CN116363330B

CN116363330B - Space reconstruction method, device, equipment and medium based on laser scanning technology

Info

Publication number: CN116363330B
Application number: CN202310226103.9A
Authority: CN
Inventors: 蔡英杰
Original assignee: Youying Intelligent Technology Shenzhen Co ltd
Current assignee: Youying Intelligent Technology Shenzhen Co ltd
Priority date: 2023-02-27
Filing date: 2023-02-27
Publication date: 2023-09-19
Anticipated expiration: 2043-02-27
Also published as: CN116363330A

Abstract

The invention relates to the field of image modeling, and discloses a space reconstruction method based on a laser scanning technology, which comprises the following steps: acquiring a three-dimensional point cloud grid data set obtained by scanning a target space region by a laser scanner, and performing filtering operation on the data set; calculating an optimal conversion matrix of the initial mapping function by using an artificial bee colony algorithm, obtaining a target mapping function according to the optimal conversion matrix, and merging a source point cloud data set and a target point cloud data set into the same coordinate system according to the target mapping function to obtain a registration point cloud data set; constructing a four-domain boundary curved surface and a basic curved surface of the point cloud segmentation area; fitting the basic curved surface by using the control vertexes of the basic curved surface to obtain a space reconstruction image. The invention also relates to a blockchain technique, and the spatial reconstruction can be stored in a blockchain node. The invention also provides a space reconstruction device, equipment and medium based on the laser scanning technology. The invention can improve the efficiency and accuracy of space reconstruction.

Description

Space reconstruction method, device, equipment and medium based on laser scanning technology

Technical Field

The present invention relates to the field of image modeling, and in particular, to a spatial reconstruction method, apparatus, device, and storage medium based on a laser scanning technique.

Background

The conventional space reconstruction method generally photographs a measured target area or a target manually to obtain photo or image data, collects information such as the contour and the height of the measured target area through the photo or image data, and performs manual modeling according to the contour, the height and the like.

On one hand, because the photo or image data taken manually are two-dimensional data, the accurate contour information and the height information of the detected area cannot be accurately identified, the quality of the photo and the image data is good and bad, and when the two-dimensional data are converted into three-dimensional data in the later period, the detail data of some detected areas cannot be acquired, so that the generated space reconstruction is inaccurate; meanwhile, due to the fact that the acquisition environments and the acquisition imaging mechanisms of the acquisition time are different, acquired data can contain noise and outliers near the same three-dimensional position, and accuracy of space reconstruction is further affected. On the other hand, the traditional method needs to manually collect and model the data of the measured target area, the data collection efficiency is low, and a great deal of manpower is consumed to verify the accuracy of the two-dimensional data, so that the space sweep period is long, and the space sweep efficiency is low.

Disclosure of Invention

The invention provides a space reconstruction method, device, equipment and storage medium based on a laser scanning technology, and aims to improve the efficiency and accuracy of space reconstruction.

In order to achieve the above object, the present invention provides a space reconstruction method based on a laser scanning technique, including:

acquiring a three-dimensional point cloud grid data set of a target space region, which is obtained by performing three-dimensional scanning on the target space region by a laser scanner, and performing filtering operation on the three-dimensional point cloud grid data set to obtain a denoising three-dimensional point cloud data set;

identifying a source point cloud data set and a target point cloud data set in the three-dimensional point cloud data set, acquiring an initial mapping function between the source point cloud data set and the target point cloud data set, calculating an optimal conversion matrix of the initial mapping function by using a manual swarm algorithm, substituting the optimal conversion matrix into the initial mapping function to obtain a target mapping function, and merging the source point cloud data set and the target point cloud data set into the same coordinate system according to the target mapping function to obtain a matching point cloud data set;

performing point cloud cutting operation on the alignment point cloud data set to obtain a plurality of point cloud segmentation areas, extracting boundary points in the plurality of point cloud segmentation areas, constructing a four-domain boundary curve according to the boundary points, and constructing a basic curved surface by utilizing the four-domain boundary curve;

And obtaining a control vertex of the basic curved surface, and fitting the basic curved surface by using the control vertex to obtain a space reconstruction diagram of the target space region.

Optionally, fitting the base curved surface with the control vertices to obtain a spatial reconstruction of the target spatial region, including:

and performing space reconstruction operation on the basic curved surface in a preset vector direction by using a least square approximation method and controlling vertexes to obtain a space reconstruction image of a target space region represented by a non-uniform rational B-spline curved surface.

Optionally, constructing a four-domain boundary curve according to the boundary points includes:

acquiring a triangular boundary curve of boundary points and boundary included angles among the boundary points;

judging whether the boundary included angle is larger than a preset included angle threshold value or not;

when the boundary included angle is larger than a preset included angle threshold, taking the boundary point as a boundary separation point of three boundary curves, and separating the triangular boundary curves according to the boundary separation point to obtain four-domain boundary curves;

and when the boundary included angle is not larger than a preset included angle threshold value, combining two triangular boundary curves adjacent to the boundary point to obtain a four-domain boundary curve.

