CN114923413A - Automatic discrimination method for point cloud steel structure quality based on three-dimensional laser scanner - Google Patents
Automatic discrimination method for point cloud steel structure quality based on three-dimensional laser scanner Download PDFInfo
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Abstract
The invention relates to a point cloud steel structure quality automatic judging method based on a three-dimensional laser scanner, which solves the problems that the existing tunnel steel structure construction quality inspection technology has larger subjective intention and can not completely express the tunnel steel structure construction quality through point cloud analysis, pretreatment, feature extraction, point cloud segmentation/single-point feature analysis, target identification, point cloud classification and judgment result acquisition.
Description
Technical Field
The invention belongs to the technical field of tunnel engineering construction, and particularly relates to a point cloud steel frame steel bar quality automatic judging method based on a three-dimensional laser scanner.
Background
In the tunnel construction process, the primary support steel arch frame spacing, the transverse vertical deviation of the steel arch frame, the perpendicularity of the steel arch frame, the steel arch frame connecting rib spacing, the lining steel bar layer spacing, the lining steel bar protective layer thickness and the like are important items for construction quality inspection. The traditional operation mode is that a site quality inspector uses a steel tape measure to measure or a total station to measure, and the traditional operation mode is a point-to-point inspection mode.
The method is limited by an field operation platform, operation efficiency and internal data processing capacity, and often, section intervals and point position intervals on the same section are large, the randomness of the determined point positions is large, and the actual situation that measured data cannot truly and completely reflect on site easily occurs, such as that key arch center distance deviation data just occurs between two points measured by a quality inspector. Meanwhile, the traditional operation mode is greatly influenced by experience, subjective will and the like of operators, and is not beneficial to the promotion of standardized, informationized and intelligent construction of safety quality management in the tunnel construction process.
Disclosure of Invention
Because the manual tape measure adopted in the prior art is used for measuring the distance between the steel bar and the steel arch frame, in tunnel construction engineering, high-altitude operation is needed in a plurality of places, the potential safety hazard caused by the high-altitude operation is large, the consumed time is long, and the site construction is influenced. Moreover, manual measurement has one-sidedness, and the quality problem cannot be comprehensively displayed, and belongs to a rough statistical method by using point and area. The invention does not need manual measurement after development and application, and adopts a 3D scanner to scan the whole point cloud, thereby consuming less time, having high safety factor and comprehensive results.
The invention provides a point cloud steel frame steel bar quality automatic distinguishing method based on a three-dimensional laser scanner, aiming at the problems, the method solves the problems that the subjective intention of the existing tunnel steel structure construction quality inspection technology is large, and the tunnel steel structure construction quality cannot be completely reflected.
The invention is realized by the following measures:
a point cloud steel structure quality automatic distinguishing method based on a three-dimensional laser scanner comprises the following steps:
step one, point cloud analysis: scanning a target by using a three-dimensional laser scanner to obtain steel structure three-dimensional point cloud data;
step two, pretreatment: importing the three-dimensional point cloud data into software and preprocessing the three-dimensional point cloud data to obtain a steel structure point cloud model;
step three, feature extraction: plane extraction, edge detection and feature descriptor calculation are mainly realized by two methods of artificial design and deep learning;
step four, point cloud segmentation: grouping points into a part or an object based on low-level attributes;
step five, target identification: identifying a steel structure in the model;
step six, obtaining a judgment result: and analyzing the steel structure to obtain the construction parameter data of the steel structure, thereby accurately judging the construction quality of the steel structure.
And further, the fifth step also comprises point cloud classification, wherein the point clouds are classified into different point cloud sets, and similar or same attributes are divided into the same point cloud set.
Further, in the step one, the target is a tunnel section.
Further, in the second step, a method for preprocessing the three-dimensional point cloud data comprises the following steps: and automatically filtering and downsampling according to the quality and scale of the point cloud data.
Further, in the third step, feature extraction is the key for describing the point cloud morphological structure, and the basis and premise for semantic information extraction.
Further, in step four, the point cloud segmentation process further processes and analyzes each object to make it richer in information than processes or analyzes each point individually.
Further, in step five, the target recognition is generally performed by performing analysis according to the results of feature extraction and segmentation, and based on a priori knowledge under given constraints and rules.
Further, in the sixth step, the construction parameter data are spacing, number, shape, diameter, length and volume.
Further, the steel structure is a steel bar or a steel arch.
Further, the point cloud model in the second step is obtained after noise reduction processing.
The method steps may be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory.
Furthermore, the method is additionally provided with a hole filling method, the method can be used for quickly primarily filling the holes by using a preset rule after point cloud triangulation is carried out, and the positions of filling points added in the primary filling process are restrained and adjusted by using the method, so that the filling part and the surrounding grids are kept consistent in shape characteristics, the holes are intelligently and quickly repaired, the filling naturalness is ensured, and the shape of the object to be measured is not changed.
