WO2018233514A1

WO2018233514A1 - Pose measurement method and device, and storage medium

Info

Publication number: WO2018233514A1
Application number: PCT/CN2018/090821
Authority: WO
Inventors: 徐坤; 周轶; 范国田
Original assignee: 中兴通讯股份有限公司
Priority date: 2017-06-21
Filing date: 2018-06-12
Publication date: 2018-12-27
Also published as: CN109099888A

Abstract

A pose measurement method and device, and a storage medium. The pose measurement method comprises: obtaining photos of an object photographed at different positions and pose parameters of the photographing device when photographing the photos (step 101); constructing a virtual three dimensional scene according to the photos of the object, and matching the virtual three dimensional scene with the real three dimensional scene according to the pose parameters of the photographing device (step 102); and calculating a pose parameter of the object according to position information of the object in the real three dimensional scene (step 103).

Description

Position measuring method, device and storage medium

Cross-reference to related applications

The present application is filed on the basis of the Chinese Patent Application No. PCT Application No.

Technical field

The present invention relates to the field of measurement technology, and in particular, to a pose measurement method, device, and storage medium.

Background technique

Object pose measurement has important application value in many fields such as modern communication, national defense and aerospace. For example, especially in communication systems, the measurement of the antenna pose is closely related to the coverage of the base station; for example, during the robot assembly process, the pose of the robot determines the accuracy of the assembly. How to achieve the pose measurement of objects has always been the focus of research.

At present, the position measurement method usually needs to be configured with relevant measurement personnel, and the relevant measurement instrument is measured on the object. However, this method can be implemented for objects that can be operated near. However, for objects such as antennas and aircraft that require long-distance measurement, such measurement methods are very dangerous, and it is impossible to ensure the personal safety of the measurement personnel, and at the same time, excessive manpower is required. Therefore, it is very necessary to provide a simple and feasible pose measurement method.

Summary of the invention

The embodiment of the invention provides a method, a device and a storage medium for measuring a pose, which solves the problem that the pose measurement method in the prior art is labor-intensive and cannot guarantee the personal safety of the measurement personnel.

The embodiment of the invention adopts the following technical solutions:

According to an aspect of an embodiment of the present invention, a pose measurement method is provided, including:

Obtaining a plurality of photos of objects taken at different positions and attitude parameters of the photographing device when the photographs are taken;

Constructing a virtual three-dimensional scene according to the object photo, and matching the virtual three-dimensional scene with the real three-dimensional environment according to the posture parameter of the photographing device;

A posture parameter of the object is calculated according to position information of the object in the real three-dimensional environment.

In an optional solution, the posture parameter of the photographing device includes rotation angle information of the photographing device; and the matching the virtual three-dimensional scene with the real three-dimensional environment according to the posture parameter of the photographing device, including:

Obtaining a first rotation matrix from a two-dimensional image plane to a virtual three-dimensional scene;

Obtaining a second rotation matrix of the two-dimensional image plane to the real three-dimensional environment according to the rotation angle information;

Calculating a rotation transformation matrix of the virtual three-dimensional scene to the real three-dimensional environment according to the first rotation matrix and the second rotation matrix.

In an optional solution, the posture parameter of the photographing device includes the positioning information of the photographing device; and the matching the virtual three-dimensional scene with the real three-dimensional environment according to the posture parameter of the photographing device, further includes:

A panning and scaling transformation matrix of the virtual three-dimensional scene to the real three-dimensional environment is determined according to the positioning information and the position of the photographing device in the virtual three-dimensional scene.

In an optional solution, the calculating a rotation transformation matrix of the virtual three-dimensional scene to the real three-dimensional environment according to the first rotation matrix and the second rotation matrix comprises:

Obtaining a rotation conversion matrix obtained by the photographing device at different positions;

The average of the rotation angles of the respective rotation conversion matrices on the three coordinate axes is calculated, and the final rotation transformation matrix is obtained based on the average value reconstruction.

In an optional solution, the calculating the attitude parameter of the object according to the location information of the object in the real three-dimensional environment comprises:

Two pairs of points with the same name are determined according to the straight line of the same name calibrated in the two target photos;

Determining the positions of two phase points in the virtual three-dimensional scene based on the two pairs of phase names of the same name;

The rotation transformation matrix and the translation and scaling transformation matrix are used to transform the positions of the centers of the two phase points, and the positioning values and altitude values of the objects in the real three-dimensional environment are obtained according to the transformed positions.

In an optional solution, the two pairs of the same name are determined according to the same-named line calibrated in the two target photos, including:

After detecting the straight line of the same name calibrated in the target photo, calculating the polar line at the two end points of the same name line in one of the target photos according to the binocular visual limit constraint principle;

The intersection of the polar line and the straight line of the same name in another target photo is the point of the same name of the end point.