Optionally, calculating an optimal transformation matrix of the initial mapping function using an artificial swarm algorithm includes:

Taking the initial mapping function as a food source, and initializing the food source to obtain a feasible solution of the food source;

carrying out neighborhood food source search on the feasible solution by using hired bees to obtain neighborhood food source candidate solutions, comparing the feasible solution with the neighborhood food source candidate solutions, and determining updated food sources according to the comparison result;

transferring the updated food sources to the following bees by employing the bees, selecting target food sources from the updated food sources by using the following bees, and carrying out neighborhood search on the target food sources to obtain secondary updated food sources;

and (3) performing iterative scout search on the secondary updated food source by using the scout bee, judging whether the number of iterative scout search meets a preset search condition, outputting an optimal food source from the secondary updated food source when the number of iterative scout search meets the search condition, and taking the position of the optimal food source as an optimal conversion matrix of the initial mapping function.

Optionally, obtaining a three-dimensional point cloud grid data set of the target space region obtained by three-dimensionally scanning the target space region by the laser scanner includes:

acquiring each three-dimensional point cloud data of a target space region obtained after a laser beam emitted by a laser scanner scans the target space region;

Calculating the resolution ratio, circumference perimeter, width of the target space region and distance between the laser scanner and the target space region by a preset formula to obtain the number of point clouds of the target space region;

and summarizing the three-dimensional point cloud data and the number of the point clouds to obtain a three-dimensional point cloud grid data set.

Optionally, constructing the base surface using the four-domain boundary curve includes:

sequentially selecting transverse guiding direction curves from four-domain boundary curves in the transverse direction;

sequentially selecting longitudinal guiding direction curves from four-domain boundary curves in the longitudinal direction;

and connecting the four-domain boundary curves in series according to the transverse guiding direction curve and the longitudinal guiding direction curve to obtain a basic curved surface.

Optionally, performing a filtering operation on the three-dimensional point cloud grid data set to obtain a denoised three-dimensional point cloud data set, including:

and respectively calculating Euclidean distance between each point cloud data in the three-dimensional point cloud grid data set and the corresponding neighborhood point, comparing the Euclidean distance with a preset noise threshold value, and removing noise point clouds in the three-dimensional point cloud grid data set according to a comparison result to obtain a denoising three-dimensional point cloud data set.

In order to solve the above problems, the present invention further provides a spatial reconstruction device based on a laser scanning technology, where the device includes:

the laser scanning module is used for acquiring a three-dimensional point cloud grid data set of the target space region, which is obtained by three-dimensional scanning of the target space region by the laser scanner, and performing filtering operation on the three-dimensional point cloud grid data set to obtain a denoising three-dimensional point cloud data set;

the point cloud registration module is used for identifying a source point cloud data set and a target point cloud data set in the three-dimensional point cloud data set, acquiring an initial mapping function between the source point cloud data set and the target point cloud data set, calculating an optimal conversion matrix of the initial mapping function by using an artificial bee colony algorithm, substituting the optimal conversion matrix into the initial mapping function to obtain a target mapping function, and merging the source point cloud data set and the target point cloud data set into the same coordinate system according to the target mapping function to obtain a registration point cloud data set;

the basic curved surface construction module is used for carrying out point cloud cutting operation on the alignment point cloud data set to obtain a plurality of point cloud segmentation areas, extracting boundary points in the plurality of point cloud segmentation areas, constructing a four-side-domain boundary curve according to the boundary points, and constructing a basic curved surface by utilizing the four-side-domain boundary curve;

And the space reconstruction module is used for acquiring control vertexes of the basic curved surface, fitting the basic curved surface by using the control vertexes, and obtaining a space reconstruction image of the target space region.

In order to solve the above problems, the present invention also provides an electronic device including:

a memory storing at least one computer program; and

And a processor executing the computer program stored in the memory to implement the spatial reconstruction method based on the laser scanning technology.

In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium in which at least one computer program is stored, the at least one computer program being executed by a processor in an electronic device to implement the above-mentioned spatial reconstruction method based on the laser scanning technique.

