VR virtual display system

Abstract

The invention discloses a VR virtual display system, and relates to the technical field of VR virtual display. According to the technical scheme, the calibration point is arranged in the calibration space, the three-dimensional coordinate of the calibration point at the moment t in the moving process is obtained, the positioning accuracy influence parameters including the installation angle theta and the vibration parameter of the positioning light tower of the calibration point and the positioning coordinate of the positioning light tower to the calibration point are obtained, and the positioning error matrix D is obtained; taking the standardized parameters of the moving speed v, the installation angle theta of the positioning light tower, the vibration amplitude and the vibration frequency as inputs, taking the standardized parameter of any element value of the positioning error matrix as an output for training to obtain a neural network model, and outputting a predicted value of the element value of the positioning error matrix according to the model. And constructing a compensation mapping relation table according to the output result of the predicted value of the positioning error matrix element value, and correcting the positioning coordinate of the positioning light tower according to the obtained compensation mapping relation table.

Description

Technical Field

[0001] The present invention relates to the field of VR virtual display technology, and in particular to a VR virtual display system. Background Art

[0002] Virtual reality, or VR for short, is the use of computer technology to simulate a three-dimensional virtual world, allowing users to perceive things in the virtual space in a timely and unrestricted manner, achieving an effect that makes users "immersive". In a dynamic VR virtual display system, the user's spatial position needs to be located so that the scene can be changed according to the user's position. The commonly used spatial positioning technology in VR technology is laser positioning, which uses a laser transmitter, i.e., a positioning light tower, to emit lasers in both horizontal and vertical directions to the space, enclosing the corresponding effective space. Multiple laser sensing receivers are placed on the located object, and the 3D coordinates of the object are obtained by calculating the angle difference between the two beams of light reaching the located object. When the object moves, the 3D coordinates will also change accordingly, thereby capturing motion information and completing virtual modeling.

[0003] The obvious advantages of laser positioning technology are low cost and high positioning accuracy. It is extremely sensitive to the static or dynamic capture of the positioning object and can locate multiple objects at the same time. However, since the laser transmitting base station uses mechanical control, it will produce slight vibrations during movement, causing the positioning light tower to shake, affecting the accuracy and stability of positioning, and even causing incorrect positioning in severe cases. To this end, we propose a VR virtual display system. Summary of the invention

[0004] The main purpose of the present invention is to provide a VR virtual display system that can effectively solve the problems in the background technology.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A VR virtual display system, comprising:

[0007] Set the calibration point in the calibration space, make the calibration point move at a fixed speed v along the calibration route, and obtain the three-dimensional coordinates (x ct ,y ct , z ct ), wherein the calibration route is inside the calibration space;

[0008] Obtaining parameters influencing the positioning accuracy of the calibration point, wherein the accuracy influencing parameters include the installation angle θ and vibration parameters of the positioning light tower, wherein the vibration parameters include vibration amplitude and vibration frequency;

[0009] Get the positioning coordinates (xrt ,y rt , z rt ), obtain the positioning error matrix D, where D = (Δx t , Δy t , Δz t ), where

[0010] The moving speed v, the installation angle θ of the positioning light tower, the vibration amplitude and the vibration frequency are respectively used as input, and the standardized parameter of any element value of the positioning error matrix is ​​used as output for training to obtain a neural network model, and the predicted value of the positioning error matrix element value is output according to the model, wherein the hidden layer node calculation formula of the neural network model is:

[0011]

[0012] Wherein, s is the number of hidden layer nodes of the neural network model; m is the number of input layer nodes, in this model, the number of input layer nodes is m=4; n is the number of output layer nodes, in this model, the number of output layer nodes is n=1; a is an integer from 1 to 9, and the standardized parameter formula of the input and output variables of the neural network model is:

[0013]

[0014] In the formula, r i is the standardized parameter of the i-th type variable; x i is the actual value of the i-th variable; x imax is the maximum value of the i-th type variable; x imin is the minimum value of the i-th type variable;

