CN109781010B - Point cloud data calculation method, device and system for large-range data splicing - Google Patents
Point cloud data calculation method, device and system for large-range data splicing Download PDFInfo
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Abstract
A point cloud data calculation method, device and system for large-range data splicing comprises the following steps: acquiring an image of an object to be scanned on an objective table, determining a region to be scanned according to the image, and calculating position data of the region to be scanned on the objective table; according to the position data and the length of the scanning line, calculating the scanning times and controlling the three-dimensional laser scanning device to start scanning; acquiring a scanned image, and calculating the initial position and the end position of an effective interval in the scanned image and point cloud data in the scanned image; the end position of the effective interval in the previous scanning image is used as a scanning starting point, the three-dimensional laser scanning device is controlled to continue scanning the next scanning image, and the start position, the end position and point cloud data of the effective interval of the next scanning image are calculated; and repeating the previous step until the scanning of the area to be scanned is finished, and accumulating the point cloud data of all the scanned images. The invention can measure the point cloud data of the object with the size larger than the length of the laser line.
Description
Technical Field
The invention relates to the technical field of visual detection and image measurement, in particular to a point cloud data calculation method, device and system for large-range data splicing.
Background
The three-dimensional line scanning technology adopts a non-contact type to acquire point cloud data of a target surface, and can continuously, automatically and quickly acquire a large amount of three-dimensional data of the target surface according to a triangular distance measurement principle. Fig. 1 shows a manner of acquiring point cloud data by using a three-dimensional laser scanner. The three-dimensional laser scanner emits a beam of laser to a target object, the laser line sequentially sweeps the surface of the target object by moving the carrying platform, and the laser scanning area is the size of point cloud data.
However, the main disadvantages of this approach are: the point cloud data after one-time motion scanning can be obtained, and when the size of the object in the laser line direction is larger than the length of the laser line, the point cloud data of the part exceeding the length of the laser line cannot be obtained, so that the measuring range is limited.
Disclosure of Invention
The invention provides a point cloud data calculation method, a point cloud data calculation device and a point cloud data calculation system for large-range data splicing, which can measure point cloud data of objects with the size larger than the length of a laser line and expand the measurement range.
According to a first aspect of the present invention, the present invention provides a point cloud data calculation method for large-scale data stitching, comprising the following steps:
(1) acquiring an image of an object to be scanned on an object stage, determining a region to be scanned according to the image, and calculating position data of the region to be scanned on the object stage;
(2) according to the position data and the length of the scanning line, calculating the scanning times, and controlling the three-dimensional laser scanning device to start scanning from the initial position of the area to be scanned;
(3) acquiring a scanned image scanned by a three-dimensional laser scanning device, and calculating the initial position and the end position of an effective interval of a scanning line in the scanned image and point cloud data in the scanned image;
(4) the end position of the effective interval of the scanning line in the previous scanning image is used as a scanning starting point, the three-dimensional laser scanning device is controlled to continue scanning the next scanning image, and the starting position and the end position of the effective interval of the scanning line in the next scanning image and point cloud data in the scanning image are calculated;
(5) and repeating the previous step until the scanning of the area to be scanned is finished, and accumulating the point cloud data of all the scanned images.
Preferably, the calculating point cloud data in the scanned image specifically includes: and calculating the distance between the starting position and the ending position of the effective interval in the scanned image, and calculating the point cloud data in the scanned image according to the distance.
Preferably, when there is a gap between the end position of the scanned image and the start position of the next scanned image, the interval of the gap is calculated, and the point cloud data generated by the interval of the gap is added when the point cloud data of all the scanned images are accumulated.
Preferably, when there is an overlapping portion between the effective sections of two adjacent scan images, the distance of the overlapping portion is calculated, and when the point cloud data of all scan images are accumulated, the point cloud data generated by the distance of the overlapping portion is subtracted.
