CN113483661B - Point cloud data acquisition method, device, equipment and storage medium - Google Patents
Point cloud data acquisition method, device, equipment and storage medium Download PDFInfo
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- CN113483661B CN113483661B CN202110761820.2A CN202110761820A CN113483661B CN 113483661 B CN113483661 B CN 113483661B CN 202110761820 A CN202110761820 A CN 202110761820A CN 113483661 B CN113483661 B CN 113483661B
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- G01—MEASURING; TESTING
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- G01B11/00—Measuring arrangements characterised by the use of optical techniques
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Abstract
The application provides a point cloud data acquisition method, a device, equipment and a storage medium, and relates to the technical field of mapping. The method comprises the following steps: acquiring a point cloud data set, wherein a plurality of original point cloud data of the point cloud data set comprise original point cloud data of each control point in a target area, the original point cloud data of each control point are acquired by matching a laser scanning device with the same reflecting sheet, the reflecting sheet is used for screening out the original point cloud data belonging to each control point, and the coordinates of each original point cloud data are relative coordinates taking the starting position of the laser scanning device as a reference; determining conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data and reference positions of the control points in the target area, wherein the coordinates of the reference positions are absolute coordinates; and obtaining absolute coordinates of the original point cloud data according to the conversion parameters. By applying the embodiment of the application, not only can the efficiency of acquiring the point cloud data be improved, but also the cost of acquiring the point cloud data can be increased and reduced.
Description
Technical Field
The application relates to the technical field of mapping, in particular to a method, a device, equipment and a storage medium for acquiring point cloud data.
Background
The point cloud data refers to a set of vectors in a three-dimensional coordinate system, and can be used for spatial positioning of other scenes such as buildings, urban roads and the like. A laser scanning device based on a SLAM (simultaneous localization and mapping), synchronous positioning and mapping system may be used to acquire point cloud data.
At present, spherical targets can be placed on a plurality of control points in a target area respectively, then point cloud data of the target area under a relative coordinate system is acquired by using a frame station type laser scanning device, and further point cloud data under an absolute coordinate system is obtained.
However, when acquiring point cloud data in a relative coordinate system by using the prior art, a plurality of spherical targets are often required to be set in a target area, which not only reduces the efficiency of acquiring the point cloud data, but also increases the cost of acquiring the point cloud data.
Disclosure of Invention
The present application aims to provide a method, a device, equipment and a storage medium for acquiring point cloud data, which can not only improve the efficiency of acquiring point cloud data, but also increase and reduce the cost of acquiring point cloud data.
In order to achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:
in a first aspect, an embodiment of the present application provides a method for acquiring point cloud data, where the method includes:
acquiring a point cloud data set, wherein the point cloud data set comprises a plurality of original point cloud data, the original point cloud data of each control point in a target area are included in the plurality of original point cloud data, the original point cloud data of each control point are acquired by matching a laser scanning device with the same reflector plate, the reflector plate is used for screening out the original point cloud data belonging to each control point, and the coordinates of the original point cloud data are relative coordinates taking the starting position of the laser scanning device as a reference;
determining conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data and reference positions of all control points in the target area, wherein the coordinates of the reference positions are absolute coordinates;
and carrying out coordinate conversion on a plurality of original point cloud data in the point cloud data set according to conversion parameters corresponding to the original point cloud data in the point cloud data set to obtain absolute coordinates of the original point cloud data.
Optionally, the determining, according to the original point cloud data and the reference position of each control point in the target area, a conversion parameter corresponding to each original point cloud data in the point cloud data set includes:
screening original point cloud data belonging to each control point from the point cloud data set based on the reflection attribute of the reflector plate, and forming a new point cloud data set from the original point cloud data belonging to each control point;
and determining conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data in the new point cloud data set and the reference positions of the control points in the target area.
Optionally, the selecting, based on the reflection attribute of the reflector, the original point cloud data belonging to each control point from the point cloud data set includes:
comparing the reflection intensity information in the original point cloud data with a reflection intensity threshold value to determine the original point cloud data larger than the reflection intensity threshold value;
and screening out the original point cloud data belonging to each control point according to the coordinate distribution of the original point cloud data which is larger than the reflection intensity threshold.
Optionally, the laser scanning device is a handheld laser scanning device; the acquiring the point cloud data set includes:
and acquiring a point cloud data set acquired by the handheld laser radar equipment after each control point is aligned with the reflector plate.
