WO2019136588A1

WO2019136588A1 - Cloud computing-based calibration method, device, electronic device, and computer program product

Info

Publication number: WO2019136588A1
Application number: PCT/CN2018/071886
Authority: WO
Inventors: 王洛威; 王恺; 廉士国
Original assignee: 深圳前海达闼云端智能科技有限公司
Priority date: 2018-01-09
Filing date: 2018-01-09
Publication date: 2019-07-18
Also published as: CN108235778A; CN108235778B

Abstract

A cloud computing-based calibration method, a device, an electronic device, and a computer program product. The method comprises: determining an existing conversion parameter G, the conversion parameter G being a conversion parameter from a tracking coordinate system to a virtual coordinate system produced by calibration of an OSTHMD on the basis of SPAAM; acquiring alignment data of a current user; and computing, on the basis of the alignment data of the current user and of the conversion parameter G, a conversion parameter G' from the tracking coordinate system to the virtual coordinate system of the current user. In the present application, the calibration of the OSTHMD of the current user can be completed by utilizing existing calibration data, the need to perform a full SPAAM calibration with respect to the current user is obviated, and only a small amount of alignment data needs to be collected, thus simplifying operation and reducing the time spent.

Description

Cloud computing-based calibration method, device, electronic device and computer program product

Technical field

The present application relates to the field of augmented reality technology, and in particular, to a cloud computing-based calibration method, apparatus, electronic device, and computer program product.

Background technique

AR (augmented reality) technology is a technology that adds computer-generated text, 3D models and other information to the real natural environment observed by users. Through AR technology, users can not only interact with the real environment, but also be close to the senses in the senses. Information such as the geometry and color of the environment enhances the user's ability to perceive the real environment. The AR needs to set the real camera and the virtual camera. When the user observes the change of the natural world view, the virtual camera parameters must be consistent with the real camera parameters. At the same time, the position and attitude parameters of the real object need to be tracked in real time, and the virtual object is updated by using these parameters. Position and posture. During the virtual and real alignment process, the internal parameters of some devices (such as cameras) in the system and the relative positional orientation between certain devices in the system remain unchanged, so these parameters can be measured or calibrated in advance.

There are two commonly used display devices in the AR system: one is VSTHMD (video see through head mounted displays); the other is OSTHMD (optical see through head mounted displays). Because the OSTHMD system allows users to directly obtain objects in the natural environment through their translucent eyepieces, their calibration cannot directly and conveniently handle features in real object images like VSTHMD. At the same time, OSTHMD calibration is mainly for human eyes. Calibration with the virtual camera composed of OTHMD, the different use of the user and the change of the user's identity will lead to potential changes in the position of the human eye. These artificial factors inevitably increase the difficulty of the OSTHMD calibration. It can be seen that the calibration of OSTHMD is more complicated and more difficult than VSTHMD.

After wearing the OSTHMD, the user sees the result that the human eye and the OTHMD work together, so the combination of the optical perspective helmet display and the human eye is defined as a virtual camera for calibration. Figure 1 shows a schematic diagram of the conversion relationship between the coordinate systems of the OTHMD system. As shown in Figure 1, the OTHMD involves three coordinate systems: the tracking coordinate system of the camera, the world coordinate system of the artificial identification, and the virtual coordinate system of the optical helmet. F is the conversion relationship between the known world coordinate system and the tracking coordinate system, which can be obtained by the camera recognition of the artificial identification in real time, and F=[R _F |T _F ], where R _F represents the world coordinate system to the tracking coordinate system. The rotation transformation, T _F represents the translational transformation of the world coordinate system to the tracking coordinate system; G is the conversion relationship of the unknown tracking coordinate system to the virtual coordinate system, and G can be expressed as G=K _G [R _G |T _G ], Where K _G represents the internal reference of the virtual camera, and the point in the virtual coordinate system 3D space can be projected onto the 2D screen, R _G represents the rotation of the tracking coordinate system to the virtual coordinate system, and T _G represents the translation of the tracking coordinate system to the virtual coordinate system. ;H is known.