Preferably, the number of the scan lines in the scan image is multiple, and the calculating the start position and the end position of the effective interval of the scan lines in the scan image specifically includes: calculating the starting point position (S1, S2,.. Sn) and the end point position (E1, E2,.. En) of each scanning line in the scanning image, calculating the confidence positions and the confidence intervals of the starting points and the end points of the scanning lines based on a normal distribution function, determining the starting position of a scanning line effective interval in the scanning image to be S +3 σ S, and determining the starting and ending positions of a scanning line effective interval in the scanning image to be E-3 σ E, wherein n is the number of the scanning lines in the scanning image, S is the average value of the starting point positions of all the scanning lines, E is the average value of the end point positions of all the scanning lines, σ S is the starting confidence interval, and σ E is the ending confidence interval.
Preferably, the calculating the number of scanning times according to the position data and the length of the scanning line specifically includes: and calculating the memory value of the area to be scanned according to the position data, calculating the memory value of a single scanning image according to the length of the scanning line, and calculating the scanning times according to the memory value of the area to be scanned and the memory value of the single scanning image.
Preferably, the determining the region to be scanned according to the image specifically includes: and receiving a scanning area selected by a user, and setting the scanning area selected by the user as the area to be scanned.
Preferably, the length of the scanning line is obtained by calculation through a preset algorithm.
According to a second aspect of the present invention, there is provided a point cloud data calculation apparatus for wide-range data stitching, comprising: the area determining module is used for acquiring an image of an object to be scanned on the objective table, determining an area to be scanned according to the image, and calculating position data of the area to be scanned on the objective table; the scanning preparation module is used for calculating the scanning times according to the position data and the length of the scanning line and controlling the three-dimensional laser scanning device to start scanning from the initial position of the area to be scanned; the scanning starting module is used for acquiring a scanning image scanned by the three-dimensional laser scanning device, and calculating the starting position and the ending position of an effective interval of a scanning line in the scanning image and point cloud data in the scanning image; the repeated scanning module is used for controlling the three-dimensional laser scanning device to continue scanning the next scanning image by taking the end position of the effective interval of the scanning line in the previous scanning image as a scanning starting point, and calculating the starting position and the end position of the effective interval of the scanning line in the next scanning image and point cloud data in the scanning image; and the data calculation module is used for repeatedly executing the steps performed by the repeated scanning module until the scanning of the area to be scanned is finished and accumulating the point cloud data of all the scanned images.
According to a third aspect of the present invention, the present invention provides a point cloud data computing system for large-scale data stitching, comprising a stage for placing an object to be scanned, a moving mechanism for moving the stage, a camera located above the stage, and a three-dimensional laser scanning device, and further comprising a controller respectively connected to the moving mechanism, the camera, and the three-dimensional laser scanning device, wherein the controller is configured to perform the following steps:
(1) acquiring an image of an object to be scanned on an object stage, determining a region to be scanned according to the image, and calculating position data of the region to be scanned on the object stage; (2) according to the position data and the length of the scanning line, calculating the scanning times, and controlling the three-dimensional laser scanning device to start scanning from the initial position of the area to be scanned; (3) acquiring a scanned image scanned by a three-dimensional laser scanning device, and calculating the initial position and the end position of an effective interval of a scanning line in the scanned image and point cloud data in the scanned image; (4) the end position of the effective interval of the scanning line in the previous scanning image is used as a scanning starting point, the three-dimensional laser scanning device is controlled to continue scanning the next scanning image, and the starting position and the end position of the effective interval of the scanning line in the next scanning image and point cloud data in the scanning image are calculated; (5) and repeating the previous step until the scanning of the area to be scanned is finished, and accumulating the point cloud data of all the scanned images.
In the invention, the three-dimensional laser scanning device scans the area to be scanned for multiple times, calculates the point cloud data of each scanning, and starts the next scanning from the termination position of the last scanning, so that the scanning area is uninterrupted, the accuracy of data calculation is improved, and finally the point cloud data of all scanned images are accumulated, the point cloud data of the object with the size larger than the length of the laser line can be calculated, and the measuring range is expanded.