Optionally, the control points are disposed at edges of the target area.
Optionally, before determining the conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data and the reference positions of the control points in the target area, the method further includes:
acquiring reference positions of the control points based on real-time differential positioning equipment arranged at the control points;
and if the reference positions of the control points do not meet the preset conditions, re-acquiring the reference positions of the new control points selected in the target area.
Optionally, the conversion parameter includes a scaling factor, an offset; the determining, according to each original point cloud data in the new point cloud data set and the reference position of each control point in the target area, a conversion parameter corresponding to each original point cloud data in the point cloud data set includes:
Determining a relative coordinate interval and an absolute coordinate interval between a first control point and a second control point according to original point cloud data corresponding to the first control point and the second control point in the new point cloud data set and corresponding reference positions, wherein the first control point and the second control point are two different control points in the target area;
determining the scaling factor according to the relative coordinate spacing and the absolute coordinate spacing between the first control point and the second control point;
and determining the offset corresponding to each original point cloud data in the point cloud data set according to the scaling coefficient, the rotation matrix, each original point cloud data in the new point cloud data set and the reference position of each control point in the target area.
In a second aspect, an embodiment of the present application further provides a point cloud data obtaining apparatus, where the apparatus includes:
the acquisition module is used for acquiring a point cloud data set, wherein the point cloud data set comprises a plurality of original point cloud data, the original point cloud data of each control point in a target area are included in the plurality of original point cloud data, the original point cloud data of each control point are acquired by matching a laser scanning device with the same reflector plate, the reflector plate is used for screening out the point cloud data belonging to each control point, and the coordinates of the original point cloud data are relative coordinates taking the starting position of the laser scanning device as a reference;
The determining module is used for determining conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data and the reference positions of the control points in the target area, and the coordinates of the reference positions are absolute coordinates;
and the conversion module is used for carrying out coordinate conversion on a plurality of original point cloud data in the point cloud data set according to conversion parameters corresponding to the original point cloud data in the point cloud data set to obtain absolute coordinates of the original point cloud data.
Optionally, the determining module is specifically configured to screen original point cloud data belonging to each control point from the point cloud data set based on the reflection attribute of the reflection sheet, and form a new point cloud data set from the original point cloud data belonging to each control point; and determining conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data in the new point cloud data set and the reference positions of the control points in the target area.
Optionally, the determining module is further specifically configured to compare the reflection intensity information in the original point cloud data with a reflection intensity threshold value, and determine the original point cloud data greater than the reflection intensity threshold value; and screening out the original point cloud data belonging to each control point according to the coordinate distribution of the original point cloud data which is larger than the reflection intensity threshold.
Optionally, the laser scanning device is a handheld laser scanning device;
correspondingly, the acquisition module is specifically configured to acquire a point cloud data set acquired by the handheld laser radar device after each control point is aligned with the reflector plate.
Optionally, the control points are disposed at edges of the target area.
Optionally, the acquiring module is further configured to acquire a reference position of each control point based on a real-time differential positioning device set at each control point; and if the reference positions of the control points do not meet the preset conditions, re-acquiring the reference positions of the new control points selected in the target area.
Optionally, the conversion parameter includes a scaling factor, an offset;
correspondingly, the determining module is further specifically configured to determine a relative coordinate distance and an absolute coordinate distance between a first control point and a second control point according to original point cloud data corresponding to the first control point and the second control point in the new point cloud data set and a corresponding reference position, where the first control point and the second control point are two different control points in the target area; determining the scaling factor according to the relative coordinate spacing and the absolute coordinate spacing between the first control point and the second control point; and determining the offset corresponding to each original point cloud data in the point cloud data set according to the scaling coefficient, the rotation matrix, each original point cloud data in the new point cloud data set and the reference position of each control point in the target area.
In a third aspect, an embodiment of the present application provides an electronic device, including: the system comprises a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, when the electronic device is running, the processor and the storage medium are communicated through the bus, and the processor executes the machine-readable instructions to execute the steps of the point cloud data acquisition method of the first aspect.
In a fourth aspect, an embodiment of the present application provides a storage medium, where a computer program is stored, where the computer program is executed by a processor to perform the steps of the point cloud data acquisition method of the first aspect.
