent/CN110400252B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794753A (en) * | 2015-04-29 | 2015-07-22 | 武汉大学 | Earth rock cofferdam breach flood risk mapping method |
| CN107730585A (en) * | 2017-11-06 | 2018-02-23 | 济南市市政工程设计研究院（集团）有限责任公司 | A kind of landform threedimensional model generation method and system |
| CN108470139A (en) * | 2018-01-25 | 2018-08-31 | 天津大学 | A kind of small sample radar image human action sorting technique based on data enhancing |
| CN109242809A (en) * | 2018-10-31 | 2019-01-18 | 安徽中科智链信息科技有限公司 | A kind of point cloud filtering system and filtering method based on RGB-D information |
Non-Patent Citations (2)
| Title |
|---|
| cdp陈东强.如何用MATLAB画山地三维曲面图、山地等高线图.《https://jingyan.baidu.com/article/c85b7a6466dfbc003bac95e0.html》.2015, * |
| 张岁兴.基于视觉和振动信息融合的地面类型在线辨识方法研究.《中国优秀博硕士学位论文全文数据库(硕士) 信息科技辑 》.2014,(第3期), * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110400252A (en) | 2019-11-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110176078B (en) | Method and device for labeling training set data | |
| CN110136273B (en) | Sample data labeling method and device used in machine learning | |
| JP6250901B2 (en) | A robot system in which a CNC and a robot controller are connected via a communication network | |
| CN108280886A (en) | Laser point cloud mask method, device and readable storage medium storing program for executing | |
| CN106251353A (en) | Weak texture workpiece and the recognition detection method and system of three-dimensional pose thereof | |
| CN109344804A (en) | A kind of recognition methods of laser point cloud data, device, equipment and medium | |
| CN110378957B (en) | Torpedo tank car visual identification and positioning method and system for metallurgical operation | |
| CN116844068B (en) | Building mapping method, system, computer equipment and storage medium | |
| CN109829458B (en) | Method for automatically generating log file for recording system operation behavior in real time | |
| CN110688687B (en) | Method for batch generation of reclaimed resource survey maps for urban and rural construction land | |
| CN110400252B (en) | Material yard contour line digitalization method and system | |
| WO2021103558A1 (en) | Rgb-d data fusion-based robot vision guiding method and apparatus | |
| CN117173125A (en) | Panoramic view-based defect point location display method, device and storage medium | |
| WO2020245889A1 (en) | Inspection device, control method, and program | |
| CN116348735A (en) | Information processing device, information processing method, and program | |
| CN108898679A (en) | A kind of method of component serial number automatic marking | |
| CN109887054B (en) | Method and device for displaying map | |
| CN110533714A (en) | Method and system based on image processing techniques detection target object maximum inscribed circle | |
| US20080125993A1 (en) | Display-Device Inspection Apparatus and Inspection Method | |
| CN110334818B (en) | Method and system for automatically identifying pipeline | |
| CN116958083A (en) | Motor car bottom bolt looseness detection method based on robot self-adaptive pose adjustment | |
| CN113256756B (en) | Map data display method, device, equipment and storage medium | |
| CN115346041A (en) | Point position marking method, device and equipment based on deep learning and storage medium | |
| Liu et al. | Multi-part deviation assessment in ship assembly structures through instance segmentation | |
| CN114353779A (en) | Method for rapidly updating local cost map of robot by point cloud projection |
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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20220330 Address after: 102208 902, unit 1, building 1, yard 1, Longyu middle street, Huilongguan town, Changping District, Beijing Applicant after: SAMSINO (BEIJING) AUTOMATION ENGINEERING TECHNOLOGY Co.,Ltd. Address before: 561503 Qingshan Town Industrial Park, Pu'an County, Guizhou Province Applicant before: Dong Li Applicant before: Bian Guyue Applicant before: Li Qiang |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20221214 Address after: 102208 902, unit 1, building 1, yard 1, Longyu middle street, Huilongguan town, Changping District, Beijing Patentee after: SAMSINO (BEIJING) AUTOMATION ENGINEERING TECHNOLOGY Co.,Ltd. Patentee after: China Power (Pu'an) Power Generation Co.,Ltd. Address before: 102208 902, unit 1, building 1, yard 1, Longyu middle street, Huilongguan town, Changping District, Beijing Patentee before: SAMSINO (BEIJING) AUTOMATION ENGINEERING TECHNOLOGY Co.,Ltd. |