Drawings
FIG. 1 is a schematic diagram of a three-dimensional laser scanning system according to the prior art;
FIG. 2 is a schematic structural diagram of a point cloud data computing system for large-scale data stitching according to an embodiment of the present invention;
FIG. 3 is a flow chart of a point cloud data calculation method for large-scale data stitching according to an embodiment of the present invention;
FIG. 4 is a schematic view of a scanned image according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a scan path according to an embodiment of the present invention;
FIG. 6 is a graphical representation of the confidence interval of FIG. 4.
Detailed Description
Before explaining the present invention in detail, it is necessary to describe the hardware configuration used in the present invention. As shown in fig. 2, the structural diagram of a point cloud data computing system for large-scale data stitching according to an embodiment includes a stage 1, a moving mechanism, a camera 2 and a three-dimensional laser scanning device 3, which are located above the stage 1, and further includes a controller respectively connected to the moving mechanism, the camera 2 and the three-dimensional laser scanning device 3. The objective table 1 is used for placing an object to be scanned, and the point cloud data calculation method is suitable for more regular objects, particularly objects with relatively flat surfaces, and can be cuboid objects. The moving mechanism is used for moving the object stage 1 on a plane, so that the object stage 1 can move transversely or longitudinally, specifically, the moving mechanism and the object stage 1 can be arranged on a base, and the moving mechanism drives the object stage 1 to move relative to the base. The camera 2 and the three-dimensional laser scanning device 3 are both fixed above the objective table 1, in the scanning process, the positions of the camera 2 and the three-dimensional laser scanning device 3 are relatively fixed, and the objective table 1 drives the object to be scanned to move relative to the three-dimensional laser scanning device 3, so that the scanning is completed through the relative movement. The camera 2 is used for taking a picture containing the object to be scanned and sending the picture to the controller. The three-dimensional laser scanning device 3 emits laser lines for scanning and can perform induction imaging according to the reflection principle. The controller may be a control chip and its related circuits, which are used for data calculation and sending out control commands.
The embodiment of the invention provides a point cloud data calculation method for large-range data splicing, which is explained from the perspective of a controller and comprises the following steps as shown in fig. 3:
s100: the method comprises the steps of obtaining an image of an object to be scanned on an object stage, determining a region to be scanned according to the image, and calculating position data of the region to be scanned on the object stage.
The camera shoots an image containing an object to be detected, and the whole object stage can be shot in the image. Since the range of the shot image is often larger than the size of the object to be detected, the whole image is unnecessary to be scanned and detected, and the position of the object to be scanned needs to be further determined. To this end, two embodiments are provided:
in one embodiment, based on the captured image, the object to be scanned in the image can be identified through an image recognition algorithm, the outline of the object to be scanned is outlined, and according to the outline, a region to be scanned larger than the object to be scanned can be automatically set, and the boundary of the region to be scanned is positioned outside the outline, so that the complete object to be scanned is contained.
In another embodiment, a setting instruction of the user may be accepted, the setting instruction includes a region selected by the user, and the region selected by the user is set as the region to be scanned. In order to prevent the error of the area selected by the user, whether the object to be scanned is completely positioned in the area selected by the user can be judged after receiving a setting instruction of the user, and if the object to be scanned is not completely positioned in the area selected by the user, an error alarm can be sent out for the user to reselect the area. And if the scanning area is completely positioned in the area selected by the user, setting the area selected by the user as the area to be scanned.
The area to be scanned can be in a square frame shape, and a certain distance is reserved between the boundary of the area to be scanned and the outline of the object to be scanned, so that the object to be scanned is prevented from not being completely contained in the area to be scanned. Once the area to be scanned is determined, subsequent calculations are performed for the area to be scanned. Further, position data of the region to be scanned on the stage is calculated, and the position data may include coordinate position of the region to be scanned on the stage coordinates, coordinates of the boundary, area of the region, and the like.
S200: and calculating the scanning times according to the position data and the length of the scanning line, and controlling the three-dimensional laser scanning device to start scanning from the initial position of the area to be scanned.