The beneficial effects of this application are:
the embodiment of the application provides a method, a device, equipment and a storage medium for acquiring point cloud data, wherein the method comprises the following steps: acquiring a point cloud data set, wherein the point cloud data set comprises a plurality of original point cloud data, the original point cloud data of each control point in a target area are included in the plurality of original point cloud data, the original point cloud data of each control point are acquired by matching a laser scanning device with the same reflecting sheet, the reflecting sheet is used for screening out the original point cloud data belonging to each control point, and the coordinates of the original point cloud data are relative coordinates taking the starting position of the laser scanning device as a reference; determining conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data and the reference positions of the control points in the target area, wherein the coordinates of the reference positions are absolute coordinates; and carrying out coordinate conversion on a plurality of original point cloud data in the point cloud data set according to conversion parameters corresponding to the original point cloud data in the point cloud data set to obtain absolute coordinates of the original point cloud data.
By adopting the point cloud data acquisition method provided by the embodiment of the application, the laser scanning device and the same reflector plate can be matched for acquisition to obtain the original point cloud data of each control point, the conversion parameters can be obtained according to the characteristics of the reflector plate which can be used for screening the original point cloud data belonging to each control point, and then the coordinate conversion can be carried out on a plurality of original point cloud data in the point cloud data set according to the conversion parameters to obtain the absolute coordinates of each original point cloud data. That is, the point cloud data with the three-dimensional coordinates corresponding to the target area as the absolute coordinates can be obtained by using one reflecting sheet, so that not only the efficiency of obtaining the point cloud data can be improved, but also the cost of obtaining the point cloud data can be increased and reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic view of a scene of point cloud data acquisition according to an embodiment of the present application;
fig. 2 is a flow chart of a method for acquiring point cloud data according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a reflective sheet according to an embodiment of the present application;
fig. 4 is a flow chart of another method for obtaining point cloud data according to an embodiment of the present application;
fig. 5 is a flowchart of another method for obtaining point cloud data according to an embodiment of the present application;
fig. 6 is a flowchart of another method for obtaining point cloud data according to an embodiment of the present application;
fig. 7 is a flowchart of another method for obtaining point cloud data according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a point cloud data acquisition device according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments.
Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
Before explaining the embodiments of the present application in detail, an application scenario of the present application will be described first. The application scene can be point cloud data required for three-dimensional reconstruction of the target object or point cloud data required for other fields, and the application is not limited to the specific application field of the point cloud data. Fig. 1 is a schematic view of a scenario of point cloud data acquisition provided in an embodiment of the present application, as shown in fig. 1, where the scenario may include a point cloud data acquisition device 101 and a point cloud data processing device 102, where the point cloud data acquisition device 101 and the point cloud data processing device 102 may be connected in a wired or wireless manner, the point cloud data acquisition device 101 may include a laser scanning device 1011, a reflective sheet 1012, and a real-time differential positioning device 1013, reference positions of control points in a target area may be acquired by the real-time differential positioning device 1013, coordinates of the reference positions are absolute coordinates, the acquired reference positions of the control points are transmitted to the point cloud data processing device 102, and the point cloud data processing device 102 may store the reference positions of the control points on a memory in advance.
The target area may include a plurality of control points, in the process of scanning by using the laser scanning device 1011, the same reflective sheet 1012 may be placed on each control point in sequence, the laser scanning device 1011 and the same reflective sheet 1012 may be matched to obtain the scanning data of the target area, the scanning data may be sent to the point cloud data processing device 102, the point cloud data processing device 102 may parse the original point cloud data set of the target area, and may also screen out the original point cloud data belonging to each control point, the point cloud data processing device 102 may obtain a conversion parameter between a relative coordinate and an absolute coordinate according to the original point cloud data of each control point and the reference position of each control point, and further may obtain the absolute coordinate of each original point cloud data according to the conversion parameter, where the conversion parameter includes a scaling coefficient, an offset, and a rotation matrix.
The point cloud data acquisition method mentioned in the application is exemplified in the following with reference to the accompanying drawings. Fig. 2 is a flow chart of a method for acquiring point cloud data according to an embodiment of the present application. As shown in fig. 2, the method may include:
s201, acquiring a point cloud data set, wherein the point cloud data set comprises a plurality of original point cloud data, and the original point cloud data of each control point in a target area are contained in the original point cloud data.
The original point cloud data of each control point are acquired by the laser scanning device in a matched mode with the same reflecting sheet, the reflecting sheet is used for screening out the original point cloud data belonging to each control point, and the coordinates of each original point cloud data are relative coordinates taking the starting position of the laser scanning device as a reference.