The final result of the calibration is that the virtual object can be correctly superimposed over the actual position. If a real 3D position Pw is in the world coordinate system and a virtual position Pv is in the virtual coordinate system, if the calibration is successful, they should eventually be in the same position in the real 3D world, ie F ^-1 *G ^-1 *Pv= Pw. For the same wearer, G is constant and unknown, that is, G needs to be obtained under the constraint of H=F*G, so the calibration of OSTHMD is mainly to obtain the conversion relationship between the tracking coordinate system and the virtual coordinate system. G.

In 2000, Tuceryan et al. proposed a user-friendly SPAAM (Single Point Active Alignment Method) to calibrate OSTHMD. The principle is to use the calibration target point in the real scene for OTHMD calibration. The user's human eye observes the calibration point through the OTHMD screen, then rotates the head to align with the calibration point on the observation screen, and confirms by pressing the confirmation key to collect the alignment data of the group. Because the alignment of the target virtual image with the target real image involves the translational rotation transformation of the target object, that is, the conversion of a point set from one coordinate system to another, a homogeneous coordinate transformation is required, so that the calibration camera is set at the calibration The homogeneous coordinate in the tracking coordinate system is P = (xyz 1) ^T . After the projection transformation of the virtual camera, the corresponding coordinate point of the corresponding image point in the virtual camera coordinate system is (u v1) ^T , then:

In the above formula (1), s is a scale factor and is a constant that is not zero. 3 reflects the G * G 4 projection matrix to track coordinates projective transformation between the virtual camera coordinate system, i.e., the calibration OSTHMD, with _{^{_{^{g 1 T, g 2 T,}}}} g 3 T 3 each represent lines of G, then:

Further available:

Since the 3*4 projection matrix has 12 unknown parameters to be determined, it can be known from the above equation (3) that each calibration point can determine two independent constraint equations. Therefore, the process of at least six single-point alignments is required to form 12 equations, and the 12 equations must be linearly independent. Then, 12 unknown parameters are calculated to obtain the solution of the 3*4 projection matrix G, thereby completing the calibration of the OSTHMD.

Because the entire calibration requires artificial alignment of the target, there may be some error, which causes the final solution equation to degenerate. Therefore, the SPAAM is subjected to more target calibration in the actual calibration process to reduce the degradation effect, and the actual operation is calibrated. Typically, 20 sets of alignment data are collected for each of the left and right eyes of each user. In addition, because different eyes of the wearer have different eye distances, visual strengths, etc., the virtual camera composed of each user's eyes and OTHMD is different, that is, G is different, so multiple sets of each OSTHMD wearer need to be performed. Align the collection of data to complete the calibration.

The shortcomings of the prior art are:

In the existing scheme based on SPAAM to implement OSTHMD calibration, each user who performs calibration involves excessive alignment data collection, which is cumbersome and time consuming.

Summary of the invention

The embodiment of the present application proposes a calibration method, device, device and computer program product based on cloud computing, which is mainly used to reduce the collection of alignment data during the OSTHMD calibration process, simplify the operation, and shorten the calibration time.

In one aspect, an embodiment of the present application provides a cloud computing-based calibration method, the method comprising: determining an existing conversion parameter G, wherein the conversion parameter G is based on a SPAAM calibration of the OTHMD calibration. Converting a coordinate system to a virtual coordinate system; acquiring alignment data of the current user; calculating a conversion coordinate G′ of the current user's tracking coordinate system to the virtual coordinate system according to the current user's alignment data and the conversion parameter G.

In another aspect, the embodiment of the present application provides a calibration device based on cloud computing, wherein the device includes: an existing parameter determination module, configured to determine an existing conversion parameter G, and the conversion parameter G It is based on the conversion parameter of the tracking coordinate system obtained by the SPAAM to the OTHMD calibration to the virtual coordinate system; the alignment data acquisition module is configured to acquire the alignment data of the current user; and the current parameter calculation module is configured to be aligned according to the current user. The data and the conversion parameter G calculate the conversion coordinate G' of the current user's tracking coordinate system to the virtual coordinate system.

In another aspect, an embodiment of the present application provides an electronic device, including: a memory, one or more processors; and one or more modules, the one or more modules being Stored in the memory and configured to be executed by the one or more processors, the one or more modules including instructions for performing the various steps of the above methods.