The radiation angle of the scanning line and the distance between the scanning line and the object stage are known, and the length of the scanning line can be calculated through the radiation angle and the distance, or the position of the scanning line is sensed by the three-dimensional laser scanning device 4, and the length of the scanning line is calculated based on the principle of triangular distance measurement. The position data calculated in step S100 includes the length of the region to be scanned, and the number of times of scanning can be calculated by dividing the length of the region to be scanned by the length of the scanning line. The controller can control the three-dimensional laser scanning device to start scanning from the initial scanning position of the area to be scanned after the scanning times are calculated.
S300: and acquiring a scanned image scanned by the three-dimensional laser scanning device, and calculating the initial position and the end position of the effective interval of the scanning line in the scanned image and point cloud data in the scanned image.
After the scanning is started, a first scanned image scanned by the three-dimensional laser scanning device is obtained, as shown in fig. 4, since a certain distance exists between the scanning line and the boundary of the scanned image, an effective interval actually effective in the scanned image of the scanning line needs to be calculated, specifically including an effective start coordinate position and an effective end coordinate position of the effective interval on the stage, and the distance of the effective interval can be calculated based on the start coordinate position and the end coordinate position.
As shown in fig. 5, the three-dimensional laser scanning device moves along the width direction of the object to be scanned, and the distance of the scanning movement matches the width of the object to be scanned, so that the product of the distance of the effective interval and the distance of the scanning movement is the point cloud data of the scanned image.
As indicated by the scanning path in fig. 5, the three-dimensional laser scanning device moves along the width direction of the object to be scanned from one end of the length direction of the object to be scanned, and after the scanning of the first image is completed, the three-dimensional laser scanning device will translate along the length direction of the object to be scanned, and perform the subsequent scanning in the shape of the path of the rectangular wave.
S400: and controlling the three-dimensional laser scanning device to continuously scan the next scanning image by taking the end position of the effective scanning line interval in the previous scanning image as a scanning starting point, and calculating the starting position and the end position of the effective scanning line interval in the next scanning image and point cloud data in the scanning image.
And continuing to scan the next scanned image, wherein the end position of the effective interval of the previous scanned image is a scanning starting point, and the scanning starting point refers to a scanning starting point in the length direction, so that two adjacent scanned images are not interrupted, and the data obtained by accumulating is more accurate. And further calculating the starting position and the ending position of the effective interval of the scanning lines in the next scanning image and the point cloud data in the scanning image.
S500: and repeating the previous step until the scanning of the area to be scanned is finished, and accumulating the point cloud data of all the scanned images.
Step S400 is repeatedly performed until the region to be scanned is completely scanned. In this way, a plurality of scan images are obtained, each scan image including the start position and the end position of the effective interval and the point cloud data in the scan image. And accumulating the point cloud data of all the scanned images to obtain the final point cloud data of the whole object to be scanned.
In an embodiment, in step S300 and step S400, the step of calculating point cloud data in the scanned image specifically includes:
the starting point and the ending point of the effective interval can be determined through an image recognition algorithm, the coordinates of the starting position and the ending position of the effective interval in the scanned image are further calculated, the distance between the starting position and the ending position is calculated, and the product of the distance and the distance of scanning movement is point cloud data of the scanned image.
Furthermore, when there is a gap between the end position of the scanned image and the start position of the next scanned image, it indicates that the scanned image cannot be scanned continuously. Since the coordinates of the start position and the end position of the effective interval of two adjacent scanned images are calculated, the coordinates of the end position of the scanned image and the coordinates of the start position of the next scanned image can be compared, and if the two coordinates are spaced apart in the length direction, a gap is determined to exist.
At this time, the distance of the gap may be calculated according to the coordinates of the ending position of the scanned image and the coordinates of the starting position of the next scanned image, and the product of the distance of the gap and the distance of the scanning movement is the point cloud data of the gap. When the point cloud data of all the scanning images are accumulated, the point cloud data generated by the interval of the gap are added, so that the accuracy of the point cloud data can be improved.
