CN114677424A - Point cloud data processing method for unattended screw ship unloader - Google Patents
Point cloud data processing method for unattended screw ship unloader Download PDFInfo
- Publication number
- CN114677424A CN114677424A CN202210580690.7A CN202210580690A CN114677424A CN 114677424 A CN114677424 A CN 114677424A CN 202210580690 A CN202210580690 A CN 202210580690A CN 114677424 A CN114677424 A CN 114677424A
- Authority
- CN
- China
- Prior art keywords
- ship
- coordinate system
- point cloud
- point
- vertical arm
- 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
- 238000003672 processing method Methods 0.000 title claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 19
- 238000012937 correction Methods 0.000 claims description 13
- 238000000034 method Methods 0.000 claims description 12
- 230000008859 change Effects 0.000 claims description 11
- 238000001914 filtration Methods 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 7
- 239000000463 material Substances 0.000 description 14
- 230000008569 process Effects 0.000 description 4
- 238000013461 design Methods 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 238000007599 discharging Methods 0.000 description 2
- 239000000428 dust Substances 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/50—Depth or shape recovery
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G67/00—Loading or unloading vehicles
- B65G67/60—Loading or unloading ships
- B65G67/606—Loading or unloading ships using devices specially adapted for bulk material
-
- 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
- G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
-
- 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
- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
- G06T3/4007—Scaling of whole images or parts thereof, e.g. expanding or contracting based on interpolation, e.g. bilinear interpolation
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G2201/00—Indexing codes relating to handling devices, e.g. conveyors, characterised by the type of product or load being conveyed or handled
- B65G2201/04—Bulk
- B65G2201/045—Sand, soil and mineral ore
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
- G06T2200/08—Indexing scheme for image data processing or generation, in general involving all processing steps from image acquisition to 3D model generation
-
- 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
- G06T2207/10028—Range image; Depth image; 3D point clouds
-
- 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
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Ocean & Marine Engineering (AREA)
- Aviation & Aerospace Engineering (AREA)
- Mechanical Engineering (AREA)
- Computer Graphics (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- Ship Loading And Unloading (AREA)
Abstract
The invention provides a point cloud data processing method for an unattended screw ship unloader, which solves the problems of data processing and the like and comprises the following steps: s1: determining a reference coordinate system of a wharf, selecting a ship mark point on a ship, and selecting a vertical arm mark point on a ship unloader; s2: measuring the positions of a ship mark point and a vertical arm mark point in real time through a positioning instrument; s3: the scanner collects a two-dimensional polar coordinate system of the ship and fuses with a reference coordinate system to form three-dimensional point cloud; s4: taking the ship mark points and the vertical arm mark points as grid reference points, and carrying out gridding processing on the reference coordinate system; s5: calculating the deviation value of the grid reference point relative to the standard coordinate system subjected to gridding processing in real time; s6: and importing the deviation value of each grid reference point into the three-dimensional point cloud, and performing linear interpolation to complete compensation. The invention has the advantages of good data processing effect and the like.
Description
Technical Field
The invention belongs to the technical field of ship unloaders, and particularly relates to a point cloud data processing method for an unattended screw ship unloader.
Background
In port industry, a conveying system has the characteristics of multiple types, large particle size deviation, large water content deviation, large conveying capacity and the like, so that the adaptability of a transfer system is high, the maintenance is simple and convenient, and the design of the transfer system is very challenging. However, although the traditional transfer system has the advantages of convenient design and installation, simple operation and the like, the material flow cannot be well controlled, the problems of material blockage, abrasion, material scattering, dust raising and the like easily occur, and in order to solve the problem of dust raising, the screw ship unloader is widely applied to closed unloading. It is a high-efficiency continuous bulk cargo ship unloader. The ship unloading machine mainly works on a horizontal screw conveyor without a flexible traction member, a vertical screw conveyor and a special material taking device. However, in the actual operation process, the unloading state needs to be observed manually, so that the unloading efficiency is low. In addition, when automatic discharging is adopted, the conventional laser scanning mode has poor processing effect on the state data of ships and internal materials.