Specifically, before the scanning operation, a scanning route in the target area can be planned, a target scanning route is determined, and appropriate control point position information is selected in the target area, and generally, the number of control points is at least 3. After the position information of each control point is determined, marking can be carried out on each control point by using a marking pen or a steel nail, and each control point can be numbered, so that the information of each control point can be conveniently stored in a memory.
When starting scanning, a worker can move the laser scanning device according to a target scanning route and sequentially place the same reflecting sheet on each control point. Fig. 3 is a schematic structural diagram of a reflective sheet provided in this embodiment of the present application, as shown in fig. 3, a central circular hole 300 is provided in the middle of a reflective sheet 1012, when the reflective sheet 1012 is placed on a control point, the central circular hole 300 on the reflective sheet 1012 can be aligned with each control point, when a laser scanning device moves to a position of a certain control point, a laser on the laser scanning device can be aligned with the reflective sheet 1012 on the control point, and the scanning lasts for a preset time, which may be 5s, so that the laser scanning device can obtain scanning data to which the control point belongs.
After the laser scanning device finishes scanning the reflective sheet 1012 on the current control point, a worker can continue to move the laser scanning device according to the target scanning route, and move the reflective sheet 1012 to the next adjacent control point, and place the reflective sheet 1012 on the control point to be reached by the laser scanning device according to the method described above, so that the laser scanning device can acquire the scanning data to which the control point belongs, and the laser scanning device can acquire the scanning data to which each control point belongs in the target area. After the staff reaches the end point of the target scanning route, the laser scanning device can acquire a scanning data set corresponding to the target area.
The laser scanning device can send the collected scanning data set corresponding to the target area to the above-mentioned point cloud data processing device, the point cloud data processing device can analyze the scanning data set to obtain a point cloud data set, the point cloud data set comprises a plurality of original point cloud data, each original point cloud data can comprise three-dimensional coordinates and reflection intensity information and can also comprise color information, wherein the three-dimensional coordinates in each original point cloud data are relative coordinates taking the initial position of the laser scanning device on the target scanning route as a reference, that is, the three-dimensional coordinates in each original point cloud data are relative coordinates.
It should be noted that, such original point cloud data cannot be directly applied by industry, and three-dimensional coordinates in each original point cloud data need to be converted into absolute coordinates in a manner described below.
S202, according to the original point cloud data and the reference position of each control point in the target area, determining conversion parameters corresponding to each original point cloud data in the point cloud data set, wherein the coordinate of the reference position is an absolute coordinate.
The conversion parameters may include, among other things, a scaling factor λ, a rotation matrix R, and offsets (Δx, Δy, Δz). The three-dimensional coordinates representing the reference position of each control point are absolute coordinates, and the three-dimensional coordinates (X control ，Y control ，Z control ) And the three-dimensional coordinates (X original ，Y original ，Z original ) Inputting a three-dimensional space similarity transformation formula:
wherein lambda is a scaling factor, R is a rotation matrix, lambda and R can be controlled by each controlThree-dimensional coordinates (X) control ，Y control ，Z control ) And the three-dimensional coordinates (X original ，Y original ，Z original ) For solving, after solving to obtain the scaling coefficient lambda and the rotation matrix R in the conversion parameters, optionally, the three-dimensional coordinates (X control ，Y control ，Z control ) Three-dimensional coordinates (X original ，Y original ，Z original ) Calculating offset (delta X, delta Y, delta Z) in the conversion parameters, and taking the offset as offset corresponding to each original point cloud data in the point cloud data set; or in the three-dimensional coordinates (X control ，Y control ，Z control ) Three-dimensional coordinates (X original ，Y original ，Z original ) After the offset (Δx, Δy, Δz) is calculated, the average offset is used as the offset corresponding to each original point cloud data in the point cloud data set.
The offset corresponds to a correspondence between the relative coordinates and the absolute coordinates, and the original point cloud data with the coordinates being the relative coordinates can be converted into the point cloud data with the absolute coordinates through the correspondence.
S203, according to conversion parameters corresponding to each original point cloud data in the point cloud data set, performing coordinate conversion on a plurality of original point cloud data in the point cloud data set to obtain absolute coordinates of each original point cloud data.
On the premise that the offset (Δx, Δy, Δz), the scaling coefficient λ, and the rotation matrix R in the conversion parameters are known, other original point cloud data in the point cloud data set may be input into the above-mentioned three-dimensional space-like transformation formula, so that an absolute coordinate of each original point cloud data may be obtained, that is, original point cloud data with a three-dimensional coordinate being a relative coordinate in the point cloud data set may be converted into point cloud data with a three-dimensional coordinate being an absolute coordinate, which may be directly applied by the industry.