Further, when there is an overlapping portion between the effective sections of two adjacent scanned images, which indicates that the scanning position of the next scanned image is closer to the previous scanned image, the coordinates of the end position of the scanned image and the coordinates of the start position of the next scanned image may be compared, and if the coordinates of the latter are located behind the former in the length direction, there is an overlapping portion.
At this time, the distance of the overlapping portion may be calculated according to the coordinates of the ending position of the scanned image and the coordinates of the starting position of the next scanned image, and the product of the distance of the overlapping portion and the distance of the scanning movement is the point cloud data of the overlapping portion. When the point cloud data of all the scanning images are accumulated, the point cloud data generated by the distance of the overlapped part is subtracted, so that the accuracy of the point cloud data can be further improved.
In one embodiment, as shown in FIG. 6, the scan lines in the scan image have a plurality of lines, step S300 and step S400. The calculating of the start position and the end position of the effective interval of the scanning line in the scanned image specifically includes:
calculating the starting point position (S1, S2,.. Sn) and the end point position (E1, E2,.. En) of each scanning line in the scanning image, calculating the confidence positions and the confidence intervals of the starting points and the end points of the scanning lines based on a normal distribution function, determining the starting position of a scanning line effective interval in the scanning image to be S +3 σ S, and determining the starting and ending positions of a scanning line effective interval in the scanning image to be E-3 σ E, wherein n is the number of the scanning lines in the scanning image, S is the average value of the starting point positions of all the scanning lines, E is the average value of the end point positions of all the scanning lines, σ S is the starting confidence interval, and σ E is the ending confidence interval. To improve the accuracy of the scanning as much as possible, n may be set to be greater than 500.
In an embodiment, in step S200, the calculating the number of scanning times according to the position data and the length of the scanning line specifically includes: and calculating the memory value of the area to be scanned according to the position data, calculating the memory value of a single scanning image according to the length of the scanning line, and calculating the scanning times according to the memory value of the area to be scanned and the memory value of the single scanning image.
In the algorithm execution process, a storage address is allocated to each image, a memory value corresponding to the image needs to be calculated, and the memory value can directly reflect the data size of the image, so that the scanning times can be obtained by dividing the memory value of the area to be scanned by the memory value of a single scanned image.
The embodiment of the present invention further provides a point cloud data computing apparatus for large-scale data stitching, as shown in fig. 6, including: the region determining module 100 is configured to acquire an image of an object to be scanned on an object stage, determine a region to be scanned according to the image, and calculate position data of the region to be scanned on the object stage; a scanning preparation module 200, configured to calculate the scanning times according to the position data and the length of the scanning line, and control the three-dimensional laser scanning device to start scanning from the initial position of the region to be scanned; a start scanning module 300, configured to obtain a scanned image scanned by the three-dimensional laser scanning device, and calculate a start position and an end position of a scan line effective interval in the scanned image and point cloud data in the scanned image; the repeated scanning module 400 is used for controlling the three-dimensional laser scanning device to continue scanning the next scanned image by taking the end position of the effective interval of the scanning line in the previous scanned image as a scanning starting point, and calculating the start position and the end position of the effective interval of the scanning line in the next scanned image and point cloud data in the scanned image; and the data calculation module 500 is configured to repeatedly execute the steps performed by the repeated scanning module 400 until the scanning of the area to be scanned is completed, and accumulate point cloud data of all scanned images.
The embodiment of the present invention further provides a point cloud data computing system for large-scale data stitching, as shown in fig. 1, including an object stage 1 for placing an object to be scanned, a moving mechanism for moving the object stage, a camera 2 and a three-dimensional laser scanning device 3, which are located above the object stage 1, and further including a controller respectively connected to the moving mechanism, the camera 2 and the three-dimensional laser scanning device 3, where the controller is configured to execute the following steps:
(1) acquiring an image of an object to be scanned on an object stage, determining a region to be scanned according to the image, and calculating position data of the region to be scanned on the object stage; (2) according to the position data and the length of the scanning line, calculating the scanning times, and controlling the three-dimensional laser scanning device to start scanning from the initial position of the area to be scanned; (3) acquiring a scanned image scanned by a three-dimensional laser scanning device, and calculating the initial position and the end position of an effective interval of a scanning line in the scanned image and point cloud data in the scanned image; (4) the end position of the effective interval of the scanning line in the previous scanning image is used as a scanning starting point, the three-dimensional laser scanning device is controlled to continue scanning the next scanning image, and the starting position and the end position of the effective interval of the scanning line in the next scanning image and point cloud data in the scanning image are calculated; (5) and repeating the previous step until the scanning of the area to be scanned is finished, and accumulating the point cloud data of all the scanned images.