[0015] The compensation mapping relationship table is constructed based on the output results of the predicted values ​​of the positioning error matrix element values, and the expression is: (v, θ, l, f) → (Δx b , Δy b , Δz b ), where l and f are the vibration amplitude and vibration frequency of the positioning light tower, respectively, Δx b , Δy b , Δz b They are the positioning error values ​​of the positioning coordinates in the three-dimensional direction respectively;

[0016] The positioning coordinates of the positioning light tower are corrected according to the obtained compensation mapping relationship table.

[0017] A VR virtual display system, comprising an influencing parameter acquisition module, a calibration coordinate acquisition module, a positioning coordinate acquisition module, an error matrix calculation module, a neural network construction module, and a positioning coordinate correction module;

[0018] The influencing parameter acquisition module is used to acquire the influencing parameters of the calibration point positioning accuracy, wherein the accuracy influencing parameters include the installation angle θ and vibration parameters of the positioning light tower, and the vibration parameters include vibration amplitude and vibration frequency;

[0019] The calibration coordinate acquisition module is used to obtain the three-dimensional coordinates (x ) of the calibration point at time t in the calibration space when the calibration point moves at a fixed speed v along the calibration route. ct ,y ct , z ct );

[0020] The positioning coordinate acquisition module is used to obtain the positioning coordinates (x) of the positioning light tower at time t during the movement of the calibration point. rt ,y rt , z rt );

[0021] The error matrix calculation module is used to calculate and obtain the positioning error matrix D according to the obtained three-dimensional coordinates and positioning coordinates of the calibration points;

[0022] The neural network building module is used to take the moving speed v, the installation angle θ of the positioning light tower, the vibration amplitude and the vibration frequency as the input, and the standardized parameter of any element value of the positioning error matrix as the output to train and obtain a neural network model, and output the predicted value of the positioning error matrix element value according to the model;

[0023] The positioning coordinate correction module is used to construct a compensation mapping relationship table according to the output result of the predicted value of the positioning error matrix element value, and correct the positioning coordinates of the positioning light tower according to the obtained compensation mapping relationship table;

[0024] The system also includes a memory, a processor, and an electronic program stored in the memory and capable of running on the processor.

[0025] The present invention has the following beneficial effects:

[0026] Compared with the prior art, the technical solution of the present invention sets a calibration point in the calibration space, makes the calibration point move at a fixed speed v along the calibration route, and obtains the three-dimensional coordinates (x ct ,y ct , z ct ), the calibration point includes the installation angle θ of the positioning light tower and the parameters affecting the positioning accuracy of the vibration parameters and the positioning coordinates (x rt ,y rt , z rt), calculate the positioning error matrix D according to the acquired data, and respectively use the moving speed v, the installation angle θ of the positioning light tower, the vibration amplitude and the standardized parameters of the vibration frequency as inputs, and the standardized parameters of any element value of the positioning error matrix as outputs to train a neural network model, output the predicted values ​​of the positioning error matrix element values ​​according to the model, and construct a compensation mapping relationship table according to the output results of the predicted values ​​of the positioning error matrix element values, and correct the positioning coordinates of the positioning light tower according to the acquired compensation mapping relationship table, so as to reduce the influence of the jitter of the positioning light tower on the accuracy and stability of positioning caused by the slight vibration of the laser transmitting base station during the movement, and improve the accuracy of spatial positioning in the dynamic VR virtual display process. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a VR virtual display system of the present invention;

[0028] Figure 2 The figure is a structural block diagram of a VR virtual display system of the present invention. DETAILED DESCRIPTION

[0029] The present invention will be further described below in conjunction with specific implementation methods, wherein the accompanying drawings are only used for exemplary descriptions and represent only schematic diagrams rather than actual drawings, and should not be understood as limiting the present invention. In order to better illustrate the specific implementation methods of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0030] Example 1