In summary, in the point cloud data acquisition method provided by the present application, the laser scanning device and the same reflector plate may be matched to acquire the original point cloud data of each control point, and according to the characteristics of the reflector plate that may be used to screen out the original point cloud data belonging to each control point, conversion parameters may be obtained, and then according to the conversion parameters, coordinate conversion may be performed on a plurality of original point cloud data in the point cloud data set, so as to obtain absolute coordinates of each original point cloud data. That is, the point cloud data with the three-dimensional coordinates corresponding to the target area as the absolute coordinates can be obtained by using one reflecting sheet, so that not only the efficiency of obtaining the point cloud data can be improved, but also the cost of obtaining the point cloud data can be increased and reduced.
Fig. 4 is a flowchart of another method for obtaining point cloud data according to an embodiment of the present application. Optionally, as shown in fig. 4, determining the conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data and the reference positions of the control points in the target area includes:
s401, based on the reflection attribute of the reflector plate, the original point cloud data belonging to each control point is screened out from the point cloud data set, and the original point cloud data belonging to each control point is formed into a new point cloud data set.
S402, according to each original point cloud data in the new point cloud data set and the reference position of each control point in the target area, determining the conversion parameters corresponding to each original point cloud data in the point cloud data set.
After the point cloud data processing device analyzes the point cloud data set corresponding to the target area, the original point cloud data belonging to each control point can be determined according to the reflection intensity information contained in each original point cloud data in the point cloud data set, and when the laser scanning device scans and collects the scanning data at each control point, the reflection sheet is placed on each control point, so that the original point cloud data belonging to each control point can be distinguished from the point cloud data belonging to other positions in the target area according to the strong reflection intensity of the reflection sheet, and the original point cloud data belonging to each control point can be selected from the point cloud data set.
After the original point cloud data of each control point is obtained, each control point corresponds to a data pair, the data pair comprises a three-dimensional coordinate in the original point cloud data corresponding to the control point and a three-dimensional coordinate corresponding to a reference position, a scaling coefficient lambda and a rotation matrix R in a conversion parameter can be obtained according to the data pair corresponding to each control point, and then the data pair corresponding to each control point is respectively input into the mentioned three-dimensional space similar transformation formula, so that the offset in the conversion parameter can be obtained.
It can be seen that, by using the reflection attribute that the reflection sheet has strong reflection intensity, the point cloud data processing apparatus can quickly determine the original point cloud data corresponding to each control point, so that the efficiency of acquiring the point cloud data can be improved.
Fig. 5 is a flowchart of another method for obtaining point cloud data according to an embodiment of the present application. Optionally, as shown in fig. 5, the selecting, based on the reflection attribute of the reflection sheet, the original point cloud data belonging to each control point from the point cloud data set includes:
s501, comparing reflection intensity information in the original point cloud data with a reflection intensity threshold value, and determining the original point cloud data larger than the reflection intensity threshold value.
S502, screening out the original point cloud data belonging to each control point according to the coordinate distribution of the original point cloud data which is larger than the reflection intensity threshold.
The specific value of the reflection intensity threshold is related to the characteristic of the reflector, the specific value is not limited by the specific value, each original point cloud data in the point cloud data set contains reflection intensity information, the reflection intensity information corresponding to each original point cloud data can be extracted and compared with the reflection intensity threshold, and the original point cloud data larger than the reflection intensity threshold is identified.
Optionally, the point cloud data processing device has a display function, and may display a position corresponding to the original point cloud data greater than the reflection intensity threshold with a larger luminance parameter, and display a position corresponding to the original point cloud data less than the reflection intensity threshold with a smaller luminance parameter.
The original point cloud data larger than the reflection intensity threshold value can be divided into a plurality of areas consistent with the number of the control points according to the number of the control points, and the original point cloud data on the central point of each area is used as the original point cloud data of the control points.
Optionally, the laser scanning device is a handheld laser scanning device; the acquiring the point cloud data set includes: and acquiring a point cloud data set acquired by the handheld laser radar equipment after each control point is aligned with the reflecting sheet.
When the staff moves to the end point of the target scanning route, the handheld laser scanning device can acquire the scanning data of each control point in the target area and the scanning data of other positions in the target area, and the point cloud data set can be obtained by analyzing the scanning data.