In order to solve the defects of the prior art, people have long searched for and put forward various solutions. For example, chinese patent literature discloses an automated material taking method [201910387658.5] for an unmanned bucket-chain type continuous ship unloader, which includes the steps of: acquiring point cloud coordinate data of the surface of the material pile; and acquiring real-time hatch boundary position data. Forming a stock pile point cloud coordinate model; layering a material pile point cloud coordinate model to obtain a plane material taking area; comparing the regional characteristics of the standard stockpile to determine the material taking process action and executing the automatic material taking task in the region; after the material taking task in the area is completed, the completion state is fed back and the next material taking operation area is applied to the scanning system; and the scanning system sends the next material taking operation area to the continuous ship unloader control system.
The scheme solves the problem of low discharging efficiency to a certain extent, but the scheme still has a plurality of defects, such as poor data processing effect and the like.
Disclosure of Invention
The invention aims to solve the problems and provides a point cloud data processing method of an unattended screw ship unloader, which is reasonable in design and good in data processing effect.
In order to achieve the purpose, the invention adopts the following technical scheme: a point cloud data processing method for an unattended screw ship unloader comprises the following steps:
s1: determining a reference coordinate system of a wharf, selecting a ship mark point on a ship, and selecting a vertical arm mark point on a ship unloader;
s2: measuring the positions of a ship mark point and a vertical arm mark point in real time through a positioning instrument;
s3: a scanner collects a two-dimensional polar coordinate system of a ship and is fused with a reference coordinate system to form three-dimensional point cloud;
s4: taking the ship mark points and the vertical arm mark points as grid reference points, and carrying out gridding processing on the reference coordinate system;
s5: calculating the deviation value of the grid reference point relative to the standard coordinate system subjected to gridding processing in real time;
s6: and importing the deviation value of each grid reference point into the three-dimensional point cloud, and performing linear interpolation to complete compensation. The method comprises the steps of measuring mark points of a ship and mark points of a vertical arm in real time, decoupling vibration of a ship sinking and floating transverse roller and the vertical arm on the sea, further decoupling three-dimensional point cloud data acquired by a scanner through gridding treatment, correcting and compensating the three-dimensional point cloud, and accordingly improving the point cloud data processing effect.
In the above point cloud data processing method for the unattended screw ship unloader, the step S1 includes the following steps:
s11: selecting base points on the wharf and the ship unloader base respectively, and establishing a reference coordinate system by taking the base points on the wharf as an original point and matching with a vertical instrument;
s12: the method comprises the following steps that a plurality of longitudinal marking points are longitudinally arranged on a ship, a plurality of transverse marking points are transversely arranged on the ship, and a plurality of vertical marking points are vertically arranged on the ship;
s13: the vertical arm of the ship unloader is provided with a plurality of vertical arm mark points along the vertical direction. After the reference coordinate system is selected, the ship and the vertical arm are subjected to multi-point marking, and data acquisition errors are reduced.
In the above point cloud data processing method for the unattended screw ship unloader, the step S2 includes the following steps:
s21: comparing the ship mark point with a reference coordinate system through a positioning instrument, and measuring the X, Y, Z coordinates of the ship, the roll angle, the pitch angle and the course angle in real time;
s22: the vertical arm mark point is compared with a reference coordinate system through the positioning instrument, and the change of the coordinates of the vertical arm X, Y, Z, the roll angle, the pitch angle and the course angle is measured in real time. And the motion state of the mark point is monitored in real time, and the distortion of the laser radar under longitudinal and transverse displacement is solved.