[0031] like Figure 1-2 As shown, the specific implementation process of the technical solution of the present invention includes the following steps:

[0032] Step 1: Set calibration points in the calibration space, so that the calibration points move at a fixed speed v along the calibration route, wherein the calibration route is inside the calibration space and can be a straight path or a curved path;

[0033] Step 2: Obtain the three-dimensional coordinates (x ct ,y ct , z ct ) and the positioning coordinates of the positioning light tower’s calibration point (x rt ,y rt , z rt );

[0034] Step 3: Calculate the positioning error matrix D at time t during the movement of the calibration point according to the acquired coordinates through the error matrix calculation module, where D = (Δx t , Δy t, Δz t ), where

[0035] Step 4: Obtain parameters influencing the positioning accuracy of the calibration point, wherein the accuracy influencing parameters include the installation angle θ and vibration parameters of the positioning light tower, and the vibration parameters include vibration amplitude and vibration frequency;

[0036] Step 5: The neural network building module is respectively input with the moving speed v, the installation angle θ of the positioning light tower, the vibration amplitude and the standardized parameters of the vibration frequency, and the standardized parameter of any element value of the positioning error matrix is ​​output to train the neural network model, and the predicted value of the element value of the positioning error matrix is ​​output according to the model. The calculation formula of the hidden layer node of the neural network model is:

[0037]

[0038] Where s is the number of hidden layer nodes of the neural network model; m is the number of input layer nodes; n is the number of output layer nodes; a is an integer from 1 to 9. In this model, the number of input layer nodes is m = 4, and the number of output layer nodes is n = 1. The standardized parameter formulas for the input and output variables of the neural network model are:

[0039]

[0040] In the formula, r i is the standardized parameter of the i-th type variable; x i is the actual value of the i-th variable; x imax is the maximum value of the i-th type variable; x imin is the minimum value of the i-th type variable;

[0041] It should be noted that in order to reduce the complexity of the neural network model, in this scheme, the standardized parameters of any element value of the positioning error matrix are used as output for training and construction, so that the number of input layer nodes of the model is 4, namely, the moving speed v, the installation angle θ of the positioning light tower, the vibration amplitude and the vibration frequency, and the output node is the coordinate error value of one dimension, that is, the number of output nodes is 1. Taking a as 4 as an example, the number of input nodes, the number of output nodes, and the number of intermediate nodes of this neural network model are 4. Structural form;

[0042] Step 6: The compensation mapping relationship table is constructed based on the output result of the predicted value of the positioning error matrix element value through the positioning coordinate correction module. The expression is: (v, θ, l, f) → (Δx b , Δy b , Δz b ), where l and f are the vibration amplitude and vibration frequency of the positioning light tower, respectively, Δx b , Δyb , Δz b are the positioning error values ​​of the positioning coordinates in the three-dimensional direction, and the positioning coordinates of the positioning light tower are corrected according to the obtained compensation mapping relationship table;

[0043] It should be noted that, according to the obtained compensation mapping relationship table, the error value of the positioning coordinates of the calibration point of the positioning light tower in the three-dimensional direction can be obtained when the moving speed v, the installation angle θ of the positioning light tower, the vibration amplitude and the vibration frequency. If it is assumed that the positioning coordinates of the calibration point of the positioning light tower obtained are (x1, y1, z1), the positioning error values ​​obtained according to the compensation mapping relationship table are (x2, y2, z2), then the actual three-dimensional coordinate values ​​should be (x1+x2, y1+y2, z1+z2).

[0044] The above solution can reduce the influence of the jitter of the positioning light tower on the accuracy and stability of positioning caused by the slight vibration of the laser transmitting base station during movement, and improve the accuracy of spatial positioning during dynamic VR virtual display.

