WO2021093593A1

WO2021093593A1 - Method and apparatus for calibrating motion data, terminal device and storage medium

Info

Publication number: WO2021093593A1
Application number: PCT/CN2020/124669
Authority: WO
Inventors: 吴迪云; 许秋子
Original assignee: 深圳市瑞立视多媒体科技有限公司
Priority date: 2019-11-12
Filing date: 2020-10-29
Publication date: 2021-05-20
Also published as: CN110956666A; CN110956666B

Abstract

A method for calibrating motion data, which is applicable to the technical field of computers, comprising: acquiring first motion data of a first number of frames of an inertial device and second motion data of a second number of frames of a rigid body (S101), wherein the difference between a first starting frame corresponding to the first motion data and a second starting frame corresponding to the second motion data is a preset number of frames; from the first motion data, sequentially intercepting multiple groups of third motion data with the same number of frames as the second number of frames, and recording the number of offset frames between a third starting frame and the second starting frame of each group of the third motion data (S102); calculating a transformation matrix in which the second motion data is transformed into each group of the third motion data, and calculating an error corresponding to the transformation matrix (S103); and using the transformation matrix that has the smallest error and the number of offset frames corresponding to the transformation matrix as a calibration result (S104). The delay relationship and attitude relationship between calibration rigid body data and inertial device data are achieved, and the problem in which there is currently no way to calibrate rigid body data and inertial device data is solved.

Description

Movement data calibration method, device, terminal equipment and storage medium

Technical field

This application belongs to the field of computer technology, and particularly relates to sports data calibration methods, devices, terminal equipment, and storage media.

Background technique

With the development of virtual reality (Virtual Reality, VR) technology, virtual-real interaction has gradually become a research hotspot and difficulty. Virtual reality interaction is a technology that combines computer-generated virtual scenes with real world scenes. It acquires the movement trajectory of physical objects in the real world, realizes the natural interaction between the real scene and the virtual scene, and presents users with a new kind of The human-computer interaction experience environment.

In the virtual-real interaction of large spaces, rigid bodies and inertial devices are set on VR props to indirectly simulate the position and posture of VR props in space. However, the way that rigid bodies and inertial devices process data is different, resulting in the data collected by rigid bodies and inertia The data collected by the body is not synchronized, and there is currently no way to calibrate the delay relationship between rigid body data and inertial device data.

Summary of the invention

The embodiments of the present application provide a method, device, terminal device, and storage medium for calibrating motion data, which can solve the current problem that there is no way to calibrate the delay relationship between rigid body data and inertial device data.

In the first aspect, an embodiment of the present application provides a method for calibrating motion data, which is applied to a terminal device, the terminal device is provided with a rigid body and an inertial device, and the method includes:

Acquire the first motion data of the first frame number of the inertial device and the second motion data of the second frame number of the rigid body, and the first start frame corresponding to the first motion data corresponds to the second motion data The difference between the second starting frame is a preset frame number, and the difference between the first frame number and the second frame number is the preset frame number;

From the first motion data, sequentially intercept multiple sets of third motion data with the same number of frames as the second frame number, and record the difference between the third start frame and the second start frame of each group of the third motion data The number of offset frames between each of the third start frames, each of the third start frames is between the first start frame and the second start frame, and the difference between each of the third start frames and the previous third start frame The difference is one frame;

Calculating a transformation matrix for transforming the second motion data into each group of the third motion data, and calculating an error corresponding to the transformation matrix;

The transformation matrix with the smallest error and the number of offset frames corresponding to the transformation matrix are used as a calibration result.

In this embodiment of the application, by acquiring the first motion data of the inertial device and the second motion data of the rigid body, from the first motion data, a plurality of sets of third motion data with the same number of frames as the second frame number are sequentially intercepted to obtain Multiple delay frames between the first motion data and the second motion data, and align the first motion data and the second motion data frame by frame; calculate the transformation matrix from the second motion data to the third motion data, and calculate each The error of the transformation matrix is obtained, and the approximate value of the transformation matrix of the first motion data and the second motion data under each delay frame is obtained, and the error of the approximate value of the transformation matrix corresponding to each delay frame is obtained. The transformation matrix with the smallest error and The offset frame number corresponding to the transformation matrix is used as the calibration result, so as to obtain the offset frame number and transformation matrix of the delay frame closest to the first motion data and the second motion data, thereby realizing the extension of the calibrated rigid body data and inertial device data. Time relationship and attitude relationship solve the problem that there is no way to calibrate rigid body data and inertial device data.