In the above point cloud data processing method for the unattended screw ship unloader, the step S3 includes the following steps:
s31: the scanner is arranged on a vertical arm and a wharf of the ship, and the ship unloader adjusts the position of the vertical arm of the ship and scans the ship from multiple directions;
s32: collecting multi-azimuth point cloud data, deleting redundant data and filtering isolated points;
s33: a ship digital-analog database is called and is fitted with the point cloud;
s34: and integrating the fitted data to a reference coordinate system to generate a three-dimensional point cloud. The ship is scanned in multiple directions, and the prestored ship data are matched, so that the point cloud is quickly fitted, and the quality of the three-dimensional point cloud is improved.
In the above point cloud data processing method for the unattended screw ship unloader, in step S31, a part of the scanner is installed on the unmanned aerial vehicle to scan dead corners of the ship. And selecting a proper scanning mode according to actual needs.
In the above point cloud data processing method for the unattended screw ship unloader, in step S32, after the point cloud data is collected, the internal empty points are compensated by linear interpolation and filtered. And the point cloud data is preprocessed after being acquired, so that the data processing quality is ensured.
In the above point cloud data processing method for the unattended screw ship unloader, the step S4 includes the following steps:
s41: a reference coordinate system divides a coordinate grid on a horizontal plane, and projection points are arranged on the coordinate grid by ship mark points and vertical arm mark points;
s42: selecting a grid where each projection point is located, taking a grid endpoint closest to the origin of the reference coordinate system as the origin, and establishing a correction coordinate system;
s43: and recording the coordinate positions of each ship mark point and each vertical arm mark point relative to the correction coordinate system. And decoupling the position change of the ship mark point and the vertical arm mark point by establishing a correction coordinate system.
In the above point cloud data processing method for the unattended screw ship unloader, the step S5 includes the following steps:
s51: monitoring the change amplitude of the ship mark point and the vertical arm mark point in a unit time relative to the correction coordinate system;
s52: and leading the change amplitudes of the ship mark points and the vertical arm mark points into the three-dimensional point cloud. And (4) performing secondary correction on the three-dimensional point cloud to effectively feed back the state of the ship.
In the above point cloud data processing method for the unattended screw ship unloader, the scanner adopts any one or more of laser scanning modeling, depth camera modeling, photography modeling and light field modeling. And selecting a proper scanning mode according to the requirement, and carrying out adaptive treatment on different ships.
In the point cloud data processing method for the unattended screw ship unloader, the reference coordinate system adopts the Beidou positioning system to determine the positions of the ship and the vertical arm. The reference coordinate system is accurately positioned by the Beidou positioning system, and external networking is facilitated.
Compared with the prior art, the invention has the advantages that: the three-dimensional point cloud is decoupled by a gridded reference coordinate system, so that a better data processing effect is achieved; the azimuth changes of the ship and the vertical arm are measured in real time, so that interference collision of the ship is avoided; and filtering the point cloud data to improve subsequent processing efficiency.
Drawings
FIG. 1 is a schematic flow diagram of the present invention.
Fig. 2 is a schematic flow chart of establishing a mark point according to the present invention.
Fig. 3 is a schematic flow chart of establishing a three-dimensional point cloud according to the present invention.
Fig. 4 is a schematic flow chart of the gridding process according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1 to 4, a point cloud data processing method for an unmanned screw ship unloader includes the following steps:
s1: determining a reference coordinate system of a wharf, selecting a ship mark point on a ship, and selecting a vertical arm mark point on a ship unloader;
s2: measuring the positions of a ship mark point and a vertical arm mark point in real time through a positioning instrument;
s3: the scanner collects a two-dimensional polar coordinate system of the ship and fuses with a reference coordinate system to form three-dimensional point cloud;
s4: taking the ship mark points and the vertical arm mark points as grid reference points, and carrying out gridding processing on the reference coordinate system;
s5: calculating the deviation value of the grid reference point relative to the standard coordinate system subjected to gridding processing in real time;
s6: and importing the deviation value of each grid reference point into the three-dimensional point cloud, and performing linear interpolation to complete compensation. After a reference coordinate system is determined, the position and the posture of a ship mark point and a vertical arm mark point are monitored in real time by a positioning instrument, a scanner scans a ship and generates three-dimensional point cloud, the reference coordinate system is subjected to gridding treatment and secondary positioning is carried out on the ship mark point and the vertical arm mark point, and the three-dimensional point cloud is subjected to correction compensation after decoupling treatment of each mark point.