In another aspect, embodiments of the present application provide a computer program product for use in conjunction with an electronic device, the computer program product comprising a computer program embedded in a computer readable storage medium, the computer program comprising An instruction to cause the electronic device to perform the various steps in the above methods.

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

In the present application, the current user's OTHMD calibration can be completed by using the existing calibration data, and it is not necessary to perform complete SPAAM calibration on the current user, and only need to collect less alignment data, which simplifies the operation and consumes a short time.

DRAWINGS

Specific embodiments of the present application will be described below with reference to the accompanying drawings, in which:

Figure 1 is a schematic diagram showing the relationship between the coordinate systems of the OSTHMD system;

FIG. 2 is a schematic flowchart diagram of a calibration method based on cloud computing in the embodiment of the present application;

FIG. 3 is a schematic diagram showing changes in imaging of a virtual coordinate system between different users in the embodiment of the present application; FIG.

4 is a schematic structural diagram of a calibration apparatus based on cloud computing in Embodiment 2 of the present application;

FIG. 5 is a schematic structural diagram of an electronic device in Embodiment 3 of the present application.

Detailed ways

The exemplary embodiments of the present application are further described in detail below with reference to the accompanying drawings, in which the embodiments described are only a part of the embodiments of the present application, but not all embodiments. An exhaustive example. And in the case of no conflict, the features in the embodiments and the embodiments in the description can be combined with each other.

The inventor noticed during the invention that in the existing scheme of implementing OSTHMD calibration based on SPAAM, each user who performs calibration involves excessive alignment data collection, which is cumbersome and time consuming.

In view of the above deficiencies, the present application provides a calibration method based on cloud computing, which acquires the conversion parameter G of the existing tracking coordinate system based on SPAAM to OSTHMD calibration to the virtual coordinate system, and obtains the current information by combining a small amount of alignment data of the current user. The user converts the parameter G' to complete the calibration of the current user. In the present application, the current user's OTHMD calibration can be completed by using the existing calibration data, and it is not necessary to perform complete SPAAM calibration on the current user, and only need to collect less alignment data, which simplifies the operation and consumes a short time.

The essence of the technical solution of the embodiment of the present invention is further clarified by specific examples below.

Embodiment 1:

FIG. 2 is a schematic flowchart of a cloud computing-based calibration method according to Embodiment 1 of the present application. As shown in FIG. 2, the cloud computing-based calibration method includes:

Step 101: Determine an existing conversion parameter G, which is a conversion parameter based on a tracking coordinate system obtained by SPAAM to OSTHMD calibration to a virtual coordinate system;

Step 102: Acquire alignment data of a current user.

Step 103: Calculate a conversion coordinate G' of the current user's tracking coordinate system to the virtual coordinate system according to the current user's alignment data and the conversion parameter G.

In step 101, an existing conversion parameter G is determined, and the conversion parameter G is a conversion parameter based on the tracking coordinate system obtained by SPAAM to OSTHMD calibration to the virtual coordinate system.

For each existing user, the calibration result of the OSTHMD calibration based on SPAAM is usually the calculated conversion parameter G of the tracking coordinate system to the virtual coordinate system when each user uses the OSTHMD. In this embodiment, an existing conversion parameter G is determined, and the parameter may be a conversion parameter obtained by other users using the OTHMD (or the same OSTHMD parameter) and based on the SPAAM calibration, or the current user uses the OTHMD. (or OSTHMD with the same parameters) Based on the conversion parameters that SPAAM has done to calibrate. The conversion parameter G is typically a 3*4 matrix for characterizing the projection transformation relationship from the tracking coordinate system to the virtual coordinate system.

When the same user's vision has not changed greatly, the conversion parameter G when wearing the same OSTHMD is usually unchanged, while another person wearing the same OTHMD, because each person's pupil distance and vision are different, so The conversion parameter G is usually also different, wherein the interpupillary distance affects the rotation and translation of the tracking coordinate system to the virtual coordinate system, resulting in a change in image position, and the visual acuity affects the size of the image.