In an optional solution, the determining, according to the two pairs of phase names of the same name, the positions of two phase points in the virtual three-dimensional scene, including:

According to the projection equation from the three-dimensional space to the two-dimensional image plane, construct a linear equation corresponding to each phase point of the same-named phase point;

A linear equation of the same name pair is formed into a linear equation group, and the linear equation group is solved by a matrix method, and the position of the phase point in the virtual three-dimensional scene is determined based on the solution.

In an optional solution, the linear equations of the same-named pair of points form a linear equation group, and the linear equations are solved by a matrix method, and the position of the phase points in the virtual three-dimensional scene is determined based on the solution, including :

According to the previous solution, the new weighting factor of each phase point is obtained. The linear equations of each phase point are respectively divided by the corresponding weighting factors to obtain a new linear equation group, and then the new solution is calculated. Repeat this step until After the new solution is equal to the previous solution, this step is repeated once again, and the calculated final solution is the position of the phase point.

Determine the position of the same line calibrated in the three target photos;

Calculating position information of the straight line in a real three-dimensional environment by using a three-focus tensor algorithm according to the position and position information of the photographing device;

The attitude angle of the object is calculated based on the position information of the straight line in the real space.

According to an aspect of an embodiment of the present invention, a pose measuring apparatus includes a camera, a processor, and a memory, wherein the memory stores a pose measurement program; the processor is configured to execute the stored in the memory The program is used to implement the steps in the pose measurement method described above.

According to an aspect of an embodiment of the present invention, a computer readable storage medium is provided, on which the pose measurement program is stored, and the pose measurement program is implemented by a processor to implement the bit described above. The steps in the attitude measurement method.

The beneficial effects of the embodiments of the present invention are as follows:

In the embodiment of the present invention, the object in different positions is photographed by the photographing device, and then the photograph of the photographed object is analyzed by combining the posture parameters of the photographing device, so that the actual pose parameter information of the object can be obtained. Therefore, the embodiment of the invention realizes the remote measurement of the pose of the object, and the operation method is very simple and convenient, and the measurement personnel are not required to climb, thereby effectively eliminating the risk of measurement.

The above description is only an overview of the technical solutions of the present invention, and the above-described and other objects, features and advantages of the present invention can be more clearly understood. Specific embodiments of the invention are set forth below.

DRAWINGS

In order to more clearly illustrate the embodiments of the present invention or the prior art, the drawings used in the embodiments or the prior description will be briefly described below. It is obvious that the drawings in the following description are only Some embodiments of the invention may also be used to obtain other figures from these figures without departing from the scope of the invention.

1 is a flowchart of a pose measurement method according to an embodiment of the present invention;

2a and 2b are schematic diagrams showing the result of calculating the phase point of the same name in an embodiment of the present invention.

3 is a flow chart of a three-focus tensor measuring antenna according to an embodiment of the present invention;

FIG. 4 is a schematic block diagram of a pose measuring device according to an embodiment of the present invention.

Detailed ways

The invention will be further described in detail below with reference to the drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

Method embodiment

As shown in FIG. 1 , the pose measurement method provided by the embodiment of the present invention includes the following steps:

Step 101: Acquire a plurality of photos of the objects photographed at different positions and posture parameters of the photographing device when the photographs are taken.

In this step, the object here is not limited to the antenna, but also an object such as a robot, an aircraft, or the like that needs to monitor the attitude parameter. For example, during the assembly process, the posture parameters of the robot arm are required to be tested to ensure the accuracy of the assembly; or, in the case of the aircraft wind test, the flight attitude is detected.

Among them, the shooting device here includes a camera, a camera, a mobile phone, and a tablet computer, and the like, and the type of the shooting device is not specifically limited. Wherein, when the photographing device is a movable device, the movable device can acquire the posture parameter information by itself. When shooting, you can choose your own shooting location and get photos of objects taken at different locations. Of course, it is also possible to collect a photo of the corresponding position when a fixed camera is set at a different position.

Here, the attitude parameters of the photographing apparatus herein include positioning information (for example, global positioning system GPS information) and information such as three rotation angles (rotation angles with respect to three coordinate axes). Wherein, when the photographing device is a movable device, the parameter can be directly obtained by the corresponding sensor. For shooting with a fixed position camera, you can pre-configure the rotation angle and GPS information, and directly obtain the configuration information when needed.

Step 102: Construct a virtual three-dimensional scene according to the object photo, and match the virtual three-dimensional scene with the real three-dimensional environment according to the posture parameter of the shooting device.