The invention has the beneficial effects that:
1. after the technology is applied to on-site actual construction, standardization and informatization of safety quality management in the tunnel construction process can be effectively improved, and quality out-of-control risks caused by quality inspection workers are reduced;
2. by adopting an integrated and standardized management program, the requirements of quality inspection personnel can be effectively reduced, the personnel structure is simplified, and the project benefit is improved;
3. the visual measurement result can effectively record various problems in the construction process, effectively improve the discovery rate of potential quality hazards in the construction process and avoid reworking caused by quality problems in the later period;
4. and carrying out noise reduction processing on the point cloud data to obtain a more effective point cloud data set.
5. The point cloud hole filling method has the advantages that after point cloud triangular meshing is conducted, the point cloud hole filling can be conducted on the hole through rapid initial filling through a preset rule, the positions of filling points added in the initial filling process are restrained and adjusted through the method, so that the filling portion and surrounding grids are kept consistent in shape characteristics, intelligent and rapid hole repairing is achieved, filling naturalness is guaranteed, and the shape of an object to be measured cannot be changed.
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FIG. 1 is a flow chart of the point cloud processing of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Based on a three-dimensional laser scanner point cloud steel structure quality automatic judging method, a steel structure is a steel bar or a steel arch, and the method comprises the following steps:
step one, point cloud analysis: scanning a target by using a three-dimensional laser scanner, wherein the target can be a tunnel section, and acquiring steel structure three-dimensional point cloud data;
step two, pretreatment: importing the three-dimensional point cloud data into software, preprocessing the three-dimensional point cloud data, and automatically filtering and downsampling according to the quality and scale of the point cloud data to obtain a steel structure point cloud model; because the steel structure is closely connected with the rock wall, the steel structure point cloud data are accurately kept, other interference point clouds are removed, the point cloud automatic filtering algorithm has high requirements, completely intelligent automatic filtering can be really achieved, a set of single algorithm can be achieved, the best implementation scheme is machine learning through a large amount of data, and before the automatic filtering is performed, computer preliminary filtering is adopted, and manual fine rejection is performed;
step three, feature extraction: plane extraction, edge detection and feature descriptor calculation are mainly realized by two methods of artificial design and deep learning; the feature extraction is a key for describing a point cloud morphological structure, and a basis and a premise for extracting semantic information;
step four, point cloud segmentation and/or single point feature analysis: grouping points into a part or an object based on low-level attributes; compared with the point cloud segmentation which processes or analyzes each point independently, the point cloud segmentation process processes and analyzes each object further, so that the point cloud segmentation has richer information;
step five, target identification: identifying a steel structure in the model; target recognition is usually performed by performing analysis based on the results of feature extraction and segmentation, and based on a priori knowledge under given constraints and rules; after the steel structure characteristic is identified, points are supplemented to the shielding part according to linearity, and thus hierarchical steel structure net structure result data can be obtained. The point-complementing algorithm comprises a parameter calculation rule, including a point cloud point-taking method, a calculation rule, a weighting condition and the like. Theoretical calculation rules can be quickly formed through a mathematical model, and then the adjustment is carried out by continuously combining practices until the algorithm result accords with the actual expectation, and the data has high confidence.
Then, point cloud classification is carried out, the point clouds are classified into different point cloud sets, and similar or same attributes are divided into the same point cloud set;
step six, obtaining a judgment result: and analyzing the steel structure to obtain the construction parameter data (spacing, quantity, shape, diameter, length and volume) of the steel structure, so that the construction quality of the steel structure is accurately judged.
Example 2
The difference between this embodiment and the specific embodiment 1 is that the point cloud model in the step two is obtained after the noise reduction processing. The specific noise reduction processing method comprises the steps of firstly reading point cloud, removing useless information by using a direct filtering method, and then removing large-scale noise by using the direct statistical filtering or radius filtering.
For the statistical filtering mode, calculating the distance value of the data point adjacent area, counting the number in the distance average value and setting a distance threshold value, removing the changed point when the distance average value is not less than the distance threshold value, and keeping the data point if the distance average value is less than the distance threshold value;
for the radius filtering mode, setting a radius, setting the number of adjacent areas of a certain data point, setting whether the number of the adjacent areas in the radius is larger than the number of the set adjacent areas, if not, removing the data point, and if so, keeping the data point;
and then removing small-size noise by using a moving least square method, estimating smoothness and detecting a denoising effect, judging whether the normal directions are consistent, returning to a statistical filtering or radius filtering mode to perform denoising again if the normal directions are inconsistent, and obtaining a denoised point cloud model until the normal directions are consistent.