In the second aspect, an embodiment of the present application provides an exercise data calibration device, including:

The acquiring module is used to acquire the first motion data of the first frame number of the inertial device and the second motion data of the second frame number of the rigid body, and the first start frame corresponding to the first motion data and the second motion data of the rigid body The difference between the second start frames corresponding to the second motion data is a preset number of frames, and the difference between the first number of frames and the second number of frames is the preset number of frames;

The interception module is used to intercept multiple sets of third motion data with the same number of frames as the second frame from the first motion data, and record the third start frame and the first frame of each third motion data The number of offset frames between the second start frames, each of the third time frames is between the first start frame and the second start frame, and each of the third start frames is different from the previous first frame. The difference between the three starting frames is one frame;

A calculation module, configured to calculate a transformation matrix from the second motion data to the third motion data, and calculate the error corresponding to the transformation matrix;

The calibration module is configured to use the transformation matrix with the smallest error and the offset frame number corresponding to the transformation matrix as the calibration result.

In a third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, The exercise data calibration method described in any one of the above-mentioned first aspects is realized.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium that stores a computer program that, when executed by a processor, implements any one of the above-mentioned aspects of the first aspect Calibration method of sports data.

In the fifth aspect, the embodiments of the present application provide a computer program product, which when the computer program product runs on a terminal device, causes the terminal device to execute the exercise data calibration method described in any one of the above-mentioned first aspects.

It can be understood that, for the beneficial effects of the second aspect to the fifth aspect described above, reference may be made to the related description in the first aspect described above, and details are not repeated here.

Description of the drawings

In order to more clearly describe the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only of the present application. For some embodiments, those of ordinary skill in the art can obtain other drawings based on these drawings without creative labor.

FIG. 1 is a schematic flowchart of a method for calibrating exercise data according to an embodiment of the present application;

2 is a schematic flowchart of a method for calibrating exercise data provided by another embodiment of the present application;

FIG. 3 is a schematic flowchart of a method for calibrating exercise data according to another embodiment of the present application;

4 is a schematic flowchart of a method for calibrating exercise data provided by another embodiment of the present application;

FIG. 5 is a schematic flowchart of a sports data calibration method provided by another embodiment of the present application;

FIG. 6 is a schematic flowchart of an exercise data calibration method provided by another embodiment of the present application;

FIG. 7 is a schematic diagram of exercise data provided by an embodiment of the present application;

FIG. 8 is a schematic structural diagram of an exercise data calibration device provided by an embodiment of the present application;

FIG. 9 is a schematic structural diagram of a terminal device provided by an embodiment of the present application.

Detailed ways

In the following description, for the purpose of illustration rather than limitation, specific details such as a specific system structure and technology are proposed for a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from obstructing the description of this application.

It should be understood that when used in the specification and appended claims of this application, the term "comprising" indicates the existence of the described features, wholes, steps, operations, elements and/or components, but does not exclude one or more other The existence or addition of features, wholes, steps, operations, elements, components, and/or collections thereof.

It should also be understood that the term "and/or" used in the specification and appended claims of this application refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

As used in the description of this application and the appended claims, the term "if" can be construed as "when" or "once" or "in response to determination" or "in response to detecting ". Similarly, the phrase "if determined" or "if detected [described condition or event]" can be interpreted as meaning "once determined" or "in response to determination" or "once detected [described condition or event]" depending on the context ]" or "in response to detection of [condition or event described]".

In addition, in the description of the specification of this application and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

References described in the specification of this application to "one embodiment" or "some embodiments", etc. mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in conjunction with the embodiment. Therefore, the sentences "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. appearing in different places in this specification are not necessarily All refer to the same embodiment, but mean "one or more but not all embodiments" unless it is specifically emphasized otherwise. The terms "including", "including", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

As related to the background technology, in the large-space virtual-real interaction, the rigid body and inertial device are set on the VR props to indirectly simulate the position and posture of the VR props in the space. However, the rigid body and the inertial device collect data in different ways. The data is calculated by the optical camera, and the data of the inertial device is calculated by the integration of the host computer. As a result, the data collected by the rigid body is not synchronized with the data collected by the inertial body, so that the virtual space display picture is made by the VR props There is a delay in the action and the user experience is very poor. However, there is currently no way to calibrate the delay relationship between the rigid body data and the inertial device data, so it is difficult to synchronize the data collected by the rigid body and the inertial device.