Specifically, step S1 includes the steps of:
s11: selecting base points on the wharf and the ship unloader base respectively, and establishing a reference coordinate system by taking the base points on the wharf as an original point and matching with a vertical instrument;
s12: the method comprises the following steps that a plurality of longitudinal marking points are longitudinally arranged on a ship, a plurality of transverse marking points are transversely arranged on the ship, and a plurality of vertical marking points are vertically arranged on the ship;
s13: the vertical arm of the ship unloader is provided with a plurality of vertical arm mark points along the vertical direction. The ship mark points are symmetrically distributed on the ship and provided with corresponding signal transmitting ends for the position locator to determine the position, and the wharf and the ship unloader base are relatively static to ensure that a reference coordinate system is fixed.
Further, step S2 includes the steps of:
s21: comparing the ship mark point with a reference coordinate system through a positioning instrument, and measuring the X, Y, Z coordinates of the ship, the roll angle, the pitch angle and the course angle in real time;
s22: the vertical arm mark point is compared with a reference coordinate system through the positioning instrument, and the change of the coordinates of the vertical arm X, Y, Z, the roll angle, the pitch angle and the course angle is measured in real time. The position of each mark point is monitored by the positioning instrument, wherein a reference coordinate system is an X axis by selecting the track position of the ship unloader cart, the sea side and the land side are Y axes, and the vertical direction of the wharf is a Z axis.
Further, step S3 includes the following steps:
s31: the scanner is arranged on a vertical arm and a wharf of the ship, and the ship unloader adjusts the position of the vertical arm of the ship and scans the ship from multiple directions;
s32: collecting multi-azimuth point cloud data, deleting redundant data and filtering isolated points;
s33: a ship digital-analog database is called and is fitted with the point cloud;
s34: and integrating the fitted data to a reference coordinate system to generate a three-dimensional point cloud.
Further, in step S31, a part of the scanner is installed on the drone to scan dead corners of the ship.
In addition, in step S32, after the point cloud data is collected, the internal empty points are compensated by linear interpolation and filtered. And compensating the point cloud by the linear interpolation, and completing point cloud data.
Meanwhile, step S4 includes the steps of:
s41: a reference coordinate system divides a coordinate grid on a horizontal plane, and projection points are arranged on the coordinate grid by ship mark points and vertical arm mark points;
s42: selecting a grid where each projection point is located, taking a grid endpoint closest to the origin of the reference coordinate system as the origin, and establishing a correction coordinate system;
s43: and recording the coordinate positions of each ship mark point and each vertical arm mark point relative to the correction coordinate system. Each mark point is relatively independently operated and coupled with surrounding point clouds, so that the correction rate of partial point clouds is improved.
As can be seen, step S5 includes the steps of:
s51: monitoring the change amplitude of the ship mark point and the vertical arm mark point in a unit time relative to the correction coordinate system;
s52: and leading the change amplitudes of the ship mark points and the vertical arm mark points into the three-dimensional point cloud. And after the change amplitude of the mark points is led into the three-dimensional point cloud, the motion interval of each point is led out, and the ship is avoided in the descending process of the vertical arm.
It will be apparent that the scanner employs any one or more of a combination of laser scanning modeling, depth camera modeling, photography modeling and light field modeling. The scanner selects laser scanning, adjusts the direction along with the movement of the vertical arm and the ship unloader, and collects ship point cloud data from multiple angles.
Preferably, the reference coordinate system adopts a Beidou positioning system to determine the positions of the ship and the vertical arm. The Beidou positioning system is matched with a Beidou position and pose positioning instrument to collect longitude, latitude, elevation and course angle of the antenna, collect roll angle and pitch angle of the mounting position of the mark point, calculate the coordinate of a reference coordinate system of the position of the antenna and simultaneously calculate the wharf coordinate system coordinate of each point of the rigid connecting part of the antenna.