In the embodiment of the invention, the three-dimensional point cloud grid data set of the target space region is obtained by scanning the target space region by utilizing the laser scanner, so that the three-dimensional data of the target space region is directly obtained, the data acquisition efficiency of the target space region can be improved, and the data quality of the point cloud data can be improved by carrying out filtering operation on the three-dimensional point cloud grid data set; secondly, calculating an optimal conversion matrix of an initial mapping function by using an artificial bee colony algorithm to obtain a target mapping function, merging a source point cloud data set and a target point cloud data set into the same coordinate system according to the target mapping function, removing noise and outliers in the point cloud data, acquiring more accurate detail data of a target space region, and improving the accuracy of a space reconstruction graph formed later; finally, by constructing a four-domain boundary surface and a basic surface of the point cloud segmentation area and by acquiring control vertexes of the basic surface, fitting the basic surface by utilizing the control vertexes to obtain a space reconstruction image of the target space area, no manual modeling is needed, the space reconstruction efficiency can be improved, and the space reconstruction image can be amplified based on detail requirements by fitting the control vertexes to the basic surface, so that the accuracy of the space reconstruction image is further improved. Therefore, the spatial reconstruction method, the device, the equipment and the storage medium based on the laser scanning technology can improve the efficiency and the accuracy of spatial reconstruction.

Drawings

Fig. 1 is a schematic flow chart of a spatial reconstruction method based on a laser scanning technique according to an embodiment of the application;

FIG. 2 is a detailed flowchart of a step in a spatial reconstruction method based on a laser scanning technique according to an embodiment of the present application;

FIG. 3 is a detailed flowchart of a step in a spatial reconstruction method based on a laser scanning technique according to an embodiment of the present application;

FIG. 4 is a schematic block diagram of a spatial reconstruction device based on a laser scanning technique according to an embodiment of the present application;

fig. 5 is a schematic diagram of an internal structure of an electronic device for implementing a spatial reconstruction method based on a laser scanning technology according to an embodiment of the present application;

the achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

The embodiment of the application provides a space reconstruction method based on a laser scanning technology. The execution subject of the spatial reconstruction method based on the laser scanning technology includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the spatial reconstruction method based on the laser scanning technology may be performed by software or hardware installed in the terminal device or the server device, and the software may be a blockchain platform. The service side includes, but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.

Referring to fig. 1, which is a schematic flow chart of a spatial reconstruction method based on a laser scanning technology according to an embodiment of the present invention, in an embodiment of the present invention, the spatial reconstruction method based on the laser scanning technology includes the following steps S1-S4:

s1, acquiring a three-dimensional point cloud grid data set of a target space region, which is obtained by three-dimensional scanning of the target space region by a laser scanner, and performing filtering operation on the three-dimensional point cloud grid data set to obtain a denoising three-dimensional point cloud data set.

In the embodiment of the invention, the three-dimensional point cloud grid data set is a set of massive continuous points expressing the spatial distribution of the target under the same three-dimensional space coordinate system, and comprises information such as three-dimensional coordinates X, Y, Z, colors, classification values, intensity values, target empty surface characteristics and the like.

In the embodiment of the invention, the three-dimensional data of the target space region can be directly obtained by obtaining the three-dimensional point cloud grid data set of the target space region obtained by three-dimensional scanning of the target space region by the laser scanner, the data acquisition efficiency of the target space region is improved, the three-dimensional point cloud grid data set is subjected to filtering operation, and the data quality of the point cloud data is improved.

In an embodiment of the invention, a scanning station with less shielding can be arranged, each scanning station is provided with a laser scanner to scan a target space region, and point cloud data is acquired at different scanning stations, so that the target space is completely scanned, and the integrity of the target space scanning point cloud is ensured, wherein the laser scanner can be a RigelVZ-400 three-dimensional laser scanner.

As one embodiment of the present invention, obtaining a three-dimensional point cloud grid data set of a target space region obtained by three-dimensionally scanning the target space region by a laser scanner includes:

acquiring each three-dimensional point cloud data of a target space region obtained after a laser beam emitted by a laser scanner scans the target space region; calculating the resolution ratio, circumference perimeter, width of the target space region and distance between the laser scanner and the target space region by a preset formula to obtain the number of point clouds of the target space region; and summarizing the three-dimensional point cloud data and the number of the point clouds to obtain a three-dimensional point cloud grid data set.

According to the embodiment of the invention, the three-dimensional point cloud data of the target space region can be obtained through the following formula:

where θ and α denote the vertical and horizontal angles of the laser beam, respectively, and S denotes the spacing between the laser scanner and the scan point of the target spatial region.

Further, the embodiment of the invention can acquire the number of the point clouds of the target space region through the following formula:

wherein N is _n Representing the number of point clouds of the target space region, N _d The resolution of the laser scanner is represented by W, the width of the target space region is represented by C, the circumferential length of the laser scanner is represented by C, and the distance between the laser scanner and the target space region is represented by O.

In detail, the three-dimensional point cloud grid data set is subjected to filtering operation, and a denoising three-dimensional point cloud data set is obtained. Comprising the following steps: and respectively calculating Euclidean distance between each point cloud data in the three-dimensional point cloud grid data set and the corresponding neighborhood point, comparing the Euclidean distance with a preset noise threshold value, and removing noise point clouds in the three-dimensional point cloud grid data set according to a comparison result to obtain a denoising three-dimensional point cloud data set.

