Panoramic VR mobile positioning method and system based on Beidou satellite

Abstract

The invention relates to the technical field of virtual reality, in particular to a panoramic VR mobile positioning method and system based on a Beidou satellite, and the method comprises the following steps: S1, obtaining real-time Beidou satellite positioning data of a user and inertial measurement unit data of the user; s2, performing optimization processing on each data source to obtain current three-dimensional space coordinates and motion parameters of the user; s3, predicting the next position of the user by using the kinematic model, and correcting the predicted position through a Kalman filtering algorithm; s4, calculating a virtual view angle of the user according to the corrected next position and motion parameters of the user in the S3; and S5, adjusting the image rendering precision and the updating frequency in the panoramic VR scene. According to the method, high-precision tracking of the user position and smooth transition of the panoramic VR visual angle in a high-speed or strenuous motion state are realized, and the stability and immersion of virtual reality experience are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of virtual reality technology, and in particular to a panoramic VR mobile positioning method and system based on Beidou satellite. Background Art

[0002] With the continuous development of virtual reality (VR) technology, especially the increasingly widespread application of panoramic VR technology, the user's immersive experience in the virtual environment has become one of the important indicators for measuring the performance of VR systems. In order to achieve a more realistic immersion, real-time tracking based on the user's position and motion state is essential. In a panoramic VR system, the user's movement and position changes directly affect the perspective adjustment, image rendering accuracy and update frequency of the virtual scene, which requires the system to be able to obtain and process the user's positioning data in real time and accurately. Especially in scenes of high-speed movement or violent acceleration, the VR system needs to be able to respond quickly to the user's movement changes to ensure smooth transition of perspective and visual stability. However, due to the existence of technical problems such as positioning accuracy, data fusion and real-time computing, achieving this goal still faces huge technical challenges.

[0003] At present, existing VR systems usually rely on multiple sensors such as GPS and inertial measurement units (IMUs) to obtain user motion information. However, since these sensors may be affected by errors under different environmental conditions, the positioning accuracy and real-time motion tracking cannot meet the needs of high-dynamic scenes; especially in high-speed motion or drastic changes in acceleration, traditional positioning algorithms are often unable to accurately predict the user's next position, which in turn affects the smoothness of perspective switching and the stability of image rendering; in addition, the adjustment of image rendering accuracy and update frequency in the existing technology usually relies on preset fixed rules, and fails to be dynamically adjusted according to the user's actual motion status and environmental conditions. Summary of the invention

[0004] Based on the above objectives, the present invention provides a panoramic VR mobile positioning method and system based on Beidou satellite.

[0005] A panoramic VR mobile positioning method based on Beidou satellite includes the following steps:

[0006] S1: Obtain the user's real-time Beidou satellite positioning data, including longitude, latitude, altitude, speed and direction, as well as the user's inertial measurement unit data;

[0007] S2: Fuse the Beidou satellite positioning data and inertial measurement unit data obtained by S1, and use the weighted average algorithm to optimize the data sources to obtain the user's current three-dimensional space coordinates and motion parameters;

[0008] S3: Based on the user's current three-dimensional space coordinates and motion parameters obtained in S2, the kinematic model is used to predict the user's next position, and the predicted position is corrected by the Kalman filter algorithm to obtain the corrected user's next position coordinates and motion parameters;

[0009] S4: Calculate the user's virtual perspective according to the next position and motion parameters of the user corrected in S3, and adjust the perspective in the panoramic VR scene through a virtual perspective conversion algorithm to ensure a smooth transition of the perspective and consistency with the user's position;

[0010] S5: According to the virtual perspective calculated in S4 and combined with the user's motion parameters, the image rendering accuracy and update frequency in the panoramic VR scene are adjusted to ensure visual stability under different speed and acceleration conditions.

[0011] Optionally, the S1 specifically includes:

[0012] S11: Receive at least three Beidou satellite signals through a Beidou satellite receiver, and obtain the longitude, latitude and altitude information of the user's current location through differential positioning technology;

[0013] S12: Calculate the user's movement speed and direction through the Beidou satellite receiver, where the movement speed is calculated by the displacement change of continuously receiving Beidou satellite signals, and the direction is calculated by a multi-base station positioning method combined with a triangulation positioning principle;

[0014] S13: Acquire acceleration, angular velocity, and tilt angle data of the user through an inertial measurement unit;

[0015] S15: Time-synchronize the acquired longitude, latitude, altitude, speed, direction, and acceleration data to ensure the temporal consistency of the data.