The point cloud data of the target area is acquired by using the handheld laser radar equipment, so that the accuracy of the point cloud data can be improved, and the point cloud data of the target area can be acquired more conveniently.
Optionally, each control point is disposed at an edge of the target area.
The number of control points to be set in the target area can be determined first, for example, 6 control points are needed in the target area, then each control point can be set at the edge of the target area according to the edge control principle, and the 6 control points can be uniformly distributed at the edge of the target area, so that the accuracy of the obtained point cloud data corresponding to the target area can be improved.
In another example, if the target area is larger, the target area may be divided into a plurality of sub-areas, and 1 or 2 common control points may be set on the area where each two adjacent sub-areas intersect, so that accuracy of the obtained point cloud data corresponding to the target area may be improved.
Fig. 6 is a flowchart of another method for obtaining point cloud data according to an embodiment of the present application. Optionally, as shown in fig. 6, before determining the conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data and the reference positions of the control points in the target area, the method further includes:
S601, acquiring reference positions of all control points based on real-time differential positioning equipment arranged at all the control points.
Before the scanning of the target area by the laser scanning device starts, the reference position of each control point can be acquired first, the reference position of each control point can be acquired by Real-Time differential positioning (RTK) equipment, specifically, the Real-Time differential positioning equipment can be sequentially arranged on each control point, and the Real-Time differential positioning equipment can upload the acquired reference position to the point cloud data processing equipment in Real Time.
S602, if the reference position of each control point does not meet the preset condition, the reference position of the new control point selected in the target area is acquired again.
The point cloud data processing device may determine a reference position of each control point, and if a value of a reference position corresponding to a certain control point does not meet a preset precision or a signal (such as a GNSS signal) cannot be acquired at the position by the real-time differential positioning device, then a worker needs to select a new control point, further set the real-time differential positioning device at the position of the new control point, acquire the reference position corresponding to the new control point, and upload the reference position corresponding to the new control point to the point cloud data processing device. Therefore, the accuracy of the conversion parameters obtained in the later period can be improved, and the accuracy of the point cloud data of the target area is further improved.
Fig. 7 is a flowchart of another method for obtaining point cloud data according to an embodiment of the present application. Optionally, as shown in fig. 7, the conversion parameters include scaling coefficients and offsets, and the conversion parameters corresponding to the original point cloud data in the point cloud data set are determined according to the original point cloud data in the new point cloud data set and the reference positions of the control points in the target area.
S701, determining the relative coordinate distance and the absolute coordinate distance between the first control point and the second control point according to the original point cloud data corresponding to the first control point and the second control point in the new point cloud data set and the corresponding reference position.
S702, determining a scaling coefficient according to the relative coordinate space and the absolute coordinate space between the first control point and the second control point.
The first control point and the second control point are two different control points in the target area. As can be seen from the above mentioned three-dimensional spatial similarity transformation formula, the offset (Δx, Δy, Δz) is obtained after the scaling factor λ and the rotation matrix R are solved. Specifically, the scaling coefficient λ may be solved by the following manner, where each control point in the target area corresponds to a three-dimensional coordinate of the original point cloud data and a three-dimensional coordinate of the reference position, then any two control points may be used as the first control point and the second control point, a relative coordinate distance between the two may be determined according to the three-dimensional coordinates of the original point cloud data of the first control point and the three-dimensional coordinates of the original point cloud data of the second control point, and an absolute coordinate distance between the two may be determined according to the three-dimensional coordinates of the reference position of the first control point and the three-dimensional coordinates of the reference position of the second control point.
Alternatively, a ratio between the absolute coordinate space and the relative coordinate space corresponding to any two control points (the first control point and the second control point) can be used as a scaling coefficient lambda; the average value of the ratio between the absolute coordinate space and the relative coordinate space corresponding to any two control points (the first control point and the second control point) can be used as the scaling coefficient lambda, so that the accuracy of the scaling coefficient lambda can be improved.
S703, determining the offset corresponding to each original point cloud data in the point cloud data set according to the scaling coefficient, the rotation matrix, each original point cloud data in the new point cloud data set and the reference position of each control point in the target area.