In summary, the principle of the present embodiment is: and determining a reference coordinate system, acquiring the three-dimensional point cloud through a scanner, meshing the reference coordinate system, decoupling each mark and the surrounding point cloud, calculating the offset in real time, compensating and correcting the three-dimensional point cloud, and improving the point cloud data processing effect.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Although the terms ship unloader, ship, etc. are used more herein, the possibility of using other terms is not excluded. These terms are used merely to more conveniently describe and explain the nature of the present invention; they are to be construed as being without limitation to any additional limitations that may be imposed by the spirit of the present invention.
Claims (10)
1. A point cloud data processing method for an unattended screw ship unloader is characterized by comprising the following steps:
s1: determining a reference coordinate system of a wharf, selecting a ship mark point on a ship, and selecting a vertical arm mark point on a ship unloader;
s2: measuring the positions of a ship mark point and a vertical arm mark point in real time through a positioning instrument;
s3: the scanner collects a two-dimensional polar coordinate system of the ship and fuses with a reference coordinate system to form three-dimensional point cloud;
s4: taking the ship mark points and the vertical arm mark points as grid reference points, and carrying out gridding processing on the reference coordinate system;
s5: calculating the deviation value of the grid reference point relative to the standard coordinate system subjected to gridding processing in real time;
s6: and importing the deviation value of each grid reference point into the three-dimensional point cloud, and performing linear interpolation to complete compensation.
2. The point cloud data processing method for the unmanned screw ship unloader as claimed in claim 1, wherein the step S1 comprises the steps of:
s11: selecting base points on the wharf and the ship unloader base respectively, and establishing a reference coordinate system by taking the base points on the wharf as an original point and matching with a vertical instrument;
s12: the method comprises the following steps that a plurality of longitudinal marking points are longitudinally arranged on a ship, a plurality of transverse marking points are transversely arranged on the ship, and a plurality of vertical marking points are vertically arranged on the ship;
s13: the vertical arm of the ship unloader is provided with a plurality of vertical arm mark points along the vertical direction.
3. The point cloud data processing method for the unmanned screw ship unloader as claimed in claim 1, wherein the step S2 comprises the steps of:
s21: comparing the ship mark point with a reference coordinate system through a positioning instrument, and measuring the X, Y, Z coordinates of the ship, the roll angle, the pitch angle and the course angle in real time;
s22: the vertical arm mark point is compared with a reference coordinate system through the positioning instrument, and the change of the coordinates of the vertical arm X, Y, Z, the roll angle, the pitch angle and the course angle is measured in real time.
4. The point cloud data processing method for the unmanned screw ship unloader as claimed in claim 1, wherein the step S3 comprises the steps of:
s31: the scanner is arranged on a vertical arm and a wharf of the ship, and the ship unloader adjusts the position of the vertical arm of the ship and scans the ship from multiple directions;
s32: collecting multi-azimuth point cloud data, deleting redundant data and filtering isolated points;
s33: a ship digital-analog database is called and is fitted with the point cloud;
s34: and integrating the fitted data to a reference coordinate system to generate a three-dimensional point cloud.
5. The method as claimed in claim 4, wherein in step S31, part of the scanner is installed on the UAV to scan dead corners of the ship.
6. The method as claimed in claim 4, wherein in step S32, the point cloud data is collected and the internal empty points are compensated by linear interpolation and filtered.
7. The point cloud data processing method for the unmanned screw ship unloader as claimed in claim 1, wherein the step S4 comprises the steps of:
s41: a coordinate grid is divided on a horizontal plane by the reference coordinate system, and projection points are arranged on the coordinate grid by the ship mark points and the vertical arm mark points;
s42: selecting a grid where each projection point is located, taking a grid endpoint closest to the origin of the reference coordinate system as the origin, and establishing a correction coordinate system;
s43: and recording the coordinate positions of each ship mark point and each vertical arm mark point relative to the correction coordinate system.
