CN114608540A - Measurement network type determining method of digital photogrammetric system - Google Patents

Measurement network type determining method of digital photogrammetric system Download PDF

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CN114608540A
CN114608540A CN202210261808.XA CN202210261808A CN114608540A CN 114608540 A CN114608540 A CN 114608540A CN 202210261808 A CN202210261808 A CN 202210261808A CN 114608540 A CN114608540 A CN 114608540A
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钱元
娄铮
曲源
左营喜
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Purple Mountain Observatory of CAS
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G01C11/04—Interpretation of pictures
    • G01C11/30—Interpretation of pictures by triangulation
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Abstract

The invention provides a measuring net type determining method of a digital photogrammetric system. The method comprises the following steps: simulating by using a light beam method adjustment model, solving error equations corresponding to all measurement points after imaging at each camera station by using a least square method to obtain the current optimal object space point coordinate and the current optimal network type, fitting the current optimal object space point coordinate and a preset theoretical digital analog to obtain the current measurement error, 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 to obtain the optimal measurement network type. The whole method can determine the most appropriate measurement net type according to the actual measurement conditions on site, so that the measurement error of the digital photogrammetric system is stable, and the measurement precision is high.

Description

Measurement network type determining method of digital photogrammetric system
Technical Field
The invention belongs to the field of computer vision and computer graphics, and relates to a method for determining a measurement network type of a digital photogrammetric system.
Background
Digital photogrammetry is a measurement technology which develops very rapidly in recent years, and mainly comprises the steps of taking a series of photos by using an optical camera, and obtaining three-dimensional coordinates of a point to be measured through computer image matching and related mathematical calculation. The measurement principle of the digital photogrammetry system is the same as that of a theodolite system, and the triangular measurement principle is adopted.
Different from the conventional measurement method, the digital photogrammetry system cannot intuitively obtain measurement data from the measurement equipment, but first needs to take a series of photos for a sample to be measured, and then solves the three-dimensional coordinates of target points on the sample to be measured by image processing and mathematical methods. The setting of the measurement net type not only affects the image point precision of the photo, but also solves important input parameters in the model, so that the measurement net type is the factor which has the greatest influence on the measurement precision from the aspect of the measurement method.
At present, the measurement net type of a digital photogrammetry system is determined mainly by the engineering experience of an operator, and the measurement net type under various conditions such as different precision requirements, different size measurement objects and the like cannot be accurately determined. The operator determines more net types to be measured or according to the triangle method measuring principle, each point to be measured needs to be solved only by intersecting two photographic light beams, so the measuring precision of the digital photographic measuring system can be improved if photographic light rays which are intersected at the point to be measured are added. However, the practical engineering application environment is more complicated, especially for large-aperture antennas or panel samples in environmental chambers, the range of the auxiliary measuring platform and the measuring space is limited, and it is difficult to realize an ideal measuring net type. Under extreme measurement conditions, when the conditions that ideal intersection angles cannot be formed by photographic light, measurement data in a single photo field are few, or complete reflecting surface coordinate information can be formed by splicing more photos and the like occur, the measurement accuracy of the digital photogrammetric system is very sensitive to the influence of the position and the posture of a camera, and at the moment, a proper measurement net type cannot be accurately determined only by the engineering experience of an operator, so that the measurement error of the digital photogrammetric system is unstable, and the measurement accuracy is low.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for determining the measurement network type of a digital photogrammetric system, which comprises the following steps:
determining initial distribution of a shooting station where a camera is located according to field measurement conditions, wherein the initial shooting station distribution comprises an initial position of the shooting station where the camera is located and an initial attitude angle of the shooting station;
simulating by using a bundle adjustment model to obtain an error equation corresponding to each measuring point on a sample piece to be measured after imaging at each camera station, wherein each measuring point on the sample piece to be measured is uniformly distributed on the surface of the sample piece to be measured according to a preset density parameter;
solving error equations corresponding to all the measurement points after imaging at each camera by using a least square method to obtain the current optimal three-dimensional coordinates of the object space point, the current optimal position of the camera and the current optimal attitude angle of the camera;
fitting the current optimal three-dimensional coordinates of the object space points with a preset theoretical digital analog to obtain a current measurement error;
