CN117191012A - Low-power-consumption outdoor large-scale map AR positioning technical method - Google Patents
Low-power-consumption outdoor large-scale map AR positioning technical method Download PDFInfo
- Publication number
- CN117191012A CN117191012A CN202311160756.8A CN202311160756A CN117191012A CN 117191012 A CN117191012 A CN 117191012A CN 202311160756 A CN202311160756 A CN 202311160756A CN 117191012 A CN117191012 A CN 117191012A
- Authority
- CN
- China
- Prior art keywords
- sensor
- frequency
- positioning
- low
- gps
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 35
- 230000000007 visual effect Effects 0.000 claims abstract description 36
- 230000003993 interaction Effects 0.000 claims abstract description 12
- 238000005457 optimization Methods 0.000 claims abstract description 11
- 230000004044 response Effects 0.000 claims description 27
- 230000035945 sensitivity Effects 0.000 claims description 23
- 230000001133 acceleration Effects 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 9
- 238000005070 sampling Methods 0.000 claims description 9
- 238000005259 measurement Methods 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 6
- 230000004927 fusion Effects 0.000 claims description 6
- 238000013459 approach Methods 0.000 claims description 5
- 238000006073 displacement reaction Methods 0.000 claims description 3
- 238000005265 energy consumption Methods 0.000 claims description 3
- 238000002474 experimental method Methods 0.000 claims description 3
- 230000002035 prolonged effect Effects 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 15
- 230000003190 augmentative effect Effects 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 3
- 230000006872 improvement Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention discloses a low-power consumption outdoor large-scale map AR positioning technical method, which utilizes a commonly used motion sensor (IMU and magnetometer) on outdoor wearable equipment to fuse a GPS algorithm to achieve the positioning accuracy of 6DOF, further fuses low-frequency visual signals to perform repositioning optimization on inertial navigation results, and enables the low-frequency visual signals to also play a role in optimizing the positioning capability of inertial navigation through the technology of combining and optimizing hybrid frequency sensors. The original inertial navigation metric scale precision is further improved to the AR positioning metric precision, whether the virtual object related to AR interaction exists in the vicinity of the position where the user is located can be perceived by combining the GPS and the low-frequency visual signal, and the visual signal frequency is reduced again after the positioning error is converged. The information frequency of the sensor is dynamically adjusted according to the actual demands of users, and the intelligent positioning accuracy and the power consumption are balanced better.
Description
Technical Field
The invention belongs to the technical field of AR positioning, and particularly relates to a low-power-consumption outdoor large-scale map AR positioning technical method.
Background
The augmented reality technology (Augmented Reality, abbreviated as AR) is a technology for calculating the position and angle of a camera image in real time and adding corresponding images, videos and 3D models, and the goal of the technology is to fit a virtual world around the real world on a screen and interact with the virtual world, and the technology is proposed in 1990. With the improvement of the CPU operation capability of the portable electronic products, the expected use of augmented reality will be wider and wider.
The mixed reality technology (MR for short) is a further development of the augmented reality technology, and it is generally considered in the industry that the display screen of the augmented reality and the virtual screen are displayed on the screen after being mixed, and the mixed display is that the real screen is seen through the lens, and the virtual screen is displayed on the lens alone or projected onto the eyes of the user. The technology is characterized in that virtual scene information is presented in a real scene, and an interactive feedback information loop is built among the real world, the virtual world and a user, so that the sense of reality of user experience is enhanced.
Now, the AR positioning technology is more and more mature, ARcore and ARkit have many cases of successful commercialized landing, and the accuracy and usability of the AR positioning technology reach a commercially available level, but the great power demand and the high power consumption are common problems of the current technical scheme, and the problems are easy scalding, great reduction of endurance, high hardware cost and the like. For the outdoor portable glasses of the main driving, the existing technical scheme is very large in hard injury, the problems of large calculation force requirement and high power consumption mainly come from the fact that the existing general technical scheme is based on the visual positioning technology (VSLAM), the camera is required to be started for a long time for video recording, and a large number of visual characteristic calculations are carried out, so that on the technical architecture level, the problems are unavoidable and are not applicable to the scenes of outdoor wearable equipment.