In one embodiment of the present invention, the Euclidean distance may be calculated by the following formula:

wherein d represents Euclidean distance between each point cloud data and the corresponding neighborhood point, x _i 、y _i 、z _i Respectively representing the coordinates of the horizontal axis, the vertical axis and the vertical axis of any point cloud data in the three-dimensional point cloud grid data set, and x _j 、y _j 、z _j And respectively representing the horizontal axis, the vertical axis and the vertical axis coordinates of the corresponding neighborhood points of the point cloud data.

Further, in the embodiment of the present invention, the euclidean average distance between each point cloud data and the corresponding neighborhood point may be calculated by the following formula:

wherein,,and (3) representing the Euclidean average distance between each point cloud data and the corresponding neighborhood point, wherein k represents the number of the neighborhood points, and d represents the Euclidean distance between each point cloud data and the corresponding neighborhood point.

The preset noise threshold is: γ=μ±n×σ

Wherein, gamma represents a noise threshold value, mu represents a distance average value of any point cloud data and the neighborhood point, n represents a constant which is not 0, and sigma represents a distance standard deviation of any point cloud data and the neighborhood point.

In one embodiment of the invention, whenWhen the noise is larger than gamma, the neighborhood point corresponding to any point cloud data is represented as a noise point, and the noise point can be removed; when->And when the data is not larger than gamma, the corresponding neighborhood points of any point cloud data are represented as noiseless points, and the neighborhood points can be reserved as a three-dimensional point cloud grid data set.

S2, identifying a source point cloud data set and a target point cloud data set in the three-dimensional point cloud data set, acquiring an initial mapping function between the source point cloud data set and the target point cloud data set, calculating an optimal conversion matrix of the initial mapping function by using a manual bee colony algorithm, substituting the optimal conversion matrix into the initial mapping function to obtain a target mapping function, and merging the source point cloud data set and the target point cloud data set into the same coordinate system according to the target mapping function to obtain a matched point cloud data set.

In the embodiment of the invention, the source point cloud data set refers to point cloud data needing rotation and translation operation, namely point cloud data needing registration. The target point cloud dataset refers to a registration target of the source point cloud dataset. The registration point cloud data set refers to an overall point cloud data set in which a source point cloud data set is registered from a current coordinate system position into a coordinate system position of a target point cloud data set and the same part is overlapped.

According to the embodiment of the invention, the source point cloud data set and the target point cloud data set in the three-dimensional point cloud data set are identified, the initial mapping function of the source point cloud data set and the target point cloud data set is obtained, the optimal conversion matrix of the initial mapping function is calculated by using the artificial bee colony algorithm, the optimal conversion matrix is substituted into the initial mapping function, the target mapping function is obtained, and the source point cloud data set and the target point cloud data set are combined into the same coordinate system according to the target mapping function, so that the problem that noise and outliers in the point cloud data are removed due to different acquisition environments and acquisition imaging mechanisms of acquisition time can be solved, the data quality is further improved, and the follow-up improvement of the accuracy of a space sweep graph is facilitated.

In one embodiment of the present invention, the initial mapping function may be expressed by the following formula:

wherein F represents an initial mapping function, n represents the point cloud logarithm of successful matching of the source point cloud data set and the target point cloud data set, and s ₀ Represents a scale factor, T represents a registration transformation matrix, p _i Represents the ith source point cloud data, q _j Represents the jth target point cloud data, R represents the spatial rotation matrix,representing the spatial translation vector.

As an embodiment of the present invention, referring to fig. 2, calculating an optimal transformation matrix of an initial mapping function using an artificial bee colony algorithm includes:

In an embodiment of the present invention, the initialization of the food source by the spy bees may be achieved by the following formula:

S _ij ＝S _minj +rand[0,1](S _maxj -S _minj )，i∈{1,2,...,m},j∈{1,2,...,d}

wherein S is _ij Representing the food source of the ith food source in the jth dimension, m representing the number of food sources, d representing the vector dimension of the food source in different positions, S _minj Representing the minimum value of the feasible solution in the j-th dimension, S _maxj Representing the maximum value of the feasible solution in the j-th dimension, rand represents taking [0,1 ]]A random number function therebetween.

Further, neighborhood food source searching for viable solutions using employment bees may be accomplished by the following formula:

wherein V is _ij Representing a neighbor food source candidate solution of the ith food source in the jth dimension, S _ij Indicating that the ith food source is in the jth dimension,the representation is [ -1,1]Random number between S _kj Meaning that the kth food source is in the jth dimension and k is not equal to i.

