CN110599449A - Gear scanning algorithm for template matching and point cloud comparison - Google Patents
Gear scanning algorithm for template matching and point cloud comparison Download PDFInfo
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- CN110599449A CN110599449A CN201910701877.6A CN201910701877A CN110599449A CN 110599449 A CN110599449 A CN 110599449A CN 201910701877 A CN201910701877 A CN 201910701877A CN 110599449 A CN110599449 A CN 110599449A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/002—Measuring arrangements characterised by the use of optical techniques for measuring two or more coordinates
- G01B11/005—Measuring arrangements characterised by the use of optical techniques for measuring two or more coordinates coordinate measuring machines
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- 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
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- 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
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- General Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Theoretical Computer Science (AREA)
- Length Measuring Devices By Optical Means (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The invention discloses a gear scanning algorithm for template matching and point cloud comparison, which comprises a line scanning algorithm, a splicing algorithm, a model matching algorithm and a model comparison algorithm, and has the following main working logics: the linear laser scans the surface profile of the workpiece through the movement of the linear module, and the process is analyzed and calculated through a linear scanning algorithm to obtain each section profile of the workpiece; the obtained section outline is spliced in real time through a splicing algorithm to obtain three-dimensional point cloud of an actual workpiece; aligning the theoretical three-dimensional model and the actual three-dimensional point cloud to a benchmark through a model matching algorithm; comparing the two three-dimensional models with the aligned reference to obtain defect information of each gear; according to the method, the rapid algorithm for scanning the gear and determining the defect position is realized through the combination of various algorithms and hardware data, the speed and the accuracy of determining the defect position of the gear are greatly improved, and the determination of the positioning parameters of the subsequently repaired gear is facilitated.
Description
Technical Field
The invention particularly relates to a scanning algorithm, in particular to a gear scanning algorithm for template matching and point cloud comparison.
Background
In the reverse engineering, a point data set of the product appearance surface obtained by a measuring instrument is also called point cloud, the number of points obtained by using a three-dimensional coordinate measuring machine is small, the distance between the points is large, and the point data set is called sparse point cloud; the point clouds obtained by using the three-dimensional laser scanner or the photographic scanner have larger and denser point quantities, and are called dense point clouds. The point cloud obtained according to the laser measurement principle comprises three-dimensional coordinates and laser reflection intensity.
After the grinder is used for a long time, the phenomena of tooth breakage, tooth missing and the like easily occur to the gear of the grinder, for the problems of tooth breakage and tooth missing, the traditional mode is to adopt manual welding and material supplementing to repair the gear, and because of adopting pure manual welding and repairing, the quality of repairing is often dependent on the technical level of workers, the artificial influence factor is large, and the repairing quality cannot be ensured; a large amount of labor is needed for repairing, and the working environment is severe; because the manual repair can not guarantee the uniformity of size, the condition of reprocessing can not be avoided, cause manpower and material resources loss, consequently, at present adopt welding robot to repair very much, but welding robot needs the data information of gear defect position, many defect position information adopt image forming analysis or the mode of scanning to analyze at present, but general defect position location is inaccurate, lack contrast controlled quantity, and the algorithm speed of acquireing the defect position is slower, consequently, to this kind of problem, we need a quick intelligent algorithm to gear defect position location.
Disclosure of Invention
The present invention aims to provide a gear scanning algorithm for template matching and point cloud comparison, so as to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
a gear scanning algorithm for template matching and point cloud comparison comprises a line scanning algorithm, a splicing algorithm, a model matching algorithm and a model comparison algorithm, and the main working logic is as follows:
a. the linear laser scans the surface profile of the workpiece through the movement of the linear module, the scanning frequency is 20 frames/s, and the process is analyzed and calculated through a linear scanning algorithm to obtain each section profile of the workpiece;
b. the obtained section outline is spliced in real time through a splicing algorithm to obtain three-dimensional point cloud of an actual workpiece;
c. aligning the theoretical three-dimensional model and the actual three-dimensional point cloud to a benchmark through a model matching algorithm;
d. and comparing the two three-dimensional models with the aligned reference to obtain the defect information of each gear.
Further scheme: the line laser includes line laser scanner, the sharp module includes linear electric motor, and line laser scanner installs the motion end at linear electric motor, and line laser scanner is just to the gear face, and line laser scanner's data output end connects the PC end that is used for handling the data point, and linear electric motor's scanning velocity of motion is held by the PC and is controlled.