Example 3
This particular example adds implementation carriers for the various steps to example 2, and the method steps in this example may be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The method can use standard programming techniques, and each program can be implemented in a high level procedural or object oriented programming language to communicate with a computer system. The program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of the processes described in the methods may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described by the present methods (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof, the computer programs comprising a plurality of instructions executable by the one or more processors.
Example 4
The embodiment is different from the above specific embodiments 1-3, and a hole filling method is additionally added, so that when the three-dimensional scanning device scans an object to be measured to generate point clouds, the generated point clouds often have holes missing due to the reason of the scanning device itself or the interference of other external factors. These holes must be filled through an algorithm for subsequent use. Aiming at the point cloud holes, the following hole repairing method can be adopted, wherein in the first step, the point cloud of the holes to be repaired is read, and the read point cloud is triangulated; secondly, searching holes needing to be filled by using the characteristics of the holes; thirdly, initially filling each hole by using a preset rule; fourthly, adjusting the positions of the filling points added in the initial filling process by utilizing the method phase constraint to ensure that the filling part is consistent with the surrounding grids on the shape characteristics; and fifthly, smoothing the filled part and the surrounding grids. By using the method, after point cloud triangulation is carried out, the hole can be rapidly initially filled by using a preset rule, and the position of a filling point added in the initial filling process is restrained and adjusted by using the method, so that the shape characteristics of a filling part and surrounding grids are kept consistent, the intelligent and rapid repairing of the hole is realized, the filling naturalness is ensured, and the shape of an object to be measured is ensured not to be changed.
Example 5
Based on embodiment 4, another point cloud point compensation method is used, and the pre-acquired point cloud is divided into a plurality of cubic blocks with mutually overlapped parts; determining a target block of the plurality of cubes and a target source block corresponding to the target block comprises: determining other cubes excluding the target block from the plurality of cubes as candidate blocks; acquiring similarity between all candidate blocks and a target block, and acquiring direct-current component differences and anisotropic map total variation difference between all candidate blocks and the target block; determining the similarity between all candidate blocks and a target block according to the direct-current component gap and the anisotropy map total variation gap; determining a source block corresponding to the target block according to the similarity; determining target source blocks according to the target blocks and the source blocks, and determining that target blocks of the target source blocks corresponding to the target blocks in the cubic blocks contain missing data; repairing the missing area in the target block according to the information in the target source block to obtain a repair result block; and replacing the target block in the point cloud by using the repair result block to obtain the repaired point cloud.
The foregoing is a further detailed description of the present application in connection with specific preferred embodiments and it is not intended to limit the specific embodiments of the application to the details shown. For those skilled in the art to which the present application pertains, several simple deductions or substitutions may be made without departing from the concept of the present application, and all should be considered as belonging to the protection scope of the present application.
Claims (10)
1. A point cloud steel structure quality automatic distinguishing method based on a three-dimensional laser scanner is characterized by comprising the following steps:
step one, point cloud analysis: scanning a target by using a three-dimensional laser scanner to obtain steel structure three-dimensional point cloud data;
step two, pretreatment: importing the three-dimensional point cloud data into software and preprocessing the three-dimensional point cloud data to obtain a steel structure point cloud model;
step three, feature extraction: plane extraction, edge detection and feature descriptor calculation;
step four, point cloud segmentation: grouping points into a part or an object based on low-level attributes;
step five, target identification: identifying a steel structure in the model;
step six, obtaining a judgment result: and analyzing the steel structure to obtain the construction parameter data of the steel structure, thereby accurately judging the construction quality of the steel structure.
2. The method of claim 1, wherein: and step five, point cloud classification is further included, the point clouds are classified into different point cloud sets, and similar or same attributes are divided into the same point cloud set.
3. The method of claim 1, wherein: in the first step, the target is a tunnel cross section.
4. The method of claim 1, wherein: in the second step, the method for preprocessing the three-dimensional point cloud data comprises the following steps: and automatically filtering and down-sampling according to the quality and scale of the point cloud data.
5. The method of claim 1, wherein: in the third step, the feature extraction is the key for describing the morphological structure of the point cloud and the basis and premise for extracting semantic information.
6. The method of claim 1, wherein: in the fourth step, the point cloud segmentation further processes and analyzes each object as compared to processing or analyzing each point separately.
7. The method of claim 1, wherein: in the fifth step, the target recognition is generally performed by performing analysis according to the results of feature extraction and segmentation, and based on a priori knowledge, under given constraints and rules.
8. The method according to claim 1 or 2, characterized in that: in the sixth step, the construction parameter data are interval, number, shape, diameter, length and volume.
9. The method of claim 1, wherein: the steel structure is a steel bar or a steel arch frame.
10. The method of claim 1, wherein: and the fourth step can also be synchronously performed by point cloud single-point feature analysis or single-point feature analysis and point cloud segmentation.
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