In an embodiment of the present invention, the greedy selection operator may be used to compare the feasible solution with the neighborhood food source candidate solution, i.e. the greedy selection operator is used to select the food source with higher profit from the profits of the food source and the neighborhood food source as the updated food source.

In the embodiment of the invention, the following bees are utilized to select a target food source from updated food sources, and the target food source can be obtained through the following formula:

wherein P is _i Representing the probability that following bees select a target food source from the ith updated food source, F (S) _i ) Indicating the benefit of the ith updated food source and n indicates the number of updated food sources.

In an embodiment of the present invention, the neighborhood search for the target food source is similar to the method of neighborhood searching for the feasible solution using employment bees, and will not be described in detail herein.

In an alternative embodiment of the invention, the iterative scout search performs revenue evaluation on the secondary updated food sources one by using the initial mapping function; the preset searching condition can be that the number of the searching steps is larger than the maximum searching step number, when the number of the iterative detection and wiping searching steps is larger than the maximum searching step number, the searching is stopped, and the optimal food source is output from the secondary updated food source; and when the number of iterative search steps is not greater than the maximum number of search steps, returning to use the hired bees to search the neighborhood food sources for the feasible solution until the loop meets the search condition.

Further, the position of the optimal food source is the optimal transformation matrix of the objective functionWherein T is _b Representing an optimal transformation matrix, pi representing the ith source point cloud data and also representing the target food source, s ₀ Represents a scale factor, R _b Representing an optimal spatial rotation matrix,/->Representing the optimal spatial translation vector.

In the embodiment of the invention, the optimal conversion matrix is substituted into the initial mapping function, and the obtained target mapping function can be expressed as:

wherein F is _b Represents a target mapping function, n represents the point cloud logarithm of successful matching of the source point cloud data set and the target point cloud data set, s ₀ Represents a scale factor, T _b Representing the optimal transformation matrix, p _i Represents the ith source point cloud data, q _j Represents jth target point cloud data, R _b Representing the optimal spatial rotation matrix,representing the optimal spatial translation vector.

And S3, performing point cloud cutting operation on the alignment point cloud data set to obtain a plurality of point cloud segmentation areas, extracting boundary points in the plurality of point cloud segmentation areas, constructing a four-domain boundary curve according to the boundary points, and constructing a basic curved surface by utilizing the four-domain boundary curve.

In the embodiment of the invention, the basic curved surface refers to a curved surface formed by connecting four-side-domain boundary curves into a quadrilateral mesh.

In an embodiment of the present invention, the point cloud cutting operation refers to a process of grouping point clouds into a plurality of portions in a high-dimensional feature space vector, where the point clouds of each portion are associated with a corresponding surface area, specifically, the registration point cloud data is divided into a plurality of point cloud division areas, where random points (i.e., overlapping point clouds) do not exist in the point cloud data set of each point cloud division area, the point clouds need to be mutually communicated, the similarity between the point clouds in each area is the same, and the point clouds with differences in similarity are divided into different areas.

According to the embodiment of the invention, the boundary points in the plurality of point cloud partition areas are extracted, the four-side boundary curve is constructed according to the boundary points, and the four-side boundary curve is utilized to construct the basic curved surface, so that the corresponding curved surface model of the point cloud area can be constructed, the basic curved surface meets the shape limitation of the boundary curve, and the accuracy of the subsequent generation of the space sweep map is improved.

In another embodiment of the present invention, since each point cloud area corresponds to a curved surface, the boundary point is extracted, i.e. Qu Miandian cloud boundary points are extracted, by using a three-dimensional network construction method to establish a triangular point cloud grid of each point cloud area, when a triangular edge belongs to a unique triangular surface, the triangular edge is described as a contour boundary of the point cloud area, and then the boundary points are two end points of the triangular edge, and the boundary contour of the point cloud area is sequentially obtained according to a boundary topological relation, so that the boundary point corresponding to each point cloud area can be sequentially extracted.

As an embodiment of the present invention, referring to fig. 3, a four-domain boundary curve is constructed according to boundary points, including:

The boundary points are two end points of the triangular boundary curve, and the boundary included angle is the included angle formed by three points in the triangular boundary curve.

In an embodiment of the present invention, since the triangle boundary curve is required to be converted into the quadrilateral curve, all the triangle boundary curves need to be combined to eliminate the included angle or separated to form a new boundary by judging whether the included angle of the boundary is greater than a preset included angle threshold, wherein the preset included angle threshold can be represented as θ, when the included angle of the boundary is smaller than θ, it is indicated that the triangle boundary curve cannot be separated, and because the included angle is smaller, two boundaries corresponding to the included angle can be combined, so that the space reconstruction can be performed subsequently without influenceThe accuracy of the subsequent spatial reconstruction; when the boundary angle is larger than θ, due to the triangular boundary curve a: f (F) _k-1 F _k And triangle boundary curve B: f (F) _k F _k+1 To connect the boundary point F between A and B _k As boundary separation points to separate AB curves _k 。

Further, constructing a base surface using four-domain boundary curves, comprising:

sequentially selecting transverse guiding direction curves from four-domain boundary curves in the transverse direction; sequentially selecting longitudinal guiding direction curves from four-domain boundary curves in the longitudinal direction; and connecting the four-domain boundary curves in series according to the transverse guiding direction curve and the longitudinal guiding direction curve to obtain a basic curved surface.