8. The point cloud data processing method for the unmanned screw ship unloader of claim 7, wherein the step S5 comprises the steps of:
s51: monitoring the change amplitude of the ship mark point and the vertical arm mark point in a unit time relative to the correction coordinate system;
s52: and leading the change amplitudes of the ship mark points and the vertical arm mark points into the three-dimensional point cloud.
9. The method for processing point cloud data of the unmanned screw ship unloader as claimed in claim 1, wherein the scanner adopts any one or more combination of laser scanning modeling, depth camera modeling, photography modeling and light field modeling.
10. The point cloud data processing method of the unmanned screw ship unloader of claim 1, wherein the reference coordinate system adopts a Beidou positioning system to determine the pose of the ship and the vertical arm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210580690.7A CN114677424B (en) | 2022-05-26 | 2022-05-26 | Point cloud data processing method for unattended screw ship unloader |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210580690.7A CN114677424B (en) | 2022-05-26 | 2022-05-26 | Point cloud data processing method for unattended screw ship unloader |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114677424A true CN114677424A (en) | 2022-06-28 |
| CN114677424B CN114677424B (en) | 2022-09-06 |
Family
ID=82079247
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210580690.7A Active CN114677424B (en) | 2022-05-26 | 2022-05-26 | Point cloud data processing method for unattended screw ship unloader |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114677424B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107883932A (en) * | 2017-11-16 | 2018-04-06 | 国家海洋局第二海洋研究所 | A kind of measuring system and method for being applicable islands and reefs and seashore |
| CN107991691A (en) * | 2017-11-13 | 2018-05-04 | 武汉华中天纬测控有限公司 | A kind of satellite navigation locating accuracy verification device and method |
| US20180332480A1 (en) * | 2017-05-10 | 2018-11-15 | Facebook, Inc. | Multi-Hop Wireless Network Planning |
| CN111380504A (en) * | 2019-12-02 | 2020-07-07 | 中国铁路沈阳局集团有限公司科学技术研究所 | Boundary gridding management method for building equipment |
| WO2020150904A1 (en) * | 2019-01-22 | 2020-07-30 | SZ DJI Technology Co., Ltd. | Neural network based obstacle detection for mobile platforms, and associated systems and methods |
| CN111750794A (en) * | 2020-06-11 | 2020-10-09 | 长江三峡通航管理局 | Ship chamber deformation monitoring method of ship lift based on point cloud data analysis |
| CN112545543A (en) * | 2021-02-19 | 2021-03-26 | 南京安科医疗科技有限公司 | Scanning motion monitoring method, system and storage medium based on sickbed motion information |
| CN113034569A (en) * | 2021-03-05 | 2021-06-25 | 武汉科技大学 | Point cloud data-based ship overrun early warning method and system |
| CN113359105A (en) * | 2021-06-15 | 2021-09-07 | 武汉米佳信息技术有限公司 | Method and system for generating three-dimensional point cloud data based on two-dimensional laser radar |
| CN114119866A (en) * | 2021-11-12 | 2022-03-01 | 东南大学 | Point cloud data-based urban road intersection visual evaluation method |
-
2022
- 2022-05-26 CN CN202210580690.7A patent/CN114677424B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180332480A1 (en) * | 2017-05-10 | 2018-11-15 | Facebook, Inc. | Multi-Hop Wireless Network Planning |
| CN107991691A (en) * | 2017-11-13 | 2018-05-04 | 武汉华中天纬测控有限公司 | A kind of satellite navigation locating accuracy verification device and method |
| CN107883932A (en) * | 2017-11-16 | 2018-04-06 | 国家海洋局第二海洋研究所 | A kind of measuring system and method for being applicable islands and reefs and seashore |
| WO2020150904A1 (en) * | 2019-01-22 | 2020-07-30 | SZ DJI Technology Co., Ltd. | Neural network based obstacle detection for mobile platforms, and associated systems and methods |
