CN115423883A - Method and device for calibrating contourgraph camera and electronic equipment - Google Patents

Method and device for calibrating contourgraph camera and electronic equipment Download PDF

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CN115423883A
CN115423883A CN202211139180.2A CN202211139180A CN115423883A CN 115423883 A CN115423883 A CN 115423883A CN 202211139180 A CN202211139180 A CN 202211139180A CN 115423883 A CN115423883 A CN 115423883A
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point cloud
cloud data
determining
camera
calibration block
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龙学雄
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Hangzhou Hikrobot Co Ltd
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Hangzhou Hikrobot Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds

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Abstract

The embodiment of the invention provides a method and a device for calibrating a contourgraph camera and electronic equipment, and specifically comprises the following steps: the method comprises the steps of acquiring three-dimensional point cloud data acquired by a contourgraph camera through a calibration block, determining model constraint conditions of each surface aiming at each surface of the calibration block, determining local point cloud data belonging to each surface of the calibration block from the three-dimensional point cloud data, determining an external parameter which enables the local point cloud data of each surface to be matched with the model constraint conditions of each surface, using the external parameter as a target external parameter of the contourgraph camera, and calibrating the contourgraph camera. Thus, by adopting the embodiment provided by the invention, the contourgraph camera can be calibrated based on the surface model constraint condition of the calibration block by virtue of the calibration block with a simple structure, the calibration cost of the contourgraph camera can be effectively reduced, and the measurement accuracy of the contourgraph camera can be effectively improved.

Description

Method and device for calibrating contourgraph camera and electronic equipment
Technical Field
The invention relates to the field of optical measurement, in particular to a method and a device for calibrating a contourgraph camera and electronic equipment.
Background
In the field of optical measurement, a profiler camera is used to measure information of a profile, a two-dimensional size, a depth, etc. of an object. If the measurement accuracy of the contourgraph camera is not accurate, the effectiveness of the acquired data is low. Therefore, in the art, the accuracy of the profiler camera is typically calibrated using standard metrology instrumentation, and this calibration process is referred to as: and (5) calibrating.
In the related art, a calibration block embedded with a steel ball or printed with a special pattern is often adopted, and the accuracy of a profile instrument camera is calibrated by taking the center of the steel ball or the special pattern in the calibration block as a characteristic point. The structure of the calibration block used in the calibration mode is complex, the processing cost is high, and the size of each characteristic point of the calibration block needs to be accurately measured by a professional measuring mechanism to ensure the accurate size of the calibration block, so that the calibration cost is high.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a device for calibrating a contourgraph camera and electronic equipment, so as to save the calibration cost of the contourgraph camera. The specific technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a method for calibrating a profiler camera, where the method includes:
acquiring three-dimensional point cloud data acquired by a contourgraph camera collecting and calibrating block;
for each surface of the calibration block, determining model constraints for the surface and determining local point cloud data belonging to the surface from the three-dimensional point cloud data;
determining an outlier that matches the local point cloud data of each surface to the model constraints of each surface as a target outlier for the profiler camera.
With reference to the first aspect, the present invention provides a second possible embodiment, in the second possible embodiment, the determining an external parameter that enables the local point cloud data of each surface to match the model constraint condition of each surface as a target external parameter of the profiler camera includes:
determining a corresponding relation between a model constraint error and an external parameter according to the model constraint condition of each surface, wherein the model constraint error is as follows: under the condition that the external parameter of the contourgraph camera is the external parameter corresponding to the model constraint error, the error of the local point cloud data of each surface to the model constraint condition of each surface;
and determining an external parameter corresponding to the minimum value of the model constraint error according to the corresponding relation, and taking the external parameter as a target external parameter of the contourgraph camera.
In combination with the first aspect, the present invention provides a third possible embodiment, in which the determining, for each surface of the calibration block, local point cloud data belonging to the surface from the three-dimensional point cloud data includes:
fitting the point cloud data in the three-dimensional point cloud data to obtain the characteristic of a fitting curve to which each point cloud data belongs, and using the characteristic as the surface characteristic of the point cloud data;
and aiming at each surface, determining point cloud data corresponding to the surface characteristics and the surface from the three-dimensional point cloud data according to a preset corresponding relation between the surface characteristics and the surface, and taking the point cloud data as local point cloud data of the surface.
With reference to the third possible embodiment of the first aspect, the present invention provides a fourth possible embodiment, in the fourth possible embodiment, the fitting the point cloud data in the three-dimensional point cloud data includes:
determining the type of each contour line of the calibration block according to the preset shape of the calibration block;
and fitting the point cloud data in the three-dimensional point cloud data into the fitting curve of the type to obtain the characteristic of the fitting curve to which each point cloud data belongs, wherein the characteristic is used as the surface characteristic of the point cloud data.
With reference to the first aspect, the present invention provides a fifth possible embodiment, in the fifth possible embodiment, the determining the model constraint condition of the surface includes:
and determining a surface equation of the surface according to the preset size and the preset shape of the surface, wherein the surface equation is used as a model constraint condition of the surface.
With reference to the first aspect, the present invention provides a sixth possible embodiment, in the sixth possible embodiment, the number of the calibration blocks is multiple;
the determining model constraints for the surface comprises:
determining a surface equation of the surface according to the preset size, the preset shape and the preset arrangement mode of each calibration block, wherein the surface equation is used as a model constraint condition of the surface
With reference to the first aspect, the present invention provides a seventh possible embodiment, where the acquiring three-dimensional point cloud data obtained by acquiring a calibration block by a profiler camera includes:
acquiring multi-frame three-dimensional point cloud data obtained by respectively acquiring calibration blocks located at different positions by a contourgraph camera;
determining local point cloud data belonging to the surface from the three-dimensional point cloud data, including:
and respectively determining local point cloud data belonging to the surface from each frame of three-dimensional point cloud data.