[0045] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A VR virtual display system, characterized in that: include: Set the calibration point in the calibration space, make the calibration point move at a fixed speed v along the calibration route, and obtain the three-dimensional coordinates (x ct ,y ct , z ct ); Obtaining parameters affecting the positioning accuracy of the calibration point, wherein the accuracy affecting parameters include the installation angle θ and vibration parameters of the positioning light tower, wherein the vibration parameters include vibration amplitude and vibration frequency; Get the positioning coordinates (x rt ,y rt , z rt ), obtain the positioning error matrix D, where D = (Δx t , Δy t , Δz t ), where The moving speed v, the installation angle θ of the positioning light tower, the vibration amplitude and the vibration frequency are respectively used as inputs, and the standardized parameter of any element value of the positioning error matrix is ​​used as output to train a neural network model, and the predicted value of the element value of the positioning error matrix is ​​output according to the model; The compensation mapping relationship table is constructed based on the output results of the predicted values ​​of the positioning error matrix element values, and the expression is: (v, θ, l, f) → (Δx b , Δy b , Δz b ), where l and f are the vibration amplitude and vibration frequency of the positioning light tower, respectively, Δx b , Δy b , Δz b They are the positioning error values ​​of the positioning coordinates in the three-dimensional direction respectively; The positioning coordinates of the positioning light tower are corrected according to the obtained compensation mapping relationship table.

2. The VR virtual display system according to claim 1, characterized in that: The calibration route is inside the calibration space.

3. The VR virtual display system according to claim 1, characterized in that: The calculation formula of the hidden layer nodes of the neural network model is: In the formula, s is the number of hidden layer nodes of the neural network model; m is the number of input layer nodes; n is the number of output layer nodes; a is an integer from 1 to 9.

4. The VR virtual display system according to claim 3, characterized in that: In the neural network model, the number of input layer nodes is m=4, and the number of output layer nodes is n=1.

5. The VR virtual display system according to claim 1, characterized in that: The standardized parameter formulas of the input and output variables of the neural network model are: In the formula, r i is the standardized parameter of the i-th type variable; x i is the actual value of the i-th variable; x imax is the maximum value of the i-th type variable; x imin is the minimum value of the i-th variable.

6. The VR virtual display system according to claim 1, characterized in that: It includes an influencing parameter acquisition module, a calibration coordinate acquisition module, a positioning coordinate acquisition module, an error matrix calculation module, a neural network construction module, and a positioning coordinate correction module; The influencing parameter acquisition module is used to acquire the influencing parameters of the calibration point positioning accuracy, wherein the accuracy influencing parameters include the installation angle θ and vibration parameters of the positioning light tower, and the vibration parameters include vibration amplitude and vibration frequency; The calibration coordinate acquisition module is used to obtain the three-dimensional coordinates (x ) of the calibration point at time t in the calibration space when the calibration point moves at a fixed speed v along the calibration route. ct ,y ct , z ct ); The positioning coordinate acquisition module is used to obtain the positioning coordinates (x rt ,y rt , z rt ); The error matrix calculation module is used to calculate and obtain the positioning error matrix D according to the obtained three-dimensional coordinates and positioning coordinates of the calibration points, where D=(Δx t , Δy t , Δz t ), where The neural network building module is used to take the moving speed v, the installation angle θ of the positioning light tower, the vibration amplitude and the vibration frequency as input, and the standardized parameter of any element value of the positioning error matrix as output to train and obtain a neural network model, and output the predicted value of the positioning error matrix element value according to the model; The positioning coordinate correction module is used to construct a compensation mapping relationship table according to the output result of the predicted value of the positioning error matrix element value, and the expression is: (v, θ, l, f) → (Δx b , Δy b , Δz b ), where l and f are the vibration amplitude and vibration frequency of the positioning light tower, respectively, Δx b , Δy b , Δz b They are the positioning error values ​​of the positioning coordinates in the three-dimensional direction, and the positioning coordinates of the positioning light tower are corrected according to the obtained compensation mapping relationship table.

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