Therefore, the embodiment of the present application provides a method for calibrating motion data to obtain the first motion data of the inertial device and the second motion data of the rigid body, offset the frames of the motion data of the two by different frames, and calculate the difference The error of the transformation matrix obtained by the offset of the frame number is the process of taking the transformation matrix with the smallest error and the number of offset frames as the calibration result, so as to realize the delay relationship between the data of the inertial device and the data of the rigid body.

The above-mentioned inertial device may be an inertial measurement unit (IMU), which is a device for measuring the three-axis attitude angle (or angular velocity) and acceleration of an object. Generally, inertial devices include a three-axis accelerometer and a three-axis gyroscope. The accelerometer detects the acceleration signal of an object in the independent three-axis coordinate system of the carrier, and the gyroscope detects the angular velocity signal of the carrier relative to the navigation coordinate system, thereby measuring the object Angular velocity and acceleration in three-dimensional space, and calculate the posture of the object based on this. It is understandable that the inertial device may only include a three-axis gyroscope, such as a gyroscope.

Among them, the above-mentioned rigid body refers to an object whose shape and size remain unchanged during movement and after being subjected to a force, and the relative positions of internal points remain unchanged. The rigid body data can be calculated by the optical camera.

Wherein, the acquired first motion data of the inertial device and the second motion data of the rigid body are both rotation change matrices. Each frame of the first motion data and the second motion data has a temporal sequence, but the time interval between every two frames is not unique.

The motion data calibration method provided by the embodiments of this application can be applied to mobile phones, tablet computers, wearable devices, in-vehicle devices, augmented reality (AR)/virtual reality (VR) equipped with inertial devices and rigid bodies. Devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (personal digital assistants, PDAs) and other mobile terminal devices, the embodiments of this application do not make specific types of terminal devices. Any restrictions.

As an example and not a limitation, when the terminal device is a virtual reality device, the virtual reality device is not only a hardware device, but also implements powerful functions through software support, data interaction, and cloud interaction. Virtual reality devices include full-featured, large-sized, complete or partial functions that can be realized without relying on smartphones, such as VR headsets. Optionally, wearable technology can be applied to intelligently design the above-mentioned virtual reality devices to develop a device with the function of collecting EEG signals, that is, the combination of the virtual reality device and the EEG signal collecting device is designed to be a kind of EEG collecting device. Signals and portable devices with virtual reality capabilities.

It should be noted that the inertial device and the rigid body are fixedly arranged in the aforementioned terminal equipment, and there is a fixed positional relationship between the inertial device and the rigid body. In the process or after the movement of the terminal device, the inertial device can be deformed, and the rigid body will not deform (it should be understood that the rigid body may deform in microseconds, rather than the absolute rigid body is not deformed at all). Both the acquired first motion data of the inertial device and the second motion data of the rigid body are rotation change matrices.

In the embodiment of the present application, the inertial device takes a gyroscope as an example, and the three-axis angular velocities collected by the gyroscope are W _x , W _y , and W _z , and the unit is radians/second. Suppose the rotation change matrix of the k-th frame in the carrier coordinate system in the gyroscope is

That is to say, the rotation change of the gyroscope from the k-1th frame to the kth frame, then:

among them,

For Euler angle rotation matrix calculation, dt(k) is the time difference between the k-th frame data and the k-1th frame data. Generally, a gyroscope will output a timestamp and obtain the time difference between two frames based on the timestamp. If the gyroscope does not output a time stamp, the time difference is calculated by the arrival time of each frame of data.

The attitude of the gyroscope is relative to the attitude change when the host computer obtains the first frame of data:

Obtain the rotation and displacement of the rigid body in the world coordinate system in the software, and set the rotation change matrix of the rigid body in the world coordinate system as

Among them, the subscript of R represents the rotation change matrix from subscript to superscript, the subscript c represents the rigid body, b represents the gyroscope, k represents the k-th frame, 0 represents the initial frame, and w represents the world coordinates.

A known:

Among them, in formula 5

Is the rotation matrix from the rigid body to the gyroscope coordinate system. According to formula 3, formula 4 and formula 5, we can get:

According to Equation 6, we can get:

Since the positional relationship between the rigid body and the gyroscope is fixed, the same rotation relationship exists at any time, namely

Further, according to Equation 7, we can get:

In order to facilitate the calculation, the formula 8 is converted into a quaternion form:

Further, by multiplying left and right quaternions, according to Equation 9, we can get:

among them,

It is the rotation quaternion of the rigid body to the gyroscope, that is, the transformation matrix of the rigid body data in the world coordinate system into the gyroscope data of the carrier coordinate system where the gyroscope is located. According to Equation 10, by collecting more than 10 frames of first motion data and second motion data, a zero-space solution can be obtained, that is, the rotation quaternion of the rigid body data transformed into the carrier coordinate system where the gyroscope is located.