The above description of the point cloud data computing device and system for large-range data stitching may refer to an embodiment of a point cloud data computing method for large-range data stitching, which is not repeated herein.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. It will be apparent to those skilled in the art that a number of simple derivations or substitutions can be made without departing from the inventive concept.
Claims (9)
1. A point cloud data calculation method for large-range data splicing is characterized by comprising the following steps:
(1) acquiring an image of an object to be scanned on an object stage, determining a region to be scanned according to the image, and calculating position data of the region to be scanned on the object stage;
(2) according to the position data and the length of the scanning line, calculating the scanning times, and controlling the three-dimensional laser scanning device to start scanning from the initial position of the area to be scanned;
(3) acquiring a scanned image scanned by a three-dimensional laser scanning device, and calculating the initial position and the end position of an effective interval of a scanning line in the scanned image and point cloud data in the scanned image;
(4) the end position of the effective interval of the scanning line in the previous scanning image is used as a scanning starting point, the three-dimensional laser scanning device is controlled to continue scanning the next scanning image, and the starting position and the end position of the effective interval of the scanning line in the next scanning image and point cloud data in the scanning image are calculated;
(5) repeatedly executing the previous step until the scanning of the area to be scanned is finished, and accumulating point cloud data of all scanned images;
the method for calculating the start position and the end position of the effective interval of the scanning lines in the scanned image comprises the following steps:
calculating the starting point position (S1, S2,.. Sn) and the end point position (E1, E2,.. En) of each scanning line in the scanning image, calculating the confidence positions and the confidence intervals of the starting points and the end points of the scanning lines based on a normal distribution function, determining the starting position of a valid interval of the scanning lines in the scanning image to be S +3 σ S, and determining the end position of the valid interval of the scanning lines in the scanning image to be E-3 σ E, wherein n is the number of the scanning lines in the scanning image, S is the average value of the starting point positions of all the scanning lines, E is the average value of the end point positions of all the scanning lines, σ S is the starting confidence interval, and σ E is the ending confidence interval.
2. The method according to claim 1, wherein the calculating point cloud data within the scanned image comprises:
and calculating the distance between the starting position and the ending position of the effective interval in the scanned image, and calculating the point cloud data in the scanned image according to the distance.
3. The method of claim 2, wherein:
when there is a gap between the end position of the scanned image and the start position of the next scanned image, the interval of the gap is calculated, and when the point cloud data of all the scanned images are accumulated, the point cloud data generated by the interval of the gap is added.
4. The method of claim 2, wherein:
when the effective intervals of two adjacent scanning images have an overlapping part, the distance of the overlapping part is calculated, and when the point cloud data of all the scanning images are accumulated, the point cloud data generated by the distance of the overlapping part is subtracted.
5. The method according to claim 1, wherein calculating the number of scans based on the position data and the length of the scan line comprises: and calculating the memory value of the area to be scanned according to the position data, calculating the memory value of a single scanning image according to the length of the scanning line, and calculating the scanning times according to the memory value of the area to be scanned and the memory value of the single scanning image.
6. The method according to claim 1, wherein the determining the region to be scanned from the image specifically comprises:
and receiving a scanning area selected by a user, and setting the scanning area selected by the user as the area to be scanned.
7. The method of claim 1, wherein:
the length of the scanning line is obtained by calculation through a preset algorithm.