Therefore, a new AR positioning technology architecture is proposed, so that the dependence on vision is fundamentally reduced under the condition of ensuring the accuracy, and the outdoor wearable device can have long-time smooth AR experience, so that a low-power-consumption outdoor large-scale map AR positioning technology method is needed.
Disclosure of Invention
The invention aims to provide a low-power-consumption outdoor large-scale map AR positioning technical method, which enables the original inertial navigation metric scale precision to be further improved to the AR positioning metric precision, and by combining a GPS and a low-frequency visual signal, whether virtual objects related to AR interaction exist near the position where a user is located or not can be perceived, so that the information frequency of a sensor is dynamically adjusted according to the actual requirements of the user, the intelligent positioning precision and the power consumption are better balanced, and the problems that the prior art brings forward the common problems that the high calculation force requirement and the high power consumption are the current technical scheme in the prior art, the subsequent problems are easy to burn, the endurance is greatly reduced, the hardware cost is high and the like are solved.
In order to achieve the above purpose, the invention adopts the following technical scheme: a low-power consumption outdoor large-scale map AR positioning technical method comprises the following steps:
step 1: the motion sensor (IMU and magnetometer) on the outdoor wearable equipment of the user fuses with the GPS algorithm to achieve the positioning accuracy of 6 DOF;
step 2: fusing low-frequency visual signals, and carrying out repositioning optimization on inertial navigation results;
step 3: through the combined optimization of the mixed frequency sensor, the common classification precision of AR positioning is further improved;
step 4: combining a GPS and a low-frequency visual signal, sensing whether a virtual object related to AR interaction exists near the position where the user is located, and reducing the frequency of the visual signal again after positioning, so that the electric energy consumption is reduced;
step 5: according to the actual demands of users, the signal frequency of the sensor is dynamically adjusted, and the positioning accuracy and the power consumption are well balanced intelligently.
Preferably, in step 1, the imu+gps fusion algorithm uses an accelerometer, a gyroscope, a magnetometer, and GPS to determine the position; setting the sampling rate, in a typical system, the accelerometer and gyroscope run at a relatively high sampling rate, the complexity of processing the data from these sensors is relatively low in a fusion algorithm in which the magnetometer and GPS samples are processed together at the same frequency, and the accelerometer and gyroscope samples are processed together at the same high rate, whereas the GPS and in some cases the magnetometer run at a relatively low sampling rate and the complexity associated with processing them is high.
Preferably, the accelerometer and gyroscope samples of the IMU are taken as inputs and the method is invoked each time the accelerometer and gyroscope are sampled, the method is based on the accelerometer and gyroscope being advanced by a time step prediction state, the error covariance of the extended kalman filter is updated here, the method takes the GPS samples as inputs, the method updates the filter state based on the GPS samples by calculating the kalman gain, which weights them according to the uncertainty of the various sensor inputs, the error covariance is also updated here, the kalman gain is also used this time, the method is similar but based on the magnetometer sample update state, the kalman gain and the error covariance, although the accelerometer and gyroscope samples are taken as inputs, they are integrated to calculate the delta speed and the triangle angle, respectively, the filter tracks the bias of the magnetometer and these integrated signals.
Preferably, the 6DOF, that is, the 6 degree of freedom, is added with 3 degrees of freedom related to the positions of 'up and down, front and back, left and right', and the like on the basis of 3DOF, the head can only detect the rotating gesture of the head from 3DOF to 6DOF, the gesture of extending the head, and the like, and the change of the displacement of the body up and down, front and back, left and right can also be detected, and the positioning mode is commonly called inertial navigation.