For a point P whose homogeneous coordinate in the tracking coordinate system is P = (x y z 1) ^T , the homogeneous coordinates of the existing user in the virtual coordinate system are P _C = (x _C , y _C , 1) ^T. Let the current user's homogeneous coordinate for imaging the P point in the virtual coordinate system be P _E =(x _E , y _E , 1) ^T , then the following relationship exists:

Where α _u and α _v are mutually perpendicular stretching scale factors; u ₀ and v ₀ are mutually perpendicular translation scale factors, as shown in FIG. 3 .

The matrix of the above formula (4) is expressed as

The matrix K is the correction parameter.

It can be seen that according to formula (1), for existing users, there are:

s*P _C =G*P (6)

In the above formula (6), s is a scale factor and is a constant that is not zero. G reflects the projection transformation relationship of the existing user's calibrated tracking coordinate system to the virtual camera coordinate system.

Also according to equation (1), for the current user, there are:

s*P _E =G'*P (7)

In the above formula (7), s is the same scale factor as in the above formula (6). G' reflects the projection transformation relationship of the current user tracking coordinate system to the virtual camera coordinate system to be calibrated.

By combining the above two forms (6) and (7), we obtain:

G ^-1 *P _C =(G') ^-1 *P _E (8)

Bring the above formula (8) into the above formula (5), and obtain:

G’=KG (9)

In step 102, the alignment data of the current user is acquired.

Data is collected for the left and right eyes of the current user respectively, and the data is collected in the same manner as the SPAAM. The OTHMD calibration is performed by using a calibration target point in the real scene, and the user's eye observes the calibration point through the OSTHMD screen, and then rotates the head. To align with the calibration point on the observation screen, confirm by pressing the confirmation key, and collect the current alignment data.

In order to calculate α _u , α _{v ,} u ₀ and v ₀ and obtain the modified parameter K, it is necessary to collect at least two sets of data of the current user, namely P _{E, 1} and P _{E, 2} , and calculate according to the corresponding existing user calibration data. Obtain P _C,1 and P _{C,2 of the} corresponding P points _, and get:

In an ideal case, only the two sets of calibration data of the current user are collected into the above equation (10) to obtain α _u , α _{v ,} u ₀ and v ₀ , and the corrected parameter K is obtained.

In step 103, the currently used conversion parameter matrix G' can be calculated using the conversion parameter matrix G in the calibration data of the existing user according to the above equation (9) and the correction parameter K, and the current user's OTHMD calibration is completed.

It should be noted that the transformation relationship between P _E and P _C caused by the difference in vision between the current user and the existing user can be expressed by the extension scale factors α _u and α _v and the translation scale factors u ₀ and v ₀ , It is also possible to use a combination of rotation and stretching or rotation and translation, or to use other functional relationships to characterize the conversion relationship between them. In these cases, the modified parameter matrix K may have other expressions, and the calculation process Different, the amount of alignment data that needs to be collected by the current user may also be different. However, it can be understood that, by using the known conversion parameter G of the existing user, the known G is corrected based on the alignment data collected by the current user, and the current user's conversion parameter G' will not require complete SPAAM calibration for the current user. Simply collecting less alignment data simplifies operation and takes less time.

In some embodiments, in the step 101, the existing conversion parameter G is determined according to the current user's vision condition, and the conversion parameter G is a conversion parameter based on the SPAAM-to-OSTHMD calibration tracking coordinate system to the virtual coordinate system. .

For users with different vision conditions, the scale and location of the perceived imaging are different, because the a priori data is used to calibrate the new wearer based on SPAAM, considering that the closer the visual conditions are, the two users perceive The smaller the difference between the imaging scale and the position, the more accurate the current user calibration accuracy can be further improved by selecting the existing calibration data of the user whose vision is closer to the current user's vision.

The visual condition usually needs to be determined with reference to the user's myopia/farsight degree and the interocular distance. In the existing calibration data, it is usually necessary to record the correspondence between the user and the existing calibration data, and record the visual acuity of the user corresponding to the calibration data, for example, the standard visual power and the distance of the user.