setting an initial measurement network type according to the current optimal position of the camera station, the current optimal attitude angle of the camera station and the density parameters, sequentially adjusting each parameter to be optimized according to a preset optimization sequence, 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 optimal measurement network type comprises the optimal position of the camera station, the optimal attitude angle of the camera station and the optimal density parameters.
Further, the initial distribution is any one of a ring shape, a column shape, a meter shape and a cross shape.
Further, the simulating by using the bundle adjustment model to obtain the error equation corresponding to each measurement point on the sample to be measured after imaging at each camera station includes:
obtaining an error equation corresponding to each measuring point on the sample piece to be measured after imaging at each camera station through the following formula:
Figure BDA0003550766450000021
wherein v isx、vyIn order to be an error, the error is,
Figure BDA0003550766450000022
is a matrix of the pose angles of the camera, consisting of the pose angles (R) of the camerax,Ry,Rz) Converted to three-dimensional coordinates, X, of measuring point X, Y, Z0、Y0、Z0Is the camera station position, f is the focal length of the camera, x and y are the image coordinates of the corresponding image point of the measuring point after the imaging of the camera station, x0、y0As principal point coordinates, Δ xr、Δxd、Δxb、Δyr、Δyd、ΔybIs the distortion parameter of the camera.
Further, the parameters to be optimized comprise shooting distance, camera direction, shooting station distribution density and measuring point distribution density.
Further, the setting of an initial measurement network type according to the current optimal position of the camera station, the current optimal attitude angle of the camera station and the density parameter, the sequential adjustment of each parameter to be optimized according to a preset optimization sequence, and the iterative solution of measurement errors until the measurement errors in the adjustment process of each parameter to be optimized all satisfy corresponding convergence conditions, to obtain an optimal measurement network type, includes:
setting an initial measurement network type according to the current optimal position of the camera station, the current optimal attitude angle of the camera 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 network type;
adjusting the orientation of the camera, and iteratively solving a 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 camera, and iteratively solving a measurement error until the measurement error meets a third preset convergence condition to obtain a third intermediate network 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 invention has the beneficial effects that: simulating by using a light beam method adjustment model, solving error equations corresponding to all measurement points after imaging at each camera station by using a least square method to obtain the current optimal object space point coordinate and the current optimal network type, fitting the current optimal object space point coordinate and a preset theoretical digital analog to obtain the current measurement error, 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 to obtain the optimal measurement network type. Therefore, the method and the device can determine the most appropriate measuring net type according to the actual field measuring conditions, so that the measuring error of the digital photogrammetric system is more stable, and the measuring precision is higher.
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In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for determining a measurement network type of a digital photogrammetric system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a bundle adjustment model;
FIG. 3 is a schematic diagram illustrating adjustment of shooting distance, camera orientation and distribution density of the stations according to an embodiment of the present invention;
fig. 4 is a schematic view of an iterative method of a measurement mesh type optimization model according to an embodiment of the present invention;
fig. 5 is a schematic flowchart of a specific process corresponding to the method for determining a measurement network type of a digital photogrammetric system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying 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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In view of the deficiencies in the prior art, embodiments of the present invention provide a method for determining a measurement network type of a digital photogrammetric system. Fig. 1 schematically illustrates a flow chart of a method for determining a measurement network type of a digital photogrammetric system according to an embodiment of the present invention, as shown in fig. 1, specifically including the following steps:
101: and determining the initial distribution of the camera station according to the field measurement condition.
The initial shooting station distribution comprises an initial position of a shooting station where the camera is located and an initial attitude angle of the shooting station.
Specifically, the initial distribution may be any one of a ring shape, a column shape, a m-shape, and a cross shape.
That is, the initial filming distribution should be selected from the conventional filming distribution schemes such as ring, column (flight band), rice-shaped and cross, in combination with the field measurement conditions.
102: and (4) 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 camera station.
And all the measuring points on the sample piece to be measured are uniformly distributed on the surface of the sample piece to be measured according to preset density parameters.
Specifically, an error equation corresponding to each measurement point on the sample to be measured after imaging at each camera station can be obtained through formula (1):
Figure BDA0003550766450000041
in the formula (1), vx、vyIn order to be an error, the error is,
Figure BDA0003550766450000042