In this step, when constructing a virtual three-dimensional scene according to the object photo, it is necessary to separately perform feature point extraction, matching, and matrix conversion on the object photos acquired at different positions to construct a sparse point cloud of the photo; and then use SFM (Structure from Motion, The motion recovery structure algorithm calculates the three-dimensional coordinates of the feature points and constructs a simple sparse virtual three-dimensional scene. Here, the process of constructing a virtual three-dimensional scene for the SFM algorithm is already well known to those skilled in the art, and thus will not be described again here.

The matching between the virtual three-dimensional scene and the real three-dimensional environment is performed according to the posture parameter of the photographing device, mainly by calculating the rotation conversion matrix according to the rotation angle information of the photographing device, and calculating the translation and zoom matrix according to the positioning information of the photographing device. The transformation of a virtual 3D scene into a real 3D scene can be achieved by rotating the transformation matrix, translation and scaling transformation matrices.

Step 103: Calculate the attitude parameter of the object according to the position information of the object in the real three-dimensional environment.

In step 102, the conversion information of the virtual three-dimensional scene to the real three-dimensional environment has been acquired, and according to the conversion information, the position information of the antenna in the real space can be obtained by the user calibrating the position of the desired measurement in the object photo. The attitude parameter of the object can be directly calculated according to the position information of the object in the real space.

Based on the above, the pose measurement method provided by the embodiment of the present invention captures an object at different positions by a photographing device, analyzes the photograph of the photographed object by combining the posture parameters of the photographing device, and finally obtains a photograph by combining a mathematical algorithm. The actual pose parameter of the medium object. The embodiment of the invention realizes the remote measurement of the posture of the object, and does not require the measuring personnel to perform the climbing operation, so the operation is very simple and convenient, the risk of measurement is effectively eliminated, and the personal safety of the measuring personnel is ensured.

The technical content of the present invention will be further described in detail below with reference to specific embodiments. In this embodiment, the antenna is taken as an example to introduce the technical solution through the pose measurement process of the antenna.

In this embodiment, a mobile phone is used as a measuring device, and a photo of the antenna is taken by using a camera of the mobile phone, and the GPS position information and posture are acquired by using related sensors (for example, a GPS sensor, a gyroscope, an accelerometer, and an electronic compass) in the mobile phone. parameter. Take several aerial photos at different locations on your phone (the number can be anywhere from 5 to 10). When taking an aerial photograph, the attitude parameter of the mobile phone is acquired and recorded, and the posture parameter here includes the GPS position and the rotation angle information of the three coordinate axes.

The above mentioned using the SFM algorithm to obtain a three-dimensional reconstruction of the scene. After constructing the three-dimensional sparse point cloud, it is necessary to rotate the sparse point cloud to make the point cloud's posture, orientation, and distribution in the real three-dimensional environment as consistent as possible.

Wherein, the rotation transformation matrix is calculated by the three rotation angle information acquired when the shooting device is photographed, and the following steps are included:

Step 21: Acquire a first rotation matrix R _{SfM of the} photographing device from the two-dimensional image plane to the virtual three-dimensional scene by using the SFM algorithm. When performing the reconstruction of the virtual three-dimensional scene, the rotation matrix (the rotation matrix of the two-dimensional image plane to the SFM space) in the external reference of each shooting device is obtained according to the SFM algorithm, and the rotation matrix is the first rotation matrix, and the matrix is passed through the matrix. The attitude of the photographing device in the virtual three-dimensional space can be known from the two-dimensional image plane.

Step 22: Acquire a second rotation matrix of the photographing device from the two-dimensional image plane to the real three-dimensional environment according to the rotation angle information of the photographing device.

In this step, according to the internal rotation conversion theorem, the rotation matrix of the photographing device in the real three-dimensional environment can be obtained according to the three rotation angles of the photographing device. Here, the following formula is used to calculate the attitude of the phone's own coordinate system with respect to the real three-dimensional environment (the geodetic coordinate system):

R _camera =R _phone *R _x (180)*R _z (-90) (1)

Where R _x (180) represents that the coordinate system first rotates 180 degrees around the x-axis of the phone's own coordinate system in a counterclockwise direction, and R _z (-90) represents the coordinate system and then rotates 90 degrees around the z-axis in a clockwise direction, R _phone Three rotation angle information for shooting with the shooting device.

Step 23: Calculate a rotation transformation matrix of the virtual three-dimensional scene to the real three-dimensional environment according to the first rotation matrix and the second rotation matrix.