Preferably, in step 2, the power consumption is reduced by using a visual signal with a low frequency, and the service time is prolonged, and the low-frequency visual signal is required to be ensured to be accurate, while the inertial navigation system is a navigation parameter resolving system for sensing devices by using a gyroscope and an accelerometer, and the system establishes a navigation coordinate system according to the output of the gyroscope, and resolves the speed and the position of the carrier in the navigation coordinate system according to the output of the accelerometer. The inertial navigation system is an autonomous navigation system which does not depend on external information and radiates energy to the outside, the working environment of the inertial navigation system not only comprises the air and the ground, but also can be underwater, the basic working principle of inertial navigation is based on Newton's law of mechanics, the acceleration of a carrier in an inertial reference system is measured, the acceleration of the carrier in the inertial reference system is integrated with time, and the acceleration is transformed into a navigation coordinate system, so that the information such as speed, yaw angle, position and the like in the navigation coordinate system can be obtained.
Preferably, in the step 3, the higher the sensitivity of the sensor is, the better the sensitivity is, because the value of the output signal corresponding to the measured change is larger only when the sensitivity is high, which is advantageous for signal processing, but it is noted that the sensitivity of the sensor is high, and external noise which is irrelevant to the measured is also easy to be mixed in, and the sensitivity is amplified by the amplifying system, which affects the measuring accuracy. Therefore, the sensor is required to have higher signal-to-noise ratio, the factory disturbance signal introduced from the outside is reduced as much as possible, the sensitivity of the sensor is directional, and when the measured sensor is unidirectional, and the requirement on the directivity is higher, the sensor with small sensitivity in other directions is selected; if the measured is a multidimensional vector, the smaller the cross sensitivity of the sensor is required to be, the better.
Preferably, the frequency response characteristic of the sensor determines the measured frequency range, the undistorted measurement condition must be maintained within the allowable frequency range, in fact, the response of the sensor always has a certain delay, and the shorter the desired delay time, the better the frequency response of the sensor, and the wider the measurable signal frequency range.
Preferably, the inertia of the mechanical system is larger due to the influence of structural characteristics, and the frequency of the measurable signal of the sensor with low frequency is lower; no sensor can guarantee absolute linearity, and the linearity is also relative, and a sensor with small nonlinear error can be approximately regarded as linear within a certain range when the required measurement accuracy is low.
Preferably, in step 4, when the user approaches the AR interaction area, the frequency of the visual signal can be adjusted to achieve higher accuracy, and the frequency of the visual signal can be reduced again after the positioning error converges.
Preferably, in step 5, the dynamic characteristic of the sensor is the characteristic of the output of the sensor when the input changes, in actual operation, the dynamic characteristic of the sensor is usually represented by the response of the sensor to certain standard input signals, the response of the sensor to the standard input signals is easy to obtain by an experiment method, and a certain relation exists between the response of the sensor to the standard input signals and the response of the sensor to any input signals, so that the former is often known to estimate the latter, and the most commonly used standard input signals are provided with both step signals and sine signals, so that the dynamic characteristic of the sensor is also usually represented by the step responses and frequency responses.
The invention has the technical effects and advantages that: compared with the prior art, the low-power-consumption outdoor large-scale map AR positioning technical method provided by the invention has the following advantages:
the positioning accuracy of 6DOF is achieved by using a commonly used motion sensor (IMU and magnetometer) on the outdoor wearable equipment together with a GPS algorithm, the repositioning optimization is further carried out on the inertial navigation result by combining the low-frequency visual signals, and the positioning capability of the inertial navigation can be optimized by using the hybrid frequency sensor combined optimization technology. The accuracy of the original inertial navigation metric scale level is further improved to the metric level accuracy of AR positioning, whether virtual objects related to AR interaction exist near the position where the user is located can be perceived by combining a GPS and a low-frequency visual signal, when the user approaches an AR interaction area, the user can achieve higher accuracy by adjusting the frequency of the visual signal, and the frequency of the visual signal is reduced again after the positioning error is converged. The information frequency of the sensor is dynamically adjusted according to the actual demands of users, and the intelligent positioning accuracy and the power consumption are balanced better.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
FIG. 1 is a flow chart of the steps of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a low-power consumption outdoor large-scale map AR positioning technical method as shown in figure 1, which comprises the following steps:
step 1: the motion sensor (IMU and magnetometer) on the outdoor wearable equipment of the user fuses with the GPS algorithm to achieve the positioning accuracy of 6 DOF;
step 2: fusing low-frequency visual signals, and carrying out repositioning optimization on inertial navigation results;
step 3: through the combined optimization of the mixed frequency sensor, the common classification precision of AR positioning is further improved;
step 4: combining a GPS and a low-frequency visual signal, sensing whether a virtual object related to AR interaction exists near the position where the user is located, and reducing the frequency of the visual signal again after positioning, so that the electric energy consumption is reduced;
step 5: according to the actual demands of users, the signal frequency of the sensor is dynamically adjusted, and the positioning accuracy and the power consumption are well balanced intelligently.