Further scheme: the line scanning algorithm comprises a scanning coordinate system which takes the inside of a line laser as an origin 0, an X, Y axis is arranged on a horizontal plane of the scanning coordinate system, an X axis is a scanning direction, a Z axis is a vertical direction, the line laser periodically emits laser pulses, a precise clock control encoder acquires a horizontal direction angle and a vertical direction angle of the emitted laser beams, the distance S from a scanning point to the origin of coordinates is calculated by the time difference from the emission of the pulse laser to the reflection of a target, from this, a calculation formula of ((x, y, z) three-dimensional coordinates of the scanning point P can be obtained: x = Scos θ cos β, Y = Scos θ sin β, Z = Ssin θ, wherein theta is an included angle between the OP and the XY surface, beta is an included angle between the projection of the OP on the XY surface and the X axis, the cross-sectional profile is a line surface formed by a plurality of P points, and the coordinates of the P points are transmitted to the PC end in a digital quantity model format for processing.
Further scheme: the splicing algorithm comprises scanning frequency, the movement speed of a linear module and coordinate data of a profile, the coordinate data of the profile formed by single scanning is line-surface data, under the driving of the linear module, line laser images the profile of the section of the gear along the scanning direction to form a plurality of profile data, the profile data are spliced along a unified coordinate system to form a complete gear three-dimensional coordinate system point cloud, and the acquisition of the gear point cloud data is completed.
Further scheme: the model matching algorithm comprises three-dimensional point cloud of a gear model and gear point cloud scanned by an actual gear, profile coordinate data formed by single scanning are compared layer by layer in a matching comparison mode, a plurality of P point coordinates acquired by a profile surface are subjected to coordinate data analysis by an F detection method in data analysis in the comparison process, the P point coordinates with obvious coordinate position difference can be determined, the position of the gear with defects is further determined, and scanning and positioning of the gear defect points are completed.
Compared with the prior art, the invention has the beneficial effects that: according to the method, the rapid algorithm for scanning the gear and determining the defect position is realized through the combination of various algorithms and hardware data, the speed and the accuracy of determining the defect position of the gear are greatly improved, and the determination of the positioning parameters of the subsequently repaired gear is facilitated.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the embodiment of the invention, a gear scanning algorithm for template matching and point cloud comparison comprises a line scanning algorithm, a splicing algorithm, a model matching algorithm and a model comparison algorithm, and the main working logic is as follows:
1. the linear laser scans the surface profile of the workpiece through the movement of the linear module, the scanning frequency is 20 frames/s, and the process is analyzed and calculated through a linear scanning algorithm to obtain each section profile of the workpiece;
2. the obtained section outline is spliced in real time through a splicing algorithm to obtain three-dimensional point cloud of an actual workpiece;
3. aligning the theoretical three-dimensional model and the actual three-dimensional point cloud to a benchmark through a model matching algorithm;
4. and comparing the two three-dimensional models with the aligned reference to obtain the defect information of each gear.
The line laser includes line laser scanner, the sharp module includes linear electric motor, and line laser scanner installs the motion end at linear electric motor, and line laser scanner is just to the gear face, and line laser scanner's data output end connects the PC end that is used for handling the data point, and linear electric motor's scanning velocity of motion is held by the PC and is controlled.
The line scanning algorithm comprises a scanning coordinate system which takes the inside of a line laser as an origin 0, an X, Y axis is arranged on a horizontal plane of the scanning coordinate system, an X axis is a scanning direction, a Z axis is a vertical direction, the line laser periodically emits laser pulses, a precise clock control encoder acquires a horizontal direction angle and a vertical direction angle of the emitted laser beams, the distance S from a scanning point to the origin of coordinates is calculated by the time difference from the emission of the pulse laser to the reflection of a target, from this, a calculation formula of ((x, y, z) three-dimensional coordinates of the scanning point P can be obtained: x = Scos θ cos β, Y = Scos θ sin β, Z = Ssin θ, wherein theta is an included angle between the OP and the XY surface, beta is an included angle between the projection of the OP on the XY surface and the X axis, the cross-sectional profile is a line surface formed by a plurality of P points, and the coordinates of the P points are transmitted to the PC end in a digital quantity model format for processing.
The splicing algorithm comprises scanning frequency, the movement speed of a linear module and coordinate data of a profile, the coordinate data of the profile formed by single scanning is line-surface data, under the driving of the linear module, line laser images the profile of the section of the gear along the scanning direction to form a plurality of profile data, the profile data are spliced along a unified coordinate system to form a complete gear three-dimensional coordinate system point cloud, and the acquisition of the gear point cloud data is completed.
The model matching algorithm comprises three-dimensional point cloud of a gear model and gear point cloud scanned by an actual gear, profile coordinate data formed by single scanning are compared layer by layer in a matching comparison mode, a plurality of P point coordinates acquired by a profile surface are subjected to coordinate data analysis by an F detection method in data analysis in the comparison process, the P point coordinates with obvious coordinate position difference can be determined, the position of the gear with defects is further determined, and scanning and positioning of the gear defect points are completed.
