CN105043393B - A kind of sensor-based pedestrian's indoor orientation method - Google Patents
A kind of sensor-based pedestrian's indoor orientation method Download PDFInfo
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- CN105043393B CN105043393B CN201510509291.1A CN201510509291A CN105043393B CN 105043393 B CN105043393 B CN 105043393B CN 201510509291 A CN201510509291 A CN 201510509291A CN 105043393 B CN105043393 B CN 105043393B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
Abstract
A kind of sensor-based pedestrian's indoor orientation method, is related to pedestrian's localization method.It is that mobile phone is put in two kinds of scenes of trouser pocket and handheld mobile phone, step 1, a learning process respectively including two kinds of application scenarios；Step 2, user's selection or allows program after the learning process of step 1 at corresponding account, and handheld mobile phone or mobile phone is placed in trouser pocket is walked, just it can be seen that the track passed by after covering；After two steps that learn and walk, track just can be plotted on mobile phone screen, and can accomplish to walk during walking while real-time rendering goes out run trace, user is with regard to that can accomplish realizing pedestrian's indoor positioning as unique hardware device by existing mobile phone without gps signal or the too weak interior that can not be properly positioned of gps signal；Coordinate indoor map again, oneself is in where now with regard to user can be allowed to understand, so as to realize indoor navigation.Power consumption is smaller, the degree of accuracy is high, stronger without new hardware, practicality.
Description
Technical field
The present invention relates to pedestrian's localization method, more particularly, to a kind of sensor-based pedestrian's indoor orientation method.
Background technology
Airmanship is essential in production and living, and positioning is the premise of navigation.In many cases, it would be desirable to
The positional information and the track situation of walking of oneself or other people walkings are obtained, to ensure to navigate under not familiar environment
Destination, or monitor other people positions to ensure its safety.Outdoor GPS positioning technology is ripe, but indoor positioning
Technology is always a difficult point, and the indoor positioning technologies that need not lay hardware infrastructure of current main flow have Wi-Fi positioning, magnetic field
Positioning, indoor navigation based on MEMS etc., its positioning precision is not up to commercial to be required.
The content of the invention
It is an object of the invention to provide with power consumption is smaller, the degree of accuracy is high, one kind stronger without new hardware, practicality
Sensor-based pedestrian's indoor orientation method.
The present invention includes two kinds of application scenarios, is that mobile phone is put in into two kinds of scenes of trouser pocket and handheld mobile phone respectively,
Comprise the following steps：
Step 1, a learning process, learning process distinguishes two kinds of different scenes：
Mobile phone is is put in trouser pocket by scene 1, if the user uses for the first time, is allowed user by built-in gyroscope and is added
It is positioned over trouser pocket after the mobile phone opening program of speedometer, and mobile phone will be placed leg steps forward a step, and this leg over there
Knee can not be bent；Mobile phone is taken out from pocket after pause 3s, the learning process is automatically performed, and learning outcome is stored in
Under the account of the user；If the next user wants to be continuing with, the account of the user need to be only selected, first just can be directly used
Secondary learning outcome；Specific learning process is as follows：
(1) the quaternary number that the angular velocity vector for measuring acceleration according to gyroscope is built makees Coordinate Conversion, measures
The horizontal direction and vertical direction acceleration of each sampled point of this step；
(2) the vertical acceleration of this step is subjected to double integral, measures vertical displacement h0；
(3) horizontal acceleration of this step is subjected to double integral, measures horizontal displacement h0；
(4) acceleration when basis is static before taking a step is compared with the acceleration after taking a step during stabilization, is surveyed
Angle, θ of the leg relative to vertical direction when going out stable0；
Scene 2 is handheld mobile phone, if the user uses for the first time, allows gyroscope and accelerometer built in user's held by both hands
Mobile phone and opening program stand erectly, any bar leg is stepped forward to an as far as possible big step, and this leg knee can not be bent, it is whole
Both hands and held mobile phone trunk geo-stationary are kept, program will be automatically performed the learning process afterwards, and learning outcome is stored
Under the account of the user；If the next user wants to be continuing with, the account of the user need to be only selected, the just can be directly used
Learning outcome once；Specific learning process is as follows：
(1) the quaternary number that the angular velocity vector for measuring acceleration according to gyroscope is built makees Coordinate Conversion, measures
The horizontal direction and vertical direction acceleration of each sampled point of this step；
(2) the vertical acceleration of this step is subjected to double integral, measures vertical displacement h0；
(3) horizontal acceleration of this step is subjected to double integral, measures horizontal displacement S0；
Step 2, user selects corresponding account or allows program after the learning process of step 1, handheld mobile phone or general
Mobile phone is placed in trouser pocket and walked, just it can be seen that the track passed by after covering；Specific trajectory calculation process equally may be used
To be divided into two kinds of different scenes.
Scene 1 is that mobile phone is put in into trouser pocket：
(1) acceleration, the gravitational vectors of each sampled point are recorded by accelerometer, gravity sensor and gyroscope
And angular velocity vector；
(2) each sampling instant vertical direction acceleration magnitude is calculated with reference to three axle gravitational vectors and 3-axis acceleration vector；
(3) by given threshold 3 and -4, the extreme point that vertical direction acceleration magnitude exceedes threshold value, the extreme value more than 3 are filtered out
Point is sole at the time of point equal with ground, and the extreme point less than -4 is heelstrike at the time of point；
(4) equation of motion is listed, acceleration and angular velocity vector are calculated into each sampling by Kalman filter
The Eulerian angles Yaw component θs of vertical direction speed and each sampling instant after the optimization at moment, while by each moment point
With drawing at the time of point contrast in (3), if coincideing, then it is assumed that vertical direction velocity amplitude is 0 this moment, draws velocity shifts this moment
Value, feeds back to Kalman filter, so as to eliminate the drift error of accelerometer；
(5) the minus part for eliminating each trough of vertical direction speed after drift error is integrated, calculated
Go out the vertical direction displacement h of each step；
(6) the step-length D of each step is
(7) starting point coordinate is set to (0,0), and the x-axis coordinate of each step adds D × sin θ, y-axis for the x-axis coordinate of back
Coordinate adds D × cos θ for the y-axis coordinate of back；
Scene 2 is handheld mobile phone：
(1) acceleration, the gravitational vectors of each sampled point are recorded by accelerometer, gravity sensor and gyroscope
And angular velocity vector；
(2) each sampling instant vertical direction acceleration magnitude is calculated with reference to three axle gravitational vectors and 3-axis acceleration vector；
(3) by given threshold 1 and -1, the extreme point that vertical direction acceleration magnitude exceedes threshold value, the extreme value more than 1 are filtered out
Point is sole at the time of point equal with ground, and the extreme point less than -1 is heelstrike at the time of point；
(4) equation of motion is listed, acceleration and angular velocity vector are calculated into each sampling by Kalman filter
The Eulerian angles Yaw component θs of vertical direction speed and each sampling instant after the optimization at moment, while by each moment point
With drawing at the time of point contrast in (3), if coincideing, then it is assumed that vertical direction velocity amplitude is 0 this moment, draws velocity shifts this moment
Value, feeds back to Kalman filter, so as to eliminate the drift error of accelerometer
(5) the minus part for eliminating each trough of vertical direction speed after drift error is integrated, calculated
Go out the vertical direction displacement h of each step；
(6) step-length of each step is
(7) starting point coordinate is set to (0,0), and the x-axis coordinate of each step adds D × sin θ, y-axis for the x-axis coordinate of back
Coordinate adds D × cos θ for the y-axis coordinate of back；
After two steps that learn and walk, track just can be plotted on mobile phone screen, and can accomplish one during walking
While one side real-time rendering of walking goes out run trace, user with regard to can accomplish no gps signal or gps signal are too weak can not be correct
Pedestrian's indoor positioning is realized as unique hardware device in the interior of positioning by existing mobile phone；Coordinate indoor map again, with regard to energy
User is allowed to understand that oneself is in where now, so as to realize indoor navigation.
The present invention proposes a kind of new inertial navigation indoor orientation method based on MEMS, is directed to improving indoor positioning essence
Degree, while the accelerometer and gyroscope hardware needed for this method have been popularized in existing cell phone platform, so user also without
Need additionally to purchase hardware again, pedestrian's indoor positioning just can be completed using mobile phone.This scheme in market and airport indoor navigation or
There is greatly effect under the scenes such as person hospital monitoring patient safety.
Embodiment
The embodiment of the present invention includes two kinds of application scenarios, is that mobile phone is put in into two kinds of trouser pocket and handheld mobile phone respectively
Scene, comprises the following steps：
Step 1, a learning process, learning process distinguishes two kinds of different scenes：
Mobile phone is is put in trouser pocket by scene 1, if the user uses for the first time, is allowed user by built-in gyroscope and is added
It is positioned over trouser pocket after the mobile phone opening program of speedometer, and mobile phone will be placed leg steps forward a step, and this leg over there
Knee can not be bent；Mobile phone is taken out from pocket after pause 3s, the learning process is automatically performed, and learning outcome is stored in
Under the account of the user；If the next user wants to be continuing with, the account of the user need to be only selected, first just can be directly used
Secondary learning outcome；Specific learning process is as follows：
(1) the quaternary number that the angular velocity vector for measuring acceleration according to gyroscope is built makees Coordinate Conversion, measures
The horizontal direction and vertical direction acceleration of each sampled point of this step；
(2) the vertical acceleration of this step is subjected to double integral, measures vertical displacement h0；
(3) horizontal acceleration of this step is subjected to double integral, measures horizontal displacement h0；
(4) acceleration when basis is static before taking a step is compared with the acceleration after taking a step during stabilization, is surveyed
Angle, θ of the leg relative to vertical direction when going out stable0；
Scene 2 is handheld mobile phone, if the user uses for the first time, allows gyroscope and accelerometer built in user's held by both hands
Mobile phone and opening program stand erectly, any bar leg is stepped forward to an as far as possible big step, and this leg knee can not be bent, it is whole
Both hands and held mobile phone trunk geo-stationary are kept, program will be automatically performed the learning process afterwards, and learning outcome is stored
Under the account of the user；If the next user wants to be continuing with, the account of the user need to be only selected, the just can be directly used
Learning outcome once；Specific learning process is as follows：
(1) the quaternary number that the angular velocity vector for measuring acceleration according to gyroscope is built makees Coordinate Conversion, measures
The horizontal direction and vertical direction acceleration of each sampled point of this step；
(2) the vertical acceleration of this step is subjected to double integral, measures vertical displacement h0；
(3) horizontal acceleration of this step is subjected to double integral, measures horizontal displacement S0；
Step 2, user selects corresponding account or allows program after the learning process of step 1, handheld mobile phone or general
Mobile phone is placed in trouser pocket and walked, just it can be seen that the track passed by after covering；Specific trajectory calculation process equally may be used
To be divided into two kinds of different scenes.
Scene 1 is that mobile phone is put in into trouser pocket：
(1) acceleration, the gravitational vectors of each sampled point are recorded by accelerometer, gravity sensor and gyroscope
And angular velocity vector；
(2) each sampling instant vertical direction acceleration magnitude is calculated with reference to three axle gravitational vectors and 3-axis acceleration vector；
(3) by given threshold 3 and -4, the extreme point that vertical direction acceleration magnitude exceedes threshold value, the extreme value more than 3 are filtered out
Point is sole at the time of point equal with ground, and the extreme point less than -4 is heelstrike at the time of point；
(4) equation of motion is listed, acceleration and angular velocity vector are calculated into each sampling by Kalman filter
The Eulerian angles Yaw component θs of vertical direction speed and each sampling instant after the optimization at moment, while by each moment point
With drawing at the time of point contrast in (3), if coincideing, then it is assumed that vertical direction velocity amplitude is 0 this moment, draws velocity shifts this moment
Value, feeds back to Kalman filter, so as to eliminate the drift error of accelerometer；
(5) the minus part for eliminating each trough of vertical direction speed after drift error is integrated, calculated
Go out the vertical direction displacement h of each step；
(6) the step-length D of each step is
(7) starting point coordinate is set to (0,0), and the x-axis coordinate of each step adds D × sin θ, y-axis for the x-axis coordinate of back
Coordinate adds D × cos θ for the y-axis coordinate of back；
Scene 2 is handheld mobile phone：
(1) acceleration, the gravitational vectors of each sampled point are recorded by accelerometer, gravity sensor and gyroscope
And angular velocity vector；
(2) each sampling instant vertical direction acceleration magnitude is calculated with reference to three axle gravitational vectors and 3-axis acceleration vector；
(3) by given threshold 1 and -1, the extreme point that vertical direction acceleration magnitude exceedes threshold value, the extreme value more than 1 are filtered out
Point is sole at the time of point equal with ground, and the extreme point less than -1 is heelstrike at the time of point；
(4) equation of motion is listed, acceleration and angular velocity vector are calculated into each sampling by Kalman filter
The Eulerian angles Yaw component θs of vertical direction speed and each sampling instant after the optimization at moment, while by each moment point
With drawing at the time of point contrast in (3), if coincideing, then it is assumed that vertical direction velocity amplitude is 0 this moment, draws velocity shifts this moment
Value, feeds back to Kalman filter, so as to eliminate the drift error of accelerometer
(5) the minus part for eliminating each trough of vertical direction speed after drift error is integrated, calculated
Go out the vertical direction displacement h of each step；
(6) step-length of each step is
(7) starting point coordinate is set to (0,0), and the x-axis coordinate of each step adds D × sin θ, y-axis for the x-axis coordinate of back
Coordinate adds D × cos θ for the y-axis coordinate of back；
After two steps that learn and walk, track just can be plotted on mobile phone screen, and can accomplish one during walking
While one side real-time rendering of walking goes out run trace, user with regard to can accomplish no gps signal or gps signal are too weak can not be correct
Pedestrian's indoor positioning is realized as unique hardware device in the interior of positioning by existing mobile phone；Coordinate indoor map again, with regard to energy
User is allowed to understand that oneself is in where now, so as to realize indoor navigation.
Claims (1)
1. a kind of sensor-based pedestrian's indoor orientation method, it is characterised in that including two kinds of application scenarios, is by hand respectively
Machine is put in two kinds of scenes of trouser pocket and handheld mobile phone, comprises the following steps：
Step 1, a learning process, learning process distinguishes two kinds of different scenes：
Mobile phone is is put in trouser pocket by scene 1, if user uses for the first time, allows user by built-in gyroscope and accelerometer
Mobile phone opening program after be positioned over trouser pocket, and mobile phone will be placed leg steps forward a step over there, and this leg knee is not
Can bending；Mobile phone is taken out from pocket after pause 3s, the learning process is automatically performed, and learning outcome is stored in the user
Account under；If the next user wants to be continuing with, the account of the user need to be only selected, first time just can be directly used
Practise result；Specific learning process is as follows：
(1) the quaternary number that the angular velocity vector for measuring acceleration according to gyroscope is built makees Coordinate Conversion, measures this
Walk the horizontal direction and vertical direction acceleration of each sampled point；
(2) the vertical acceleration of this step is subjected to double integral, measures vertical displacement h0；
(3) horizontal acceleration of this step is subjected to double integral, measures horizontal displacement S0；
(4) acceleration when basis is static before taking a step is compared with the acceleration after taking a step during stabilization, is measured steady
Angle, θ of the timing leg relative to vertical direction0；
Scene 2 is handheld mobile phone, if the user uses for the first time, allows the hand of gyroscope and accelerometer built in user's held by both hands
Machine simultaneously stood erectly by opening program, and any bar leg is stepped forward to an as far as possible big step, and this leg knee can not be bent, and whole process is kept
Both hands and held mobile phone trunk geo-stationary, program will be automatically performed the learning process afterwards, and learning outcome is stored in into this
Under the account of user；If the next user wants to be continuing with, the account of the user need to be only selected, just can be directly using for the first time
Learning outcome；Specific learning process is as follows：
(1) the quaternary number that the angular velocity vector for measuring acceleration according to gyroscope is built makees Coordinate Conversion, measures this
Walk the horizontal direction and vertical direction acceleration of each sampled point；
(2) the vertical acceleration of this step is subjected to double integral, measures vertical displacement h0；
(3) horizontal acceleration of this step is subjected to double integral, measures horizontal displacement S0；
Step 2, user's selection or allows program after the learning process of step 1 at corresponding account, handheld mobile phone or by mobile phone
It is placed in trouser pocket and walks, the track passed by just is seen after covering；Specific trajectory calculation process is equally divided into two kinds not
Same scene；
Scene 1 is that mobile phone is put in into trouser pocket：
(1) acceleration, gravitational vectors and the angle of each sampled point are recorded by accelerometer, gravity sensor and gyroscope
Velocity；
(2) each sampling instant vertical direction acceleration magnitude is calculated with reference to three axle gravitational vectors and 3-axis acceleration vector；
(3) by given threshold 3 and -4, the extreme point that vertical direction acceleration magnitude exceedes threshold value is filtered out, the extreme point more than 3 is
It is sole at the time of point equal with ground, the extreme point less than -4 is heelstrike at the time of point；
(4) equation of motion is listed, acceleration and angular velocity vector are calculated into each sampling instant by Kalman filter
Optimization after vertical direction speed and each sampling instant Eulerian angles Yaw component θs, while by each moment point with
(3) point is contrasted at the time of being drawn in, if coincideing, then it is assumed that vertical direction velocity amplitude is 0 this moment, draws velocity shifts this moment
Value, feeds back to Kalman filter, so as to eliminate the drift error of accelerometer；
(5) the minus part for eliminating each trough of vertical direction speed after drift error is integrated, calculated every
The vertical direction displacement h of one step；
(6) the step-length D of each step is
(7) starting point coordinate is set to (0,0), and the x-axis coordinate of each step adds D × sin θ, y-axis coordinate for the x-axis coordinate of back
D × cos θ are added for the y-axis coordinate of back；
Scene 2 is handheld mobile phone：
(1) acceleration, gravitational vectors and the angle of each sampled point are recorded by accelerometer, gravity sensor and gyroscope
Velocity；
(2) each sampling instant vertical direction acceleration magnitude is calculated with reference to three axle gravitational vectors and 3-axis acceleration vector；
(3) by given threshold 1 and -1, the extreme point that vertical direction acceleration magnitude exceedes threshold value is filtered out, the extreme point more than 1 is
It is sole at the time of point equal with ground, the extreme point less than -1 is heelstrike at the time of point；
(4) equation of motion is listed, acceleration and angular velocity vector are calculated into each sampling instant by Kalman filter
Optimization after vertical direction speed and each sampling instant Eulerian angles Yaw component θs, while by each moment point with
(3) point is contrasted at the time of being drawn in, if coincideing, then it is assumed that vertical direction velocity amplitude is 0 this moment, draws velocity shifts this moment
Value, feeds back to Kalman filter, so as to eliminate the drift error of accelerometer
(5) the minus part for eliminating each trough of vertical direction speed after drift error is integrated, calculated every
The vertical direction displacement h of one step；
(6) step-length of each step is
(7) starting point coordinate is set to (0,0), and the x-axis coordinate of each step adds D × sin θ, y-axis coordinate for the x-axis coordinate of back
D × cos θ are added for the y-axis coordinate of back；
Through study and two steps of walking after, track just can be plotted on mobile phone screen, and is accomplished during walking while walking one
Side real-time rendering goes out run trace, and user is with regard to that can accomplish without gps signal or the too weak room that can not be properly positioned of gps signal
It is interior to realize pedestrian's indoor positioning as unique hardware device by existing mobile phone；Coordinate indoor map again, with regard to that user can be made bright
Oneself where is in now in vain, so as to realize indoor navigation.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2189758A2 (en) * | 2008-11-19 | 2010-05-26 | Fujitsu Limited | Apparatus, method and program for calculating absolute movement path |
| CN102168986A (en) * | 2010-01-19 | 2011-08-31 | 精工爱普生株式会社 | Method of estimating stride length, method of calculating movement trajectory, and stride length estimating device |
| CN104061934A (en) * | 2014-06-10 | 2014-09-24 | 哈尔滨工业大学 | Pedestrian indoor position tracking method based on inertial sensor |
| CN104197935A (en) * | 2014-05-29 | 2014-12-10 | 成都旗客科技有限公司 | Indoor localization method based on mobile intelligent terminal |
-
2015
- 2015-08-19 CN CN201510509291.1A patent/CN105043393B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2189758A2 (en) * | 2008-11-19 | 2010-05-26 | Fujitsu Limited | Apparatus, method and program for calculating absolute movement path |
| CN102168986A (en) * | 2010-01-19 | 2011-08-31 | 精工爱普生株式会社 | Method of estimating stride length, method of calculating movement trajectory, and stride length estimating device |
| CN104197935A (en) * | 2014-05-29 | 2014-12-10 | 成都旗客科技有限公司 | Indoor localization method based on mobile intelligent terminal |
| CN104061934A (en) * | 2014-06-10 | 2014-09-24 | 哈尔滨工业大学 | Pedestrian indoor position tracking method based on inertial sensor |
Non-Patent Citations (3)
| Title |
|---|
| 基于嵌入式传感器的室内定位技术的研究;欧阳斌;《中国优秀硕士学位论文全文数据库 信息科技辑》;20150515(第5期);11-33页 * |
| 基于惯性测量的主动室内定位系统研究;陈天啸;《中国优秀硕士学位论文全文数据库 信息科技辑》;20150215(第2期);7-12页 * |
| 室内定位系统中的行人航迹推算研究;王克己;《中国优秀硕士学位论文全文数据库 信息科技辑》;20150815(第8期);9-23页 * |
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