The rotation matrix R may be solved according to the original point cloud data and the reference position corresponding to each control point, and may be solved specifically by a four-element method, and a specific process for solving the rotation matrix R will not be described herein. After the rotation matrix R and the scaling coefficient lambda are determined, the three-dimensional coordinates (X) of the original point cloud data corresponding to each control point in the new point cloud data set original ，Y original ，Z original ) And the three-dimensional coordinates (X control ，Y control ，Z control ) The offset (DeltaX, deltaY, deltaZ) can be derived by inputting the mentioned three-dimensional spatial similarity transformation formulas respectively.
Fig. 8 is a schematic structural diagram of a point cloud data acquisition device according to an embodiment of the present application. As shown in fig. 8, the apparatus may include:
an obtaining module 801, configured to obtain a point cloud data set, where the point cloud data set includes a plurality of original point cloud data, and the plurality of original point cloud data includes original point cloud data of each control point in a target area;
the determining module 802 is configured to determine conversion parameters corresponding to each original point cloud data in the point cloud data set according to the original point cloud data and a reference position of each control point in the target area, where a coordinate of the reference position is an absolute coordinate;
the conversion module 803 is configured to perform coordinate conversion on a plurality of original point cloud data in the point cloud data set according to conversion parameters corresponding to each original point cloud data in the point cloud data set, so as to obtain absolute coordinates of each original point cloud data.
Optionally, the determining module 802 is specifically configured to screen original point cloud data belonging to each control point from the point cloud data set based on the reflection attribute of the reflector, and form a new point cloud data set from the original point cloud data belonging to each control point; and determining conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data in the new point cloud data set and the reference positions of the control points in the target area.
Optionally, the determining module 802 is further specifically configured to compare the reflection intensity information in the original point cloud data with a reflection intensity threshold, and determine the original point cloud data greater than the reflection intensity threshold; and screening out the original point cloud data belonging to each control point according to the coordinate distribution of the original point cloud data larger than the reflection intensity threshold.
Optionally, the laser scanning device is a handheld laser scanning device;
correspondingly, the acquiring module 801 is specifically configured to acquire a point cloud data set acquired by the handheld laser radar device after each control point is aligned with the reflector.
Optionally, each control point is disposed at an edge of the target area.
Optionally, the acquiring module 801 is further configured to acquire a reference position of each control point based on a real-time differential positioning device set at each control point; and if the reference position of each control point does not meet the preset condition, re-acquiring the reference position of the new control point selected in the target area.
Optionally, the determining module 802 is further specifically configured to determine, according to the first control point in the new point cloud data set, original point cloud data corresponding to the second control point, and a corresponding reference position, a relative coordinate distance and an absolute coordinate distance between the first control point and the second control point, where the first control point and the second control point are two different control points in the target area; determining a scaling factor according to the relative coordinate spacing and the absolute coordinate spacing between the first control point and the second control point; and determining conversion parameters corresponding to the original point cloud data in the point cloud data set according to the scaling coefficient, the rotation matrix, the original point cloud data in the new point cloud data set and the reference positions of the control points in the target area.
The foregoing apparatus is used for executing the method provided in the foregoing embodiment, and its implementation principle and technical effects are similar, and are not described herein again.
The above modules may be one or more integrated circuits configured to implement the above methods, for example: one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASICs), or one or more microprocessors, or one or more field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGAs), etc. For another example, when a module above is implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a central processing unit (Central Processing Unit, CPU) or other processor that may invoke the program code. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
Fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application, as shown in fig. 9, the electronic device may include: processor 901, storage medium 902, and bus 903, storage medium 902 storing machine-readable instructions executable by processor 901, processor 901 executing machine-readable instructions to perform the steps of the method embodiments described above when the electronic device is operating, communicating between processor 901 and storage medium 902 via bus 903. The specific implementation manner and the technical effect are similar, and are not repeated here.