Before the current user calibration, the user may be required to log in to his own account. If the user is a historical user, the existing calibration data and the visual acuity of the user are saved in the account, and the existing calibration data is directly used. If the current user is a newly registered user, it needs to be measured by OTHMD or the user inputs his or her vision. For example, the user's distance is detected by OTHMD, and the visual power of both eyes is input by the user; OSTHMD is in the database according to the user's vision. Searching for the visual acuity of each user who has the calibration data, and selecting the existing calibration data of the user closest to the current user's visual acuity as a priori data, for example, determining the existing user who is the same or closest to the current user's distance, Among the users, the existing user who determines that the binocular vision is the same as the current user or has the smallest gap, and the conversion parameter G in the existing user's existing calibration data to the virtual coordinate system is used as the calculation of the subsequent steps in this embodiment. Basic to complete the calibration of the current user. When there are multiple existing users whose visual conditions are closest to the current user, the calibrated data of one of the users is randomly selected.

When a large existing user's calibration database is established, it will be easy to find the calibration data of the existing user whose current visual situation is similar to the current new user to complete the current user calibration. The calibration database can be stored in the cloud server, and the cloud server can select the user whose visual condition is close.

In some embodiments, the step 101 further includes:

Step 1011, determining an eyesight situation score corresponding to each existing conversion parameter;

Step 1012, determining a current user's visual condition score;

Step 1013, determining that the conversion parameter corresponding to any score within the preset score range of the current user's visual condition score is the existing conversion parameter G, or determining the closest to the current user's visual condition score. The conversion parameter corresponding to the score is the existing conversion parameter G.

The sequence of steps 1011 and 1012 is not limited. In step 1011, the visual grading score of the existing user and the calibration data of each user are determined (including the conversion of the tracking coordinate system based on SPAAM to OSTHMD calibration to the virtual coordinate system). Parameter G). In step 1012, the current user's visual condition score is determined, which may be pre-tested and input by the current user, or may be detected and calculated by the OTHMD for the current user. The visual acuity score here is a comprehensive consideration of various aspects of the user's vision, such as myopia/farsightedness, interocular distance, and other factors calculated by a preset algorithm, which characterizes the visual acuity used. Users with identical vision conditions have identical visual acuity scores, and the difference in visual acuity scores between the two users with greater differences in visual acuity.

In step 1013, the existing conversion parameter G is determined based on the preset score range, or the conversion parameter G of the user closest to the current user's vision condition score is determined to complete the subsequent steps.

In the solution for determining the existing conversion parameter G based on the preset score range, the range may be a simple determination based on the threshold, for example, the preset threshold is recorded as threshold, and the current user's visual condition score is score, in the cloud database. Look for users whose visual scores are in the range of [score-threshold, score+threshold]. If there are no users in the range, expand the threshold threshold and continue searching until they are found. One of the users whose visual acuity score is within the threshold range is randomly selected, and the conversion parameter G in the calibration data is used as the calculation basis of the subsequent steps in the present embodiment. The preset score range may also be determined based on a relatively complicated manner, for example, determining a preset score range according to the “Quadruple Elimination Extreme Value” method, that is, finding a visual score in the cloud database [score-threshold, score+threshold] Within the scope of the user, the visual acuity score of each user is recorded as score _i , and the subscript i represents the i-th eligible user. Sort the data from small to large to get {score ₁ , score ₂ , score ₃ ,..., score _n }, and then divide the data into two sets of data to get {score ₁ ,...,score _m ,} and {score _m+1 ,...,score _n }, respectively, the median values of the two sets of data are recorded as m1 and m2, and the visual acuity score is less than m1-(m2-m1)*0.5 or the visual acuity score is greater than m2+(m2-m1)*0.5 The culling is performed, and a user corresponding to the score is randomly selected within the subsequent score range, and the conversion parameter G in the calibration data is used as the calculation basis of the subsequent steps in the embodiment.

In addition, the visual condition score closest to the current user's visual condition score can be directly determined, and the conversion parameter G in the existing calibration data of the user to which the score belongs is used as the calculation basis of the subsequent steps in this embodiment.