is a matrix of the pose angles of the camera, consisting of the pose angles (R) of the camerax，Ry，Rz) Converted to three-dimensional coordinates, X, of measuring point X, Y, Z0、Y0、Z0Is the camera station position, f is the focal length, x, y are the image coordinates of the corresponding image point of the measuring point after the camera station imaging, x0、y0As principal point coordinates, Δ xr、Δxd、Δxb、Δyr、Δyd、ΔybIs the distortion parameter of the camera.
The process of simulating the bundle adjustment model is described below.
The principle of the bundle adjustment model is shown in fig. 2, and it is assumed that a measurement point on a sample to be measured is P, the three-dimensional coordinates of the global coordinate system are (X, Y, Z), P is an image point corresponding to P after imaging at a certain camera station, the image coordinates of P are (X, Y), and the camera station coordinates include the camera station position (X, Y)0,Y0,Z0) And attitude angle (R)x,Ry,Rz) If the coordinates of the point P in the camera coordinate system are (X ', Y ', Z '), and f is the focal length of the camera, the relationship shown in the following equation (2) exists:
Figure BDA0003550766450000043
in the formula (2), X and Y are image coordinates of the image point P, X ', Y ' and Z ' are coordinates of the point P in the camera station coordinate system, and f is the focal length of the camera.
The coordinates X ', Y ', Z ' of the point P in the camera coordinate system can be expressed by the following formula (3):
Figure BDA0003550766450000051
from the equations (2) and (3), the following equation (4) can be obtained:
Figure BDA0003550766450000052
in the formula (3) and the formula (4),
Figure BDA0003550766450000053
is a matrix of the pose angles of the camera, consisting of the pose angles (R) of the camerax，Ry，Rz) Converted into three-dimensional coordinates, X, X, Y, Z for the measurement point0、Y0、Z0F is the focal length of the camera, and x and y are the image coordinates of the image point p.
In consideration of the image principal point position and image distortion, equation (4) is modified as follows:
Figure BDA0003550766450000054
in the formula (5), the first and second groups,
Figure BDA0003550766450000055
is a matrix of the pose angles of the camera, consisting of the pose angles (R) of the camerax,Ry,Rz) Converted to three-dimensional coordinates, X, of measuring point X, Y, Z0、Y0、Z0Is the camera station position, f is the focal length of the camera, x, y are the image coordinates of the image point p, x0、y0As principal point coordinates, Δ xr、Δxd、Δxb、Δyr、Δyd、ΔybIs the distortion parameter of the camera.
Wherein, the radial distortion correction amount formula can be expressed by formula (6):
Figure BDA0003550766450000056
the tangential distortion correction amount formula can be expressed by formula (7):
Figure BDA0003550766450000057
the image plane correction amount formula can be expressed by formula (8):
Figure BDA0003550766450000058
in addition, some of the parameters in equation (6), equation (7), and equation (8) are calculated by equation (9):
Figure BDA0003550766450000059
in the formula (6), the formula (7), the formula (8) and the formula (9), Δ xr、ΔyrFor radial distortion correction, K1、K2、K3As radial distortion parameter, x, y are image coordinates of image point p, x0、y0As principal point coordinates, Δ xd、ΔydFor tangential distortion correction, P1、P2As a tangential distortion parameter, Δ xb、ΔybAs an amount of image plane correction, b1、b2Is the image plane distortion parameter.
Thus, the error equation shown in equation (1) can be obtained from equation (5).
103: and solving error equations corresponding to all the measurement points after imaging at each camera by using a least square method to obtain the current optimal three-dimensional coordinates of the object space point, the current optimal position of the camera and the current optimal attitude angle of the camera.
The current optimal three-dimensional coordinates of the object space point comprise the solved three-dimensional coordinates of all measuring points on the sample piece to be measured.
Specifically, firstly, aiming at any camera station, an error equation of any measuring point on a sample piece to be measured at the camera station is established by utilizing a light beam adjustment method.
And then, according to the error equations of all the measuring points at all the shooting stations, the current optimal position and current optimal attitude angle of each shooting station and the three-dimensional coordinates of each measuring point can be obtained by using a least square method.
104: and fitting the current optimal three-dimensional coordinate of the object space point with a preset theoretical digital analog to obtain the current measurement error.
Wherein, the sample to be measured is processed according to a theoretical digital model.
105: setting an initial measurement network type according to the current optimal position of the camera station, the current optimal attitude angle of the camera station and the density parameter, sequentially adjusting each parameter to be optimized according to a preset optimization sequence, and iteratively solving a measurement error until the measurement error in the adjustment process of each parameter to be optimized meets the corresponding convergence condition to obtain the optimal measurement network type.
The optimal measurement network type comprises a shooting station optimal position, a shooting station optimal attitude angle and an optimal density parameter.
Specifically, the parameters to be optimized include a photographing distance, a camera pointing direction, a shooting station distribution density, and a measurement point distribution density.
Fig. 3 exemplarily illustrates a schematic diagram of adjusting photographing distances, camera orientations, and distribution densities of the photographing stations according to an embodiment of the present invention, where, as shown in fig. 3, different photographing distances indicate that distances between the photographing stations and a sample to be measured are adjusted, different distribution densities of the photographing stations indicate that distances between the photographing stations are adjusted, and different camera orientations indicate that camera attitude angles of the respective photographing stations are adjusted.