Optionally, the present application further provides a storage medium, on which a computer program is stored, which when being executed by a processor performs the steps of the above-mentioned method embodiments.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the indirect coupling or communication connection of devices or elements may be in the form of electrical, mechanical, or otherwise.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application 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 may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (english: processor) to perform part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is merely a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and variations may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. The foregoing is merely a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and variations may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (8)
1. A method for obtaining point cloud data, the method comprising:
acquiring a point cloud data set, wherein the point cloud data set comprises a plurality of original point cloud data, the original point cloud data of each control point in a target area are included in the plurality of original point cloud data, the original point cloud data of each control point are acquired by matching a laser scanning device with the same reflector plate, the reflector plate is used for screening out the original point cloud data belonging to each control point, and the coordinates of the original point cloud data are relative coordinates taking the starting position of the laser scanning device as a reference;
Determining conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data and reference positions of all control points in the target area, wherein the coordinates of the reference positions are absolute coordinates;
according to conversion parameters corresponding to each original point cloud data in the point cloud data set, carrying out coordinate conversion on a plurality of original point cloud data in the point cloud data set to obtain absolute coordinates of each original point cloud data;
the determining conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data and the reference positions of the control points in the target area includes:
screening original point cloud data belonging to each control point from the point cloud data set based on the reflection attribute of the reflector plate, and forming a new point cloud data set from the original point cloud data belonging to each control point;
determining conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data in the new point cloud data set and the reference positions of the control points in the target area;
the conversion parameters comprise scaling factors and offsets; the determining, according to each original point cloud data in the new point cloud data set and the reference position of each control point in the target area, a conversion parameter corresponding to each original point cloud data in the point cloud data set includes:
Determining a relative coordinate interval and an absolute coordinate interval between a first control point and a second control point according to original point cloud data corresponding to the first control point and the second control point in the new point cloud data set and corresponding reference positions, wherein the first control point and the second control point are two different control points in the target area;
determining the scaling factor according to the relative coordinate spacing and the absolute coordinate spacing between the first control point and the second control point;
and determining the offset corresponding to each original point cloud data in the point cloud data set according to the scaling coefficient, the rotation matrix, each original point cloud data in the new point cloud data set and the reference position of each control point in the target area.
2. The method of claim 1, wherein the screening the original point cloud data belonging to each control point from the point cloud data set based on the reflection attribute of the reflection sheet comprises:
comparing the reflection intensity information in the original point cloud data with a reflection intensity threshold value to determine the original point cloud data larger than the reflection intensity threshold value;
And screening out the original point cloud data belonging to each control point according to the coordinate distribution of the original point cloud data which is larger than the reflection intensity threshold.
3. The method of claim 1, wherein the laser scanning device is a handheld laser scanning device; the acquiring the point cloud data set includes:
and acquiring a point cloud data set acquired by the handheld laser scanning device after each control point is aligned with the reflector plate.
4. The method of claim 1, wherein the control points are disposed at edges of the target area.
5. The method according to claim 1, wherein before determining the conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data and the reference positions of the control points in the target area, the method further comprises:
acquiring reference positions of the control points based on real-time differential positioning equipment arranged at the control points;
and if the reference positions of the control points do not meet the preset conditions, re-acquiring the reference positions of the new control points selected in the target area.
6. A point cloud data acquisition apparatus, the apparatus comprising:
the acquisition module is used for acquiring a point cloud data set, wherein the point cloud data set comprises a plurality of original point cloud data, the original point cloud data of each control point in a target area are included in the plurality of original point cloud data, the original point cloud data of each control point are acquired by matching a laser scanning device with the same reflector plate, the reflector plate is used for screening out the point cloud data belonging to each control point, and the coordinates of the original point cloud data are relative coordinates taking the starting position of the laser scanning device as a reference;
the determining module is used for determining conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data and the reference positions of the control points in the target area, and the coordinates of the reference positions are absolute coordinates;
the conversion module is used for carrying out coordinate conversion on a plurality of original point cloud data in the point cloud data set according to conversion parameters corresponding to the original point cloud data in the point cloud data set to obtain absolute coordinates of the original point cloud data;
the determining module is specifically configured to screen original point cloud data belonging to each control point from the point cloud data set based on the reflection attribute of the reflector plate, and form a new point cloud data set from the original point cloud data belonging to each control point; determining conversion parameters corresponding to the original point cloud data in the point cloud data set according to the original point cloud data in the new point cloud data set and the reference positions of the control points in the target area;
The conversion parameters comprise scaling factors and offsets; the determining module is further specifically configured to determine a relative coordinate distance and an absolute coordinate distance between a first control point and a second control point in the new point cloud data set according to original point cloud data corresponding to the first control point and the second control point and corresponding reference positions, where the first control point and the second control point are two different control points in the target area; determining the scaling factor according to the relative coordinate spacing and the absolute coordinate spacing between the first control point and the second control point; and determining the offset corresponding to each original point cloud data in the point cloud data set according to the scaling coefficient, the rotation matrix, each original point cloud data in the new point cloud data set and the reference position of each control point in the target area.
7. An electronic device, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating over the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the steps of the point cloud data acquisition method of any of claims 1-5.
8. A storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the point cloud data acquisition method according to any of claims 1-5.
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