The working principle of the invention is as follows: according to the invention, the gear is scanned layer by matching the motion of the linear module through the line laser, the contour coordinate system parameters scanned layer by layer are spliced into point cloud data of the whole gear through the line scanning algorithm and the splicing algorithm, data comparison is carried out according to standard model gear point cloud data in a PC terminal, and the coordinate difference of the actually scanned gear of the model gear box is found out through the model matching algorithm, so that the defect position of the actual gear is determined.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (5)
1. A gear scanning algorithm for template matching and point cloud comparison comprises a line scanning algorithm, a splicing algorithm, a model matching algorithm and a model comparison algorithm, and is characterized in that the main working logic is as follows:
a. the linear laser scans the surface profile of the workpiece through the movement of the linear module, the scanning frequency is 20 frames/s, and the process is analyzed and calculated through a linear scanning algorithm to obtain each section profile of the workpiece;
b. the obtained section outline is spliced in real time through a splicing algorithm to obtain three-dimensional point cloud of an actual workpiece;
c. aligning the theoretical three-dimensional model and the actual three-dimensional point cloud to a benchmark through a model matching algorithm;
d. and comparing the two three-dimensional models with the aligned reference to obtain the defect information of each gear.
2. The gear scanning algorithm for template matching and point cloud comparison as claimed in claim 1, wherein the line laser comprises a line laser scanner, the linear module comprises a linear motor, the line laser scanner is installed at a moving end of the linear motor, the line laser scanner is opposite to the gear surface, a data output end of the line laser scanner is connected with a PC end for processing data points, and a scanning moving speed of the linear motor is controlled by the PC end.
3. The gear scanning algorithm for template matching and point cloud comparison according to claim 1, wherein the line scanning algorithm comprises a scanning coordinate system with the inside of the line laser as an origin 0, X, Y axes are on a horizontal plane of the scanning coordinate system, the X axis is a scanning direction, the Z axis is a vertical direction, laser pulses are periodically emitted by the line laser, the precise clock control encoder obtains a horizontal direction angle and a vertical direction angle of the emitted laser beams, and the distance S from the scanning point to the coordinate origin is calculated from the time difference between the emission of the pulse laser and the receipt of the reflection by the target, thereby obtaining a calculation formula of ((X, Y, Z) three-dimensional coordinates of the scanning point P, wherein X = Scos θ cos β, Y = Scos θ sin β, Z = Ssin θ, where θ is an angle between the OP and the XY plane, β is an angle between the projection of the OP on the XY plane and the X axis, and the section profile is a line plane composed of a plurality of P points, and the coordinates of the point P are transmitted to the PC end in a digital quantity model format for processing.
4. The gear scanning algorithm for template matching and point cloud comparison as claimed in claim 3, wherein the stitching algorithm comprises scanning frequency, motion speed of the linear module and coordinate data of the profile, the coordinate data of the profile formed by single scanning is line-surface data, under the driving of the linear module, line laser images the profile of the section of the gear along the scanning direction to form a plurality of profile data, and the plurality of profile data are stitched along a uniform coordinate system to form a complete gear three-dimensional coordinate system point cloud, thereby completing the collection of the gear point cloud data.
5. The gear scanning algorithm for template matching and point cloud comparison as claimed in claim 3, wherein the model matching algorithm comprises three-dimensional point cloud of the gear model and gear point cloud of actual gear scanning, the matching and comparison mode is to compare layer by layer with contour coordinate data formed by single scanning, and coordinate data analysis is performed on a plurality of P point coordinates acquired by the contour surface in the comparison process by using an F detection method in data analysis, so that the P point coordinates with obvious coordinate position difference can be determined, the position of the gear with defects is further determined, and the scanning and positioning of the gear defect points are completed.
