CN110763148A - Automatic extraction method for multi-station three-dimensional laser point cloud target ball data - Google Patents
Automatic extraction method for multi-station three-dimensional laser point cloud target ball data Download PDFInfo
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- CN110763148A CN110763148A CN201911058569.2A CN201911058569A CN110763148A CN 110763148 A CN110763148 A CN 110763148A CN 201911058569 A CN201911058569 A CN 201911058569A CN 110763148 A CN110763148 A CN 110763148A
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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
- G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
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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
- G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
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- General Physics & Mathematics (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention discloses a method for automatically extracting multi-station three-dimensional laser point cloud target ball data. The method adopts the algorithms of intensity threshold filtering, region growing method segmentation, PCA, least square iterative elimination, overall least square fitting calculation and the like to realize the automatic extraction of the multi-station three-dimensional laser point cloud target sphere data. The method changes the traditional method of manually selecting the point cloud data of the target sphere and then performing the sphere center fitting calculation, and can efficiently realize the automatic extraction of the multi-target and the fitting calculation of the sphere center.
Description
Technical Field
The invention belongs to the technical field of automatic extraction of three-dimensional laser point cloud target ball data, and particularly relates to an automatic extraction method of multi-station three-dimensional laser point cloud target ball data.
Background
The three-dimensional laser scanner is different from a general total station and cannot acquire the position of a fixed point through a prism. In consideration of the view angle deformation factor, the three-dimensional laser scanner is usually used for measuring coordinates of a certain point in actual engineering, and the fitted sphere centers are still the same point theoretically no matter which view angle the target sphere point cloud data is acquired from. In the field of three-dimensional modeling, registration of multi-station data and transformation of a geographic reference coordinate system are generally realized by adopting a target ball. High-precision deformation monitoring can be carried out on the fixed point through the target ball in the field of deformation monitoring.
In the prior art, the method of manual frame selection is basically adopted to confirm the point cloud data of the target sphere and then the sphere center is obtained through fitting calculation. The method needs to find a target ball which is relatively much smaller in a large scene, and the target ball is distinguished by human eyes, so that time and labor are wasted, misjudgment is possible to occur on the target ball with poor data, the method cannot be integrated into other algorithms, and the method becomes an obstacle of an automatic process.
Disclosure of Invention
In order to solve the problems, the invention discloses an automatic extraction method of multi-station three-dimensional laser point cloud target ball data, which changes the conventional method that the target ball point cloud data needs to be manually selected and then the center of sphere is fitted and calculated, and can efficiently realize the automatic extraction of multi-targets and the fitting and calculation of the center of sphere.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a multi-station three-dimensional laser point cloud target ball data automatic extraction method comprises the following steps:
(1) filtering the point cloud data by using an intensity threshold value to simplify the data and separate different point cloud blocks;
(2) dividing and storing different point cloud blocks by using a region growing method;
(3) removing non-target point cloud blocks by utilizing PCA-LS iterative calculation, and providing high-quality sphere center parameters for subsequent overall least square fitting sphere center calculation;
(4) and calculating and resolving the sphere center parameters and the coincidence precision information by adopting integral least square fitting, and realizing full-automatic extraction of the multi-station three-dimensional laser point cloud target sphere data.
The invention has the beneficial effects that:
the method for automatically extracting the multi-station three-dimensional laser point cloud target ball data can realize the full-automatic extraction of the three-dimensional laser point cloud target ball data under the multi-station data, and improves the measurement efficiency and the accuracy of the data; the overall error of the extracted data is reduced.
Drawings
FIG. 1 is a block diagram of the present invention.
Detailed Description
The present invention will be further illustrated with reference to the accompanying drawings and specific embodiments, which are to be understood as merely illustrative of the invention and not as limiting the scope of the invention.
The invention relates to a method for automatically extracting multi-station three-dimensional laser point cloud target ball data, which comprises the following steps of:
1. and reading the point cloud data file, and setting information such as an intensity threshold, an accuracy threshold, a PCA threshold, a known radius and the like according to known information.
2. And the intensity threshold is utilized to dig and fetch the high-intensity point cloud block and perform thinning according to the density, so that the operation data amount is reduced. And then filtering by using an intensity threshold value, filtering edge points with weak intensity information, and separating different point cloud blocks.
3. And (3) dividing different point cloud blocks by using a region growing method, setting thresholds such as point number, xyz component and volume, and preliminarily removing non-standard target point cloud blocks.
4. And assuming that the point cloud is a target ball point cloud, calculating the approximate sphere center of the point cloud according to a certain algorithm, and providing an initial value of the sphere center for the subsequent LS.
5. And performing PCA calculation on the point cloud block data, and eliminating the point cloud with the ratio of the maximum component to the second maximum component exceeding a threshold value. And performing fitting calculation and error analysis on the obtained point cloud data by using least squares, and performing iterative calculation until no coarse difference point and no error in unit weight change amount are smaller than a certain threshold. And judging whether the internal coincidence precision exceeds the limit, if so, rejecting the internal coincidence precision, and continuously processing the next point cloud block.
6. And carrying out integral least square adjustment on the point cloud blocks meeting the conditions, and carrying out iterative calculation until the error change amount in the unit weight is smaller than a certain threshold value. And storing the target point cloud and the sphere center information, and continuously processing the next point cloud block.
7. And repeating the steps 4 to 6 until all the point cloud blocks which are segmented are processed.
As shown in the figure, the first and second,
"input parameters" refers to setting parameters according to known information, such as threshold of intensity filtering, PCA threshold, least square fit precision threshold, etc.;
the 'region growing method segmentation storage' refers to storing segmented point cloud blocks meeting certain conditions;
the 'approximate sphere center calculation' means that the point cloud is assumed to be a target sphere point cloud, and the approximate sphere center of the point cloud is calculated according to a certain algorithm to provide an initial value of the sphere center for the subsequent LS;
the 'threshold overrun' means that PCA (principal component analysis) and LS (least squares) iterative computation are carried out on the point cloud to respectively obtain the point cloud dimension distribution proportion and the internally-conforming precision information, and the point clouds with uneven normal plane dimension distribution and over-low precision are removed; and traversing all the segmented target point cloud blocks until all the point cloud blocks are processed.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features.
Claims (1)
1. A multi-station three-dimensional laser point cloud target ball data automatic extraction method comprises the following steps:
(1) filtering the point cloud data by using an intensity threshold value to simplify the data and separate different point cloud blocks;
(2) dividing and storing different point cloud blocks by using a region growing method;
(3) removing non-target point cloud blocks by utilizing PCA-LS iterative calculation, and providing high-quality sphere center parameters for subsequent overall least square fitting sphere center calculation;
(4) and calculating and resolving the sphere center parameters and the coincidence precision information by adopting integral least square fitting, and realizing full-automatic extraction of the multi-station three-dimensional laser point cloud target sphere data.
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| CN201911058569.2A CN110763148A (en) | 2019-11-01 | 2019-11-01 | Automatic extraction method for multi-station three-dimensional laser point cloud target ball data |
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| CN201911058569.2A CN110763148A (en) | 2019-11-01 | 2019-11-01 | Automatic extraction method for multi-station three-dimensional laser point cloud target ball data |
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| CN110763148A true CN110763148A (en) | 2020-02-07 |
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Application publication date: 20200207 |