In step 21 and step 22, respectively, to obtain the photographing apparatus in the rotation matrix R _SfM SFM space (virtual three-dimensional scene) is, the rotation matrix R _camera imaging device to the real three dimensional environment according to the formula (2) can be obtained rotational transformation Matrix R _trans :

R _trans =R _camera *(R _SfM ) ^-1 (2)

In an optional embodiment, since the photographing device photographs the object at different positions (multiple positions), several rotation conversion matrices can be obtained according to the photographing devices of different positions, and the present invention is ensured in order to ensure the accuracy of the rotation conversion matrix. In an embodiment, the average rotation transformation matrix of the plurality of rotation conversion matrices obtained from different positions is required to obtain a final rotation transformation matrix. Specifically, the average values of the rotation angles of the respective rotation conversion matrices on the three coordinate axes are acquired, and the final rotation transformation matrix is obtained based on the average value reconstruction.

In this embodiment, each rotation transformation matrix is decomposed into three rotation angles R _x , R _y , R _z , which are averaged in three directions and then averaged according to three rotation angles. The value is re-reconstructed to obtain the final rotation transformation matrix. The conversion process of the matrix and the rotation angle here is a technique well known to those skilled in the art and will not be described in detail herein. After the rotation transformation, the coordinate system of the real space is that the Y axis represents the true north direction, the X axis represents the true east direction, and the Z axis represents the vertical direction from the center of the earth to the sky. It can be seen that the side of the building is nearly perpendicular to the Z axis, so a more accurate conversion is made.

It can be seen that by decomposing the rotation angle of the rotation transformation matrix and then inversely transforming according to the angle average to obtain the final rotation transformation matrix, the final calculation error can be effectively reduced, and the accuracy of the subsequent attitude parameters can be improved.

In an optional embodiment, in order to make the size and position of the rotated sparse point cloud conform to the real world and ensure the accuracy of the accuracy of the GPS, the position of the point cloud needs to be translated and scaled. When calculating the conversion parameters, since the scaling parameters are the same ratio of the respective coordinate axes, the value in the Z-axis direction can be ignored. Here, only the numerical calculations of the X-axis and the Y-axis are explained.

The position of the shooting device in meters after conversion is (X _i , Y _i ) (that is, the position of the shooting device in the real three-dimensional environment can be obtained according to the GPS information of the shooting device), and the same can be obtained in the three-dimensional reconstruction space. The position of the device (x _i , y _i ), where the scaling factor is S and the translation vector is (T _x , T _y ), the following linear equation can be established:

Where n represents the number of photographs of the object, and the above formula is an overdetermined equation. The solution can be solved by using the least square method, QR decomposition or singular value decomposition (SVD) and other common solutions in matrix theory, so that the target can be translated and scaled. Conversion parameters. The solution process for the above equations is a technique well known to those skilled in the art and therefore will not be described here.

Since the best GPS accuracy is only 3m when taking aerial photographs, the accuracy of the obtained GPS is very inaccurate. By combining all the GPS information obtained by re-determining the calculation target, a very accurate GPS value can be obtained.

In addition to the above method of calculating the pan and zoom parameters, an interface for the user to input the zoom parameters can be set, and the user provides the average moving distance between each of the two antenna photos at the time of shooting, and this parameter is divided by the neighboring photographing device in the virtual three-dimensional scene. The average distance between them to get the scaling parameter. Normally, the average moving distance of the user is between 0.3 and 1 meter. After the scaling parameter is obtained, the translation parameter can also be solved using the above formula. The reason for this method is that the GPS positioning accuracy is low. For example, the user has taken 5 photos, and the moving distance of the adjacent photos is 0.5 meters, which moves within a total range of 2.5 meters. However, the GPS accuracy is often accurate at 3 meters. Left and right, it is very likely that user movement occurs but the GPS reading is too dense.

Here, the antenna attitude parameters include the attitude angle of the antenna, as well as GPS and altitude (height) information. Here we first introduce the acquisition of GPS and altitude (height) information.

After passing the SFM algorithm, the internal and external parameters of the shooting device can be obtained. Under this premise, it is necessary to obtain the phase name of the same name representing the antenna position to calculate the three-dimensional spatial position of the antenna target, which includes the following:

Determining the positions of two phase points in the virtual three-dimensional scene based on two phase pairs of the same name;

The rotation transformation matrix, translation and scaling transformation matrix are used to transform the position of the center of the two phase points, and the GPS and altitude values of the antenna in the real three-dimensional environment are obtained according to the transformed position.

Here, after taking a picture of the antenna, the user selects two photos from the captured photos as the target picture, and calibrates the straight line of the same name indicating the position of the antenna in the target picture. The final GPS and altitude values are determined based on the information of the line of the same name.