8. A point cloud data computing device using extensive data stitching according to the method of claim 1, comprising:
the area determining module is used for acquiring an image of an object to be scanned on the objective table, determining an area to be scanned according to the image, and calculating position data of the area to be scanned on the objective table;
the scanning preparation module is used for calculating the scanning times according to the position data and the length of the scanning line and controlling the three-dimensional laser scanning device to start scanning from the initial position of the area to be scanned;
the scanning starting module is used for acquiring a scanning image scanned by the three-dimensional laser scanning device, and calculating the starting position and the ending position of an effective interval of a scanning line in the scanning image and point cloud data in the scanning image;
the repeated scanning module is used for controlling the three-dimensional laser scanning device to continue scanning the next scanning image by taking the end position of the effective interval of the scanning line in the previous scanning image as a scanning starting point, and calculating the starting position and the end position of the effective interval of the scanning line in the next scanning image and point cloud data in the scanning image;
and the data calculation module is used for repeatedly executing the steps performed by the repeated scanning module until the scanning of the area to be scanned is finished and accumulating the point cloud data of all the scanned images.
9. A point cloud data computing system using extensive data stitching according to the method of claim 1, wherein:
the device comprises an object stage for placing an object to be scanned, a moving mechanism for moving the object stage, a camera positioned above the object stage, a three-dimensional laser scanning device and a controller respectively connected with the moving mechanism, the camera and the three-dimensional laser scanning device, wherein the controller is configured to execute the following steps:
(1) acquiring an image of an object to be scanned on an object stage, determining a region to be scanned according to the image, and calculating position data of the region to be scanned on the object stage;
(2) according to the position data and the length of the scanning line, calculating the scanning times, and controlling the three-dimensional laser scanning device to start scanning from the initial position of the area to be scanned;
(3) acquiring a scanned image scanned by a three-dimensional laser scanning device, and calculating the initial position and the end position of an effective interval of a scanning line in the scanned image and point cloud data in the scanned image;
(4) the end position of the effective interval of the scanning line in the previous scanning image is used as a scanning starting point, the three-dimensional laser scanning device is controlled to continue scanning the next scanning image, and the starting position and the end position of the effective interval of the scanning line in the next scanning image and point cloud data in the scanning image are calculated;
(5) and repeating the previous step until the scanning of the area to be scanned is finished, and accumulating the point cloud data of all the scanned images.
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| JP4665350B2 (en) * | 2001-07-06 | 2011-04-06 | コニカミノルタホールディングス株式会社 | measuring device |
| JP4429184B2 (en) * | 2005-02-02 | 2010-03-10 | Ｎｅｃエンジニアリング株式会社 | Three-dimensional shape measurement system and measurement method |
| CN103679741B (en) * | 2013-12-30 | 2017-01-18 | 北京建筑大学 | Method for automatically registering cloud data of laser dots based on three-dimensional line characters |
| CN103955939B (en) * | 2014-05-16 | 2018-06-19 | 重庆理工大学 | 3 D scanning system midpoint cloud edge feature point method for registering |
| CN104006762B (en) * | 2014-06-03 | 2017-01-04 | 大族激光科技产业集团股份有限公司 | Obtain the methods, devices and systems of object dimensional information |
| CN104408055B (en) * | 2014-10-29 | 2018-03-13 | 中国石油天然气股份有限公司 | The storage method and device of a kind of laser radar point cloud data |
| CN106091976B (en) * | 2016-05-27 | 2017-07-25 | 武汉大学 | The automatic detection of cuboid and three-dimensional reconfiguration system and method |
| CN106441087B (en) * | 2016-08-15 | 2019-04-16 | 南京工业大学 | A kind of more size Multi-parameter Measurement Methods of workpiece based on image procossing |
| CN106338245B (en) * | 2016-08-15 | 2019-05-10 | 南京工业大学 | A kind of non-contact traverse measurement method of workpiece |
| CN109238168B (en) * | 2018-08-06 | 2019-11-26 | 大连理工大学 | Large-scale metrology part surface three dimension shape high-precision measuring method |
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