Fig. 1 shows a schematic flowchart of the exercise data calibration method provided by the present application. As an example and not a limitation, the method can be applied to the above-mentioned virtual reality device. As shown in Fig. 1, the method includes steps S101-S104, and each step is specifically explained below.

S101. Acquire first motion data of a first frame number of the inertial device and second motion data of a second frame number of the rigid body, and the first start frame and the second motion data corresponding to the first motion data The difference between the second start frames corresponding to the data is a preset frame number, and the difference between the first frame number and the second frame number is the preset frame number;

In the above S101, the first motion data includes the rotation change matrix of the inertial device in each frame relative to the previous frame

The second motion data includes the rotation change matrix of the rigid body of each frame relative to the previous frame

The preset number of frames is a preset offset frame used for data offset. The above-mentioned acquisition process is a process in which the terminal device performs data processing on the original data collected by the inertial device and the rigid body, and stores the motion data. It is understandable that the terminal device always performs data processing on the original data. Only when the acquisition operation is performed, the motion data processed at the current time is acquired and the motion data is stored, while the motion data processed before the acquisition operation is Do not store.

As shown in Figure 7, the preset number of frames is 50 frames, A is the frame number sequence of the first motion data, B is the frame number sequence of the second motion data, and the second motion data is after the 50 frames of the first motion data are acquired. Just started to get it. The above-mentioned frame number sequence has a time sequence. Specifically, for the motion data acquired first, the time corresponding to the frame number is first, and the motion data acquired later has the time corresponding to the frame number last.

S102. From the first motion data, sequentially intercept multiple sets of third motion data with the same number of frames as the second frame, and record the third start frame and the second start of each group of the third motion data. The number of offset frames between frames, each of the third starting frames is between the first starting frame and the second starting frame, and each of the third starting frames is different from the previous third starting frame. The frame difference is one frame;

In the above S102, as shown in FIG. 7, for example, if the third motion data of 50 frames is acquired, the data from the 0th frame to the 50th frame in the first motion data can be intercepted for the first time, and the first motion data can be intercepted for the second time. From the first frame of data to the 51st frame of data in the data, until the 49th to the 99th frame of the first motion data is intercepted, 50 sets of third motion data are finally obtained, and each group of third motion data has 50 Frame data. At the same time, the number of offset frames for each intercepted data is recorded. The number of offset frames for the first interception of data is 50 frames, the number of offset frames for the second interception of data is 49 frames, and the number of offset frames for the last time is 1 frame. .

It should be understood that the foregoing data interception method is only used for illustration, and is not used as a specific implementation means for limiting the embodiments of the present application.

S103: Calculate a transformation matrix for transforming the second motion data into each group of the third motion data, and calculate an error corresponding to the transformation matrix;

In the above S103, the above transformation matrix is the rotation quaternion in the process of transforming the second motion data into the third motion data, that is, the aforementioned zero-space solution

Optionally, the transformation matrix is calculated by the aforementioned formula 10. Since the first motion data and second motion data of multiple frames are calculated by formula 10, the zero-space solution is calculated. Therefore, there is an error between the first motion data and second motion data of each frame and the final calculated zero-space solution. The null-space solution and the multi-frame first motion data and second motion data for calculating the null-space solution are substituted into Equation 10 again to calculate the average error corresponding to the null-space solution.

S104. Use the transformation matrix with the smallest error and the number of offset frames corresponding to the transformation matrix as a calibration result.

In the above S104, when the error is smaller, it means that the transformation matrix calculated based on the first motion data and the second motion data of the corresponding delayed frame (offset frame number) is more accurate, that is, under the corresponding delayed frame, the rigid body data and the inertia If the device data is closest to synchronization, the corresponding calibration result is more accurate.

On the basis of the embodiment shown in FIG. 1, FIG. 2 shows a schematic flowchart of another exercise data calibration method provided by an embodiment of the present application. The first motion data includes fourth motion data and fifth motion data. As shown in FIG. 2, the above step S101 specifically includes S201 and S202. It should be noted that the steps are the same as those in the embodiment shown in FIG. 1, and will not be repeated here, please refer to the foregoing.