Among them, the method of inputting the same name straight line pair and the binocular visual limit constraint is used to obtain the same name phase point, including the following:

After the straight line of the same name calibrated in the target photo is detected, the polar line at the two end points of the same name line in one of the target photos is calculated according to the binocular vision limit constraint principle;

The intersection of the polar line and the line of the same name in the other target photo is the point of the same name of the endpoint.

For example, as shown in FIG. 2a and FIG. 2b, the user selects two photos as the target photo, and selects an antenna target in each target photo, and selects a straight line pair of the same name representing the antenna in the antenna target.

The constraint limit binocular vision principle, the same name is calculated 2a a line l at the end of the electrode line _a in FIG. L; FIG. 2b and the entry of the same name polar straight line l 'intersects the intersection points a', thereby to obtain a point of the same name

Can also get a straight line pair with the same name

Based on the above, the acquisition method of the above-mentioned same-named phase has increased the difficulty of the user's operation. However, the current linear matching algorithm cannot accurately match the corresponding straight line, and the method of the embodiment of the present invention ensures the accuracy of the straight line matching. The precision of the sex and the same name.

After obtaining the two pairs of the same name in the selected two target photos, it is necessary to determine the three-dimensional spatial position coordinates of the corresponding two phase points according to the positions of the two same-named points in the photo. Optionally, the embodiment of the present invention is implemented as follows:

The linear equations of the same-named pair are formed into a linear equation, and the linear equations are solved by the matrix method. The position of the phase points in the virtual three-dimensional scene is determined based on the solution result.

Each phase point here corresponds to a shooting angle, that is, the shooting angle of the two target photos (different shooting positions) mentioned above. Specifically, the projection equation according to the three-dimensional space and the two-dimensional image plane has x=PX; wherein x is a homogeneous coordinate and x=w(u, v, 1), and (u, v) is a coordinate point on the photo ( Two-dimensional image plane coordinates), w represents the projection depth, ie the scaling factor. X is the position coordinate of the three-dimensional space of the phase point to be calculated. In order to be able to use the matrix for calculation, it is usually expressed in homogeneous coordinates, that is, a four-dimensional vector such as X=(x, y, z, 1); P is a projection matrix. (according to the SFM algorithm), here the i-th line

Representation, then the projection equation can be rewritten as:

For the above projection equation, the parameter w is eliminated, and the following two linear equations are obtained:

Wherein, if it is necessary to calculate the value of X, at least two phase points of the same name phase point and projection matrix are needed. The above formula (5) represents two linear equations acquired at a certain angle of view; if there are two angles of view, four linear equations can be obtained, and by moving the item on the right side of the equation to the left, The four equations are converted to the form of AX=0.

Where A is a 4 x 4 matrix. Normally, due to the presence of noise, this equation cannot be completely equivalent. You can find the smallest X that makes ||AX|| and satisfy the constraint ||X||=1. The optimal solution of this equation is the eigenvector corresponding to the minimum eigenvalue of the matrix A ^T A. The general solution such as SVD, QR decomposition can be used to solve X.

Among them, after calculating X, it is a homogeneous vector, dividing each element of the vector by the last element, and the resulting first three elements represent the final target position. That is, the solved X=(a, b, c, d), the homogeneous coordinate X1=(a/d, b/d, c/d, d/d), and the final target coordinate T=(a/d) , b/d, c/d).

It should be noted that the above principle is explained from two angles of view (two target photos). When there are multiple angles of view, six or eight linear equations can be constructed. At this time, A is an overdetermined matrix. , can also be solved using the least squares method. Singular value decomposition SVD, QR method, etc. can be used to solve.

In an alternative embodiment, one problem with the linear method is that the minimized ||AX|| has no geometric meaning, does not match the minimized objective function, and multiplies each row of the matrix A by a weighting factor, The results are not the same. Therefore, in order to solve this problem, in an embodiment of the present invention, the weighting factor of the linear equation is continually changed by a linear iterative method, so that the weighted equation can be unified with the error of the photograph coordinate point.

For example, the spatial point X calculated at the beginning does not fully satisfy the linear equation and there is an error.

What you want to minimize is the projection point of the real image point x and X.

Distance, ie

This means that the linear equation is divided by the weighting factor.

Then the final error is to minimize the meaning of the photo.

In order to minimize the error, the embodiment of the present invention is implemented according to an iterative method: a new weighting factor for each view is obtained according to the previous solution result, and the linear equations of each view are respectively divided by the corresponding weighting factors to obtain a new one. The linear equations are recalculated to obtain a new solution. Repeat this step until the new solution is equal to the previous solution. Then, the solution step of the linear equations is repeated according to the new weighting factor, and the final solution is calculated.