In a second aspect, an embodiment of the present invention provides a calibration apparatus for a profiler camera, where the apparatus includes:
the acquisition module is used for acquiring three-dimensional point cloud data acquired by the contourgraph camera through acquiring the calibration block;
a first determination module for determining, for each surface of the calibration block, a model constraint for the surface; determining local point cloud data belonging to the surface from the three-dimensional point cloud data;
a second determining module for determining an external parameter that matches the local point cloud data of each surface with the model constraint condition of each surface as a target external parameter of the profiler camera.
With reference to the second aspect, the present invention provides a second possible embodiment, in which the second determining module is specifically configured to:
determining a corresponding relation between a model constraint error and an external parameter according to the model constraint condition of each surface, wherein the model constraint error is as follows: under the condition that the external parameter of the contourgraph camera is the external parameter corresponding to the model constraint error, the error of the local point cloud data of each surface to the model constraint condition of each surface;
and determining an external parameter corresponding to the minimum value of the model constraint error according to the corresponding relation, and taking the external parameter as a target external parameter of the contourgraph camera.
With reference to the second aspect, the present invention provides a third possible embodiment, in which the first determining module is specifically configured to:
fitting the point cloud data in the three-dimensional point cloud data to obtain the characteristic of a fitting curve to which each point cloud data belongs, and using the characteristic as the surface characteristic of the point cloud data;
and aiming at each surface, determining point cloud data corresponding to the surface characteristics and the surface from the three-dimensional point cloud data according to a preset corresponding relation between the surface characteristics and the surface, and taking the point cloud data as local point cloud data of the surface.
With reference to the third possible embodiment of the second aspect, the present invention provides a fourth possible embodiment, in the fourth possible embodiment, the fitting the point cloud data in the three-dimensional point cloud data includes:
determining the type of each contour line of the calibration block according to the preset shape of the calibration block;
and fitting the point cloud data in the three-dimensional point cloud data into the fitting curve of the type to obtain the characteristic of the fitting curve to which each point cloud data belongs, wherein the characteristic is used as the surface characteristic of the point cloud data.
With reference to the second aspect, the present invention provides a fifth possible embodiment, in a fifth possible embodiment, the first determining module is further configured to:
and determining a surface equation of the surface according to the preset size and the preset shape of the surface, wherein the surface equation is used as a model constraint condition of the surface.
With reference to the second aspect, the present invention provides a sixth possible embodiment, in the sixth possible embodiment, the number of the calibration blocks is multiple; the first determining module is further configured to:
and determining a surface equation of the surface according to the preset size, the preset shape and the preset arrangement mode of each calibration block, wherein the surface equation is used as a model constraint condition of the surface.
With reference to the second aspect, the present invention provides a seventh possible embodiment, where the obtaining module is configured to obtain multiple frames of three-dimensional point cloud data obtained by acquiring calibration blocks located at different positions by a profiler camera respectively;
the first determining module is used for determining local point cloud data belonging to the surface from each frame of three-dimensional point cloud data.
In a third aspect, an embodiment of the present invention provides an electronic device, where the electronic device includes a processor and a memory, where the memory is used for storing a computer program; and the processor is used for realizing the steps of the calibration method of the contourgraph camera in the first aspect when executing the program stored in the memory.
In a fourth aspect, this embodiment provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the steps of the calibration method for a profiler camera according to the first aspect are implemented.
The embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a method, a device and equipment for calibrating a contourgraph camera, wherein the method specifically comprises the steps of acquiring three-dimensional point cloud data obtained by acquiring a calibration block by the contourgraph camera, determining a model constraint condition of each surface aiming at each surface of the calibration block, determining local point cloud data belonging to each surface of the calibration block from the three-dimensional point cloud data, and then determining an external parameter which enables the local point cloud data of each surface to be matched with the model constraint condition of each surface to serve as a target external parameter of the contourgraph camera. Since the model constraint conditions of the surfaces of the calibration block are determined according to the actual surface size of the calibration block, the simpler the shape of the calibration block is, the simpler and more accurate the corresponding model constraint condition determination process of the surfaces is.
Thus, by using the calibration method of the contourgraph camera provided by the embodiment of the invention, the external parameters of the contourgraph camera can be adjusted based on the model constraint condition of the surface of the calibration block by directly using the calibration block with a simple structure, so that the three-dimensional point cloud data acquired by the adjusted contourgraph camera is matched with the model constraint condition of the actual surface of the calibration block, and the calibration of the contourgraph camera is further realized. The measuring accuracy of the contourgraph camera can be improved while the calibration cost of the contourgraph camera can be effectively reduced.
Of course, it is not necessary for any product or method to achieve all of the above-described advantages at the same time for practicing the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other embodiments can be obtained by referring to these drawings.
FIG. 1 is a schematic flow chart of a possible method for calibrating a profiler camera according to the present invention;
FIG. 2a is a schematic diagram illustrating a possible process of determining local point cloud data according to the present invention;
FIG. 2b is a schematic diagram of a possible fitting curve feature provided by the present invention;
FIG. 3a is a schematic diagram illustrating a possible process of determining surface features of point cloud data according to the present invention;
FIG. 3b is a schematic diagram of another possible characteristic of a fitted curve provided by the present invention;
FIG. 4 is a schematic diagram of a possible calibration block shape provided by the present invention;
FIG. 5 is a schematic diagram of a possible surface model provided by the present invention;
FIG. 6 is a schematic diagram of a possible arrangement of calibration blocks according to the present invention;
FIG. 7 is a schematic view of one possible installation of multiple profiler cameras according to the present invention;
FIG. 8 is a schematic diagram of another possible arrangement of calibration blocks provided by the present invention;
FIG. 9 is a schematic view of another possible installation of a multi-profiler camera according to the present invention;
FIG. 10 is a schematic diagram of a possible structure of a profiler camera calibration device provided by the present invention;
fig. 11 is a schematic diagram of a possible structure of an electronic device provided in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by one skilled in the art based on the embodiments of the present invention, are within the scope of the present invention.