| CN111380504A (en) * | 2019-12-02 | 2020-07-07 | 中国铁路沈阳局集团有限公司科学技术研究所 | Boundary gridding management method for building equipment |
| CN111750794A (en) * | 2020-06-11 | 2020-10-09 | 长江三峡通航管理局 | Ship chamber deformation monitoring method of ship lift based on point cloud data analysis |
| CN112545543A (en) * | 2021-02-19 | 2021-03-26 | 南京安科医疗科技有限公司 | Scanning motion monitoring method, system and storage medium based on sickbed motion information |
| CN113034569A (en) * | 2021-03-05 | 2021-06-25 | 武汉科技大学 | Point cloud data-based ship overrun early warning method and system |
| CN113359105A (en) * | 2021-06-15 | 2021-09-07 | 武汉米佳信息技术有限公司 | Method and system for generating three-dimensional point cloud data based on two-dimensional laser radar |
| CN114119866A (en) * | 2021-11-12 | 2022-03-01 | 东南大学 | Point cloud data-based urban road intersection visual evaluation method |
Non-Patent Citations (2)
| Title |
|---|
| SHI SHUSHAN等: "A Method of Power Line Hanging Point Location Based on LiDAR Data", 《2019 IEEE 3RD ADVANCED INFORMATION MANAGEMENT, COMMUNICATES, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IMCEC)》 * |
| 王林: "基于多模信息融合的采煤机精准定位方法", 《中国优秀硕士学位论文全文数据库》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114677424B (en) | 2022-09-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111090103B (en) | Three-dimensional imaging device and method for dynamically and finely detecting underwater small target | |
| CN105352476B (en) | Boat-carrying waterfront line underwater integrated measuring system integrated approach waterborne | |
| CN102980512A (en) | Fixed type automatic volume measurement system and measuring method thereof | |
| CN112363153B (en) | Material pile edge detection method and system | |
| US20230348237A1 (en) | Mapping of a Crane Spreader and a Crane Spreader Target | |
| CN111829434B (en) | Material flow metering detection method and system | |
| CN113625299A (en) | Three-dimensional laser radar-based method and device for detecting height and unbalance loading of loading material | |
| CN112858483B (en) | Phased array probe scanning track self-correcting system and method | |
| CN112945137A (en) | Storage ore deposit scanning equipment based on single line laser radar and distancer | |
| CN112669376B (en) | Method and system for calculating layer height of residual stack of material pile | |
| CN115908539A (en) | Target volume automatic measurement method and device and storage medium | |
| CN112462373A (en) | Cabin position detection method based on multi-sensor fusion | |
| CN114677424B (en) | Point cloud data processing method for unattended screw ship unloader | |
| US11783465B2 (en) | Flying drone for inspecting surfaces, and method for inspecting surfaces by such a flying drone | |
| CN112829891A (en) | High-precision ship draught real-time measuring system based on inertial sensing technology | |
| CN209871804U (en) | Scanning identification system of unmanned chain bucket type continuous ship unloader | |
| CN111694009A (en) | Positioning system, method and device | |
| AU2017274080B2 (en) | Three dimensional object mapping | |
| CN114782359A (en) | Industrial conveyor belt surface defect detection method based on data heterogeneous fusion | |
| CN114879180A (en) | Seamless situation perception method for real-time fusion of unmanned ship-borne multi-element multi-scale radar | |
| CN213677075U (en) | High-precision ship draught real-time measuring system based on inertial sensing technology | |
| Chotiros et al. | Seafloor roughness measurement from a ROV | |
| CN113848825B (en) | AGV state monitoring system and method for flexible production workshop | |
| CN114067080B (en) | Method and system for acquiring material pile elevation model | |
| CN113313745B (en) | Stockpile identification system and method for stock yard |
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 |