Specifically, the initial weighting factor w ₀ =w ₀ '=1 is set, the solution of the above linear equations is calculated, and the solution is made the initial solution X ₀ .

First view equations divided by weighting factors

Also divide the equation of the second perspective by the weighting factor

Thus, a new system of linear equations is obtained, and the solution X _{i is} obtained, where X _i-1 is the result of the last calculation. Repeat this step until convergence yields X _i =X _i-1 and there is

Error at this time

It is the minimized photo error, which is the expected error.

The optimal weighting factor obtained by the iterative method is divided into the optimal weighting factors of the respective angles of view, and the obtained solution is the position of the final target space point. Based on this method, the minimized equation can be made to conform to the coordinate error in the photographic sense. The spatial iteration method calculated by such a linear iterative method has high accuracy, and generally can achieve convergence with a small number of iterations, and the implementation is simple and the program is simple.

After the position of the opposite end point of the corresponding spatial line in the virtual three-dimensional scene is obtained by the phase name of the same name at both ends of the antenna, the GPS position and altitude in the real three-dimensional scene are calculated according to the position coordinates of the center of the phase connecting the two ends, thereby representing the The GPS position and altitude of the antenna. Methods as below:

For the three-dimensional point (x, y) of the center position of the connection, the (X, Y) of the real space coordinates obtained after the rotation transformation is obtained according to the translation and scaling matrix obtained in step 102, and is obtained as:

X=x*S+t _x , Y=y*S+t _y ;

The (X, Y) is subjected to back projection transformation to obtain the GPS value. At the same altitude Z ₀ when shooting, according to the X, Y, Z axis and other scaling principles, the target's altitude is:

Z=Z ₀ +(zz _camera )*S

Next, the process of calculating the attitude angle of the antenna based on the positions of the same straight line calibrated in different antenna photographs will be described.

Optionally, after obtaining the two phase points representing the spatial straight line by the above method, the spatial position coordinates of the two phase points may be utilized, and then the rotation transformation matrix, the translation and the scaling matrix are used to perform the rotation transformation. The two phase points are directly at the coordinate points of the real three-dimensional environment, and the attitude angle of the antenna can be directly calculated.

Optionally, the coordinate point representing the spatial straight line is re-acquired by the three-focus tensor method, and the attitude angle of the antenna is calculated according to the coordinate point. Specifically, determining the position of the same straight line calibrated in the three target photos; calculating the position information of the straight line in the real three-dimensional environment by using the three-focus tensor algorithm according to the position and the position information of the photographing device; according to the position of the straight line in the real space The information calculates the attitude angle of the antenna.

Since the three-focus tensor represents the mutual correspondence between the three views, this is a spatial geometric relationship that does not depend on the structure of the measured object itself. As shown in FIG. 3, where the straight lines l ₁ , l ₂ , l ₃ are projections of the spatial straight line L on each photo, the projection matrix of the three photographing devices centered at {C ₀ , C ₁ , C ₂ } is P ₁ , P ₂ , P ₃ , the projection matrix can be calculated by the internal and external parameters estimated by the SFM algorithm, specifically P=K[R t], and the trifocal tensor matrix is constructed as:

Decompose W with singular value decomposition to obtain [u, s, v] = SVD(W). The homogeneous coordinates of the two spatial points correspond to the last two columns of v, the 4-dimensional vector X _a = v(:, 3), X _b =v(:,4), the two 4-dimensional vectors are non-homogeneous to obtain two points of the space line, and also use this to indicate the position of the calibration line in space, thereby further solving the straight line by simple mathematical methods. The attitude angle, which is the attitude angle of the antenna (direction angle and pitch angle). The process for solving the attitude angle from the spatial point is well known to those skilled in the art and will not be described here.

Equipment example

The embodiment of the present invention further provides a pose measuring device for implementing the above-described pose measurement method. As shown in FIG. 4, the device includes a processor 42 and a memory 41 storing instructions executable by the processor 42. among them,

The processor 42 may be a general-purpose processor, such as a central processing unit (CPU), or may be a digital signal processor (DSP), an application specific integrated circuit (ASIC), or One or more integrated circuits configured to implement embodiments of the present invention.

The memory 41 is configured to store the program code and transmit the program code to the CPU. The memory 41 may include a volatile memory such as a random access memory (RAM); the memory 41 may also include a non-volatile memory such as a read-only memory (read- Only memory, ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); the memory 41 may also include a combination of the above types of memories.