The four-side boundary curves can be connected into a basic curved surface only by determining the serial direction of the curves because the triangular curves are separated by the four-side boundary curves, and the basic curved surface can be a coons curved surface.

S4, obtaining control vertexes of the basic curved surface, and fitting the basic curved surface by using the control vertexes to obtain a space reconstruction diagram of the target space region.

In the embodiment of the invention, the control vertexes form a control grid in a topological rectangular array, and the main function is to carry out fitting reconstruction on a basic curved surface; the spatial reconstruction map refers to a three-dimensional model map of the target spatial region.

In the embodiment of the invention, the control vertexes are used for fitting the basic curved surface to obtain the space reconstruction of the target space region, the curve degree of the basic curved surface can be better controlled through the control vertexes to obtain the fitting reconstruction curved surface with high precision, the scan space map can be enlarged based on detail requirements, and the accuracy of the space scan map is improved.

As one embodiment of the invention, the method for obtaining the space reconstruction of the target space region by utilizing the control vertex to fit the basic curved surface comprises the following steps:

In an embodiment of the present invention, the spatial reconstruction operation may be implemented by the following least squares approximation formula:

wherein S (u, v) represents a non-uniform rational B-spline surface, E _i.j Represents the control vertex, D _i.j The weight associated with the control vertex is represented by i, the number of control vertices in the U vector direction (i=1, 2,., n), j, the number of control vertices in the V vector direction (j=1, 2,., m), k, the B-spline basis function power of the U vector, l, the B-spline basis function power of the V vector, C _i,k (U) B-spline basis function representing k-th order on vector U, C _j,l (v) Representing the B-spline basis function of the V vector l-order.

In an embodiment of the present invention, the control vertex is also obtained by a least squares approximation formula.

In the embodiment of the invention, the non-uniform rational B-spline surface is an NURBS surface, the final structure of the target space region can be represented by the NURBS surface, and the space reconstruction can be enlarged or reduced according to detail requirements by controlling the vertexes and the corresponding weight factors, so that the accuracy of the space reconstruction is improved.

In the embodiment of the invention, the three-dimensional point cloud grid data set of the target space region is obtained by scanning the target space region by utilizing the laser scanner, so that the three-dimensional data of the target space region is directly obtained, the data acquisition efficiency of the target space region can be improved, and the data quality of the point cloud data can be improved by carrying out filtering operation on the three-dimensional point cloud grid data set; secondly, calculating an optimal conversion matrix of an initial mapping function by using an artificial bee colony algorithm to obtain a target mapping function, merging a source point cloud data set and a target point cloud data set into the same coordinate system according to the target mapping function, removing noise and outliers in the point cloud data, acquiring more accurate detail data of a target space region, and improving the accuracy of a space reconstruction graph formed subsequently; finally, by constructing a four-domain boundary surface and a basic surface of the point cloud segmentation area and by acquiring control vertexes of the basic surface, fitting the basic surface by utilizing the control vertexes to obtain a space reconstruction image of the target space area, no manual modeling is needed, the space reconstruction efficiency can be improved, and the space reconstruction image can be amplified based on detail requirements by fitting the control vertexes to the basic surface, so that the accuracy of the space reconstruction image is further improved. Therefore, the spatial reconstruction method based on the laser scanning technology provided by the embodiment of the invention can improve the efficiency and the accuracy of spatial reconstruction.

The spatial reconstruction device 100 based on the laser scanning technology of the present invention can be installed in an electronic apparatus. Depending on the functions implemented, the spatial reconstruction device based on the laser scanning technology may include a laser scanning module 101, a point cloud registration module 102, a basic curved surface construction module 103, and a spatial reconstruction module 104, where the modules may also be referred to as units, refer to a series of computer program segments capable of being executed by a processor of an electronic device and of performing a fixed function, and stored in a memory of the electronic device.

In the present embodiment, the functions concerning the respective modules/units are as follows:

the laser scanning module 101 is configured to obtain a three-dimensional point cloud grid data set of a target space region obtained by performing three-dimensional scanning on the target space region by using a laser scanner, and perform filtering operation on the three-dimensional point cloud grid data set to obtain a denoised three-dimensional point cloud data set.