In the present application, the current user's OTHMD calibration can be completed by using the existing calibration data, and it is not necessary to perform complete SPAAM calibration on the current user, and only need to collect less alignment data, which simplifies the operation and consumes a short time. When selecting the existing calibration data, it is possible to select the existing calibration data of the user who is closer to the current user's visual acuity, and improve the current user calibration accuracy; characterizing the visual condition of the user with the visual condition score can simplify the user's operation, and When the user replaces different types of OTHMD, it is easier to make OTHMD obtain the multi-dimensional visual information of the user and complete the relevant calibration process as soon as possible.

Embodiment 2:

Based on the same inventive concept, a calibration device based on cloud computing is also provided in the embodiment of the present application. Since the principle of solving the problem is similar to the calibration method based on cloud computing, the implementation of these devices can be referred to the implementation of the method. It will not be repeated here. As shown in FIG. 4, the cloud computing-based calibration apparatus 200 includes:

The existing parameter determining module 201 is configured to determine an existing conversion parameter G, and the conversion parameter G is a conversion parameter based on a tracking coordinate system and a virtual coordinate system obtained by the SPAAM to the OTHMD calibration;

An alignment data obtaining module 202, configured to acquire alignment data of a current user;

The current parameter calculation module 203 is configured to calculate a conversion coordinate G' of the current user's tracking coordinate system to the virtual coordinate system according to the current user's alignment data and the conversion parameter G.

In some embodiments, the existing parameter determination module 201 is configured to determine an existing conversion parameter G according to the current user's vision condition.

In some embodiments, the existing parameter determination module 201 includes:

The first determining unit 2011 is configured to determine a visual condition score corresponding to each existing conversion parameter;

a second determining unit 2012, configured to determine a current user's visual condition score;

The existing parameter determining unit 2013 is configured to determine that the conversion parameter corresponding to any score within the preset score range of the current user's visual condition score is the existing conversion parameter G, or determine the current user's The conversion parameter corresponding to the closest score of the visual condition score is the existing conversion parameter G.

In some embodiments, the current parameter calculation module 203 includes:

a correction parameter calculation unit 2031, configured to calculate a correction parameter K according to the alignment data of the current user and the conversion parameter G;

The conversion parameter calculation unit 2032 is configured to calculate a conversion coordinate G' of the current user's tracking coordinate system to the virtual coordinate system according to the correction parameter K and the conversion parameter G.

In some embodiments, the correction parameter K is:

Wherein α _u and α _v are mutually perpendicular stretching scale factors; u ₀ and v ₀ are mutually perpendicular translation scale factors;

The conversion parameter calculation unit 2032 is configured to calculate a conversion coordinate G' of the current user's tracking coordinate system to the virtual coordinate system according to the correction parameter K and the conversion parameter G based on G'=K*G.

Embodiment 3:

Based on the same inventive concept, an electronic device is also provided in the embodiment of the present application. Since the principle is similar to the calibration method based on the cloud computing, the implementation of the method may refer to the implementation of the method, and the repeated description is not repeated. As shown in FIG. 5, the electronic device 300 includes: a memory 301, one or more processors 302; and one or more modules, the one or more modules being stored in the memory and configured to Executed by the one or more processors, the one or more modules include instructions for performing the various steps of any of the above methods.

Embodiment 4:

Based on the same inventive concept, an embodiment of the present application further provides a computer program product for use in combination with an electronic device, the computer program product comprising a computer program embedded in a computer readable storage medium, the computer program comprising An instruction to cause the electronic device to perform each of the steps of any of the above methods.

For the convenience of description, the various parts of the above-described apparatus are separately described by functions into various modules. Of course, the functions of each module or unit may be implemented in one or more software or hardware when implementing the present application.

Those skilled in the art will appreciate that embodiments of the present application can be provided as a method, system, or computer program product. Thus, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment in combination of software and hardware. Moreover, the application 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, CD-ROM, optical storage, etc.) including computer usable program code.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (system), and computer program products according to embodiments of the present application. 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.

