CN112114660A - Method for realizing large-scale movement of virtual world character by utilizing motion of human foot in small space range - Google Patents
Method for realizing large-scale movement of virtual world character by utilizing motion of human foot in small space range Download PDFInfo
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Abstract
The invention discloses a method for realizing large-scale movement of a virtual world figure by utilizing the motion of human feet in a small space range, wherein in the running process of a system, a foot displacement sensing module utilizes an inertial navigation technology to carry out real-time calculation on pedestrian displacement in a built-in MCU (microprogrammed control unit); then judging the static moment of the trip person in the motion process by a static detection technology; carrying out zero-speed correction by utilizing a Kalman filtering technology at the detected static moment to obtain an accurate pedestrian displacement result, and simultaneously obtaining gait information of a single foot by utilizing the obtained accurate pedestrian displacement result through a gait recognition algorithm; the gait information of the single foot is sent to the central processing module through Bluetooth, the central processing module obtains the gait information of the pedestrian through a biped gait fusion recognition algorithm, and finally the gait information is reported to the virtual displacement display software for real-time display.
Description
Technical Field
The invention relates to the technical field of virtual world character control, in particular to a method for realizing large-scale movement of a virtual world character by utilizing the motion of human feet in a small space range.
Background
With the technological progress in recent years, Virtual Reality (VR) technology has gradually become a research hotspot, which utilizes a computer to simulate a Virtual environment with which a user can interact through a perception organ, so that the user can generate a "real" experience in the VR. With the continuous development of VR technology, VR technology has been widely applied to the fields of military, production, medical treatment, education, entertainment, etc., such as virtual battlefield simulation training, virtual competitive games, etc. And the applications need to realize the virtualization of the motion trail of the human body. The virtual nature of the human motion trajectory is to acquire and process the motion data of the human body through various devices to realize the motion trajectory simulation.
Currently, the most widely used in commerce is the optical motion capture system. The optical motion capture system needs to wear a mark point on a tracked object and then track the mark point to realize motion capture of a human body. The optical motion capture system is based on the computer vision principle, and the task of motion capture is completed by monitoring and tracking target feature points from different angles by a plurality of high-speed cameras. When the camera is continuously taking pictures at a sufficiently high rate, the motion trajectory of the point can be derived from the sequence of images. Usually 6 to 8 cameras are used distributed around the scene, and in order to obtain more accurate motion data, the shooting rate of each camera should not be lower than 60 frames/second. The current common technical means are divided into an active form and a passive form, the main difference is the light emitting form of the mark points, the passive mark points reflect external light sources, and the active mark points are emitted by light emitting diodes.
Although the optical motion capture system is currently most widely used commercially, it has many disadvantages due to the characteristics of the technology itself, mainly including the following four aspects:
1. because the optical signal is easily interfered by illumination conditions, visual angles and shadows and is easily blocked by middle obstacles, the optical motion capture system is sensitive to light rays in a capture visual field, has high requirements on lamplight and reflection conditions of the field, and is frequently confused and shielded by mark points in the using process to cause loss of motion information or wrong calculation, so that more manual intervention in the later period is needed;
2. the marking points need to be identified, tracked and the spatial coordinates need to be calculated, so that the workload of algorithm processing is large and the real-time performance is poor;
3. the equipment is expensive, the measuring distance is limited, the installation and the positioning are more complicated, and the carrying is inconvenient;
4. in the case of a large application scene, the range of motion of people is enlarged, so that the people cannot use the device or the required installation equipment is increased, so that the cost is high and the application is limited.
Disclosure of Invention
The present invention provides a method for realizing a large-scale movement of a virtual world character by using the motion of human feet in a small space range, aiming at the above technical problems of the existing optical motion capture system.
The technical problem to be solved by the invention can be realized by the following technical scheme:
a method for realizing the large-scale movement of a virtual world character by utilizing the motion of human feet in a small space range comprises the following steps:
step 1: sensing the displacement of the human foot in a small space range and resolving the sensed displacement of the human foot in the small space range in real time to obtain IMU (inertial measurement unit) original data of the human foot in the small space range motion process;
step 2: detecting static moment data in IMU original data by a static detection method;
and step 3: performing zero-speed correction on the static moment data detected in the step 2 by using a Kalman filtering algorithm to obtain an accurate human foot displacement result;
and 4, step 4: acquiring gait information of a single foot of the human body by using the accurate human body foot displacement result obtained in the step 3 through a gait recognition algorithm;
and 5: acquiring the gait information of the human body by the gait fusion recognition algorithm of the biped of the human body obtained in the step 4;
step 6: and (5) reporting the gait information of the human body obtained in the step (5) to virtual displacement display software for real-time display.
In a preferred embodiment of the present invention, in step 1, the displacement of the human foot in the small space range includes one or a combination of any two or more of forward movement, backward movement, left side movement, right side movement, stepping in place, jumping in place, kicking, and acceleration of motion of the human leg. Preferably stepping in place.
In a preferred embodiment of the present invention, in step 1, the device for sensing the displacement of the human foot in the small space range is a foot displacement sensing module bound on the human foot.
In a preferred embodiment of the present invention, the foot displacement sensing module is a MEMS inertial device.
In a preferred embodiment of the present invention, in step 1, the steps of sensing the displacement of the human foot in the small space range and calculating the sensed displacement of the human foot in the small space range in real time to obtain the IMU raw data of the human foot in the small space range include:
step 1.1: the velocity, position and attitude of the movement of the human foot is sensed by the gyroscope and accelerometer in the MEMS inertial device, and the IMU raw data is shown in fig. 4a and 4 b.
In a preferred embodiment of the present invention, in step 1, the device for sensing the displacement of the human foot in the small space range is a foot pad provided with five sensing areas.
In a preferred embodiment of the present invention, in step 1, the device for sensing the displacement of the human foot in the small space range is an infrared sensing device or a laser sensing device.
The gait recognition is based on the premise that the complete gait can be detected, and analysis of IMU original data shows that a small section of static interval exists after each gait is finished, and the static interval is required on the premise of zero-speed correction. Therefore, the stillness detection technology is very important, and in a preferred embodiment of the present invention, the specific steps of detecting the still time data in the IMU raw data by the stillness detection method are as follows:
and calculating the detection value at each moment by a static interval detection method, and determining that the detection value is static when the detection value is smaller than a set threshold value.
The stationary interval detection method adopts an Angular velocity Energy discrimination method (Angular Rate Energy), and records that the Angular velocity at the time k is omegakAnd if the detection window is N, the sum of angular velocity energy in the window is:
if the angular velocity is almost zero in the stationary region, the angular velocity threshold must be smaller than a threshold, so the criterion of the angular velocity energy discrimination (ARE) is:
in a preferred embodiment of the present invention, in step 3, the specific steps of performing zero-speed correction on the static moment data detected in step 2 by using a kalman filter algorithm to obtain an accurate human foot displacement result are as follows:
the zero-speed correction algorithm estimates errors of speed, position and attitude through a Kalman filter by using observed quantity (namely, the speed is zero) obtained when the pedestrian is static, and feeds the estimated error parameters back to an inertial navigation system, so that the pedestrian displacement calculation precision is improved.
Kalman filtering is a recursive linear minimum variance estimation, and has the advantages that: in the time domain, a filter is designed by adopting a state space method, and the signal characteristics are described by adopting a state transfer equation, so that the decomposition of a signal power spectrum is avoided. Given a stochastic system state space model:
in the formula, XkIs a state vector, ZkIs a measurement vector, phik/k-1Is a one-step transition matrix of states,k/k-1is the system noise distribution matrix, HkIs a measurement matrix, Wk-1Is the system noise vector, VkIs a measurement noise vector, both are gaussian white noise vector sequences with zero mean (obeying normal distribution), and they are not correlated with each other, namely:
the kalman filtering process can be divided into five basic formulas, as follows:
state one-step prediction:
state one-step prediction mean square error:
filter gain:
state estimation:
state estimation mean square error: pk＝(I-KkHk)Pk/k-1
The zero-speed correction algorithm utilizes the observed quantity (namely, the speed is zero) obtained in the stationary time, estimates other error parameters and feeds the estimated error parameters back to the inertial navigation system, so that the pedestrian displacement calculation precision is improved.
In a preferred embodiment of the present invention, in step 4, the specific steps of obtaining the gait information of the human single foot by using the accurate human foot displacement result obtained in step 3 through a gait recognition algorithm are as follows:
when the single foot is still, the movement of the single foot is finished or not started, and when the single foot is detected to be still, the gait information of the single foot can be judged by comparing the displacement result at the moment with the displacement result at the last time of still.
In a preferred embodiment of the present invention, in step 5, the specific step of obtaining the gait information of the human body from the gait information of the single foot of the human body obtained in step 4 by the bipedal gait fusion recognition algorithm is:
the same principle as the judgment principle of the single-foot gait information is adopted, and the obtained two pieces of single-foot gait information are compared again to finally obtain the human body gait information.
In a preferred embodiment of the present invention, in step 6, the step of reporting the gait information of the human body obtained in step 5 to the virtual displacement display software for real-time display specifically comprises:
the gait information of the human body is sent to the software through Bluetooth according to a specified protocol, after the software receives the data packet, the software firstly analyzes the data packet according to the protocol to obtain the gait information of the human body, and then displays the gait information of the human body.
Due to the adoption of the technical scheme, the invention aims to realize the requirement that a real world figure controls the virtual scene figure to move in a large range by using the technology of human motion gait, direction identification and the like based on the MEMS inertial device, reduce the space requirement, and simultaneously can obtain the actions of human jumping, kicking legs and the like and the gaits of left movement, right movement, forward movement, backward movement, stepping and the like, and technically overcome the defects caused by the optical technology.
The invention is different from the current mainstream optical motion capture system, and has the following main advantages:
(1) the device is not influenced by illumination, shading and other environmental factors;
(2) the device can be used indoors and outdoors and can work all the day;
(3) the appearance is small, the carrying is easy, the use and the operation are simple, and the price is low;
(4) the required space is small, and the application scene is not restricted.
Drawings
FIG. 1 is a schematic view of a foot displacement sensor module of the present invention being worn on a human foot.
FIG. 2 is a flow chart of the method for realizing the large-scale movement of the virtual world character by utilizing the motion of the human foot in a small space range.
Fig. 3 is a schematic diagram of a MEMS inertial device of the present invention.
FIGS. 4a and 4b are schematic views of IMU raw data during motion of a human foot according to the present invention.
FIG. 5 is a diagram illustrating the detection result of the static region of the human foot according to the present invention.
FIG. 6 is a schematic view of virtual displacement of the human foot in the forward mode according to the present invention.
Fig. 7 is a schematic view of the virtual displacement of the human foot in the backward mode according to the present invention.
FIG. 8 is a schematic view of the virtual displacement of the human foot in the left displacement mode according to the present invention.
FIG. 9 is a schematic view of the virtual displacement of the human foot in the right displacement mode according to the present invention.
FIG. 10 is a schematic view of virtual displacement of the foot of the human body in the stepping mode in situ according to the present invention.
Detailed Description
Aiming at the defects of the existing optical motion capture system, the invention adopts MEMS inertial devices and the technologies of inertial navigation, human motion gait and direction recognition and the like, and technically overcomes the defects caused by the optical technology.
The specific implementation mode of the invention realizes that the real person controls the large-scale displacement of the virtual character under the condition of small space by utilizing the MEMS inertial device, so that the application scene is not limited by the use space any more, and the virtualization of the human motion track can be realized in the small-scale space even if the application scene is large-scale. However, it is not excluded to use other devices to realize the real person to control the virtual character to move in a large range under the condition of a small space, for example, a foot pad provided with five sensing areas, an infrared sensing device or a laser sensing device for sensing the displacement of the human foot in the small space range, and the like are adopted.
The invention discloses a system for realizing the control of a virtual character to move in a large range by a real person under a small space condition by utilizing an MEMS inertial device.
Referring to fig. 1, before use, the foot displacement sensing module 10 is bound to the instep of the human foot 20, one for each of the left and right feet; connecting the central processing module with a computer through a USB interface; and installing virtual displacement display software on the computer. When the foot-moving type foot-moving sensing device is used, the feet keep still, and the two foot-moving sensing modules 10 are opened for use. In the moving process of the pedestrian, the foot displacement sensing module 10 can calculate the motion information of the single foot in real time through the built-in MCU, and sends the motion information of the single foot to the central processing module through the Bluetooth, and the central processing module performs fusion recognition after obtaining the motion information of the two feet to finally obtain the displacement information of the pedestrian and reports the information to the virtual displacement display software for real-time display.
The invention relates to a method for realizing large-scale movement of a virtual world character by utilizing the motion of human feet in a small space range, which is shown in figure 2 and adopts a flow schematic diagram of a method for realizing the large-scale movement of the virtual world character by utilizing the motion of the human feet in the small space range, and the method for realizing the large-scale movement of the virtual world character by utilizing the motion of the human feet in the small space range mainly comprises four parts: firstly, inertial navigation technology; secondly, static detection technology; thirdly, zero-speed correction based on Kalman filtering; and fourthly, a moving gait and direction recognition algorithm.
The method for realizing the large-scale movement of the virtual world character by utilizing the motion of the human foot in the small space range is described in detail by the following specific steps:
step 1: sensing the displacement of the human foot 20 in a small space range by adopting the foot displacement sensing module 10, and calculating the sensed displacement of the human foot 20 in the small space range in real time to obtain IMU (inertial measurement unit) original data of the human foot 20 in the small space range motion process (see fig. 4a and 4 b);
the method comprises the following steps: in the operation process of the system, the foot displacement sensing module 10 utilizes an inertial navigation method to perform real-time calculation of the displacement of the human foot 20 in a built-in MCU. Referring to fig. 3, the inertial navigation system of the present invention adopts the existing strapdown inertial navigation system, which is an autonomous navigation method for measuring speed, positioning and attitude of a carrier by using the measurements of a gyroscope 30 and an accelerometer 40.
When the feet of the human body step in situ, the acceleration and angular velocity information of the human body measured by the gyroscope 30 and the accelerometer 40 are error-compensated by the compensation coefficients built in the error compensation module 50, then input to the direction cosine matrix module 60 and the acceleration coordinate conversion module 70 from the carrier coordinate system to the navigation coordinate system to perform direction cosine matrix transformation and acceleration coordinate conversion from the carrier coordinate system to the navigation coordinate system, and then input to the attitude calculation module 80 and the navigation calculation module 90 to perform attitude calculation and navigation calculation, and then output attitude data, velocity data and position data, and output IMU raw data consisting of the attitude data, the velocity data and the position data through the navigation output module 100 (see fig. 4a and 4 b).
Step 2: detecting static moment data in IMU original data by a static detection method; then judging the static moment of the trip person in the motion process by a static detection technology;
the gait recognition is based on the premise that the complete gait can be detected, and analysis of IMU original data shows that a small section of static interval exists after each gait is finished, and the static interval is required on the premise of zero-speed correction. The stationary detection technique is therefore very important.
The specific steps of detecting the static moment data in the IMU original data by the static detection method are as follows:
and calculating the detection value at each moment by a static interval detection method, and determining that the detection value is static when the detection value is smaller than a set threshold value.
The stationary interval detection method adopts an Angular velocity Energy discrimination method (Angular Rate Energy), and records that the Angular velocity at the time k is omegakAnd if the detection window is N, the sum of angular velocity energy in the window is:
if the angular velocity is almost zero in the stationary region, the angular velocity threshold must be smaller than a threshold, so the criterion of the angular velocity energy discrimination (ARE) is:
and step 3: performing zero-speed correction on the static moment data detected in the step 2 by using a Kalman filtering algorithm to obtain an accurate human foot displacement result; the method comprises the following specific steps:
the zero-speed correction algorithm estimates errors of speed, position and attitude through a Kalman filter by using observed quantity (namely, the speed is zero) obtained when the pedestrian is static, and feeds the estimated error parameters back to an inertial navigation system, so that the pedestrian displacement calculation precision is improved.
Kalman filtering is a recursive linear minimum variance estimation, and has the advantages that: in the time domain, a filter is designed by adopting a state space method, and the signal characteristics are described by adopting a state transfer equation, so that the decomposition of a signal power spectrum is avoided. Given a stochastic system state space model:
in the formula, XkIs a state vector, ZkIs a measurement vector, phik/k-1Is a one-step transition of stateThe matrix is shifted in a direction that is orthogonal to the direction of the motion,k/k-1is the system noise distribution matrix, HkIs a measurement matrix, Wk-1Is the system noise vector, VkIs a measurement noise vector, both are gaussian white noise vector sequences with zero mean (obeying normal distribution), and they are not correlated with each other, namely:
the kalman filtering process can be divided into five basic formulas, as follows:
state one-step prediction:
state one-step prediction mean square error:
filter gain:
state estimation:
state estimation mean square error: pk＝(I-KkHk)Pk/k-1
The zero-speed correction algorithm utilizes the observed quantity (namely, the speed is zero) obtained in the stationary time, estimates other error parameters and feeds the estimated error parameters back to the inertial navigation system, so that the pedestrian displacement calculation precision is improved.
The currently recognizable pedestrian movement modes comprise eight modes of advancing, retreating, left side moving, right side moving, stepping, in-situ jumping, kicking and movement acceleration. The eight modes are divided into two types, wherein one type is a motion mode which comprises forward movement, backward movement, left side movement and right side movement; another category is special modes, including stepping, take-off-seat, kicking, and acceleration of movement. Since the stepping mode can be identified, a motion mode keeping strategy is added into the gait identification algorithm, namely, the user can keep the existing motion mode by stepping during the use process. The space range required by the simulated scene can be greatly reduced through the strategy, so that the application scene is not limited by the simulated scene any more, and the control of the virtual character to move in a large range under the condition of small space can be realized.
And 4, step 4: and 3, obtaining the gait information of the single foot of the human body by using the accurate human body foot displacement result obtained in the step 3 through a gait recognition algorithm. The method comprises the following specific steps:
when the single foot is still, the movement of the single foot is finished or not started, and when the single foot is detected to be still, the gait information of the single foot can be judged by comparing the displacement result at the moment with the displacement result at the last time of still.
And 5: acquiring the gait information of the human body by the gait fusion recognition algorithm of the biped of the human body obtained in the step 4; the method comprises the following specific steps:
the same principle as the judgment principle of the single-foot gait information is adopted, and the obtained two pieces of single-foot gait information are compared again to finally obtain the human body gait information.
Step 6: the specific steps of reporting the human body gait information obtained in the step 5 to virtual displacement display software for real-time display are as follows:
the gait information of the human body is sent to the software through Bluetooth according to a specified protocol, after the software receives the data packet, the software firstly analyzes the data packet according to the protocol to obtain the gait information of the human body, and then displays the gait information of the human body.
Claims (12)
1. A method for realizing the large-scale movement of a virtual world character by utilizing the motion of human feet in a small space range comprises the following steps:
step 1: sensing the displacement of the human foot in a small space range and resolving the sensed displacement of the human foot in the small space range in real time to obtain IMU (inertial measurement unit) original data of the human foot in the small space range motion process;
step 2: detecting static moment data in IMU original data by a static detection method;
and step 3: performing zero-speed correction on the static moment data detected in the step 2 by using a Kalman filtering algorithm to obtain an accurate human foot displacement result;
and 4, step 4: acquiring gait information of a single foot of the human body by using the accurate human body foot displacement result obtained in the step 3 through a gait recognition algorithm;
and 5: acquiring the gait information of the human body by the gait fusion recognition algorithm of the biped of the human body obtained in the step 4;
step 6: and (5) reporting the gait information of the human body obtained in the step (5) to virtual displacement display software for real-time display.
2. The method for realizing the large-scale movement of the virtual world character by utilizing the motion of the human foot in the small space range according to the claim 1, wherein in the step 1, the displacement of the human foot in the small space range comprises one or the combination of more than two of the forward movement, the backward movement, the left side movement, the right side movement, the stepping in place, the jumping in place, the leg kicking and the acceleration of the motion of the human leg. Preferably stepping in place.
3. The method as claimed in claim 1, wherein the means for sensing the displacement of the human foot in the small space range in step 1 is a foot displacement sensing module bound on the human foot.
4. The method as claimed in claim 3, wherein the foot displacement sensor module is a MEMS inertial device.
5. The method for realizing the large-scale movement of the virtual world character by utilizing the motion of the human foot in the small space range as claimed in claim 1, wherein the steps of sensing the displacement of the human foot in the small space range and calculating the sensed displacement of the human foot in the small space range in real time to obtain the IMU raw data of the motion process of the human foot in the small space range in step 1 are as follows:
step 1.1: sensing the moving speed, position and posture of the foot part of the human body by using a gyroscope and an accelerometer in the MEMS inertial device;
IMU raw data is as in fig. 4a and 4 b.
6. The method as claimed in claim 1, wherein the means for sensing the displacement of the human foot in the small space range in step 1 is a foot pad having five sensing areas.
7. The method for achieving the large-scale movement of the virtual world character through the small-space range motion of the human foot according to claim 1, wherein in the step 1, the device for sensing the small-space range displacement of the human foot is an infrared sensing device or a laser sensing device.
8. The method for realizing the large-scale movement of the character in the virtual world by utilizing the motion of the human foot in the small space range as claimed in claim 1, wherein the step 2 of detecting the static moment data in the IMU raw data by the static detection method comprises the following specific steps:
and calculating the detection value at each moment by a static interval detection method, and determining that the detection value is static when the detection value is smaller than a set threshold value.
The stationary interval detection method adopts an Angular velocity Energy discrimination method (Angular Rate Energy), and records that the Angular velocity at the time k is omegakAnd if the detection window is N, the sum of angular velocity energy in the window is:
if the angular velocity is almost zero in the stationary region, the angular velocity threshold must be smaller than a threshold, so the criterion of the angular velocity energy discrimination (ARE) is:
9. the method for realizing the large-scale movement of the virtual world character by utilizing the motion of the human foot in the small space range according to the claim 1, wherein in the step 3, the specific steps of performing zero-speed correction on the static moment data detected in the step 2 by utilizing a Kalman filtering algorithm to obtain an accurate human foot displacement result are as follows:
the zero-speed correction algorithm estimates errors of speed, position and attitude through a Kalman filter by using observed quantity (namely, the speed is zero) obtained when the pedestrian is static, and feeds the estimated error parameters back to an inertial navigation system, so that the pedestrian displacement calculation precision is improved.
Kalman filtering is a recursive linear minimum variance estimation, and has the advantages that: in the time domain, a filter is designed by adopting a state space method, and the signal characteristics are described by adopting a state transfer equation, so that the decomposition of a signal power spectrum is avoided. Given a stochastic system state space model:
in the formula, XkIs a state vector, ZkIs a measurement vector, phik/k-1Is a one-step transition matrix of states,k/k-1is the system noise distribution matrix, HkIs a measurement matrix, Wk-1Is the system noise vector, VkIs amount ofThe noise vector measurement and noise vector measurement are both Gaussian white noise vector sequences with zero mean (obeying normal distribution) and are mutually uncorrelated, namely, the following conditions are satisfied:
the kalman filtering process can be divided into five basic formulas, as follows:
state one-step prediction:
state one-step prediction mean square error:
filter gain:
state estimation:
state estimation mean square error: pk＝(I-KkHk)Pk/k-1
The zero-speed correction algorithm utilizes the observed quantity (namely, the speed is zero) obtained in the stationary time, estimates other error parameters and feeds the estimated error parameters back to the inertial navigation system, so that the pedestrian displacement calculation precision is improved.
10. The method for achieving the large-scale movement of the virtual world character through the motion of the human foot in the small space range according to the claim 1, wherein in the step 4, the specific steps of obtaining the gait information of the human single foot through the gait recognition algorithm by using the accurate human foot displacement result obtained in the step 3 are as follows:
when the single foot is still, the movement of the single foot is finished or not started, and when the single foot is detected to be still, the gait information of the single foot can be judged by comparing the displacement result at the moment with the displacement result at the last time of still.
11. The method for achieving the large-scale movement of the virtual world character by utilizing the motion of the human foot in the small space range according to claim 1, wherein in the step 5, the step of obtaining the gait information of the human body by the gait fusion recognition algorithm of the biped through the gait information of the single foot of the human body obtained in the step 4 comprises the following specific steps:
the same principle as the judgment principle of the single-foot gait information is adopted, and the obtained two pieces of single-foot gait information are compared again to finally obtain the human body gait information.
12. The method for realizing the large-scale movement of the virtual world figure by utilizing the motion of the human foot in the small space range as claimed in claim 1, wherein in the step 6, the step of reporting the gait information of the human body obtained in the step 5 to the virtual displacement display software for real-time display comprises the following specific steps:
the gait information of the human body is sent to the software through Bluetooth according to a specified protocol, after the software receives the data packet, the software firstly analyzes the data packet according to the protocol to obtain the gait information of the human body, and then displays the gait information of the human body.
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