As one embodiment of the present invention, the laser scanning module 101 acquires a three-dimensional point cloud grid data set of a target space region obtained by three-dimensionally scanning the target space region by a laser scanner by performing operations including:

In detail, the three-dimensional point cloud grid data set is subjected to filtering operation, and a denoising three-dimensional point cloud data set is obtained. Comprising the following steps:

wherein,,representing each point cloud data and corresponding neighborhood pointThe Euclidean average distance between the two points, k represents the number of the neighborhood points, and d represents the Euclidean distance between each point cloud data and the corresponding neighborhood point.

The preset noise threshold is: γ=μ±n×σ

The point cloud registration module 102 is configured to identify a source point cloud data set and a target point cloud data set in the three-dimensional point cloud data set, obtain an initial mapping function between the source point cloud data set and the target point cloud data set, calculate an optimal transformation matrix of the initial mapping function by using an artificial bee colony algorithm, substitute the optimal transformation matrix into the initial mapping function to obtain a target mapping function, and combine the source point cloud data set and the target point cloud data set into the same coordinate system according to the target mapping function to obtain a registration point cloud data set.

As one embodiment of the present invention, the point cloud registration module 102 calculates an optimal transformation matrix for the initial mapping function using an artificial bee colony algorithm by performing the following operations, including:

The basic curved surface construction module 103 is configured to perform a point cloud cutting operation on the alignment point cloud data set to obtain a plurality of point cloud segmentation areas, extract boundary points in the plurality of point cloud segmentation areas, construct a four-sided boundary curve according to the boundary points, and construct a basic curved surface by using the four-sided boundary curve.

As one embodiment of the present invention, the basic curved surface construction module 103 constructs a four-domain boundary curve from boundary points by performing operations including:

In an embodiment of the present invention, since the triangle boundary curve is required to be converted into the quadrilateral curve, all the triangle boundary curves need to be combined to eliminate the included angle or separated to form a new boundary by judging whether the included angle of the boundary is greater than a preset included angle threshold, wherein the preset included angle threshold can be represented as θ, when the included angle of the boundary is smaller than θ, it is indicated that the triangle boundary curve cannot be separated, and because the included angle is smaller, two boundaries corresponding to the included angle can be combined, and when space reconstruction is performed subsequentlyThe accuracy of the subsequent space reconstruction is not affected; when the boundary angle is larger than θ, due to the triangular boundary curve a: f (F) _k-1 F _k And triangle boundary curve B: f (F) _k F _k+1 To connect the boundary point F between A and B _k As boundary separation points to separate AB curves _k 。

The space reconstruction module 104 is configured to obtain a control vertex of the basic surface, and fit the basic surface with the control vertex to obtain a space reconstruction image of the target space region.

As one embodiment of the present invention, the spatial reconstruction module 104 fits the base surface with control vertices to obtain a spatial reconstruction of the target spatial region by performing operations including:

In the embodiment of the invention, the three-dimensional point cloud grid data set of the target space region is obtained by scanning the target space region by utilizing the laser scanner, so that the three-dimensional data of the target space region is directly obtained, the data acquisition efficiency of the target space region can be improved, and the data quality of the point cloud data can be improved by carrying out filtering operation on the three-dimensional point cloud grid data set; secondly, calculating an optimal conversion matrix of an initial mapping function by using an artificial bee colony algorithm to obtain a target mapping function, merging a source point cloud data set and a target point cloud data set into the same coordinate system according to the target mapping function, removing noise and outliers in the point cloud data, acquiring more accurate detail data of a target space region, and improving the accuracy of a space reconstruction graph formed subsequently; finally, by constructing a four-domain boundary surface and a basic surface of the point cloud segmentation area and by acquiring control vertexes of the basic surface, fitting the basic surface by utilizing the control vertexes to obtain a space reconstruction image of the target space area, no manual modeling is needed, the space reconstruction efficiency can be improved, and the space reconstruction image can be amplified based on detail requirements by fitting the control vertexes to the basic surface, so that the accuracy of the space reconstruction image is further improved. Therefore, the spatial reconstruction device based on the laser scanning technology provided by the embodiment of the invention can improve the efficiency and the accuracy of spatial reconstruction.

Fig. 5 is a schematic structural diagram of an electronic device for implementing a spatial reconstruction method based on a laser scanning technology according to the present invention.

The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a spatial reconstruction program based on laser scanning technology.

The memory 11 includes at least one type of medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a local magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various types of data, such as codes of a spatial reconstruction program based on a laser scanning technique, but also for temporarily storing data that has been output or is to be output.

The processor 10 may in some embodiments be comprised of integrated circuits, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functionality, including one or more central processing units (Central Processing Unit, CPU), microprocessors, digital processing chips, graphics processors, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules (e.g., a spatial reconstruction program based on a laser scanning technique, etc.) stored in the memory 11, and calling data stored in the memory 11.