[0016] Optionally, the S2 specifically includes:

[0017] S21: According to the measurement accuracy of Beidou satellite positioning data and inertial measurement unit data, weight values ​​are set for these two types of data respectively; calculated by the following formula:

[0018]

[0019] Among them, w GPS is the weight of Beidou satellite positioning data, w IMU is the weight of the inertial measurement unit data, σ λ , σ h ,σ v ,σ θare the standard deviations of the measurement errors of longitude, latitude, altitude, speed and direction in Beidou satellite positioning data; σ a ,σ ω ,σ α are the standard deviations of the measurement errors of acceleration, angular velocity, and tilt angle in the inertial measurement unit data, respectively;

[0020] S22: According to the weight value calculated in S21, the Beidou satellite positioning data and the inertial measurement unit data are fused using the weighted average method. The user's three-dimensional spatial coordinates (x, y, z) and motion parameters, including speed v and orientation θ, are calculated using the following formulas:

[0021]

[0022] Among them, x GPS ,y GPS ,z GPS are the three-dimensional coordinates obtained by Beidou satellite positioning, x IMU ,y IMU ,z IMU are the three-dimensional coordinates calculated by the inertial measurement unit; v GPS and v IMU are speed data; θ GPS and θ IMU They are the direction data respectively.

[0023] Optionally, the S3 specifically includes:

[0024] S31: Based on the current three-dimensional spatial coordinates and motion parameters of the user obtained in S2, a kinematic model is used to predict the next position of the user, wherein the kinematic model presupposes that the user moves at a constant speed along the current speed direction, and calculates the predicted next position through the current position and the motion speed;

[0025] S32: The next position predicted by S31 is input into the Kalman filter, and the position is corrected according to the accuracy and error of Beidou satellite positioning and inertial measurement unit data, so as to obtain the corrected user's next position coordinates and motion parameters.

[0026] Optionally, the S31 specifically includes:

[0027] S311: Calculate the user's velocity vector according to the user's current three-dimensional space coordinates and motion parameters obtained in step S2. The formula is: Among them, v current is the user's current speed, θ is the user's heading angle, and are unit vectors, representing the components in the east-west and north-south directions respectively;

[0028] S312: Using the calculated velocity vector V, predict the next position of the user based on the kinematic model. Assuming that the user moves at a constant speed along the current velocity direction within the next time interval Δt, the next position (x next ,y next , z next ) is calculated as:

[0029] x next =x+v current ·cos(θ)·Δt;

[0030] y next =y+v current ·sin(θ)·Δt;

[0031] z next =z+v zcurrent ·Δt; where x, y, z are the current three-dimensional coordinates of the user, v zcurrent is the user's velocity component in the vertical direction, and Δt is the time interval.

[0032] Optionally, the S32 specifically includes:

[0033] S321: Using the predicted position (x) obtained in S31 next ,y next , z next ) and motion parameters, construct the state estimation of the Kalman filter, and set the state vector of the Kalman filter to be x pred =[x next ,y next , z next , v next ,θ next ] T , where x next ,y next ,z next is the predicted position coordinate, v next is the predicted speed, θ next is the predicted direction;

[0034] S322: Construct the observation value of the Kalman filter according to the accuracy and error of Beidou satellite positioning and inertial measurement unit data; let the observation value be z obs =[x obs ,y obs ,z obs ] T , where x obs ,y obs ,z obs It is the positioning data of actual measurement;

[0035] S323: Calculate the Kalman gain matrix K to correct the predicted position. The calculation formula of the Kalman gain is: K = P pred ·(H T ·(H·P pred ·H T +R) -1 ), where P pred is the prediction error covariance matrix, R is the error matrix of the observations, and H is the observation matrix;

[0036] S324: The predicted state vector is corrected by using the Kalman gain to obtain the corrected user position coordinates and motion parameters. The corrected state vector is: corr =x pred +K·(z obs -H·x pred );

[0037] S325: From the corrected state vector x corr Extract the corrected user position coordinates (x corr ,y corr , z corr ) and motion parameters (v corr ,θ corr ) as the final output of the user's next position coordinates and motion parameters.

[0038] Optionally, the S4 specifically includes:

[0039] S41: Based on the corrected coordinates of the user's next location obtained in S3 and motion parameters to calculate the user's virtual perspective, which is determined by the user's current position and orientation. The calculation formula is: θ view =θ corr +Δθ, where θ view is the user’s perspective in the virtual environment, θ corr is the corrected orientation, Δθ is the fine-tuning value based on the orientation of the user's head or eyes at the current user position;

[0040] S42: Use the virtual perspective conversion algorithm to adjust the perspective in the panoramic VR scene to ensure that it matches the user's motion state. Based on the relationship between the virtual perspective and the actual physical position, the user's orientation and motion trajectory are calculated to smoothly transition to the next perspective. Specifically, the perspective conversion formula is: θ scene =θ view +α·(θ target -θ view ), where θ scene is the target viewing angle in the panoramic VR scene, θ target is the viewing angle of the target position, and α is the smoothing factor, which is used to control the speed of the smooth transition.

[0041] Optionally, the S5 specifically includes:

[0042] S51: Based on the virtual perspective calculated in S4 and in combination with the user's current motion parameters, the user's motion state in the current time interval is evaluated, and according to the user's speed and acceleration change trend, whether the image rendering accuracy and update frequency need to be adjusted;

[0043] S52: When the user's speed exceeds 10m / s or the acceleration exceeds 2m / s 2 When the image is in high speed or high acceleration state, the image rendering accuracy is increased to more than 1.5 times the original accuracy, and the update frequency is increased to 60 frames per second.

[0044] S53: When the user's speed is less than 5 m / s or the acceleration is less than 0.5 m / s 2 When the image is rendered at a low or constant speed, the image rendering accuracy is reduced to less than 1 times the original accuracy, and the update frequency is reduced to 30 frames per second.

[0045] Optionally, the S51 specifically includes:

[0046] S511: Calculate the user's current motion state based on the virtual perspective obtained in S4 and the user's current motion parameters. The formula is: Among them, S state is the comprehensive exercise state value, indicating the user's exercise intensity, v current is the user's current speed, a current is the user's current acceleration;

[0047] S512: Analyze the speed and acceleration change trend of the user, if the speed change rate in the current time period exceeds a certain set threshold Δv threshold When the acceleration rate exceeds the set threshold Δa threshold , it is considered that the user is experiencing acceleration or deceleration; the specific change trend calculation formula is:

[0048]

[0049] Where Δv current and Δa current are the velocity change rate and acceleration change rate, v previous and a previous are the velocity and acceleration of the previous moment respectively, and Δt is the current time interval;

[0050] S513: Determine whether to adjust the image rendering accuracy and update frequency based on the speed and acceleration change trend of the user. If the speed and acceleration change rate of the user exceeds the set threshold within the current time interval, the image rendering accuracy and update frequency need to be increased.

[0051] A panoramic VR mobile positioning system based on Beidou satellite, used to implement the above-mentioned panoramic VR mobile positioning method based on Beidou satellite, includes the following modules:

[0052] Positioning data acquisition module: used to receive positioning data from Beidou satellites, including the user's longitude, latitude, altitude, speed and direction, as well as acceleration and angular velocity data from the user's inertial measurement unit;

[0053] Data fusion module: used to fuse Beidou satellite positioning data and inertial measurement unit data, and use weighted average algorithm to optimize each data source to obtain the user's current three-dimensional space coordinates and motion parameters;

[0054] Position prediction and correction module: Based on the user's current three-dimensional spatial coordinates and motion parameters obtained by the data fusion module, the kinematic model is used to predict the user's next position, and the predicted position is corrected through the Kalman filter algorithm to obtain the corrected user's next position coordinates and motion parameters;

[0055] Virtual perspective calculation module: used to calculate the user's virtual perspective based on the corrected user's next position coordinates and motion parameters, and adjust the perspective in the panoramic VR scene through the virtual perspective conversion algorithm to ensure a smooth transition of the perspective and consistency with the user's position;

[0056] Rendering and updating module: It is used to dynamically adjust the image rendering accuracy and update frequency in the panoramic VR scene according to the virtual perspective provided by the virtual perspective calculation module and the user's motion parameters to ensure visual stability under different speeds and acceleration conditions.

[0057] Beneficial effects of the present invention:

[0058] The present invention integrates Beidou satellite positioning and inertial measurement unit ( I MU) data, combined with weighted average algorithm and Kalman filtering technology, can achieve high-precision user position tracking; compared with traditional positioning technology, it can effectively eliminate positioning errors, improve positioning stability and real-time performance, and thus provide more accurate user position data for panoramic VR systems.

[0059] The present invention dynamically adjusts the image rendering accuracy and update frequency to adapt to the motion state under different speeds and acceleration conditions, thereby effectively solving the common problems of visual instability, screen freeze and blur in existing VR systems during motion. By real-time evaluation of the user's motion state and change trend, the system can intelligently adjust the rendering parameters according to the motion intensity to ensure that the image remains clear and smooth under high-speed movement or drastic changes in acceleration, thereby providing a more stable and realistic virtual reality experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 This is a schematic diagram of a panoramic VR mobile positioning method according to an embodiment of the present invention;

[0062] Figure 2 Schematic diagram of a panoramic VR mobile positioning system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0064] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0065] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0066] like Figure 1 As shown, a panoramic VR mobile positioning method based on Beidou satellite includes the following steps:

[0067] S1: Obtain the user's real-time Beidou satellite positioning data, including longitude, latitude, altitude, speed and direction, as well as the user's inertial measurement unit (IMU) data;

[0068] S2: Fuse the Beidou satellite positioning data and inertial measurement unit data obtained by S1, and use the weighted average algorithm to optimize the data sources to obtain the user's current three-dimensional space coordinates and motion parameters (including speed and direction);

[0069] S3: Based on the user's current three-dimensional space coordinates and motion parameters obtained in S2, the kinematic model is used to predict the user's next position, and the predicted position is corrected by the Kalman filter algorithm to obtain the corrected user's next position coordinates and motion parameters;

[0070] S4: Calculate the user's virtual perspective according to the next position and motion parameters of the user corrected in S3, and adjust the perspective in the panoramic VR scene through a virtual perspective conversion algorithm to ensure a smooth transition of the perspective and consistency with the user's position;

[0071] S5: According to the virtual perspective calculated in S4 and combined with the user's motion parameters, the image rendering accuracy and update frequency in the panoramic VR scene are adjusted to ensure visual stability under different speed and acceleration conditions.

[0072] S1 specifically includes:

[0073] S11: Receive at least three Beidou satellite signals through a Beidou satellite receiver, and obtain the longitude, latitude and altitude information of the user's current location through differential positioning technology, where the differential positioning technology adopts a real-time kinematic positioning (RTK) method, and specifically calculates the longitude, latitude and altitude of the user's current location through the following formula: Among them, Δλ and Δφ represent the changes in longitude and latitude respectively, R is the radius of the earth, and ΔDistance is the change in distance between the user and the base station. RTK technology provides accurate distance changes;

[0074] S12: Calculate the user's movement speed and direction through the Beidou satellite receiver, where the movement speed is calculated by the displacement change of continuously receiving Beidou satellite signals, and the direction is calculated by combining the multi-base station positioning method with the triangulation positioning principle to ensure the accuracy of the direction data; assuming that the displacement change is Δd, the calculation formula is: Where v is the user's speed, Δd is the displacement of the user's position, and Δt is the time interval for receiving the signal. Let the user's direction be θ, where the calculation formula is: Among them, (x1, y1) and (x2, y2) are the plane coordinates of two consecutive time points;

[0075] S13: The user's acceleration, angular velocity and tilt angle data are obtained through the inertial measurement unit. The IMU data realizes real-time monitoring of the user's movement through a three-axis accelerometer and a three-axis gyroscope. The acceleration data is used to calculate the user's instantaneous movement speed, and the angular velocity and tilt angle are used to calculate the user's posture change; specifically, the acceleration a, angular velocity ω and tilt angle π are calculated by the following formula:

[0076] Acceleration:

[0077] Angular velocity:

[0078] Tilt Angle:

[0079] Among them, a x ,a y ,a z They are the three axial acceleration data of the accelerometer, ω x ,ω y is the angular velocity data of the gyroscope;

[0080] S15: Time-synchronize the acquired longitude, latitude, altitude, speed, direction, and acceleration data to ensure the temporal consistency of the data, so that the data processing in the subsequent steps can accurately reflect the user's motion status;

[0081] Through the above steps, the accuracy and stability of user positioning can be effectively improved; RTK differential positioning technology ensures the accuracy of positioning data by calculating longitude, latitude and altitude with high precision; the calculation of movement speed and direction combines the displacement changes of continuous satellite signals to provide users with accurate movement information; IMU data combines acceleration, angular velocity and tilt angle, and can monitor the user's dynamic state in real time, thereby providing highly accurate data support for subsequent VR scene positioning and perspective adjustment.

[0082] S2 specifically includes:

[0083] S21: According to the measurement accuracy of Beidou satellite positioning data and inertial measurement unit data, weight values ​​are set for these two types of data respectively; calculated by the following formula:

[0084]

[0085] Among them, w GPS is the weight of Beidou satellite positioning data, w IMU is the weight of the inertial measurement unit data, σ λ , σ h ,σ v ,σ θ are the standard deviations of the measurement errors of longitude, latitude, altitude, speed and direction in Beidou satellite positioning data; σ a ,σ ω ,σ α are the standard deviations of the measurement errors of acceleration, angular velocity, and tilt angle in the inertial measurement unit data. The weight value reflects the reliability of each data source. The smaller the measurement error, the greater the weight.

[0086] S22: According to the weight value calculated in S21, the Beidou satellite positioning data and the inertial measurement unit data are fused using the weighted average method. The user's three-dimensional spatial coordinates (x, y, z) and motion parameters, including speed v and orientation θ, are calculated using the following formulas:

[0087]

[0088] Among them, x GPS ,y GPS ,z GPS are the three-dimensional coordinates obtained by Beidou satellite positioning, x IMU ,y IMU ,z IMU are the three-dimensional coordinates calculated by the inertial measurement unit; v GPS and v IMU are speed data; θ GPS and θ IMU They are orientation data respectively; the fused three-dimensional space coordinates and motion parameters are the user's current precise positioning data, which serve as input data for virtual perspective calculation and image rendering in subsequent steps.

[0089] S3 specifically includes:

[0090] S31: Based on the current three-dimensional spatial coordinates and motion parameters of the user obtained in S2, a kinematic model is used to predict the next position of the user. The kinematic model presupposes that the user moves at a constant speed along the current speed direction, and the predicted next position is calculated by the current position and the motion speed.

[0091] S32: The next position predicted by S31 is input into the Kalman filter, and the position is corrected according to the accuracy and error of Beidou satellite positioning and inertial measurement unit data to obtain the corrected user's next position coordinates and motion parameters; by combining the kinematic model with the Kalman filter, the user's next position can be accurately predicted and corrected, overcoming the influence of sensor noise and measurement errors, providing high-precision user positioning data, and ensuring the real-time and accuracy of viewing angles and image rendering in panoramic VR scenes.

[0092] S31 specifically includes:

[0093] S311: Calculate the user's velocity vector according to the user's current three-dimensional space coordinates and motion parameters obtained in step S2. The formula is: Among them, v current is the user's current speed, θ is the user's heading angle, and are unit vectors, representing the components in the east-west and north-south directions respectively;

[0094] S312: Using the calculated velocity vector V, predict the next position of the user based on the kinematic model. Assuming that the user moves at a constant speed along the current velocity direction within the next time interval Δt, the next position (x next ,y next , z next ) is calculated as:

[0095] x next =x+v current ·cos(θ)·Δt;

[0096] y next =y+v current ·sin(θ)·Δt;

[0097] z next =z+v zcurrent ·Δt; where x, y, z are the current three-dimensional coordinates of the user, v zcurrent is the velocity component of the user in the vertical direction (altitude), and Δt is the time interval. Through the above steps, the spatial position of the user in the next time interval can be accurately predicted based on the user's current spatial coordinates and motion parameters, avoiding the reuse of parameters and improving the clarity and accuracy of the calculation.

[0098] S32 specifically includes:

[0099] S321: Using the predicted position (x) obtained in S31 next ,y next , z next) and motion parameters, construct the state estimation of the Kalman filter, and set the state vector of the Kalman filter to be x pred =[x next ,y next , z next , v next ,θ next ] T , where x next ,y next ,z next is the predicted position coordinate, v next is the predicted speed, θ next is the predicted direction;

[0100] S322: According to Beidou satellite positioning and inertial measurement unit ( I MU) data accuracy and error, construct the observation value of the Kalman filter; let the observation value be z obs =[x obs ,y obs ,z obs ] T , where x obs ,y obs ,z obs is the actual measured positioning data. At this time, the error matrix R of the observation value is estimated by the satellite positioning accuracy and the noise of the IMU sensor;

[0101] S323: Calculate the Kalman gain matrix K to correct the predicted position. The calculation formula of the Kalman gain is: K = P pred ·(H T ·(H·P pred ·H T +R) -1 ), where P pred is the prediction error covariance matrix, R is the error matrix of the observations, and H is the observation matrix, which represents the relationship between the prediction values ​​and the actual observation values;

[0102] S324: The predicted state vector is corrected by using the Kalman gain to obtain the corrected user position coordinates and motion parameters. The corrected state vector is: corr =x pred +K·(z obs -H·x pred );

[0103] S325: From the corrected state vector x corr Extract the corrected user position coordinates (x corr ,y corr , z corr ) and motion parameters (v corr ,θ corr), as the final output of the user's next position coordinates and motion parameters, provides subsequent steps for virtual perspective adjustment and image rendering accuracy calculation; the above steps correct the predicted position through the Kalman filter, which can effectively integrate Beidou satellite and inertial measurement unit (IMU) data, compensate for sensor errors and noise, and provide more accurate user position coordinates and motion parameters. The weighted update mechanism of the Kalman filter has significant advantages in dynamic environments and can effectively eliminate external interference.

[0104] S4 specifically includes:

[0105] S41: Based on the corrected coordinates of the user's next location obtained in S3 and motion parameters (including the corrected orientation θ corr ), calculate the user's virtual perspective, which is determined by the user's current position and orientation. The calculation formula is: θ view =θ corr +Δθ, where θ view is the user’s perspective in the virtual environment, θ corr is the corrected orientation, Δθ is the fine-tuning value based on the orientation of the user's head or eyes at the current user position, usually obtained from sensor data (such as an IMU or an angle sensor of a head-mounted display);

[0106] S42: Use the virtual perspective conversion algorithm to adjust the perspective in the panoramic VR scene to ensure that it matches the user's motion state. Based on the relationship between the virtual perspective and the actual physical position, the user's orientation and motion trajectory are calculated to smoothly transition to the next perspective. Specifically, the perspective conversion formula is: θ scene =θ view +α·(θ target -θ view ), where θ scene is the target viewing angle in the panoramic VR scene, θ target is the viewing angle of the target position, and α is the smoothing factor, which is used to control the speed of the smooth transition. The value range of the smoothing factor is 0 to 1. The larger the value, the smoother the transition, and the smaller the value, the faster the transition. The above steps can ensure that the perspective change in the panoramic VR scene is closely integrated with the actual motion state of the user by accurately calculating the user's virtual perspective and using the virtual perspective conversion algorithm for smooth transition, providing a smooth visual experience and effectively eliminating the perspective inconsistency or freeze caused by the user's rapid movement or turning.

[0107] S5 specifically includes:

[0108] S51: Based on the virtual perspective calculated in S4 and in combination with the user's current motion parameters, the user's motion state in the current time interval is evaluated, and according to the user's speed and acceleration change trend, whether the image rendering accuracy and update frequency need to be adjusted;

[0109] S52: When the user's speed exceeds 10m / s (about 36km / h) or the acceleration exceeds 2m / s 2 When the image is in high speed or high acceleration state, the image rendering accuracy is increased to more than 1.5 times the original accuracy, and the update frequency is increased to 60 frames per second (fps) to ensure smooth transition and detail presentation.

[0110] S53: When the user's speed is lower than 5m / s (i.e. 18km / h) or the acceleration is lower than 0.5m / s 2 When the user is in a low or uniform speed state, the image rendering accuracy is reduced to less than 1 times the original accuracy, and the update frequency is reduced to 30 frames per second (fps) to optimize computing resources and improve system efficiency. The above steps dynamically adjust the rendering accuracy and update frequency in the panoramic VR scene to ensure that no matter what motion state the user is in, the visual effect always remains stable and matches the actual motion state, avoiding visual dislocation, blur or freeze caused by image processing delays or insufficient calculations.

[0111] The step of determining whether it is necessary to adjust the image rendering accuracy and update frequency in S51 specifically includes:

[0112] S511: Calculate the user's current motion state based on the virtual perspective obtained in S4 and the user's current motion parameters. The formula is: Among them, S state is the comprehensive exercise state value, indicating the user's exercise intensity, v current is the user's current speed, a current The current acceleration of the user. The comprehensive motion state value comprehensively reflects the motion state of the user, including the influence of speed and acceleration;

[0113] S512: Analyze the speed and acceleration change trend of the user, if the speed change rate in the current time period exceeds a certain set threshold Δv threshold (such as 1m / s 2 ), or the acceleration change rate exceeds the set threshold Δa threshold (e.g. 0.5m / s 3 ), the user is considered to be experiencing acceleration or deceleration; the specific change trend calculation formula is:

[0114]

[0115] Where Δvcurrent and Δa current are the velocity change rate and acceleration change rate, v previous and a previous are the velocity and acceleration of the previous moment respectively, Δt is the current time interval, if Δv current or Δa current If the set threshold is exceeded, it is considered that the user's motion state has changed significantly;

[0116] S513: Determine whether it is necessary to adjust the image rendering accuracy and update frequency based on the speed and acceleration change trends of the user. If the speed and acceleration change rates of the user exceed the set thresholds within the current time interval, it is necessary to increase the image rendering accuracy and update frequency. The above steps can accurately evaluate the motion state based on the speed and acceleration change trends of the user, and can dynamically adjust the image rendering accuracy and update frequency, thereby ensuring visual stability and smoothness in the panoramic VR scene.

[0117] like Figure 2 As shown, a panoramic VR mobile positioning system based on Beidou satellite is used to implement the above-mentioned panoramic VR mobile positioning method based on Beidou satellite, including the following modules:

[0118] Positioning data acquisition module: used to receive positioning data from Beidou satellites, including the user's longitude, latitude, altitude, speed and direction, as well as acceleration and angular velocity data from the user's inertial measurement unit (IMU);

[0119] Data fusion module: used to fuse Beidou satellite positioning data and inertial measurement unit data, and use weighted average algorithm to optimize each data source to obtain the user's current three-dimensional space coordinates and motion parameters;

[0120] Position prediction and correction module: Based on the user's current three-dimensional spatial coordinates and motion parameters obtained by the data fusion module, the kinematic model is used to predict the user's next position, and the predicted position is corrected through the Kalman filter algorithm to obtain the corrected user's next position coordinates and motion parameters;

[0121] Virtual perspective calculation module: used to calculate the user's virtual perspective based on the corrected user's next position coordinates and motion parameters, and adjust the perspective in the panoramic VR scene through the virtual perspective conversion algorithm to ensure a smooth transition of the perspective and consistency with the user's position;

[0122] Rendering and updating module: It is used to dynamically adjust the image rendering accuracy and update frequency in the panoramic VR scene according to the virtual perspective provided by the virtual perspective calculation module and the user's motion parameters to ensure visual stability under different speeds and acceleration conditions.

[0123] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0124] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A panoramic VR mobile positioning method based on Beidou satellite, characterized in that: The following steps are involved: S1: Obtain the user's real-time Beidou satellite positioning data, including longitude, latitude, altitude, speed and direction, as well as the user's inertial measurement unit data; S2: Fuse the Beidou satellite positioning data and inertial measurement unit data obtained by S1, and use the weighted average algorithm to optimize the data sources to obtain the user's current three-dimensional space coordinates and motion parameters; S3: Based on the user's current three-dimensional space coordinates and motion parameters obtained in S2, the kinematic model is used to predict the user's next position, and the predicted position is corrected by the Kalman filter algorithm to obtain the corrected user's next position coordinates and motion parameters; S4: Calculate the user's virtual perspective according to the next position and motion parameters of the user corrected in S3, and adjust the perspective in the panoramic VR scene through a virtual perspective conversion algorithm to ensure a smooth transition of the perspective and consistency with the user's position; S5: According to the virtual perspective calculated in S4 and combined with the user's motion parameters, the image rendering accuracy and update frequency in the panoramic VR scene are adjusted to ensure visual stability under different speeds and acceleration conditions.

2. According to claim 1, a Beidou satellite-based panoramic VR mobile positioning method is characterized in that: The S1 specifically includes: S11: Receive at least three Beidou satellite signals through a Beidou satellite receiver, and obtain the longitude, latitude and altitude information of the user's current location through differential positioning technology; S12: Calculate the user's movement speed and direction through the Beidou satellite receiver, where the movement speed is calculated by the displacement change of continuously receiving Beidou satellite signals, and the direction is calculated by a multi-base station positioning method combined with a triangulation positioning principle; S13: Acquire acceleration, angular velocity, and tilt angle data of the user through an inertial measurement unit; S15: Time-synchronize the acquired longitude, latitude, altitude, speed, direction, and acceleration data to ensure the temporal consistency of the data.

3. The panoramic VR mobile positioning method based on Beidou satellite according to claim 1 is characterized in that: The S2 specifically includes: S21: According to the measurement accuracy of Beidou satellite positioning data and inertial measurement unit data, weight values ​​are set for these two types of data respectively; calculated by the following formula: Among them, w GPS is the weight of Beidou satellite positioning data, w IMU is the weight of the inertial measurement unit data, σ λ , σ h ,σ v ,σ θ are the standard deviations of the measurement errors of longitude, latitude, altitude, speed and direction in Beidou satellite positioning data; σ a ,σ ω ,σ α are the standard deviations of the measurement errors of acceleration, angular velocity, and tilt angle in the inertial measurement unit data, respectively; S22: According to the weight value calculated in S21, the Beidou satellite positioning data and the inertial measurement unit data are fused using the weighted average method. The user's three-dimensional spatial coordinates (x, y, z) and motion parameters, including speed v and orientation θ, are calculated using the following formulas: Among them, x GPS ,y GPS ,z GPS are the three-dimensional coordinates obtained by Beidou satellite positioning, x IMU ,y IMU ,z IMU are the three-dimensional coordinates calculated by the inertial measurement unit; v GPS and v IMU are speed data; θ GPS and θ IMU They are the direction data respectively.

4. The panoramic VR mobile positioning method based on Beidou satellite according to claim 1 is characterized in that: The S3 specifically includes: S31: Based on the current three-dimensional spatial coordinates and motion parameters of the user obtained in S2, a kinematic model is used to predict the next position of the user, wherein the kinematic model presupposes that the user moves at a constant speed along the current speed direction, and calculates the predicted next position through the current position and the motion speed; S32: The next position predicted by S31 is input into the Kalman filter, and the position is corrected according to the accuracy and error of Beidou satellite positioning and inertial measurement unit data, so as to obtain the corrected user's next position coordinates and motion parameters.

5. The method for panoramic VR mobile positioning based on Beidou satellite according to claim 4 is characterized in that: The S31 specifically includes: S311: Calculate the user's velocity vector according to the user's current three-dimensional space coordinates and motion parameters obtained in step S2. The formula is: Among them, v current is the user's current speed, θ is the user's heading angle, and are unit vectors, representing the components in the east-west and north-south directions respectively; S312: Using the calculated velocity vector V, predict the next position of the user based on the kinematic model. Assuming that the user moves at a constant speed along the current velocity direction within the next time interval Δt, the next position (x next ,y next , z next ) is calculated as: x next =x+v current ·cos(θ)·Δt; and next =y+v current ·sin(θ)·Δt; Among them, x, y, z are the current three-dimensional coordinates of the user, v zcurrent is the user's velocity component in the vertical direction, and Δt is the time interval.

6. The panoramic VR mobile positioning method based on Beidou satellite according to claim 5 is characterized in that: The S32 specifically includes: S321: Using the predicted position (x) obtained in S31 next ,y next , z next ) and motion parameters, construct the state estimation of the Kalman filter, and set the state vector of the Kalman filter to be x pred =[x next ,y next , z next , v next ,θ next ] T , where x next ,y next ,z next is the predicted position coordinate, v next is the predicted speed, θ next is the predicted direction; S322: Construct the observation value of the Kalman filter according to the accuracy and error of Beidou satellite positioning and inertial measurement unit data; let the observation value be z obs =[x obs ,y obs ,z obs ] T , where x obs ,y obs ,z obs It is the positioning data of actual measurement; S323: Calculate the Kalman gain matrix K to correct the predicted position. The calculation formula of the Kalman gain is: K = P pred ·(H T ·(H·P pred ·H T +R) -1 ), where P pred is the prediction error covariance matrix, R is the error matrix of the observations, and H is the observation matrix; S324: The predicted state vector is corrected by using the Kalman gain to obtain the corrected user position coordinates and motion parameters. The corrected state vector is: corr =x pred +K·(z obs -H·x pred ); S325: From the corrected state vector x corr Extract the corrected user position coordinates (x corr ,y corr , z corr ) and motion parameters (v corr ,θ corr ) as the final output of the user's next position coordinates and motion parameters.

7. The panoramic VR mobile positioning method based on Beidou satellite according to claim 1 is characterized in that: The S4 specifically includes: S41: Based on the corrected user next position coordinates (x corr ,y corr ,z corr ) and motion parameters to calculate the user's virtual perspective, which is determined by the user's current position and orientation. The calculation formula is: θ view =θ corr +Δθ, where θ view is the user’s perspective in the virtual environment, θ corr is the corrected orientation, Δθ is the fine-tuning value based on the orientation of the user's head or eyes at the current user position; S42: Use the virtual perspective conversion algorithm to adjust the perspective in the panoramic VR scene to ensure that it matches the user's motion state. Based on the relationship between the virtual perspective and the actual physical position, the user's orientation and motion trajectory are calculated to smoothly transition to the next perspective. Specifically, the perspective conversion formula is: θ scene =θ view +α·(θ target -θ view ), where θ scene is the target viewing angle in the panoramic VR scene, θ target is the viewing angle of the target position, and α is the smoothing factor, which is used to control the speed of the smooth transition.

8. The method for panoramic VR mobile positioning based on Beidou satellite according to claim 7, characterized in that: The S5 specifically includes: S51: Based on the virtual perspective calculated in S4 and in combination with the user's current motion parameters, the user's motion state in the current time interval is evaluated, and according to the user's speed and acceleration change trend, whether the image rendering accuracy and update frequency need to be adjusted; S52: When the user's speed exceeds 10m / s or the acceleration exceeds 2m / s 2 When the image is in high speed or high acceleration state, the image rendering accuracy is increased to more than 1.5 times the original accuracy, and the update frequency is increased to 60 frames per second. S53: When the user's speed is less than 5 m / s or the acceleration is less than 0.5 m / s 2 When the image is rendered at a low or constant speed, the image rendering accuracy is reduced to less than 1 times the original accuracy, and the update frequency is reduced to 30 frames per second.

9. The method for panoramic VR mobile positioning based on Beidou satellite according to claim 8, characterized in that: The S51 specifically includes: S511: Calculate the user's current motion state based on the virtual perspective obtained in S4 and the user's current motion parameters. The formula is: Among them, S state is the comprehensive exercise state value, indicating the user's exercise intensity, v current is the user's current speed, a current is the user's current acceleration; S512: Analyze the speed and acceleration change trend of the user, if the speed change rate in the current time period exceeds a certain set threshold Δv threshold When the acceleration rate exceeds the set threshold Δa threshold , it is considered that the user is experiencing acceleration or deceleration; the specific change trend calculation formula is: Where Δv current and Δa current are the velocity change rate and acceleration change rate, v previous and a previous are the velocity and acceleration of the previous moment respectively, and Δt is the current time interval; S513: Determine whether to adjust the image rendering accuracy and update frequency based on the speed and acceleration change trend of the user. If the speed and acceleration change rate of the user exceeds the set threshold within the current time interval, the image rendering accuracy and update frequency need to be increased.

10. A panoramic VR mobile positioning system based on Beidou satellite, used to implement a panoramic VR mobile positioning method based on Beidou satellite as claimed in any one of claims 1 to 9, characterized in that: Includes the following modules: Positioning data acquisition module: used to receive positioning data from Beidou satellites, including the user's longitude, latitude, altitude, speed and direction, as well as acceleration and angular velocity data from the user's inertial measurement unit; Data fusion module: used to fuse Beidou satellite positioning data and inertial measurement unit data, and use weighted average algorithm to optimize each data source to obtain the user's current three-dimensional space coordinates and motion parameters; Position prediction and correction module: Based on the user's current three-dimensional spatial coordinates and motion parameters obtained by the data fusion module, the kinematic model is used to predict the user's next position, and the predicted position is corrected through the Kalman filter algorithm to obtain the corrected user's next position coordinates and motion parameters; Virtual perspective calculation module: used to calculate the user's virtual perspective based on the corrected user's next position coordinates and motion parameters, and adjust the perspective in the panoramic VR scene through the virtual perspective conversion algorithm to ensure a smooth transition of the perspective and consistency with the user's position; Rendering and updating module: It is used to dynamically adjust the image rendering accuracy and update frequency in the panoramic VR scene according to the virtual perspective provided by the virtual perspective calculation module and the user's motion parameters to ensure visual stability under different speeds and acceleration conditions.

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