In the related technology, a calibration block embedded with a steel ball or printed with a special pattern is adopted, and the principle of calibrating the profile instrument camera is as follows: the method comprises the steps of taking a steel ball or a special case on a calibration block as a feature point, scanning the calibration block by using a contourgraph camera to obtain three-dimensional point cloud data of the calibration block, registering by using a position relation between coordinates of the feature point extracted from the three-dimensional point cloud data and an actual feature point of the calibration block, and adjusting external parameters of the contourgraph camera by using a registration result so that the three-dimensional point cloud data acquired by the adjusted contourgraph camera can accurately represent the actual size of the calibration block.
By adopting the calibration block inlaid with the steel ball or printed with the special pattern, on one hand, the structure of the calibration block is usually complex, so that the processing period of the calibration block is long and the processing cost is high. On the other hand, because the steel ball or the special pattern is used as the feature point, before calibrating the profile gauge camera, the size of the feature point, such as the center coordinates of the steel ball and the diameter of the steel ball, needs to be accurately determined in advance. This means that the processed calibration block needs to be sent to a professional precision measurement mechanism for dimension measurement, and a model coordinate system is constructed according to the designed calibration block, so that accurate measurement reference data can be obtained. Therefore, the measuring period is increased inevitably, the measuring cost is increased inevitably, and the calibration cost of the contourgraph camera is further increased.
In view of this, in order to save the calibration cost of the profiler camera, the present invention provides a method for calibrating a profiler camera, which is applied to any electronic device supporting the calibration of the profiler camera, including but not limited to electronic devices such as a mobile terminal, a personal computer, or a server, and may also be any measurement system supporting the calibration of the profiler camera, and the embodiment does not limit this.
In a possible embodiment, the method for calibrating a profiler camera provided by the present invention can be as shown in fig. 1, and specifically includes the following steps:
s110, acquiring three-dimensional point cloud data obtained by acquiring a calibration block by a contourgraph camera;
s120, determining a surface model constraint condition and local point cloud data belonging to each surface of the calibration block;
s130, determining external parameters which enable the local point cloud data of the surfaces to be matched with the model constraint conditions of the surfaces, and using the external parameters as target external parameters of the contourgraph camera.
The embodiment of the invention is selected, the surface model constraint conditions of the actual surfaces of the calibration block are matched with the local point cloud data of the surfaces, which is equivalent to the surface model constraint conditions of the actual surfaces of the calibration block, and the parameters of the contourgraph camera are constrained to calibrate the contourgraph camera. Therefore, the local point cloud data of each surface acquired by the calibrated contourgraph camera accords with the surface model constraint condition of the actual surface of the calibration block, namely the acquired local point cloud data of each surface can accurately represent the actual surface characteristics of the calibration block, and further the calibration of the contourgraph camera is completed.
By adopting the embodiment of the invention, the calibration block is not required to have a specific structure or pattern, so that the calibration block with a simple structure can be used for calibrating the contourgraph camera, the processing cost and the measurement cost of the calibration block can be effectively saved, and the calibration cost of the contourgraph camera is further saved.
In order to clearly illustrate the calibration method of the profiler camera provided by the embodiment of the present invention, the foregoing steps S110 to S130 will be described below:
in the embodiment of the present invention, the profiler camera may be any device with three-dimensional information acquisition function, including but not limited to a line laser profiler, a line laser sensor, a three-dimensional scanner, a depth camera, and the like. In the embodiment of the present invention, the number of the profiler cameras may be one or multiple, and the specific type, model and number of the profiler cameras may be selected according to actual measurement requirements, which is not specifically limited in the present invention.
In a possible situation, the contourgraph camera optically scans the object to be detected by emitting light rays with a specific wavelength to the surface of the object to be detected, and generates three-dimensional point cloud data of the object to be detected according to the scanning result. The generated three-dimensional point cloud data of the object to be detected is actually: and the set of all points which are positioned in the same spatial reference system and express the spatial distribution of the object to be detected and the spectral characteristics of the surface of the object to be detected.
Specifically, in step S110, data acquisition may be performed on the calibration block by the profiler camera, so as to obtain three-dimensional point cloud data of the calibration block. The three-dimensional point cloud data of the calibration block can be understood as: in the image coordinate system of the contourgraph camera, a set consisting of a large number of points exists, and the distribution condition of each point in the set can represent the spatial distribution and the surface spectrum of the calibration block. The spatial distribution of the calibration block includes shape information of the calibration block, the shape information of the calibration block may specifically be size information of the calibration block, surface distribution of the calibration block, and the like, and the surface distribution of the specific calibration block may include: the number of calibration block surfaces, the characteristics of the calibration block surfaces, the spatial relationship formed between the surfaces of the calibration block, and the like. The surface spectrum of the calibration block can reflect the surface roughness, the material and the like of the calibration block.
Since the three-dimensional point cloud data of the calibration block acquired in step S110 is a set formed by a large number of points, the distribution of each point can represent the spatial distribution and the surface spectrum of the calibration block. In step S120, local point cloud data belonging to the respective surfaces of the calibration block may be determined for each surface of the calibration block. Specifically, each point in the three-dimensional point cloud data of the calibration block acquired in step S110 is divided according to the spatial distribution of the calibration block, and the points belonging to the same surface are divided into a point cloud data set, so as to obtain local point cloud data of each surface.
For example, taking a calibration block as a conical block as an example, data acquisition is performed on the calibration block through a contourgraph camera, so that three-dimensional point cloud data consisting of a large number of points can be obtained, and the three-dimensional point cloud data can represent the spatial distribution condition of the calibration block, that is, the three-dimensional point cloud can represent that the calibration block is a spatial structure formed by splicing circles of a conical surface and a bottom surface. Therefore, the surface attribution condition of each point cloud can be determined according to the space distribution condition of the calibration block, and the points belonging to the same surface are divided into a point cloud set. That is, in step S120, the point belonging to the conical surface in the three-dimensional point cloud can be determined as the first local point cloud data, and the point belonging to the circle with the remaining part belonging to the bottom surface can be determined as the second local point cloud data.
In one possible embodiment, as shown in fig. 2a, for each surface of the calibration block, the step S120 of determining local point cloud data belonging to the surface from the three-dimensional point cloud data may be implemented by:
and S121, fitting the point cloud data in the three-dimensional point cloud data to obtain the characteristics of a fitting curve to which each point cloud data belongs, and using the characteristics as the surface characteristics of the point cloud data.
The curved line herein does not particularly denote a curved line, but includes a straight line. Similarly, the surface herein may be a surface with any shape, that is, the surface may be a plane or a curved surface, which is not limited in this respect.
It will be appreciated that the shapes of the different surfaces are different and therefore the curves on the different surfaces have different shapes, and for example, taking the calibration block as a cone, the base of the cone is a circular plane, so that the line connecting any two points on the base is a straight line, and the curve of the side of the cone, so that the line connecting two points on the side is a conic curve. In other words, if the point cloud data belonging to the bottom surface is fitted, the fitted curve obtained by fitting is a straight line, and if the point cloud data belonging to the side surface is fitted, the fitted curve obtained by fitting is a conical curve, as shown in fig. 2b for example. Since the fitted curves obtained by fitting the point cloud data of different surfaces have different shapes, the shapes can be regarded as the characteristics of the fitted curves, and meanwhile, as analyzed in the foregoing manner, the characteristics depend on the surface to which the point cloud data belongs, so that the characteristics can reflect the surface of the point cloud data to a certain extent, and therefore the characteristics are referred to as surface characteristics in the present document.
And S122, aiming at each surface, determining point cloud data corresponding to the surface features and the surface from the three-dimensional point cloud data according to the preset corresponding relation between the surface features and the surface, and taking the point cloud data as local point cloud data of the surface.
As described above, the surface features of the point cloud data depend on the surface to which the point cloud data belongs, and therefore the point cloud data of a specific surface will have specific surface features, that is, there is a correspondence between the surface and the surface features, and according to the correspondence and the surface features obtained by fitting, the local point cloud data of each surface can be determined. Illustratively, still taking the example of the cone as an example, if the surface features of the point cloud data indicate that the fitting curve obtained by fitting the point cloud data is a conic curve, the point cloud data belongs to the side surface of the cone, and if the surface features of the point cloud data indicate that the fitting curve obtained by fitting the point cloud data is a straight line, the point cloud data belongs to the bottom surface of the conic curve.
By adopting the embodiment, the point cloud data can be fitted to obtain the surface characteristics of each point cloud data, and the surface to which each point cloud data belongs can be accurately determined according to the surface characteristics of the point cloud data by utilizing the characteristic that the surface characteristics depend on the surface, namely, the local point cloud data of each surface can be accurately determined.
In the foregoing S121, different ways may be adopted for fitting according to different application scenarios, for example, in one possible embodiment, the three-dimensional point cloud data is respectively fitted to curves of multiple different shapes, the matching degrees between the three-dimensional point cloud data and the curves obtained by fitting are respectively calculated, and the curve with the highest matching degree is used as the fitting curve of the three-dimensional point cloud data.
In another possible embodiment, as shown in fig. 3a, the above S121 may also be implemented by S1211-S1212:
s1211 determines a type of a contour line of the calibration block according to the preset shape of the calibration block.
The contour line of the calibration block is a projection of the surface of the index calibration block on a tangent plane, which is any plane perpendicular to the platform on which the calibration block is placed, it can be understood that the type of contour line of the same calibration block will be different depending on the tangent plane, and exemplarily, as shown in fig. 3b, the upper left part of fig. 3b shows the contour lines of different tangent planes in case the calibration block is in the shape of a trapezoidal table. The upper right part of fig. 3b shows the contour lines of the different cut planes in the case of a calibration block in the shape of a circular mountain. The lower left part of fig. 3b shows the contour lines of the different cut planes in the case of a calibration block with a conical shape. The lower right part of fig. 3b shows the contour lines of different cut planes in the case of a spherical cap shape of the calibration block.
And S1212, fitting the point cloud data in the three-dimensional point cloud data into a fitting curve of each contour line type to obtain the characteristics of the fitting curve to which the point cloud data belongs, and using the characteristics as the surface characteristics of the point cloud data.
The three-dimensional point cloud data acquired by the contourgraph camera is point cloud data of the surface of the calibration block, so that a fitting curve obtained by fitting the three-dimensional point cloud data should be a contour line of the calibration block theoretically, namely the type of the fitting curve should be the same as that of the contour line of the calibration block. Therefore, the type of the determined contour line can be regarded as the type of the fitted curve.
Therefore, the embodiment is selected, the type of the fitting curve can be determined before the point cloud data is fitted based on the priori knowledge of the preset shape of the calibration block, and therefore the fitting efficiency of the point cloud data is improved. For example, compared with the manner of respectively fitting the three-dimensional point cloud data into curves of various different shapes and then determining the fitted curve from the fitted curves, the method of the embodiment does not need to respectively fit the same point cloud data into the curves of various different shapes, so that the fitting efficiency is higher.
In step S130, the model constraint condition is used to characterize a condition that each point on the same surface in the spatial coordinate system should satisfy. For example, since each point on a surface is located on the surface, each point on the surface should satisfy the surface equation of the surface, which is an equation for describing the shape of the surface, the surface equation of the surface can be used as a model constraint. For another example, if the surface 1 is parallel to the surface 2 and the distance between the surface 1 and the surface 2 is s, the distance from each point on the surface 1 to the surface 2 should also be s, so that the distance from the surface 2 to s can also be used as a model constraint condition.
It will be appreciated that since the surface equation of a surface depends on the size and shape of the surface, the distance between the surfaces depends on the pose of the calibration blocks, whereas in the case of fixed calibration blocks, the size and shape of the surfaces of the calibration blocks are fixed, but the pose is limited to the specific placement of the calibration blocks and is difficult to know in advance. Therefore, it is easier to implement a surface equation determined from the size and shape of the surface as a model constraint than a distance to the surface as a model constraint. Namely, the surface equation is used as the model constraint condition, so that the complexity of the calibration method of the contourgraph camera can be effectively reduced.
Taking the calibration block as the cone block as an example, calculating a surface equation of the bottom surface of the cone block model according to the actual bottom surface diameter of the cone block as a model constraint condition of the bottom surface, and determining a surface equation of the side surface of the cone block model according to the actual bottom surface diameter of the cone block and the height of the cone block as a model constraint condition of the side surface.
It will be appreciated that the model constraints depend on the calibration block, and thus the model constraints are conditions in a coordinate system that is stationary with respect to the calibration block (hereinafter referred to as the calibration block coordinate system), and for example, the model constraints are surface equations, which are equations in a coordinate system that is stationary with respect to the calibration block. The local point cloud data is acquired by the profiler camera and is therefore point cloud data in a stationary coordinate system (hereinafter referred to as a sensor coordinate system) with respect to the profiler camera. The conversion relation between the sensor coordinate system and the calibration block coordinate system depends on external parameters of a camera of the contourgraph, so that the coordinates of the local point cloud data under the calibration block coordinate system depend on the external parameters, and the coordinates of the local point cloud data under the calibration block coordinate system directly influence whether the local point cloud data is matched with the model constraint condition or not because the model constraint condition is the condition under the calibration block coordinate system.
For the case where there are multiple calibration blocks, illustratively, as shown in fig. 4, different calibration block coordinate systems may be set for different calibration blocks in one possible embodiment due to the different locations of the different calibration blocks. Illustratively, for each calibration block, a calibration block coordinate system is constructed with the center of the calibration block as the origin. In this example, the model constraints of the surfaces of different calibration blocks are conditions in different calibration block coordinate systems, and thus the respective model constraints are independent of each other.
In another possible embodiment, the surface equations of the surfaces are determined according to the preset size, the preset shape and the preset arrangement of the calibration blocks, and used as the model constraints of the surfaces, so that the model constraints of the surfaces are integrated into the same calibration block coordinate system, for example, as shown in fig. 5, in this example, the calibration block coordinate system is constructed by using the center of the calibration block at the upper left as the origin, and the model constraints of the surfaces of the four calibration blocks are integrated into the calibration block coordinate system.
It will be appreciated that a constraining relationship also exists between the surfaces. Illustratively, still taking fig. 5 as an example, the horizontal (i.e., left-right direction in fig. 5) distance of the local point cloud data on the left side surface of the calibration block at the upper right of fig. 5 from the left side surface of the calibration block at the upper left should be dx, and similarly, the vertical (i.e., up-down direction in fig. 5) distance of the local point cloud data on the left side surface of the calibration block at the lower left of fig. 5 from the left side surface of the calibration block at the upper left should be dy. The model constraint conditions of the surfaces of the plurality of calibration blocks are integrated into the coordinate system of the same calibration block, and the model constraint condition of one surface can not only constrain the local point cloud data of the surface, but also constrain the local point cloud data of other surfaces. I.e. enriching the model constraints.
It can be understood that the more the model constraint conditions, the higher the possibility that the target external parameters matched with the local point cloud data and the model constraint conditions are the real external parameters of the profiler camera, that is, the embodiment is selected, and the model constraint conditions are enriched as much as possible by integrating the surface equations into the same calibration block coordinate system, so that the accuracy of the determined target external parameters is improved, that is, the accuracy of the profiler camera calibration is further improved.
It is understood that the arrangement shown in fig. 4 and 5 is only one possible arrangement, and in other possible embodiments, the arrangement of the plurality of calibration blocks may not be as good as that shown in fig. 4 and 5, for example, and in some possible embodiments, the arrangement of the plurality of calibration blocks may be as shown in any one of fig. 6, 7, 8 and 9.
Also, since the greater the number of calibration blocks, the greater the number of surfaces, and therefore the greater the model constraints will be. Therefore, in one possible embodiment, a larger number of calibration blocks may be provided in order to enrich the model constraints to improve the accuracy of the calibration.
In another possible embodiment, the calibration block may be provided with a moving capability, i.e. the calibration block is located at a plurality of different positions at a plurality of different times, for example, as shown in fig. 6 to 9, the calibration block is placed on the conveyor belt, so that the calibration block moves under the driving of the conveyor belt. In the example, calibration blocks at different positions are respectively acquired by a contourgraph camera to obtain multiple frames of three-dimensional point cloud data, local point cloud data belonging to the surface are respectively determined from each frame of three-dimensional point cloud data, and the target pose is determined according to the local point cloud data and the surface constraint conditions of each surface.
It can be understood that when the calibration blocks are located at different positions, the surfaces on the calibration blocks are also located at different positions, and the model constraint conditions of the same surface at different positions are also different, so that the embodiment is adopted, the calibration blocks are respectively located at different positions, the model constraint conditions are enriched, the calibration accuracy of the contourgraph camera is further improved, and meanwhile, the calibration blocks with a large number are not required to be arranged, and therefore the calibration cost of the contourgraph camera is effectively reduced.
The local point cloud data is point cloud data of the surface of the calibration block, so that if the external parameter is the real external parameter of the contourgraph camera, the local point cloud data should be matched with the model constraint condition, otherwise, the external parameter of the local point cloud data which should be matched with the model constraint condition can be regarded as the real external parameter of the contourgraph camera. Therefore, the external parameter that matches the local point cloud data with the model constraint condition can be used as the target external parameter in S130.
It can be understood that when the local point cloud data of each surface matches the model constraint condition of the surface, an error between the local point cloud data of each surface and the model constraint condition of the surface is small, otherwise, when the local point cloud data of each surface does not match the model constraint condition of the surface, an error between the local point cloud data of each surface and the model constraint condition of the surface is large, so in a possible embodiment, whether the local point cloud data of each surface matches the model constraint condition of the surface can be judged according to the error between the local point cloud data of each surface and the model constraint condition of the surface. Hereinafter, for convenience of description, an error between the local point cloud data of each surface and the model constraint condition of the surface is referred to as a model constraint error.
For example, in one possible embodiment, the foregoing S130 may be implemented by S131-S132:
s131, determining the corresponding relation between the model constraint error and the external parameter according to the model constraint condition of each surface.
Wherein, the model constraint error is: the external parameter of the contourgraph camera is the external parameter corresponding to the model constraint error
As the above analysis, the coordinate of the local point cloud data in the coordinate system of the calibration block directly affects whether the local point cloud data matches the model constraint condition, so that model constraint errors are different under different external parameters, that is, there is a corresponding relationship between the model constraint errors and the external parameters.
By selecting the embodiment, the external parameter which enables the model constraint error to be minimum is determined and obtained as the target external parameter according to the corresponding relation between the model constraint error and the external parameter which is obtained through establishment, so that the determined target external parameter can be as close to the real external parameter of the contourgraph camera as possible, and the calibration accuracy of the contourgraph camera is further improved.
For a clearer explanation of S131, how to determine the correspondence between the model constraint error and the external parameter will be described below with reference to a specific application scenario:
assuming that there are n calibration blocks in total, each calibration block includes m surfaces, the n calibration blocks are in a moving state, such as the n calibration blocks are placed on a conveyor belt and moved by the conveyor belt. And the contourgraph camera acquires each calibration block at p different moments respectively to obtain three-dimensional point cloud data.
Assuming that the coordinate of the local point cloud data of the kth surface of the jth calibration block at the jth moment under the sensor coordinate system is X s,i,j,k And then, the coordinates of the local point cloud data under the coordinate system of the calibration block satisfy the formula (1):
X o,i,j,k ＝f(X s,i,j,k )…(1)
wherein, X o,i,j,k For the coordinates of the local point cloud data in the calibration block coordinate system, f (-) is a coordinate transfer function between the sensor coordinate system and the calibration block coordinate system, which depends on the external parameters of the profiler camera.
Then, assuming that the model constraint condition of the surface in the local point cloud data is the surface equation of the surface, and the surface equation of the surface is expressed in the form of formula (2):
g(X)＝0…(2)
where g (-) varies with the shape of the surface, X is the coordinate in the calibration block coordinate system, and for example, if the surface is a plane, equation (2) can be rewritten as equation (3):
Figure BDA0003852734070000121
wherein a, b, c and d are parameters in a planar surface equation, and X, y and z are X component, y component and z component of X respectively.
If the surface is a conical surface, equation (2) can be rewritten as equation (4):
Figure BDA0003852734070000122
wherein D and h are parameters in a surface equation of the conical surface.
If the surface is spherical, equation (2) can be rewritten as equation (5):
Figure BDA0003852734070000123
wherein x is 0 、y 0 、z 0 Is a parameter of the surface equation of a sphere.
Mixing X o,i,j,k Substituting function g (-) as X yields equation (6):
g(X o,i,j,k )＝e i,j,k …(6)
wherein e is i,j,k To be X o,i,j,k Input to the result obtained by function g (-). As analyzed above, when X o,i,j,k When matching the model constraints of the surface, e i,j,k Is 0, on the contrary, when X is o,i,j,k When there is no match with the model constraints of the surface, e i,j,k Is not 0, and X o,i,j,k The more mismatched the model constraints with the surface, the more e i,j,k The larger the value of (c). See, e i,j,k Whether the local point cloud data for the surface matches the model constraints for the surface.
By extending the formula (6) to the local point cloud data of each surface, the formula (7) can be obtained:
Figure BDA0003852734070000131
where T denotes transposition. As described above in relation to e i,j,k As a result, it is possible to determine whether the local point cloud data of each surface matches the model constraint condition of each surface. And due to e i,j,k To be X o,i,j,k Input to the result obtained by function g (-), while X o,i,j,k Is obtained according to the function f (·), which depends on the external parameters, so equation (7) can be regarded as the corresponding relationship between the model constraint error and the external parameters.
And S132, determining the external parameter corresponding to the minimum value of the model constraint error according to the corresponding relation, and taking the external parameter as the target external parameter of the contourgraph camera.
Still taking the correspondence relationship expressed in the form of the foregoing formula (7) as an example, the external parameter capable of minimizing E is determined as the target external parameter of the profiler camera. That is, the objective external parameter should satisfy formula (8):
Figure BDA0003852734070000132
wherein, T g To target external ginseng, argmin T Means that the value of an external parameter T can be minimized, e.g. argmin T E is an external parameter T which can enable the value of E to be minimum.
It is understood that, in some application scenarios, the coordinates of the local point cloud data in the calibration block coordinate system may not be calculated according to formula (1), and in an exemplary embodiment, the coordinates are calculated according to formula (9):
X o,i,j,k ＝f OM (X M,i,j,k )…(9)
wherein, X M,i,j,k For the coordinates of the local point cloud data in the world coordinate system, function f OM (. Is) a coordinate between the world coordinate system and the calibration block coordinate systemAnd (4) calibrating the conversion function. X M,i,j,k Calculated by equation (10):
X M,i,j,k ＝f MS (X s,i,j,k )…(10)
wherein the function f MS (. Cndot.) is a coordinate transfer function between the sensor coordinate system and the world coordinate system. Then in this embodiment E depends on the function f OM (. O) and function f MS (-) and assuming that in this embodiment the step size of the motion of the calibration blocks between each two instants is step, since the surface equation of the surfaces depends on step, and thus E also depends on step, in this embodiment the right part of the first equal sign in equation (8) can be rewritten as equation (11):
Figure BDA0003852734070000133
due to the function f OM The value of (DEG) and step is a fixed value, and the function f MS The value of E is still determined to be the minimum value by the formula (11) because the value of (beta) depends on the external parameter, and the target external parameter can be calculated according to the formula (11).
In some application scenarios there may be multiple profiler cameras, and in one possible embodiment, each profiler camera may be calibrated separately according to equation (11). As with the foregoing analysis, the more model constraints, the more accurate the profiler camera calibration. Therefore, in another possible embodiment, the local point cloud data collected by all the profiler cameras can be integrated into the same calibration block coordinate system, and then equation (12) can be obtained:
Figure BDA0003852734070000141
wherein, E r For determining the obtained model constraint error based on the three-dimensional point cloud data acquired by the r-th contourgraph camera, reference may be made to the foregoing description for how to determine the obtained model constraint error based on the three-dimensional point cloud data, and details are not repeated here. q is the phase of the contourgraphNumber of machines, E tot And determining the sum of the obtained constraint errors of the models based on the three-dimensional point cloud data acquired by the contourgraph cameras. e.g. of the type r,i,j,k To be X o,r,i,j,k The result obtained by inputting the function g (-) can be referred to the related explanation of the aforementioned S131, X o,r,i,j,k And coordinates of local point cloud data of the kth surface of the jth calibration block acquired by the ith contourgraph camera at the ith moment in a calibration block coordinate system.
Analysis as described above, E r Dependent on a function f OM With step and depends on the function f MSr (. O), wherein the function f MSr (. Cndot.) is the coordinate transfer function between the sensor coordinate system and the world coordinate system of the r-th profiler camera. Thus, E tot Dependent on a function f OM (. H) function f MS1 (. Cndot.), function f MS2 (. The one), 8230, function f MSq (. Cndot.) and step size step. Wherein the function f MS1 (. H) function f MS2 (. The one), 8230, function f MSq (. DEG) depends on the external parameters of the profiler cameras, respectively, and the function f OM The value of (t) and step is fixed, and as the above analysis shows, the model constraint error is the smallest in the case where the external parameter is the true external parameter of the profiler camera, so that the external parameter that can minimize the sum of the model constraint errors can be regarded as the true external parameter of the profiler camera, and thus, the value of E can be determined according to equation (13) to be the value of E tot Function f of minimum MS1 (. Cndot.), function f MS2 (. The one), 8230, function f MSq (·)：
Figure BDA0003852734070000142
Then according to the determined function f MS1 (. Cndot.), function f MS2 (. The one), 8230, function f MSq Determining an external parameter of each profiler camera as a target external parameter of each profiler camera. Since only one profiler camera can be calibrated at a time according to equation (11), calibrating a profiler camera according to equation (11) is referred to as single camera calibration, whereas multiple cameras can be calibrated at once according to equation (13)The profiler camera, and therefore calibrating the profiler camera according to equation (13), is referred to as multi-camera calibration.
Moreover, for a scene with a plurality of profiler cameras, each profiler camera can be respectively calibrated according to single-camera calibration, or a plurality of profiler cameras can be simultaneously calibrated according to a plurality of profiler cameras. Or calibrating each profiler camera according to single camera calibration, and determining the external parameter of each profiler camera obtained by calibration as the initial parameter of each profiler camera in formula (13) to determine the external parameter E tot Function f of minimum MS1 (. H) function f MS2 (. The one), 8230, function f MSq (. O) and a function f obtained from the determination MS1 (. Cndot.), function f MS2 (. The one), 8230, function f MSq Determining the external parameters of each contourgraph camera as the target external parameters of each contourgraph camera.
On the other hand, as shown in fig. 10, an embodiment of the present invention further provides a calibration apparatus for a profiler camera, where the calibration apparatus includes:
the acquisition module 101 is used for acquiring three-dimensional point cloud data obtained by acquiring the calibration block by the contourgraph camera;
a first determining module 102, configured to determine, for each surface of the calibration block, a model constraint condition of each surface, and determine, from the three-dimensional point cloud data, local point cloud data belonging to each surface;
and a second determining module 103, configured to determine an external parameter that matches the local point cloud data of each surface with the model constraint condition of each surface, as a target external parameter of the profiler camera.
In a possible embodiment, the second determining module 103 is specifically configured to:
determining a corresponding relation between a model constraint error and an external parameter according to the model constraint condition of each surface, wherein the model constraint error is as follows: under the condition that the external parameter of the contourgraph camera is the external parameter corresponding to the model constraint error, the error of the local point cloud data of each surface to the model constraint condition of each surface;
and determining an external parameter corresponding to the minimum value of the model constraint error according to the corresponding relation, and taking the external parameter as a target external parameter of the contourgraph camera.
In a possible embodiment, the first determining module 102 is specifically configured to:
fitting the point cloud data in the three-dimensional point cloud data to obtain the characteristics of a fitting curve to which each point cloud data belongs, and taking the characteristics as the surface characteristics of the point cloud data;
and aiming at each surface, determining point cloud data corresponding to the surface features and the surface from the three-dimensional point cloud data according to the preset corresponding relation between the surface features and the surface, and taking the point cloud data as local point cloud data of the surface.
In one possible embodiment, fitting point cloud data in three-dimensional point cloud data includes:
determining the type of each contour line of the calibration block according to the preset shape of the calibration block;
and fitting the point cloud data in the three-dimensional point cloud data into a type fitting curve to obtain the characteristics of the fitting curve to which each point cloud data belongs, and taking the characteristics as the surface characteristics of the point cloud data.
In a possible embodiment, the first determining module 102 is further configured to:
and determining a surface equation of the surface according to the preset size and the preset shape of the surface, wherein the surface equation is used as a model constraint condition of the surface.
In a possible embodiment, the number of scaling blocks is multiple, and the first determining module 102 is further configured to:
and determining a surface equation of the surface as a model constraint condition of the surface according to the preset size, the preset shape and the preset arrangement mode of each calibration block.
In a possible embodiment, the obtaining module 101 is configured to obtain multiple frames of three-dimensional point cloud data obtained by acquiring calibration blocks located at different positions by a profiler camera respectively; the first determining module 102 is further configured to determine local point cloud data belonging to each surface of the calibration block from each frame of three-dimensional point cloud data.
An embodiment of the present invention further provides an electronic device, as shown in fig. 11, including a memory 111 for storing a computer program;
the processor 112 is configured to implement the following steps when executing the program stored in the memory 111:
acquiring three-dimensional point cloud data acquired by a calibration block acquired by a contourgraph camera;
determining a model constraint condition of the surface and local point cloud data belonging to the surface from the three-dimensional point cloud data aiming at each surface of the calibration block;
and determining the external parameters which enable the local point cloud data of each surface to be matched with the model constraint conditions of each surface, and using the external parameters as the target external parameters of the contourgraph camera.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this is not intended to represent only one bus or type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
In another embodiment of the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any one of the above-mentioned calibration methods for a profiler camera.
In a further embodiment of the present invention, there is also provided a computer program product containing instructions which, when run on a computer, cause the computer to perform the method of calibrating a profiler camera according to any of the above embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), among others.
It is noted that, herein, relational terms such as first and second, and the like may be 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. Also, 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 phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (11)

1. A method for calibrating a profiler camera, the method comprising:
acquiring three-dimensional point cloud data acquired by a contourgraph camera collecting and calibrating block;
for each surface of the calibration block, determining model constraints for the surface and determining local point cloud data belonging to the surface from the three-dimensional point cloud data;
determining an outlier that matches the local point cloud data for each surface to the model constraints for each surface as a target outlier for the profiler camera.
2. The method of claim 1, wherein the determining an external parameter that matches the local point cloud data for each surface to the model constraints for each surface as a target external parameter for the profiler camera comprises:
determining a corresponding relation between a model constraint error and an external parameter according to the model constraint condition of each surface, wherein the model constraint error is as follows: under the condition that the external parameter of the contourgraph camera is the external parameter corresponding to the model constraint error, the error of the local point cloud data of each surface to the model constraint condition of each surface;
and determining an external parameter corresponding to the minimum value of the model constraint error according to the corresponding relation, and taking the external parameter as a target external parameter of the contourgraph camera.
3. The method of claim 1, wherein determining, for each surface of the calibration block, local point cloud data belonging to the surface from the three-dimensional point cloud data comprises:
fitting the point cloud data in the three-dimensional point cloud data to obtain the characteristic of a fitting curve to which each point cloud data belongs, and using the characteristic as the surface characteristic of the point cloud data;
and aiming at each surface, determining point cloud data corresponding to the surface characteristics and the surface from the three-dimensional point cloud data according to a preset corresponding relation between the surface characteristics and the surface, and taking the point cloud data as local point cloud data of the surface.
4. The method of claim 3, wherein fitting the point cloud data in the three-dimensional point cloud data comprises:
determining the type of each contour line of the calibration block according to the preset shape of the calibration block;
and fitting the point cloud data in the three-dimensional point cloud data into the fitting curve of the type to obtain the characteristic of the fitting curve to which each point cloud data belongs, wherein the characteristic is used as the surface characteristic of the point cloud data.
5. The method of claim 1, wherein determining model constraints for the surface comprises:
and determining a surface equation of the surface according to the preset size and the preset shape of the surface, wherein the surface equation is used as a model constraint condition of the surface.
6. The method of claim 1, wherein the number of scaling blocks is plural;
the determining of the model constraints of the surface comprises:
and determining a surface equation of the surface according to the preset size, the preset shape and the preset arrangement mode of each calibration block, wherein the surface equation is used as a model constraint condition of the surface.
7. The method of claim 1, wherein acquiring three-dimensional point cloud data from a calibration block acquired by a profiler camera comprises:
acquiring multi-frame three-dimensional point cloud data obtained by respectively acquiring calibration blocks located at different positions by a contourgraph camera;
determining local point cloud data belonging to the surface from the three-dimensional point cloud data, including:
and respectively determining local point cloud data belonging to the surface from each frame of three-dimensional point cloud data.
8. A device for calibrating a profiler camera, the device comprising:
the acquisition module is used for acquiring three-dimensional point cloud data acquired by the contourgraph camera through acquiring the calibration block;
a first determination module for determining, for each surface of the calibration block, a model constraint for the surface; determining local point cloud data belonging to the surface from the three-dimensional point cloud data;
a second determining module for determining an external parameter that matches the local point cloud data of each surface with the model constraint condition of each surface as a target external parameter of the profiler camera.
9. The apparatus of claim 8, wherein the second determining module is specifically configured to:
determining a corresponding relation between a model constraint error and an external parameter according to the model constraint condition of each surface, wherein the model constraint error is as follows: under the condition that the external parameter of the contourgraph camera is the external parameter corresponding to the model constraint error, the error of the local point cloud data of each surface to the model constraint condition of each surface;
determining an external parameter corresponding to the minimum value of the model constraint error according to the corresponding relation, and using the external parameter as a target external parameter of the contourgraph camera;
the first determining module is specifically configured to:
fitting the point cloud data in the three-dimensional point cloud data to obtain the characteristic of a fitting curve to which each point cloud data belongs, and using the characteristic as the surface characteristic of the point cloud data;
for each surface, determining point cloud data corresponding to the surface features and the surface from the three-dimensional point cloud data according to a preset corresponding relation between the surface features and the surface, and taking the point cloud data as local point cloud data of the surface;
the fitting of the point cloud data in the three-dimensional point cloud data comprises:
determining the type of each contour line of the calibration block according to the preset shape of the calibration block;
fitting the point cloud data in the three-dimensional point cloud data into the fitting curve of the type to obtain the characteristic of the fitting curve to which each point cloud data belongs, and taking the characteristic as the surface characteristic of the point cloud data;
the first determining module is further configured to:
determining a surface equation of the surface according to the preset size and the preset shape of the surface, wherein the surface equation is used as a model constraint condition of the surface;
the number of the calibration blocks is multiple; the first determining module is further configured to:
determining a surface equation of the surface according to the preset size, the preset shape and the preset arrangement mode of each calibration block, wherein the surface equation is used as a model constraint condition of the surface;
the acquisition module is used for acquiring multi-frame three-dimensional point cloud data obtained by respectively acquiring calibration blocks located at different positions by a contourgraph camera;
the first determining module is further configured to:
and respectively determining local point cloud data belonging to the surface from each frame of three-dimensional point cloud data.
10. An electronic device, characterized in that the electronic device comprises:
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1 to 7 when executing a program stored in the memory.
11. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 7.
CN202211139180.2A 2022-09-19 2022-09-19 Method and device for calibrating contourgraph camera and electronic equipment Pending CN115423883A (en)

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