The communication bus 12 may be a peripheral component interconnect standard (PerIPheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable a connection communication between the memory 11 and the at least one processor 10 etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.

Fig. 5 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 5 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.

For example, although not shown, the electronic device may further include a power source (such as a battery) for powering the respective components, and the power source may be logically connected to the at least one processor 10 through a power management device, so as to perform functions of charge management, discharge management, and power consumption management through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may also include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described in detail herein.

Optionally, the communication interface 13 may comprise a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices.

Optionally, the communication interface 13 may further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.

It should be understood that the examples are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.

The spatial reconstruction program based on the laser scanning technique stored in the memory 11 of the electronic device is a combination of a plurality of computer programs, which, when run in the processor 10, can realize:

In particular, the specific implementation method of the processor 10 on the computer program may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.

Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable medium. The computer readable medium may be non-volatile or volatile. The computer readable medium may include: any entity or device capable of carrying computer program code to be recorded, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).

Embodiments of the present invention may also provide a computer-readable storage medium storing a computer program which, when executed by a processor of an electronic device, may implement:

Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.

In the several embodiments provided by the present invention, it should be understood that the disclosed media, devices, apparatuses, and methods may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of modules is merely a logical function division, and other manners of division may be implemented in practice.

The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.

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 characteristics 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. Any reference signs in the claims shall not be construed as limiting the claim concerned.

The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.

Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A spatial reconstruction method based on a laser scanning technology, which is characterized by comprising the following steps:

Performing point cloud cutting operation on the alignment point cloud data set to obtain a plurality of point cloud segmentation areas, extracting boundary points in the plurality of point cloud segmentation areas, obtaining triangular boundary curves of the boundary points and boundary angles among the boundary points, judging whether the boundary angles are larger than a preset angle threshold value, taking the boundary points as boundary separation points of three boundary curves when the boundary angles are larger than the preset angle threshold value, separating the triangular boundary curves according to the boundary separation points to obtain four-side boundary curves, merging two triangular boundary curves adjacent to the boundary points to obtain four-side boundary curves when the boundary angles are not larger than the preset angle threshold value, sequentially selecting transverse guiding direction curves from the four-side boundary curves in the transverse direction, sequentially selecting longitudinal guiding direction curves from the four-side boundary curves in the longitudinal direction, and connecting the four-side boundary curves in series according to the transverse guiding direction curves and the longitudinal guiding direction curves to obtain a basic curved surface;

2. The spatial reconstruction method based on the laser scanning technique as claimed in claim 1, wherein calculating the optimal transformation matrix of the initial mapping function using the artificial bee colony algorithm comprises:

3. The spatial reconstruction method based on the laser scanning technology as set forth in claim 1 or 2, wherein obtaining a three-dimensional point cloud grid data set of the target spatial region obtained by three-dimensionally scanning the target spatial region by the laser scanner includes:

4. The spatial reconstruction method based on the laser scanning technology as set forth in claim 1 or 2, wherein the filtering operation is performed on the three-dimensional point cloud grid data set to obtain a denoised three-dimensional point cloud data set, and the method includes:

5. A spatial reconstruction device based on a laser scanning technique, the device comprising:

the basic curved surface construction module is used for carrying out point cloud cutting operation on the alignment point cloud data set to obtain a plurality of point cloud segmentation areas, extracting boundary points in the plurality of point cloud segmentation areas, obtaining triangular boundary curves of the boundary points and boundary angles between the boundary points, judging whether the boundary angles are larger than a preset angle threshold value, taking the boundary points as boundary separation points of three boundary curves when the boundary angles are larger than the preset angle threshold value, separating the triangular boundary curves according to the boundary separation points to obtain four-domain boundary curves, merging two triangular boundary curves adjacent to the boundary points to obtain four-domain boundary curves when the boundary angles are not larger than the preset angle threshold value, sequentially selecting transverse guiding direction curves from the four-domain boundary curves in the transverse direction, sequentially selecting longitudinal guiding direction curves from the four-domain boundary curves in the longitudinal direction, and connecting the four-domain boundary curves in series according to the transverse guiding direction curves and the longitudinal guiding direction curves to obtain the basic curved surface;

And the space reconstruction module is used for carrying out space reconstruction operation on the basic curved surface in a preset vector direction through a least square approximation method and control vertexes to obtain a space reconstruction image of a target space region represented by the non-uniform rational B-spline curved surface.

6. An electronic device, characterized in that the electronic device comprises:

at least one processor; the method comprises the steps of,

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the laser scanning technology based spatial reconstruction method as claimed in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the spatial reconstruction method based on the laser scanning technique as claimed in any one of claims 1 to 4.