CN114608540B - Measurement net type determining method for digital photogrammetry system - Google Patents
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Abstract
The invention provides a measurement network type determining method of a digital photogrammetry system. The method comprises the following steps: and simulating by using a beam method adjustment model, solving error equations corresponding to all measurement points after imaging at each shooting station by using a least square method to obtain current optimal object point coordinates and current optimal net shape, fitting the current optimal object point coordinates with a preset theoretical digital model to obtain current measurement errors, sequentially adjusting each parameter to be optimized according to a preset optimization sequence, and iteratively solving the measurement errors until the measurement errors in the adjustment process of each parameter to be optimized meet corresponding convergence conditions to obtain the optimal measurement net shape. The whole method can determine the most suitable measurement net type according to the field actual measurement conditions, so that the measurement error of the digital photogrammetry system is stable, and the measurement accuracy is high.
Description
Technical Field
The invention belongs to the fields of computer vision and computer graphics, and relates to a measurement net type determining method of a digital photogrammetry system.
Background
Digital photogrammetry is a measurement technology which has been developed very rapidly in recent years, mainly taking a series of photos by using an optical camera, and obtaining three-dimensional coordinates of a to-be-measured point after computer image matching and related mathematical calculation. The measurement principle of the digital photogrammetry system is the same as that of the theodolite system, and the triangle measurement principle is adopted.
Unlike conventional measurement methods, digital photogrammetry systems cannot intuitively obtain measurement data from measurement devices, but first take a series of pictures for a sample to be measured, and then solve three-dimensional coordinates of target points on the sample to be measured through image processing and mathematical methods, based on which a spatial network formed by all the shooting points, the points to be measured and photographic light is called a measurement network, and the measurement network mainly includes shooting positions and gesture distribution (i.e., shooting station distribution) of cameras in a shooting station, target distribution (i.e., measurement point distribution) of the sample to be measured, and position (reference layout) of a scale. The setting of the measurement network type not only affects the image point precision of the photo, but also solves important input parameters in the model, so that the measurement network type is the factor with the greatest influence on the measurement precision from the aspect of a measurement method.
The current measurement network type of the digital photogrammetry system is determined, and the measurement network type under various conditions such as different precision requirements, different size measurement objects and the like cannot be accurately determined mainly by means of engineering experience of operators. More operators can determine the measurement network type according to the triangle measurement principle, and each point to be measured at least needs to be intersected by two photographic beams to be solved, so that if photographic beams intersected at the point to be measured are increased, the measurement accuracy of the digital photogrammetry system can be improved. However, the practical engineering application environment is more complex, especially for large-caliber antennas or panel samples in an environmental cabin, and is limited by an auxiliary measurement platform and a measurement space range, so that an ideal measurement net type is difficult to realize. Under extreme measurement conditions, when conditions such as ideal intersection angles cannot be formed by photographic light, measurement data in a single photo view field are less or complete reflection surface coordinate information can be formed by splicing more photos are needed, the measurement accuracy of the digital photographic measurement system is very sensitive to the influence of the position and the posture of a camera, and at the moment, a proper measurement net cannot be accurately determined only by relying on engineering experience of an operator, so that the measurement error of the digital photographic measurement system is unstable and the measurement accuracy is low.
Disclosure of Invention
The invention provides a method for determining a measurement network type of a digital photogrammetry system, aiming at the defects in the prior art, comprising the following steps:
according to the site measurement conditions, determining initial distribution of the cameras at the cameras, wherein the initial distribution of the cameras comprises initial positions of the cameras at the cameras and initial attitude angles of the cameras;
simulating by using a beam method adjustment model to obtain an error equation corresponding to each measuring point on a sample to be measured after imaging at each shooting station, wherein each measuring point on the sample to be measured is uniformly distributed on the surface of the sample to be measured according to preset density parameters;
solving error equations corresponding to all measurement points after imaging of each shooting station by using a least square method to obtain a current optimal three-dimensional coordinate of an object point, a current optimal position of the shooting station and a current optimal attitude angle of the shooting station;
fitting the current optimal three-dimensional coordinate of the object point with a preset theoretical digital model to obtain a current measurement error;
setting an initial measurement net type according to the current optimal position of the shooting station, the current optimal attitude angle of the shooting station and the density parameter, sequentially adjusting each parameter to be optimized according to a preset optimization sequence, and iteratively solving measurement errors until the measurement errors in the adjustment process of each parameter to be optimized meet corresponding convergence conditions, so as to obtain the optimal measurement net type, wherein the optimal measurement net type comprises the optimal position of the shooting station, the optimal attitude angle of the shooting station and the optimal density parameter.