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| CN201910701877.6A CN110599449A (en) | 2019-07-31 | 2019-07-31 | Gear scanning algorithm for template matching and point cloud comparison |
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| CN201910701877.6A CN110599449A (en) | 2019-07-31 | 2019-07-31 | Gear scanning algorithm for template matching and point cloud comparison |
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112598668A (en) * | 2021-03-02 | 2021-04-02 | 北京大成国测科技有限公司 | Defect identification method and device based on three-dimensional image and electronic equipment |
| CN112734662A (en) * | 2020-12-30 | 2021-04-30 | 北京航空航天大学 | Machine vision detection method and system for bevel gear abrasion |
| CN112907508A (en) * | 2021-01-14 | 2021-06-04 | 中国第一汽车股份有限公司 | Point cloud virtual matching device and method with tool as carrier |
| CN113720398A (en) * | 2021-11-01 | 2021-11-30 | 南京光衡科技有限公司 | Full-automatic tile multi-dimensional defect online measurement method |
| CN113781467A (en) * | 2021-09-18 | 2021-12-10 | 崇左南方水泥有限公司 | Gear tooth surface wear condition visual analysis method and system |
| CN113776453A (en) * | 2021-07-28 | 2021-12-10 | 贵阳铝镁设计研究院有限公司 | Aluminum electrolysis cell scanning result automatic splicing method based on laser line profiler array |
| CN113790669A (en) * | 2021-07-28 | 2021-12-14 | 贵阳铝镁设计研究院有限公司 | Aluminum electrolysis cell furnace building result detection method based on laser line profiler array scanning |
| CN114384075A (en) * | 2021-12-06 | 2022-04-22 | 西安理工大学 | Track slab defect online detection system and detection method based on three-dimensional laser scanning |
| CN114413774A (en) * | 2021-12-29 | 2022-04-29 | 国能铁路装备有限责任公司 | Method and device for measuring assembly gap width of bogie to be measured in railway wagon |
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| CN114663403A (en) * | 2022-03-25 | 2022-06-24 | 北京城建设计发展集团股份有限公司 | Prefabricated part assembling surface local defect identification method based on dense scanning data |
| CN115131344A (en) * | 2022-08-25 | 2022-09-30 | 泉州华中科技大学智能制造研究院 | Method for extracting shoe-making molding rubber thread through light intensity data |
| CN115908182A (en) * | 2022-11-25 | 2023-04-04 | 哈尔滨鑫润工业有限公司 | Mold abrasion repairing method based on digital model |
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Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112734662B (en) * | 2020-12-30 | 2022-11-15 | 北京航空航天大学 | Machine vision detection method and system for bevel gear abrasion |
| CN112734662A (en) * | 2020-12-30 | 2021-04-30 | 北京航空航天大学 | Machine vision detection method and system for bevel gear abrasion |
| CN112907508A (en) * | 2021-01-14 | 2021-06-04 | 中国第一汽车股份有限公司 | Point cloud virtual matching device and method with tool as carrier |
| CN112598668A (en) * | 2021-03-02 | 2021-04-02 | 北京大成国测科技有限公司 | Defect identification method and device based on three-dimensional image and electronic equipment |
| CN113776453A (en) * | 2021-07-28 | 2021-12-10 | 贵阳铝镁设计研究院有限公司 | Aluminum electrolysis cell scanning result automatic splicing method based on laser line profiler array |
| CN113790669A (en) * | 2021-07-28 | 2021-12-14 | 贵阳铝镁设计研究院有限公司 | Aluminum electrolysis cell furnace building result detection method based on laser line profiler array scanning |
| CN113781467A (en) * | 2021-09-18 | 2021-12-10 | 崇左南方水泥有限公司 | Gear tooth surface wear condition visual analysis method and system |
| CN113720398A (en) * | 2021-11-01 | 2021-11-30 | 南京光衡科技有限公司 | Full-automatic tile multi-dimensional defect online measurement method |
| CN114384075A (en) * | 2021-12-06 | 2022-04-22 | 西安理工大学 | Track slab defect online detection system and detection method based on three-dimensional laser scanning |
| CN114384075B (en) * | 2021-12-06 | 2024-05-14 | 西安理工大学 | Track slab defect online detection system and detection method based on three-dimensional laser scanning |
| CN114413774A (en) * | 2021-12-29 | 2022-04-29 | 国能铁路装备有限责任公司 | Method and device for measuring assembly gap width of bogie to be measured in railway wagon |
| CN114519711A (en) * | 2022-02-22 | 2022-05-20 | 中冶赛迪重庆信息技术有限公司 | Method, system, medium and electronic terminal for measuring steel coils in depot area |
| CN114519711B (en) * | 2022-02-22 | 2024-07-02 | 中冶赛迪信息技术(重庆)有限公司 | Method, system, medium and electronic terminal for measuring steel coil in reservoir area |
| CN114663403A (en) * | 2022-03-25 | 2022-06-24 | 北京城建设计发展集团股份有限公司 | Prefabricated part assembling surface local defect identification method based on dense scanning data |
| CN114663403B (en) * | 2022-03-25 | 2022-11-18 | 北京城建设计发展集团股份有限公司 | Prefabricated part assembling surface local defect identification method based on dense scanning data |
| CN115131344A (en) * | 2022-08-25 | 2022-09-30 | 泉州华中科技大学智能制造研究院 | Method for extracting shoe-making molding rubber thread through light intensity data |
| CN115131344B (en) * | 2022-08-25 | 2022-11-08 | 泉州华中科技大学智能制造研究院 | Method for extracting shoe-making molding rubber thread through light intensity data |
| CN115908182A (en) * | 2022-11-25 | 2023-04-04 | 哈尔滨鑫润工业有限公司 | Mold abrasion repairing method based on digital model |
| CN115908182B (en) * | 2022-11-25 | 2023-12-29 | 哈尔滨鑫润工业有限公司 | Mold abrasion repairing method based on digital model |
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