A pose measuring device according to an embodiment of the present invention includes a camera, a processor and a memory, wherein a pose measurement program is stored in the memory; and the processor is configured to execute the pose measurement program stored in the memory, and the method is as follows:

Obtaining a plurality of photographs of objects taken at different positions and attitude parameters of the photographing device when photographing;

Constructing a virtual three-dimensional scene according to the photo of the object, and matching the virtual three-dimensional scene with the real three-dimensional environment according to the posture parameter of the shooting device;

The attitude parameter of the object is calculated according to the position information of the object in the real three-dimensional environment.

Optionally, the posture parameter of the photographing device includes rotation angle information of the photographing device; and matching the virtual three-dimensional scene with the real three-dimensional environment according to the posture parameter of the photographing device, including:

Obtaining a first rotation matrix in a two-dimensional image plane to a virtual three-dimensional scene by using an SFM algorithm;

A rotation transformation matrix of the virtual three-dimensional scene to the real three-dimensional environment is calculated according to the first rotation matrix and the second rotation matrix.

Optionally, the posture parameter of the shooting device includes positioning information of the shooting device; matching the virtual three-dimensional scene with the real three-dimensional environment according to the posture parameter of the shooting device, further includes:

The translation and scaling transformation matrix of the virtual three-dimensional scene to the real three-dimensional environment is determined according to the positioning information and the position of the photographing device in the virtual three-dimensional scene.

Optionally, calculating a rotation transformation matrix of the virtual three-dimensional scene to the real three-dimensional environment according to the first rotation matrix and the second rotation matrix, including:

The average of the rotation angles of the respective rotation conversion matrices in the three coordinate axes is calculated, and the final rotation transformation matrix is obtained based on the average value reconstruction.

Optionally, calculating the attitude parameter of the object according to the position information of the object in the real three-dimensional environment, including:

The rotation transformation matrix and the translation and scaling transformation matrix are used to transform the position of the center of the two phase points, and the position value and the altitude value of the object in the real three-dimensional environment are obtained according to the transformed position.

Optionally, two pairs of the same name are determined according to the straight line of the same name calibrated in the two target photos, including:

After detecting the straight line of the same name in the target photo, calculating the polar line at the two end points of the same name line in one of the target photos according to the binocular vision limit constraint principle;

Optionally, determining the positions of the two phase points in the virtual three-dimensional scene based on the two pairs of the same name, including:

The linear equations of the same-named pair are formed into a linear system of equations, and the linear equations are solved by the matrix method. The position of the phase points in the virtual three-dimensional scene is determined based on the solution.

Optionally, a linear equation of the same name pair is formed into a linear equation group, and the matrix equation is used to solve the linear equation group, and the position of the phase point in the virtual three-dimensional scene is determined based on the solution, including:

According to the previous solution, the new weighting factor of each phase point is obtained. The linear equations of each phase point are respectively divided by the corresponding weighting factors to obtain a new linear equation group, and then the new solution is calculated. Repeat this step until After the new solution is equal to the previous solution, this step is repeated, and the calculated final solution is the position of the phase point.

Determine the position of the same line calibrated in the three target photos;

Calculating position information of the straight line in the real three-dimensional environment by using a three-focus tensor algorithm according to the position and the position information of the photographing device;

The attitude angle of the object is calculated from the position information of the straight line in the real space.

Storage medium embodiment

The embodiment of the invention further provides a computer readable storage medium. The computer readable storage medium herein stores one or more programs. The computer readable storage medium may include a volatile memory such as a random access memory; the memory may also include a non-volatile memory such as a read only memory, a flash memory, a hard disk or a solid state hard disk; the memory may also include the above categories a combination of memory. One or more programs in a computer readable storage medium may be executed by one or more processors to implement the pose measurement methods provided in the method embodiments.

A person skilled in the art can understand that all or part of the process of implementing the above embodiment method can be completed by a computer program to instruct related hardware, and the program can be stored in a computer readable storage medium. The flow of an embodiment of the methods as described above may be included.

While the present invention has been described by the embodiments of the invention, it will be understood that Thus, it is intended that the present invention cover the modifications and modifications of the invention

Those skilled in the art will appreciate that embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention can take the form of a hardware embodiment, a software embodiment, or a combination of software and hardware. Moreover, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) including computer usable program code.

The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (system), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or FIG. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine for the execution of instructions for execution by a processor of a computer or other programmable data processing device. Means for implementing the functions specified in one or more of the flow or in a block or blocks of the flow chart.

The computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device. The apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.

These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device. The instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

The above is only the preferred embodiment of the present invention and is not intended to limit the scope of the present invention.

Industrial applicability

In the embodiment of the present invention, the actual pose parameter information of the object can be obtained by shooting the object at different positions by the photographing device and analyzing the photograph of the photographed object in combination with the posture parameter of the photographing device. The remote measurement of the pose of the object is realized, and the operation method is very simple and convenient, and no measurement personnel are required to climb, which effectively eliminates the risk of measurement.

Claims

A pose measurement method comprising:

Obtaining a plurality of photos of objects taken at different positions and attitude parameters of the photographing device when the photographs are taken;

Constructing a virtual three-dimensional scene according to the object photo, and matching the virtual three-dimensional scene with the real three-dimensional environment according to the posture parameter of the photographing device;

A posture parameter of the object is calculated according to position information of the object in the real three-dimensional environment.
The method of claim 1, wherein the attitude parameter of the photographing device comprises rotation angle information of the photographing device;

The matching the virtual three-dimensional scene with the real three-dimensional environment according to the posture parameter of the photographing device includes:

Obtaining a first rotation matrix from a two-dimensional image plane to a virtual three-dimensional scene;

Obtaining a second rotation matrix of the two-dimensional image plane to the real three-dimensional environment according to the rotation angle information;

Calculating a rotation transformation matrix of the virtual three-dimensional scene to the real three-dimensional environment according to the first rotation matrix and the second rotation matrix.
The method of claim 2, wherein the attitude parameter of the photographing device comprises positioning information of the photographing device;

The matching the virtual three-dimensional scene with the real three-dimensional environment according to the posture parameter of the photographing device further includes:

A panning and scaling transformation matrix of the virtual three-dimensional scene to the real three-dimensional environment is determined according to the positioning information and the position of the photographing device in the virtual three-dimensional scene.
The method of claim 2, wherein the calculating a rotation transformation matrix of the virtual three-dimensional scene to the real three-dimensional environment according to the first rotation matrix and the second rotation matrix comprises:

Obtaining a rotation conversion matrix obtained by the photographing device at different positions;

The average of the rotation angles of the respective rotation conversion matrices in the three coordinate axes is calculated, and the rotation transformation matrix of the virtual three-dimensional scene obtained from the average value to the real three-dimensional environment is calculated.
The method of claim 3, wherein the calculating the attitude parameter of the object according to the position information of the object in the real three-dimensional environment comprises:

Determining two pairs of the same name by a line of the same name calibrated in the two target photos, the target photo being any two of the plurality of object photos;

Determining the positions of two phase points in the virtual three-dimensional scene based on the two pairs of phase names of the same name;

The rotation transformation matrix, the translation and the scaling transformation matrix are used to transform the positions of the centers of the two phase points, and the positioning values and altitude values of the objects in the real three-dimensional environment are obtained according to the transformed positions.
The method of claim 5 wherein said determining a pair of points of the same name based on a line of the same name calibrated in the two target photos comprises:

After detecting the straight line of the same name calibrated in the target photo, calculating the polar line at the two end points of the same name line in one of the target photos according to the binocular visual limit constraint principle;

The intersection of the polar line and the straight line of the same name in another target photo is the point of the same name of the end point.
The method according to claim 5 or 6, wherein said determining a position of two phase points in the virtual three-dimensional scene based on said two pairs of phase names of the same name comprises:

According to the projection equation from the three-dimensional space to the two-dimensional image plane, construct a linear equation corresponding to each phase point of the same-named phase point;

A linear equation of the same name pair is formed into a linear equation group, and the linear equation group is solved by a matrix method, and the position of the phase point in the virtual three-dimensional scene is determined based on the solution result.
The method according to claim 7, wherein said linear equations of the same-named phase pairs form a linear equation group, and said linear equations are solved by a matrix method, and the position of the phase points in the virtual three-dimensional scene is determined based on the solution result. ,include:

According to the previous solution result, the new weighting factor of each phase point is obtained. The linear equations of each phase point are respectively divided by the corresponding weighting factors to obtain a new linear equation group, and then the new solution is calculated. Repeat this step. This step is repeated until the new solution is equal to the previous solution, and the calculated final solution is the position of the phase point.
The method of claim 1, wherein the calculating the attitude parameter of the object according to the position information of the object in the real three-dimensional environment comprises:

Determining a position of the same straight line calibrated in the three target photos, the three target photos being any three of the plurality of object photos;

Calculating position information of the straight line in a real three-dimensional environment by using a three-focus tensor algorithm according to the position and position information of the photographing device;

The attitude angle of the object is calculated based on the position information of the straight line in the real space.
A pose measuring device comprising a camera, a processor and a memory, wherein the memory stores a pose measurement program; the processor is configured to execute the program stored in the memory to implement the claim 1 The step in the pose measurement method of any of the nine.
A computer readable storage medium storing a pose measurement program, wherein the pose measurement program is executed by a processor to implement the pose measurement method according to any one of claims 1 to 9. The steps in .