The optimization sequence can be set as the shooting distance, the camera pointing direction, the shooting station distribution density and the measuring point distribution density in sequence from first to last.
Further, the optimal measurement net shape can be obtained by the following steps:
step one, setting an initial measurement network 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 step two, adjusting the shooting 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 shooting distance can be embodied by the shooting station position coordinates.
Specifically, the adjustment may be performed according to a preset parameter increment.
The first predetermined convergence condition may be that the measurement accuracy reaches a peak value within a predetermined parameter threshold range, i.e., convergence is considered.
And step three, adjusting the orientation of the camera, and iteratively solving the measurement error until the measurement error meets a second preset convergence condition to obtain a second intermediate network type.
Specifically, the adjustment may be performed according to a preset parameter increment.
The second predetermined convergence condition may be that the measurement accuracy reaches a peak value within a predetermined parameter threshold range, i.e., convergence is considered.
And step four, adjusting the distribution density of the camera stations, and iteratively solving the measurement error until the measurement error meets a third preset convergence condition to obtain a third intermediate network type.
Wherein, the distribution density of the shooting stations can also be embodied by the coordinates of the shooting stations.
Specifically, the adjustment may be performed according to a 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 precision and field measurement conditions.
And step five, 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.
Specifically, the adjustment may be performed according to a preset parameter increment.
The fourth preset convergence condition may be that a ratio between the measurement accuracy increment and the parameter increment within the preset parameter threshold is smaller than a design value. Wherein the design value can be determined according to the requirement of measurement precision and field measurement conditions.
In addition, other optimization sequences may also be adopted to adjust each parameter to be optimized, and the details are not limited.
Fig. 4 exemplarily shows a schematic diagram of an iterative method of a measurement mesh type optimization model provided by the embodiment of the present invention, and as shown in fig. 4, an optimization iteration sequence provided by the embodiment of the present invention sequentially includes a shooting distance, a camera orientation, a shooting station distribution density, and a measurement point distribution density, iteration convergence conditions corresponding to the shooting distance adjustment and the camera orientation adjustment are both set parameter thresholds, and a measurement accuracy peak value in a threshold range is considered to be convergence. The iteration convergence conditions corresponding to the distribution density adjustment of the shooting station and the distribution density adjustment of the measuring points are set parameter threshold values, and whether the ratio of the measurement precision increment to the parameter increment in the threshold value range is smaller than a design value or not can be determined according to the measurement precision requirement and the field condition.
To more clearly illustrate the method for determining a measurement network type provided by the embodiment of the present invention, fig. 5 exemplarily shows a specific flowchart corresponding to the method for determining a measurement network type of a digital photogrammetric system provided by the embodiment of the present invention, and as shown in fig. 5, the specific flowchart is: finishing the reference layout of a measurement network type, determining the distribution of the camera stations according to the boundary of a field measurement condition, determining the error of image points according to the distribution of the camera stations and the distribution of the measurement points, adjusting an error equation of an imaging model according to a beam method, solving by using least square iteration, calculating surface shape error calculation and data statistics by using ICP iteration to obtain the measurement error of the imaging model and a sample model, judging whether the convergence condition of the iteration model is met, if not, continuing to adjust parameters such as the layout mode, the distribution of the measurement points, the distribution of the camera stations and the like until the measurement error meets the convergence condition of the iteration model, and outputting the optimal shooting network type.
The beneficial effects of the invention are: simulating by using a light beam method adjustment model, solving error equations corresponding to all measurement points after imaging at each camera by using a least square method to obtain a current optimal object space point coordinate and a current optimal net shape, fitting the current optimal object space point coordinate and a preset theoretical digital model to obtain a current measurement error, 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 to obtain the optimal measurement net shape. Therefore, the method can determine the most appropriate measurement network type according to the actual field measurement conditions, so that the measurement error of the digital photogrammetric system is more stable, and the measurement precision is higher.
The invention has been described in detail with reference to specific embodiments and illustrative examples, but the description is not intended to be construed in a limiting sense. Those skilled in the art will appreciate 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, which fall within the scope of the present invention. The scope of the invention is defined by the appended claims.