S201: Acquire fourth motion data of a preset number of frames of the inertial device;

In the above S201, the fourth motion data of the preset frame number is used as the data calculated after the frame number is offset, and the specific value can be set according to the frequency of the inertial device and the rigid body collecting data. For example, if the gyroscope data update frequency is 100Hz, the rigid body data refresh rate is also set to 100Hz, that is, the interval between two frames of data is 0.01 second, then 50 frames of data is 0.5 seconds, and the delay between the rigid body and the gyroscope is estimated to be within 0.5 seconds , So you can set the preset number of frames to 50 frames.

S202. After the end frame corresponding to the fourth motion data, acquire the fifth motion data of the inertial device and the second motion data of the rigid body, and the number of frames of the fifth motion data is the same as that of the first motion data. The number of frames of the second motion data is the same.

In the above S202, the time when the fifth motion data and the second motion data are acquired are the same, and the number of acquired data frames is also the same, but the speed of the optical camera calculation and the integration calculation of the host computer is different, so although The time when the fifth movement data and the second movement data are acquired are the same, but the collection time of the original data corresponding to the two is different.

Based on the embodiment shown in FIG. 2, FIG. 3 shows a schematic flowchart of another exercise data calibration method provided by an embodiment of the present application. As shown in FIG. 3, the above step S201 specifically includes S301 and S302. It should be noted that the steps are the same as those in the embodiment shown in FIG. 1 and will not be repeated here, please refer to the foregoing.

S301: Collect first attitude angle change data of the inertial device or the rigid body;

In the above S301, the first attitude angle change data is the change angle of a certain moment relative to the initial moment, which can be calculated according to the rotation change matrix of a certain frame relative to the initial frame.

S302: When the posture angle change data is greater than a first preset angle, obtain fourth motion data of the inertial device with a preset number of frames.

In the above S302, the first preset angle may be 20°, that is, when the change angle of the gyroscope or rigid body is greater than 20°, the fourth motion data will be acquired to avoid other reasons causing the gyroscope or rigid body to appear tiny Unnecessary data is acquired when the angle is changed. For example, when the attitude angle of a swing terminal device, a gyroscope or a rigid body is greater than 20° from the initial moment, the fourth movement data is acquired.

On the basis of the embodiment shown in FIG. 2, FIG. 4 shows a schematic flowchart of another exercise data calibration method provided by an embodiment of the present application. As shown in Fig. 4, the above step S202 specifically includes S401-S403. It should be noted that the steps are the same as those in the embodiment shown in FIG. 2 and will not be repeated here, please refer to the foregoing.

S401: Collect a preset number of groups of the second attitude angle change data of the inertial device or the rigid body;

In the foregoing S401, the second attitude angle change data is a change angle at a certain time relative to the initial time, and the preset number of groups may be 50 groups.

S402. Acquire the sixth movement data of the inertial device and the seventh movement data of the rigid body when each set of the second attitude angle change data is greater than a second preset angle, wherein the sixth movement acquired each time The number of frames of data is the same as the number of frames of the seventh motion data;

In the above S402, when the second preset angle is 5-10°, there is a better calibration effect, for example, 7°. It should be understood that the second preset angle is not limited to 5-10°. Since the terminal device is moving, it may be a small movement, and the difference between such data in the real space and the virtual space is very small, so you can choose not to use the data during the small movement, only when the second attitude angle change data is greater than 7° , The corresponding motion data is acquired, and when the second attitude angle change data is less than 7°, the corresponding motion data is not acquired. In order to ensure that the collected motion data is sufficient for calibration, it is divided into multiple groups for acquisition.

S403, composing all the sixth movement data into the fifth movement data, and composing all the seventh movement data into the second movement data.

In the above 403, according to the chronological order when the sixth motion data and the fifth motion data are acquired, all the sixth motion data are formed into the fifth motion data, and all the seventh motion data are formed into all the The second movement data.

On the basis of the embodiment shown in FIG. 2, FIG. 5 shows a schematic flowchart of another exercise data calibration method provided by an embodiment of the present application. As shown in FIG. 5, the above step S102 specifically includes S501 and S502. It should be noted that the steps are the same as those in the embodiment shown in FIG. 2 and will not be repeated here, please refer to the foregoing.