In step 1, an imu+gps fusion algorithm, using accelerometers, gyroscopes, magnetometers and GPS to determine position; setting the sampling rate, in a typical system, the accelerometer and gyroscope run at a relatively high sampling rate, the complexity of processing the data from these sensors is relatively low in a fusion algorithm in which the magnetometer and GPS samples are processed together at the same frequency, and the accelerometer and gyroscope samples are processed together at the same high rate, whereas the GPS and in some cases the magnetometer run at a relatively low sampling rate and the complexity associated with processing them is high.
The method is invoked each time an accelerometer and gyroscope are sampled, and is based on the accelerometer and gyroscope being advanced by a time step prediction state, where the error covariance of the extended Kalman filter is updated, and the method takes GPS samples as input, and updates the GPS sample based filter state by computing Kalman gains that weight them based on the uncertainty of the various sensor inputs, where the error covariance is also updated, and this time also uses Kalman gain, and is similar, but based on the magnetometer sample update state, kalman gain, and error covariance, although the accelerometer and gyroscope samples are taken as inputs, they are integrated to calculate the delta speed and triangle angle, respectively, and the filter tracks the bias of the magnetometer and these integrated signals.
The three-dimensional inertial navigation system has the advantages that 6DOF is 6 degrees of freedom, 3 degrees of freedom related to the upper and lower positions, the front and rear positions, the left and right positions and the like are added on the basis of 3DOF, the head can only detect the head rotation posture from 3DOF to 6DOF, the posture such as the telescopic head can be detected, and the change of the upper and lower, front and rear, left and right displacement of the body can also be detected.
In step 2, the power consumption is reduced by adopting a visual signal with lower frequency, the service time is prolonged, the low-frequency visual signal is needed to ensure the accuracy, and the inertial navigation system is a navigation parameter resolving system which uses a gyroscope and an accelerometer as sensitive devices, the system establishes a navigation coordinate system according to the output of the gyroscope, and the speed and the position of the carrier in the navigation coordinate system are resolved according to the output of the accelerometer. The inertial navigation system is an autonomous navigation system which does not depend on external information and radiates energy to the outside, the working environment of the inertial navigation system not only comprises the air and the ground, but also can be underwater, the basic working principle of inertial navigation is based on Newton's law of mechanics, the acceleration of a carrier in an inertial reference system is measured, the acceleration of the carrier in the inertial reference system is integrated with time, and the acceleration is transformed into a navigation coordinate system, so that the information such as speed, yaw angle, position and the like in the navigation coordinate system can be obtained.
In step 3, it is desirable that the higher the sensitivity of the sensor is, the better the sensitivity is, because the value of the output signal corresponding to the measured change is larger only when the sensitivity is high, which is advantageous for signal processing, but it is noted that the sensitivity of the sensor is high, and external noise which is not related to the measured is also liable to be mixed in, and is amplified by the amplifying system, which affects the measurement accuracy. Therefore, the sensor is required to have higher signal-to-noise ratio, the factory disturbance signal introduced from the outside is reduced as much as possible, the sensitivity of the sensor is directional, and when the measured sensor is unidirectional, and the requirement on the directivity is higher, the sensor with small sensitivity in other directions is selected; if the measured is a multidimensional vector, the smaller the cross sensitivity of the sensor is required to be, the better.