Claims (5)

1. A method for determining a measurement network type of a digital photogrammetric system is characterized by comprising the following steps:
determining initial distribution of a shooting station where a camera is located according to field measurement conditions, wherein the initial shooting station distribution comprises an initial position of the shooting station where the camera is located and an initial attitude angle of the shooting station;
simulating by using a light beam method adjustment model to obtain an error equation corresponding to each measuring point on a sample to be measured after imaging at each camera 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 a preset density parameter;
solving error equations corresponding to all the measuring points after imaging in each camera by using a least square method to obtain the current optimal three-dimensional coordinates of the object space point, the current optimal position of the camera and the current optimal attitude angle of the camera;
fitting the current optimal three-dimensional coordinate of the object space point with a preset theoretical digital analog to obtain a current measurement error;
setting an initial measurement network type according to the current optimal position of the camera station, the current optimal attitude angle of the camera station and the density parameters, sequentially adjusting each parameter to be optimized according to a preset optimization sequence, 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 optimal measurement network type comprises the optimal position of the camera station, the optimal attitude angle of the camera station and the optimal density parameters.
2. The method of claim 1, wherein the initial distribution is any one of a ring, a column, a m-shape, and a cross.
3. The method of claim 1, wherein the simulating by using the bundle adjustment model to obtain the error equation corresponding to each measurement point on the sample to be measured after imaging at each camera station comprises:
obtaining an error equation corresponding to each measuring point on the sample piece to be measured after imaging at each camera station through the following formula:
Figure FDA0003550766440000011
wherein v isx、vyIn order to be an error, the error is,
Figure FDA0003550766440000012
is a matrix of the pose angles of the camera, consisting of the pose angles (R) of the camerax,Ry,Rz) Converted to three-dimensional coordinates, X, of measuring point X, Y, Z0、Y0、Z0Is the station position, f is the focal length of the camera, x and y are the image coordinates of the corresponding image points of the measuring points after the imaging of the station, x0、y0As principal point coordinates, Δ xr、Δxd、Δxb、Δyr、Δyd、ΔybIs the distortion parameter of the camera.
4. The method of claim 1, wherein the parameters to be optimized include camera range, camera pointing direction, camera distribution density, and measurement point distribution density.
5. The method according to claim 4, wherein the setting of 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, the sequential adjustment of each parameter to be optimized according to a preset optimization sequence, and the iterative solution of measurement errors until the measurement errors in the adjustment process of each parameter to be optimized all satisfy corresponding convergence conditions, obtains an optimal measurement network type, comprises:
setting an initial measurement network type according to the current optimal position of the camera station, the current optimal attitude angle of the camera 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 network type;
adjusting the orientation of the camera, and iteratively solving a 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 camera, and iteratively solving a measurement error until the measurement error meets a third preset convergence condition to obtain a third intermediate network 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.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116822357A (en) * 2023-06-25 2023-09-29 成都飞机工业（集团）有限责任公司 Photogrammetry station layout planning method based on improved wolf algorithm

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101298979A (en) * 2008-07-01 2008-11-05 北京林业大学 Estimate survey technique using metric camera cooperating with theodolite
JP2011022062A (en) * 2009-07-17 2011-02-03 Topcon Corp Position measurement method and position measuring instrument
US9857172B1 (en) * 2017-09-25 2018-01-02 Beijing Information Science And Technology University Method for implementing high-precision orientation and evaluating orientation precision of large-scale dynamic photogrammetry system
US20210041236A1 (en) * 2018-04-27 2021-02-11 China Agricultural University Method and system for calibration of structural parameters and construction of affine coordinate system of vision measurement system
CN113324528A (en) * 2021-05-18 2021-08-31 武汉大学 Close-range photogrammetry target positioning method and system with known camera station position
CN114092563A (en) * 2021-10-25 2022-02-25 南京航空航天大学 Photogrammetry beam method adjustment optimization method based on T-MAC

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101298979A (en) * 2008-07-01 2008-11-05 北京林业大学 Estimate survey technique using metric camera cooperating with theodolite
JP2011022062A (en) * 2009-07-17 2011-02-03 Topcon Corp Position measurement method and position measuring instrument
US9857172B1 (en) * 2017-09-25 2018-01-02 Beijing Information Science And Technology University Method for implementing high-precision orientation and evaluating orientation precision of large-scale dynamic photogrammetry system
US20210041236A1 (en) * 2018-04-27 2021-02-11 China Agricultural University Method and system for calibration of structural parameters and construction of affine coordinate system of vision measurement system
CN113324528A (en) * 2021-05-18 2021-08-31 武汉大学 Close-range photogrammetry target positioning method and system with known camera station position
CN114092563A (en) * 2021-10-25 2022-02-25 南京航空航天大学 Photogrammetry beam method adjustment optimization method based on T-MAC

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
王旋: "用于天文摄影测量的相机标定研究", 《中国优秀硕士学位论文全文数据库基础科学辑》, pages 6 - 13 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116822357A (en) * 2023-06-25 2023-09-29 成都飞机工业（集团）有限责任公司 Photogrammetry station layout planning method based on improved wolf algorithm

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