While the preferred embodiment of the present application has been described, it will be apparent that those skilled in the art can make further changes and modifications to the embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and the modifications and

Claims

A cloud computing-based calibration method, the method comprising:

Determining an existing conversion parameter G, which is a conversion parameter based on a tracking coordinate system obtained by SPAAM to OSTHMD calibration to a virtual coordinate system;

Obtain alignment data of the current user;

The conversion parameter G' of the current user's tracking coordinate system to the virtual coordinate system is calculated based on the current user's alignment data and the conversion parameter G.
The method of claim 1 wherein said determining said existing conversion parameter G comprises:

The existing conversion parameter G is determined according to the current user's vision.
The method of claim 2, wherein the determining the existing conversion parameter G according to the current user's vision condition comprises:

Determining the visual condition score corresponding to each existing conversion parameter;

Determine the current user's visual condition score;

Determining, by the current user, the conversion parameter corresponding to any score within the preset score range of the current user is the existing conversion parameter G, or

The conversion parameter corresponding to the score closest to the current user's visual condition score is determined as the existing conversion parameter G.
The method according to any one of claims 1 to 3, wherein the calculating the conversion parameter G of the current user's tracking coordinate system to the virtual coordinate system according to the current user's alignment data and the conversion parameter G ',include:

Calculating a correction parameter K according to the alignment data of the current user and the conversion parameter G;

The conversion parameter G' of the current user's tracking coordinate system to the virtual coordinate system is calculated based on the correction parameter K and the conversion parameter G.
The method of claim 4 wherein said correction parameter K is:

Wherein α u and α v are mutually perpendicular stretching scale factors; u 0 and v 0 are mutually perpendicular translation scale factors;

The calculating the conversion parameter G' of the current user's tracking coordinate system to the virtual coordinate system according to the correction parameter K and the conversion parameter G includes:

Based on the G'=K*G, the tracking parameter system of the current user is converted to the conversion parameter G' of the virtual coordinate system based on the correction parameter K and the conversion parameter G.
A calibration device based on cloud computing, characterized in that the device comprises:

An existing parameter determining module is configured to determine an existing conversion parameter G, wherein the conversion parameter G is a conversion parameter based on a tracking coordinate system obtained by SPAAM to OSTHMD calibration to a virtual coordinate system;

Aligning the data acquisition module, configured to acquire alignment data of the current user;

The current parameter calculation module is configured to calculate a conversion coordinate G' of the current user's tracking coordinate system to the virtual coordinate system according to the current user's alignment data and the conversion parameter G.
The device of claim 6 wherein:

The existing parameter determining module is configured to determine an existing conversion parameter G according to the current user's visual condition.
The device of claim 7, wherein the existing parameter determination module comprises:

a first determining unit, configured to determine a visual condition score corresponding to each existing conversion parameter;

a second determining unit, configured to determine a current user's visual condition score;

The existing parameter determining unit is configured to determine that the conversion parameter corresponding to any score within the preset score range of the current user's visual condition score is the existing conversion parameter G, or

The conversion parameter corresponding to the score closest to the current user's visual condition score is determined as the existing conversion parameter G.
The device according to any one of claims 6 to 8, wherein the current parameter calculation module comprises:

a correction parameter calculation unit, configured to calculate a correction parameter K according to the alignment data of the current user and the conversion parameter G;

The conversion parameter calculation unit is configured to calculate a conversion coordinate G' of the current user's tracking coordinate system to the virtual coordinate system according to the correction parameter K and the conversion parameter G.
The apparatus of claim 9 wherein said correction parameter K is:

Wherein α u and α v are mutually perpendicular stretching scale factors; u 0 and v 0 are mutually perpendicular translation scale factors;

The conversion parameter calculation unit is configured to calculate a conversion coordinate G' of the current user's tracking coordinate system to the virtual coordinate system based on the correction parameter K and the conversion parameter G based on G'=K*G.
An electronic device, comprising:

a memory, one or more processors; and one or more modules, the one or more modules being stored in the memory and configured to be executed by the one or more processors, the one or The plurality of modules includes instructions for performing the various steps of the method of any of claims 1 to 5.
A computer program product for use in conjunction with an electronic device, the computer program product comprising a computer program embedded in a computer readable storage medium, the computer program comprising means for causing the electronic device to perform claims 1 to 5 The instructions of the various steps in any of the methods described.