The frequency response characteristics of the sensor determine the measured frequency range, and the undistorted measurement conditions must be maintained within the allowable frequency range, and in practice, the response of the sensor always has a certain delay, and the shorter the desired delay time, the better the frequency response of the sensor, and the wider the measurable signal frequency range.
Due to the influence of structural characteristics, the inertia of the mechanical system is larger, and the frequency of the measurable signal of the sensor with low frequency is lower; no sensor can guarantee absolute linearity, and the linearity is also relative, and a sensor with small nonlinear error can be approximately regarded as linear within a certain range when the required measurement accuracy is low.
In step 4, when the user approaches the AR interaction area, the frequency of the visual signal can be adjusted to achieve higher accuracy, and the frequency of the visual signal can be reduced again after the positioning error converges.
In step 5, the dynamic characteristic of the sensor is dynamically adjusted, that is, the output characteristic of the sensor when the input changes, in actual operation, the dynamic characteristic of the sensor is usually represented by the response of the sensor to certain standard input signals, the response of the sensor to the standard input signals is easy to obtain by an experiment method, and a certain relation exists between the response of the sensor to the standard input signals and the response of the sensor to any input signals, so that the former is often known to estimate the latter, the most commonly used standard input signals have two types of step signals and sine signals, and the dynamic characteristic of the sensor is also usually represented by the step response and the frequency response
Working principle: the positioning accuracy of 6DOF is achieved by using a commonly used motion sensor (IMU and magnetometer) on the outdoor wearable equipment together with a GPS algorithm, the repositioning optimization is further carried out on the inertial navigation result by combining the low-frequency visual signals, and the positioning capability of the inertial navigation can be optimized by using the hybrid frequency sensor combined optimization technology. The accuracy of the original inertial navigation metric scale level is further improved to the metric level accuracy of AR positioning, whether virtual objects related to AR interaction exist near the position where the user is located can be perceived by combining a GPS and a low-frequency visual signal, when the user approaches an AR interaction area, the user can achieve higher accuracy by adjusting the frequency of the visual signal, and the frequency of the visual signal is reduced again after the positioning error is converged. The information frequency of the sensor is dynamically adjusted according to the actual demands of users, and the intelligent positioning accuracy and the power consumption are balanced better.
Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present invention.
Claims (10)
1. A low-power consumption outdoor large-scale map AR positioning technical method is characterized by comprising the following steps of: the method comprises the following steps:
step 1: the motion sensor (IMU and magnetometer) on the outdoor wearable equipment of the user fuses with the GPS algorithm to achieve the positioning accuracy of 6 DOF;
step 2: fusing low-frequency visual signals, and carrying out repositioning optimization on inertial navigation results;
step 3: through the combined optimization of the mixed frequency sensor, the common classification precision of AR positioning is further improved;
step 4: combining a GPS and a low-frequency visual signal, sensing whether a virtual object related to AR interaction exists near the position where the user is located, and reducing the frequency of the visual signal again after positioning, so that the electric energy consumption is reduced;
step 5: according to the actual demands of users, the signal frequency of the sensor is dynamically adjusted, and the positioning accuracy and the power consumption are well balanced intelligently.
2. The method for positioning the low-power-consumption outdoor large-scale map AR according to claim 1, which is characterized by comprising the following steps of: in the step 1, an IMU+GPS fusion algorithm is used for determining the position by using an accelerometer, a gyroscope, a magnetometer and a GPS; setting the sampling rate, in a typical system, the accelerometer and gyroscope run at a relatively high sampling rate, the complexity of processing the data from these sensors is relatively low in a fusion algorithm in which the magnetometer and GPS samples are processed together at the same frequency, and the accelerometer and gyroscope samples are processed together at the same high rate, whereas the GPS and in some cases the magnetometer run at a relatively low sampling rate and the complexity associated with processing them is high.