S501. Taking the fourth starting frame of the fourth motion data as a starting point, each frame of the fourth motion data is shifted by one frame in a direction close to the second starting frame, until the offset The total number of frames reaches the preset number of frames;

S502, taking the fourth starting frame after each offset as a starting point, and using the first motion data of the same number of frames as the second number of frames as the third motion data.

In the above S501 and S502, as shown in FIG. 7, the fourth starting frame is the 0th frame, and each frame of the fourth motion data is moved to the right by one frame as a whole, then in time, each frame is moved to the right One frame, but the first motion data remains unchanged. For example, the first motion data corresponding to the 0th frame is x, and the first motion data corresponding to the 1st frame is y. After each frame is shifted to the right, the 0th frame replaces the original 1st frame, and the 0th frame corresponds to The first movement data of becomes y, and so on.

Based on the embodiment shown in FIG. 2, FIG. 6 shows a schematic flowchart of another exercise data calibration method provided by an embodiment of the present application. As shown in FIG. 6, the above step S102 specifically includes S601 and S602. It should be noted that the steps are the same as those in the embodiment shown in FIG. 2 and will not be repeated here, please refer to the foregoing.

S601. Taking the fifth starting frame of the fifth motion data as a starting point, each frame of the fifth motion data is shifted by one frame in a direction away from the second starting frame, until the offset The total number of frames reaches the preset number of frames;

S602, taking the fifth starting frame after each offset as a starting point, and using the first motion data of the same number of frames as the second number of frames as the third motion data.

In the above S601 and S602, as shown in Figure 7, the fifth starting frame is the 500th frame, and each frame of the fifth motion data is moved one frame to the left as a whole, then in time, each frame is moved to the left One frame, but the first motion data remains unchanged.

On the basis of the embodiment shown in FIG. 1, an embodiment of another exercise data calibration method provided in the embodiment of the present application. The calculation of the transformation matrix for transforming the second motion data into each group of the third motion data in the above step S103 specifically includes S1031. It should be noted that the steps are the same as those in the embodiment shown in FIG. 1 and will not be repeated here, please refer to the foregoing.

S1031: Calculate one transformation matrix according to each frame of the third motion data in a set of the third motion data and the second motion data of the corresponding frame, and there are multiple sets of the third motion data, Then, multiple transformation matrices are obtained.

In the above S1031, the third motion data and the second motion data of the corresponding frame can be substituted into the aforementioned formula 10. Then the third motion data and the second motion data of more than 10 frames can be calculated to obtain the zero-space solution, that is, the corresponding Transformation matrix.

On the basis of the embodiment shown in FIG. 1, an embodiment of another exercise data calibration method provided in the embodiment of the present application. The calculation of the error corresponding to the transformation matrix in the foregoing step S103 specifically includes S1032. It should be noted that the steps are the same as those in the embodiment shown in FIG. 1 and will not be repeated here, please refer to the foregoing.

S1032: Calculate the error of each transformation matrix according to each transformation matrix, the corresponding first motion data and the second motion data.

In the above S1032, each of the transformation matrix, the corresponding first motion data and the second motion data can be substituted into the foregoing formula 10, because each group of the first motion data and the second motion data has multiple frames Data, therefore, according to Equation 10, we can get:

Among them, n is the number of offset frames, i is set according to the number of offset frames, and j is set according to the rotation interval after the i frame.

It should be understood that the size of the sequence number of each step in the foregoing embodiment does not mean the order of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

Corresponding to the exercise data calibration method described in the above embodiment, FIG. 6 shows a structural block diagram of the exercise data calibration device 800 provided in an embodiment of the present application. For ease of description, only the parts related to the embodiment of the present application are shown. .

Referring to Figure 8, the device includes:

The acquiring module 801 is configured to acquire the first motion data of the first frame number of the inertial device and the second motion data of the second frame number of the rigid body, and the first start frame corresponding to the first motion data and the second motion data of the rigid body The difference between the second starting frames corresponding to the second motion data is a preset number of frames, and the difference between the first number of frames and the second number of frames is the preset number of frames;

The interception module 802 is configured to intercept multiple sets of third motion data with the same number of frames as the second frame from the first motion data, and record the third start frame of each third motion data and the The number of offset frames between the second start frames, each of the third time frames is between the first start frame and the second start frame, and each of the third start frames is different from the previous one. The difference of the third starting frame is one frame;

A calculation module 803, configured to calculate a transformation matrix from the second motion data to the third motion data, and calculate an error corresponding to the transformation matrix;

The calibration module 804 is configured to use the transformation matrix with the smallest error and the offset frame number corresponding to the transformation matrix as the calibration result.

It should be noted that the information interaction and execution process between the above-mentioned devices/units are based on the same concept as the method embodiment of this application, and its specific functions and technical effects can be found in the method embodiment section. I won't repeat it here.

Those skilled in the art can clearly understand that for the convenience and conciseness of description, only the division of the above functional units and modules is used as an example. In practical applications, the above functions can be allocated to different functional units and modules as required. Module completion, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist alone physically, or two or more units can be integrated into one unit. The above-mentioned integrated units can be hardware-based Formal realization can also be realized in the form of software functional units. In addition, the specific names of the functional units and modules are only used to facilitate distinguishing from each other, and are not used to limit the protection scope of the present application. For the specific working process of the units and modules in the foregoing system, reference may be made to the corresponding process in the foregoing method embodiment, which will not be repeated here.

FIG. 9 is a schematic structural diagram of a terminal device provided by an embodiment of the application. As shown in FIG. 9, the terminal device 9 of this embodiment includes: at least one processor 90 (only one is shown in FIG. 9), a processor, a memory 91, and a processor stored in the memory 91 and capable of being processed in the at least one processor. A computer program 92 running on the processor 90, when the processor 90 executes the computer program 92, the steps in any of the foregoing motion data calibration method embodiments are implemented.

The terminal device 9 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server, specifically, such as the aforementioned virtual reality device. The terminal device may include, but is not limited to, a processor 90 and a memory 91. Those skilled in the art can understand that FIG. 9 is only an example of the terminal device 9 and does not constitute a limitation on the terminal device 9. It may include more or less components than shown in the figure, or a combination of certain components, or different components. , For example, can also include input and output devices, network access devices, and so on.

The so-called processor 90 may be a central processing unit (Central Processing Unit, CPU), and the processor 90 may also be other general-purpose processors, digital signal processors (Digital Signal Processors, DSPs), and application specific integrated circuits (Application Specific Integrated Circuits). , ASIC), ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.

The memory 91 may be an internal storage unit of the terminal device 9 in some embodiments, such as a hard disk or memory of the terminal device 9. In other embodiments, the memory 91 may also be an external storage device of the terminal device 9, such as a plug-in hard disk equipped on the terminal device 9, a smart media card (SMC), and a secure digital (Secure Digital, SD) card, flash card (Flash Card), etc. Further, the memory 91 may also include both an internal storage unit of the terminal device 9 and an external storage device. The memory 91 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 91 can also be used to temporarily store data that has been output or will be output.

The embodiments of the present application also provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in each of the foregoing method embodiments can be realized.

The embodiments of the present application provide a computer program product. When the computer program product runs on a mobile terminal, the steps in the foregoing method embodiments can be realized when the mobile terminal is executed.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the implementation of all or part of the processes in the above-mentioned embodiment methods in this application can be accomplished by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. The computer program can be stored in a computer-readable storage medium. When executed by the processor, the steps of the foregoing method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate forms. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to the photographing device/terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), and random access memory (RAM, Random Access Memory), electric carrier signal, telecommunications signal and software distribution medium. For example, U disk, mobile hard disk, floppy disk or CD-ROM, etc. In some jurisdictions, according to legislation and patent practices, computer-readable media cannot be electrical carrier signals and telecommunication signals.

In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail or recorded in an embodiment, reference may be made to related descriptions of other embodiments.

A person of ordinary skill in the art may realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of this application.

In the embodiments provided in this application, it should be understood that the disclosed apparatus/network equipment and method may be implemented in other ways. For example, the device/network device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units. Or components can be combined or integrated into another system, or some features can be omitted or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, a person of ordinary skill in the art should understand that it can still implement the foregoing The technical solutions recorded in the examples are modified, or some of the technical features are equivalently replaced; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the application, and should be included in Within the scope of protection of this application.

Claims

A method for calibrating motion data, characterized in that it is applied to a terminal device, the terminal device is provided with a rigid body and an inertial device, and the method includes:

Acquire the first motion data of the first frame number of the inertial device and the second motion data of the second frame number of the rigid body, and the first start frame corresponding to the first motion data corresponds to the second motion data The difference between the second starting frame is a preset frame number, and the difference between the first frame number and the second frame number is the preset frame number;

From the first motion data, sequentially intercept multiple sets of third motion data with the same number of frames as the second frame number, and record the difference between the third start frame and the second start frame of each group of the third motion data The number of offset frames between each of the third start frames, each of the third start frames is between the first start frame and the second start frame, and the difference between each of the third start frames and the previous third start frame The difference is one frame;

Calculating a transformation matrix for transforming the second motion data into each group of the third motion data, and calculating an error corresponding to the transformation matrix;

The transformation matrix with the smallest error and the number of offset frames corresponding to the transformation matrix are used as a calibration result.
The motion data calibration method of claim 1, wherein the first motion data includes fourth motion data and fifth motion data, and the first motion data of the first frame number of the inertial device is acquired And the second motion data of the second frame number of the rigid body, including:

Acquiring the fourth motion data of the preset frame number of the inertial device;

After the end frame corresponding to the fourth motion data, the fifth motion data of the inertial device and the second motion data of the rigid body are acquired, and the number of frames of the fifth motion data is different from the second motion data. The number of frames of data is the same.
The method for calibrating motion data according to claim 2, wherein said obtaining the fourth motion data of the preset frame number of the inertial device comprises:

Collecting the first attitude angle change data of the inertial device or the rigid body;

When the posture angle change data is greater than the first preset angle, the fourth motion data of the preset frame number of the inertial device is acquired.
3. The motion data calibration method according to claim 2, wherein said acquiring said fifth motion data of said inertial device and said second motion data of said rigid body comprises:

Collecting a preset number of groups of the second attitude angle change data of the inertial device or the rigid body;

Acquire the sixth motion data of the inertial device and the seventh motion data of the rigid body when each set of the second attitude angle change data is greater than the second preset angle, where the sixth motion data acquired each time is The number of frames is the same as the number of frames of the seventh motion data;

All the sixth motion data are composed of the fifth motion data, and all the seventh motion data are composed of the second motion data.
The method for calibrating motion data according to claim 2, wherein the intercepting multiple sets of third motion data with the same number of frames as the second frame number from the first motion data comprises:

Taking the fourth starting frame of the fourth motion data as a starting point, each frame of the fourth motion data is shifted by one frame toward the direction of the second starting frame each time, until the shifted total frame The number reaches the preset number of frames;

Taking the fourth starting frame after each offset as a starting point, the first motion data of the same number of frames as the second number of frames is used as the third motion data.
The method for calibrating motion data according to claim 2, wherein the intercepting multiple sets of third motion data with the same number of frames as the second frame number from the first motion data comprises:

Taking the fifth starting frame of the fifth motion data as a starting point, each frame of the fifth motion data is shifted by one frame in a direction away from the second starting frame each time, until the shifted total frame The number reaches the preset number of frames;

Taking the fifth starting frame after each offset as a starting point, the first motion data of the same number of frames as the second number of frames is used as the third motion data.
The method for calibrating motion data according to claim 1, wherein said calculating a transformation matrix for transforming said second motion data into each group of said third motion data comprises:

According to each frame of the third motion data in a set of the third motion data and the second motion data of the corresponding frame, a transformation matrix is obtained by calculation, and there are multiple sets of the third motion data, then A plurality of said transformation matrices.
The method for calibrating sports data according to claim 1, wherein said calculating the error corresponding to said transformation matrix comprises:

According to each transformation matrix, the corresponding first motion data and the second motion data, the error of each transformation matrix is calculated.
A motion data calibration device, characterized in that the device includes a rigid body and an inertial device, and the device further includes:

The acquiring module is used to acquire the first motion data of the first frame number of the inertial device and the second motion data of the second frame number of the rigid body, and the first start frame corresponding to the first motion data and the second motion data of the rigid body The difference between the second start frames corresponding to the second motion data is a preset number of frames, and the difference between the first number of frames and the second number of frames is the preset number of frames;

The interception module is used to intercept multiple sets of third motion data with the same number of frames as the second frame from the first motion data, and record the third start frame and the first frame of each third motion data The number of offset frames between the second start frame, each of the third start frames is between the first start frame and the second start frame, and each of the third start frames is different from the previous first The difference between the three starting frames is one frame;

A calculation module, configured to calculate a transformation matrix from the second motion data to the third motion data, and calculate the error corresponding to the transformation matrix;

The calibration module is configured to use the transformation matrix with the smallest error and the offset frame number corresponding to the transformation matrix as the calibration result.
A terminal device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program as claimed in claims 1 to 8. The method of any one.
A computer-readable storage medium storing a computer program, wherein the computer program implements the method according to any one of claims 1 to 8 when the computer program is executed by a processor.