Further, the initial distribution is any one of a ring shape, a column shape, a Chinese character 'mi' shape and a cross shape.
Further, the simulating by using the beam method adjustment model to obtain an error equation corresponding to each measurement point on the sample to be measured after each imaging station, including:
obtaining an error equation corresponding to each measuring point on the sample to be measured after imaging at each shooting station through the following formula:
wherein v is x 、v y As a result of the error in the error,
for a matrix of the pose angles of the camera, a matrix of the pose angles of the camera (R x ,R y ,R z ) Converted into X, Y, Z as the three-dimensional coordinate of the measuring point, X 0 、Y 0 、Z 0 For the position of the shooting station, f is the focal length of the camera, x and y are the image coordinates of the image point corresponding to the measurement point after the shooting station is imaged, and x 0 、y 0 As the principal point coordinates of the image, deltax r 、Δx d 、Δx b 、Δy r 、Δy d 、Δy b Is a distortion parameter of the camera.
Further, the parameters to be optimized include a photographing distance, a camera pointing direction, a camera station distribution density and a measurement point distribution density.
Further, setting an initial measurement network type according to the current optimal position of the camera, the current optimal attitude angle of the camera and the density parameter, sequentially adjusting each parameter to be optimized according to a preset optimization sequence, and iteratively solving measurement errors until the measurement errors in the adjustment process of each parameter to be optimized meet corresponding convergence conditions, thereby obtaining the optimal measurement network type, including:
setting an initial measurement net type according to the current optimal position of the shooting station, the current optimal attitude angle of the shooting station and the density parameter;
adjusting the photographing distance, and iteratively solving a measurement error until the measurement error meets a first preset convergence condition to obtain a first intermediate net type;
adjusting the camera orientation, and iteratively solving the measurement error until the measurement error meets a second preset convergence condition to obtain a second intermediate network type;
adjusting the distribution density of the shooting stations, and iteratively solving a measurement error until the measurement error meets a third preset convergence condition to obtain a third intermediate net type;
and adjusting the distribution density of the measuring points, and iteratively solving the measuring error until the measuring error meets a fourth preset convergence condition to obtain the optimal measuring net type.
The beneficial effects of the invention are as follows: and simulating by using a beam method adjustment model, solving error equations corresponding to all measurement points after imaging at each shooting station by using a least square method to obtain current optimal object point coordinates and current optimal net shape, fitting the current optimal object point coordinates with a preset theoretical digital model to obtain current measurement errors, sequentially adjusting each parameter to be optimized according to a preset optimization sequence, and iteratively solving the measurement errors until the measurement errors in the adjustment process of each parameter to be optimized meet corresponding convergence conditions to obtain the optimal measurement net shape. Therefore, the invention can determine the most suitable measurement net type according to the field actual measurement condition, thereby ensuring that the measurement error of the digital photogrammetry system is more stable and the measurement precision is higher.
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In order to more clearly illustrate the technical solution of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic flow chart of a measurement network type determining method of a digital photogrammetry system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a beam method adjustment model;
FIG. 3 is a schematic diagram illustrating adjustment of camera distance, camera pointing and distribution density of camera stations according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an iterative method of a measurement network optimization model according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a specific flow corresponding to a measurement network type determining method of a digital photogrammetry system according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Aiming at the defects in the prior art, the embodiment of the invention provides a measurement network type determining method of a digital photogrammetry system. Fig. 1 is a schematic flow chart of a measurement network type determining method of a digital photogrammetry system according to an embodiment of the present invention, as shown in fig. 1, specifically including the following steps:
101: and determining initial distribution of the cameras at the stations according to the field measurement conditions.
The initial camera station distribution comprises an initial position of a camera station and an initial attitude angle of the camera station.
Specifically, the initial distribution may be any one of a ring shape, a column shape, a zigzag shape, and a cross shape.
That is, the initial substation distribution should be combined with the field measurement condition, and may be selected from conventional substation distribution schemes such as annular, column (ribbon), zig-zag, and cross.
102: and simulating by using a beam method adjustment model to obtain an error equation corresponding to each measuring point on the sample to be measured after imaging at each shooting station.
Wherein, each measuring point on the sample to be measured is evenly distributed on the surface of the sample to be measured according to the preset density parameter.
Specifically, an error equation corresponding to each measurement point on the sample to be measured after imaging at each station can be obtained through the formula (1):
in the formula (1), v x 、v y As a result of the error in the error,
for a matrix of the pose angles of the camera, a matrix of the pose angles of the camera (R x ，R y ，R z ) Converted into X, Y, Z as the three-dimensional coordinate of the measuring point, X 0 、Y 0 、Z 0 For the position of the shooting station, f is the focal length, x and y are the image coordinates of the image point corresponding to the measurement point after the shooting station is imaged, and x 0 、y 0 As the principal point coordinates of the image, deltax r 、Δx d 、Δx b 、Δy r 、Δy d 、Δy b Is a distortion parameter of the camera.
The process of simulating the beam method adjustment model is described below.
Model principle of beam method adjustmentAs shown in FIG. 2, assume that a certain measurement point on the sample to be measured is P, its global coordinate system three-dimensional coordinates are (X, Y, Z), P is the corresponding image point of P after a certain camera is imaged, the image coordinates of P are (X, Y), and the camera position coordinates include the camera position (X 0 ,Y 0 ,Z 0 ) And attitude angle (R) x ,R y ,R z ) The coordinates of the point P in the camera coordinate system are (X ', Y ', Z '), and f is the focal length of the camera, and there is a relationship shown in the following formula (2):
in the formula (2), X and Y are image coordinates of an image point P, X ', Y ', and Z ' are coordinates of the point P in a camera coordinate system, and f is a camera focal length.
The coordinates X ', Y ', Z ' of the P point in the camera coordinate system can be expressed by the following formula (3):
the following equation (4) can be obtained from equation (2) and equation (3):
in the formula (3) and the formula (4),
for a matrix of the pose angles of the camera, a matrix of the pose angles of the camera (R x ，R y ，R z ) Converted into X, Y, Z as the three-dimensional coordinate of the measuring point, X 0 、Y 0 、Z 0 For the position of the camera, f is the focal length of the camera, and x, y are the image coordinates of the image point p.
Considering the principal point position and the image distortion, the following distortion is made to the formula (4):
in the formula (5) of the present invention,
for a matrix of the pose angles of the camera, a matrix of the pose angles of the camera (R x ,R y ,R z ) Converted into X, Y, Z as the three-dimensional coordinate of the measuring point, X 0 、Y 0 、Z 0 For the position of the camera, f is the focal length of the camera, x, y are the image coordinates of the image point p, x 0 、y 0 As the principal point coordinates of the image, deltax r 、Δx d 、Δx b 、Δy r 、Δy d 、Δy b Is a distortion parameter of the camera.
Wherein the radial distortion correction formula can be expressed by formula (6):
the tangential distortion correction formula can be expressed by formula (7):
the image plane correction formula can be expressed by formula (8):
furthermore, some of the parameters in equation (6), equation (7) and equation (8) are calculated by equation (9):
in the formula (6), the formula (7), the formula (8) and the formula (9), Δx r 、Δy r K is the radial distortion correction 1 、K 2 、K 3 As radial distortion parameters, x and y are the image coordinates of the image point p, x 0 、y 0 As the principal point coordinates of the image, deltax d 、Δy d For tangential distortion correction, P 1 、P 2 As tangential distortion parameter Deltax b 、Δy b B for image plane correction 1 、b 2 Is an image plane distortion parameter.
Thus, the error equation shown in the formula (1) can be obtained according to the formula (5).
103: and solving error equations corresponding to all the measurement points after imaging of each shooting station by using a least square method to obtain the current optimal three-dimensional coordinates of the object point, the current optimal position of the shooting station and the current optimal attitude angle of the shooting station.
The current optimal three-dimensional coordinates of the object point comprise the solved three-dimensional coordinates of all the measuring points on the sample to be measured.
Specifically, firstly, for any shooting station, an error equation of any measuring point on a sample to be measured at the shooting station is established by using a beam adjustment method simulation.
And then according to the error equation of all the measuring points at each station, the current optimal position and the current optimal attitude angle of each station and the three-dimensional coordinates of each measuring point can be obtained by using a least square method.
104: fitting the current optimal three-dimensional coordinate of the object point with a preset theoretical digital model to obtain a current measurement error.
The sample to be measured is processed according to theoretical digital-analog.
105: setting an initial measurement network type according to the current optimal position of the photographing station, the current optimal attitude angle of the photographing station and the density parameter, sequentially adjusting each parameter to be optimized according to a preset optimization sequence, and iteratively solving the measurement error until the measurement error in the adjustment process of each parameter to be optimized meets the corresponding convergence condition, thereby obtaining the optimal measurement network type.
The optimal measurement network comprises an optimal position of a shooting station, an optimal attitude angle of the shooting station and an optimal density parameter.
In particular, the parameters to be optimized include camera distance, camera pointing, camera station distribution density, and measurement point distribution density.
Fig. 3 is a schematic diagram illustrating adjustment of shooting distance, camera orientation and distribution density of the shooting stations according to an embodiment of the present invention, where different shooting distances represent adjustment of distances between the shooting stations and a sample to be measured, different distribution densities of the shooting stations represent adjustment of distances between the shooting stations, and different camera orientations represent adjustment of camera attitude angles of the respective shooting stations, as shown in fig. 3.
The optimization order may be set to be, in order from front to back, the camera pointing, the workstation distribution density, and the measurement point distribution density.
Further, the optimal measurement net shape can be obtained by the following steps:
step one, setting an initial measurement net type according to the current optimal position of the camera station, the current optimal attitude angle of the camera station and the density parameter.
And secondly, adjusting the photographing distance, and iteratively solving the measurement error until the measurement error meets a first preset convergence condition to obtain a first intermediate net type.
Wherein the camera distance may be embodied by the camera position coordinates.
Specifically, the adjustment can be performed according to the preset parameter increment.
The first preset convergence condition may be that the measurement accuracy reaches a peak value within a preset parameter threshold range, and convergence may be considered.
And thirdly, adjusting the camera orientation, and iteratively solving the measurement error until the measurement error meets a second preset convergence condition to obtain a second intermediate net type.
Specifically, the adjustment can be performed according to the preset parameter increment.
The second preset convergence condition may be that the measurement accuracy reaches a peak value within a preset parameter threshold range, that is, convergence may be considered.
And step four, adjusting the distribution density of the shooting stations, and iteratively solving the measurement error until the measurement error meets a third preset convergence condition to obtain a third intermediate net type.
Wherein, the distribution density of the shooting stations can also be represented by the position coordinates of the shooting stations.
Specifically, the adjustment can be performed according to the preset parameter increment.
The third preset convergence condition may be that a ratio between the measurement accuracy increment and the parameter increment within a preset parameter threshold range is smaller than a design value. Wherein the design value can be determined according to the requirement of measurement accuracy and field measurement conditions.
And fifthly, adjusting the distribution density of the measurement points, and iteratively solving the measurement error until the measurement error meets a fourth preset convergence condition to obtain an optimal measurement net type.
Specifically, the adjustment can be performed according to the preset parameter increment.
The fourth preset convergence condition may be that a ratio between the measurement accuracy increment and the parameter increment within a preset parameter threshold range is smaller than a design value. Wherein the design value can be determined according to the requirement of measurement accuracy and field measurement conditions.
In addition, other optimization orders can be adopted to adjust each parameter to be optimized, and the method is not particularly limited.
Fig. 4 is a schematic diagram illustrating an iteration method of a measurement network optimization model according to an embodiment of the present invention, where, as shown in fig. 4, an optimization iteration sequence provided by an embodiment of the present invention is a photographing distance, a camera pointing direction, a substation distribution density, and a measurement point distribution density in sequence, iteration convergence conditions corresponding to the photographing distance adjustment and the camera pointing direction adjustment are set parameter thresholds, and measurement accuracy peaks are considered to be converged within a threshold range. The iteration convergence conditions corresponding to the adjustment of the distribution density of the shooting station and the adjustment of the distribution density of the measuring point are set parameter thresholds, and whether the ratio between the increment of the measuring precision and the increment of the parameter in the threshold range is smaller than a design value or not is determined according to the measuring precision requirement and the field condition.
In order to more clearly illustrate the measurement network type determining method provided by the embodiment of the present invention, fig. 5 schematically illustrates a specific flow diagram corresponding to the measurement network type determining method of the digital photogrammetry system provided by the embodiment of the present invention, and as shown in fig. 5, the specific flow is as follows: and finishing the reference layout of the measurement network type, determining the distribution of the shooting stations according to the boundary of the field measurement condition, determining the image point errors according to the distribution of the shooting stations and the distribution of the measurement points, carrying out adjustment of an error equation of an imaging model according to a beam method, carrying out iterative solution by utilizing least squares, carrying out iterative calculation on the surface shape errors, calculating and carrying out data statistics by utilizing ICP (inductively coupled plasma) to obtain the measurement errors of the imaging model and the sample model, judging whether the measurement errors meet the convergence condition of the iterative model, and if the measurement errors do not meet the convergence condition of the iterative model, continuously adjusting parameters such as the layout mode, the distribution of the measurement points, the distribution of the shooting stations and the like until the measurement errors meet the convergence condition of the iterative model, and outputting the optimal shooting network type.
The beneficial effects of the invention are as follows: and simulating by using a beam method adjustment model, solving error equations corresponding to all measurement points after imaging at each shooting station by using a least square method to obtain current optimal object point coordinates and current optimal net shape, fitting the current optimal object point coordinates with a preset theoretical digital model to obtain current measurement errors, sequentially adjusting each parameter to be optimized according to a preset optimization sequence, and iteratively solving the measurement errors until the measurement errors in the adjustment process of each parameter to be optimized meet corresponding convergence conditions to obtain the optimal measurement net shape. Therefore, the invention can determine the most suitable measurement net type according to the field actual measurement condition, thereby ensuring that the measurement error of the digital photogrammetry system is more stable and the measurement precision is higher.
The invention has been described in detail in connection with the specific embodiments and exemplary examples thereof, but such description is not to be construed as limiting the invention. It will be understood by those skilled in the art that various equivalent substitutions, modifications or improvements may be made to the technical solution of the present invention and its embodiments without departing from the spirit and scope of the present invention, and these fall within the scope of the present invention. The scope of the invention is defined by the appended claims.
Claims (2)
1. A measurement web type determining method of a digital photogrammetry system, comprising:
according to the site measurement conditions, determining initial distribution of the cameras at the cameras, wherein the initial distribution of the cameras comprises initial positions of the cameras at the cameras and initial attitude angles of the cameras;
simulating by using a beam method adjustment model to obtain an error equation corresponding to each measuring point on a sample to be measured after imaging at each shooting station, wherein each measuring point on the sample to be measured is uniformly distributed on the surface of the sample to be measured according to preset density parameters;
solving error equations corresponding to all measurement points after imaging of each shooting station by using a least square method to obtain a current optimal three-dimensional coordinate of an object point, a current optimal position of the shooting station and a current optimal attitude angle of the shooting station;
fitting the current optimal three-dimensional coordinate of the object point with a preset theoretical digital model to obtain a current measurement error;
setting an initial measurement net type according to the current optimal position of the shooting station, the current optimal attitude angle of the shooting station and the density parameter, sequentially adjusting each parameter to be optimized according to a preset optimization sequence, and iteratively solving measurement errors until the measurement errors in the adjustment process of each parameter to be optimized meet corresponding convergence conditions to obtain an optimal measurement net type, wherein the optimal measurement net type comprises the optimal position of the shooting station, the optimal attitude angle of the shooting station and the optimal density parameter;
the method for simulating the adjustment model by using the beam method to obtain an error equation corresponding to each measuring point on the sample to be tested after imaging at each shooting station comprises the following steps:
obtaining an error equation corresponding to each measuring point on the sample to be measured after imaging at each shooting station through the following formula:
wherein v is x 、v y As a result of the error in the error,
for the matrix of the attitude angles of the camera, the matrix is composed of the attitude angles R of the camera x ,R y ,R z Converted into X, Y, Z as the three-dimensional coordinate of the measuring point, X 0 、Y 0 、Z 0 For the position of the shooting station, f is the focal length of the camera, x and y are the image coordinates of the image point corresponding to the measurement point after the shooting station is imaged, and x 0 、y 0 As the principal point coordinates of the image, deltax r 、Δx d 、Δx b 、Δy r 、Δy d 、Δy b Is a distortion parameter of the camera;
the parameters to be optimized comprise shooting distance, camera pointing, distribution density of a shooting station and distribution density of measuring points;
setting an initial measurement network type according to the current optimal position of the shooting station, the current optimal attitude angle of the shooting station and the density parameter, sequentially adjusting each parameter to be optimized according to a preset optimization sequence, and iteratively solving measurement errors until the measurement errors in the adjustment process of each parameter to be optimized meet corresponding convergence conditions to obtain the optimal measurement network type, wherein the method comprises the following steps:
setting an initial measurement net type according to the current optimal position of the shooting station, the current optimal attitude angle of the shooting station and the density parameter;
adjusting the photographing distance, and iteratively solving a measurement error until the measurement error meets a first preset convergence condition to obtain a first intermediate net type;
adjusting the camera orientation, and iteratively solving the measurement error until the measurement error meets a second preset convergence condition to obtain a second intermediate network type;
adjusting the distribution density of the shooting stations, and iteratively solving a measurement error until the measurement error meets a third preset convergence condition to obtain a third intermediate net type;
and adjusting the distribution density of the measuring points, and iteratively solving the measuring error until the measuring error meets a fourth preset convergence condition to obtain the optimal measuring net type.
2. The method of claim 1, wherein the initial distribution is any one of a circular shape, a column shape, a zig-zag shape, and a cross shape.
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