3. The method for positioning the low-power-consumption outdoor large-scale map AR according to claim 2, characterized in that: the accelerometer and gyroscope samples of the IMU are taken as inputs and the method is invoked each time the accelerometer and gyroscope are sampled, the method advances the state of prediction by one time step based on the accelerometer and gyroscope, the error covariance of the extended kalman filter is updated here, the method takes the GPS samples as inputs, the method updates the state of the filter based on the GPS samples by calculating the kalman gain, which weights them based on the uncertainty of the various sensor inputs, the error covariance is updated here, the kalman gain is used this time, the method is similar, but the state, kalman gain and error covariance are updated based on the magnetometer samples, although the accelerometer and gyroscope samples are taken as inputs, they are integrated to calculate the delta speed and triangle angle, respectively, the filter tracks the bias of the magnetometer and these integrated signals.
4. The method for positioning the low-power-consumption outdoor large-scale map AR according to claim 1, which is characterized by comprising the following steps of: the 6DOF is 6 degrees of freedom, 3 degrees of freedom related to the positions of 'up and down, front and back, left and right' and the like are added on the basis of the 3DOF, the head can only detect the head rotation gesture from the 3DOF to the 6DOF, the gesture of extending the head, the head retraction gesture and the like can be detected, and the change of the up and down, front and back, left and right displacement of the body movement can also be detected.
5. The method for positioning the low-power-consumption outdoor large-scale map AR according to claim 1, which is characterized by comprising the following steps of: in step 2, the power consumption is reduced by adopting a visual signal with lower frequency, the service time is prolonged, the low-frequency visual signal is needed to be ensured under the condition of precision, the inertial navigation system is a navigation parameter resolving system which uses a gyroscope and an accelerometer as sensitive devices, the system establishes a navigation coordinate system according to the output of the gyroscope, and the speed and the position of a carrier in the navigation coordinate system are resolved according to the output of the accelerometer. The inertial navigation system is an autonomous navigation system which does not depend on external information and radiates energy to the outside, the working environment of the inertial navigation system not only comprises the air and the ground, but also can be underwater, the basic working principle of inertial navigation is based on Newton's law of mechanics, the acceleration of a carrier in an inertial reference system is measured, the acceleration of the carrier in the inertial reference system is integrated with time, and the acceleration is transformed into a navigation coordinate system, so that the information such as speed, yaw angle, position and the like in the navigation coordinate system can be obtained.
6. The method for positioning the low-power-consumption outdoor large-scale map AR according to claim 1, which is characterized by comprising the following steps of: in step 3, the higher the sensitivity of the sensor is, the better the sensitivity is, because the value of the output signal corresponding to the measured change is larger only when the sensitivity is high, which is beneficial to signal processing, but it is noted that the sensitivity of the sensor is high, and external noise unrelated to the measured is easy to mix in, and the sensitivity is amplified by the amplifying system, which affects the measuring accuracy. Therefore, the sensor is required to have higher signal-to-noise ratio, the factory disturbance signal introduced from the outside is reduced as much as possible, the sensitivity of the sensor is directional, and when the measured sensor is unidirectional, and the requirement on the directivity is higher, the sensor with small sensitivity in other directions is selected; if the measured is a multidimensional vector, the smaller the cross sensitivity of the sensor is required to be, the better.
7. The method for positioning the low-power-consumption outdoor large-scale map AR according to claim 6, which is characterized in that: the frequency response characteristics of the sensor determine the measured frequency range, and the undistorted measurement conditions must be maintained within the allowable frequency range, and in fact, the response of the sensor always has a certain delay, and the shorter the desired delay time, the better the frequency response of the sensor, and the wider the measurable signal frequency range.
8. The method for positioning the low-power-consumption outdoor large-scale map AR according to claim 7, wherein: the inertia of the mechanical system is larger due to the influence of structural characteristics, and the frequency of the measurable signal of the sensor with low frequency is lower; no sensor can guarantee absolute linearity, and the linearity is also relative, and a sensor with small nonlinear error can be approximately regarded as linear within a certain range when the required measurement accuracy is low.
9. The method for positioning the low-power-consumption outdoor large-scale map AR according to claim 1, which is characterized by comprising the following steps of: in step 4, when the user approaches the AR interaction area, the frequency of the visual signal can be adjusted to achieve higher accuracy, and the frequency of the visual signal is reduced again after the positioning error converges.
10. The method for positioning the low-power-consumption outdoor large-scale map AR according to claim 1, which is characterized by comprising the following steps of: in step 5, the dynamic characteristic of the sensor is that the sensor outputs when the input changes, in actual operation, the dynamic characteristic of the sensor is usually represented by its response to some standard input signals, the response of the sensor to the standard input signals is easy to find by an experiment method, and a certain relation exists between the response of the sensor to the standard input signals and the response of the sensor to any input signals, so that the former is often known to estimate the latter, and the most commonly used standard input signals have two types of step signals and sine signals, so that the dynamic characteristic of the sensor is also usually represented by step responses and frequency responses.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311160756.8A CN117191012A (en) | 2023-09-11 | 2023-09-11 | Low-power-consumption outdoor large-scale map AR positioning technical method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311160756.8A CN117191012A (en) | 2023-09-11 | 2023-09-11 | Low-power-consumption outdoor large-scale map AR positioning technical method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117191012A true CN117191012A (en) | 2023-12-08 |
Family
ID=88984510
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311160756.8A Pending CN117191012A (en) | 2023-09-11 | 2023-09-11 | Low-power-consumption outdoor large-scale map AR positioning technical method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117191012A (en) |
-
2023
- 2023-09-11 CN CN202311160756.8A patent/CN117191012A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR101778807B1 (en) | Motion capture pointer with data fusion | |
| CN110133582B (en) | Compensating for distortion in electromagnetic tracking systems | |
| US8957909B2 (en) | System and method for compensating for drift in a display of a user interface state | |
| US9459692B1 (en) | Virtual reality headset with relative motion head tracker | |
| US8619023B2 (en) | Method and device for inputting force intensity and rotation intensity based on motion sensing | |
| US9316513B2 (en) | System and method for calibrating sensors for different operating environments | |
| CN108375370A (en) | A kind of complex navigation system towards intelligent patrol unmanned plane | |
| US20110307213A1 (en) | System and method of sensing attitude and angular rate using a magnetic field sensor and accelerometer for portable electronic devices | |
| US20140214357A1 (en) | Portable electronic device adapted to provide an improved attitude matrix | |
| KR101922700B1 (en) | Method and Apparatus for calculation of angular velocity using acceleration sensor and geomagnetic sensor | |
| EP2291819A2 (en) | Dynamic selection of sensitivity of tilt functionality | |
| KR20070077598A (en) | Method and apparatus for estimating 2-dimension trajectory of a gesture | |
| EP3767435B1 (en) | 6-dof tracking using visual cues | |
| US20180253161A1 (en) | Spatial motion-based user interactivity | |
| US20200334837A1 (en) | Method for predicting a motion of an object, method for calibrating a motion model, method for deriving a predefined quantity and method for generating a virtual reality view | |
| CN117191012A (en) | Low-power-consumption outdoor large-scale map AR positioning technical method | |
| CN114777773A (en) | Camera position and posture compensation method and device, electronic equipment and readable storage medium | |
| CN111183336A (en) | Information processing apparatus, information processing method, and program | |
| KR20230030472A (en) | Method for creating user defined gesture profile based on user's repetitive motion and recognizing gesture | |
| CN115480483A (en) | Method, device, equipment and medium for identifying kinetic parameters of robot | |
| US10503277B1 (en) | Accelerometer-based content display adjustment | |
| CN118379865B (en) | Direction remote control method, device, equipment and storage medium | |
| CN114791284B (en) | Calibration method and device of electronic compass in robot and robot | |
| KR20220131658A (en) | Wearable device that determines motion signal for controlling host device based on machine learning | |
| CN118212263A (en) | Video dynamic capture optimization method, equipment and device based on ground sensing and dynamics |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |