CN109031263A - A kind of indoor fingerprint map constructing method based on mobile gunz perception data - Google Patents
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Abstract
The present invention relates to a kind of indoor fingerprint map constructing method based on mobile gunz perception data, the present invention includes the acquisition of intelligent perception data, building of the closed loop detection with figure, the figure optimization algorithm based on Graph SLAM.Using the mobile subscriber in environment, perceptually unit, the automatic collection of realization finger print data and user trajectory, exploration workload needed for can reducing fingerprint location are conducive to rapid deployment of the fingerprint location system in circumstances not known.
Description
Technical field
The present invention relates to indoor fingerprint location technology fields, and in particular to a kind of interior based on mobile gunz perception data
Fingerprint map constructing method.
Background technique
With Internet of Things development and based on location-based service application demand, indoor positioning in recent years by people increasingly
More concerns.It is no longer available indoors as the outdoor positioning technology of representative using GPS due to blocking for City Building, because of this person
Need a kind of convenient, pervasive, accurately indoor positioning technologies scheme.In fact, researchers at home and abroad before more than 20 years just
Start to explore indoor positioning.It is some to attempt to dispose special hardware device including some key positions indoors, than
Positioning can be realized by measurement distance or orientation in such as magnetic marker, infrared, RFID and laser sensor.Although these technologies
Good positioning accuracy can be provided, but additional hardware spending, special equipment and deployment in advance all limits these sides
The promotion and application of method.
A large amount of mobile communication equipment (smart phone, laptop) has all embedded the sensing of cheap multiplicity in the market
Device, such as Wi-Fi module, motion sensor, GPS and camera etc..And most of indoor environment is all deployed with Wi-Fi access point
(access point, AP), therefore the indoor positioning based on Wi-Fi becomes the hot spot studied at present.In fact, Wi-Fi is
By many companies (including Google, Apple, Microsoft, Baidu and Skyhook) for indoor positioning and navigation.Mesh
Before, location fingerprint method is to be widely adopted to need with state-of-the-art Wi-Fi indoor orientation method, this method in advance to indoor environment
It is surveyed, establishes the corresponding finger print information of each location point in fingerprint map record environment, pass through fingerprint matching
Realize the positioning of equipment.The biggest problem that this method faces is the building of huge fingerprint database.Especially City Building
Huge and structure is extremely complex, and the exploration of environment generally requires special equipment and technical professional, therefore surveys work
Journey expends huge.With the variation of indoor environment, the manned surveys being updated to fingerprint map He repeatedly are needed, it has also become limit
The bottleneck that fingerprint positioning method processed is promoted.
SLAM thought is introduced into Wi-Fi positioning in some researchs both domestic and external, by utilizing Gaussian process hidden variable
The signal strength space of higher-dimension is mapped to the two-dimensional position space of low-dimensional by model.These methods generally require to rely on signal propagation
Model needs to need to estimate simultaneously the signal propagation model parameter of AP while realizing self poisoning.
Summary of the invention
Technical problem to be solved by the invention is to provide a kind of indoor fingerprint maps based on mobile gunz perception data
Construction method solves the problems, such as that surveying engineering is great in existing fingerprint positioning method.
The technical scheme to solve the above technical problems is that a kind of interior based on mobile gunz perception data refers to
Line map constructing method, comprising the following steps:
S1, the finger print information and dead reckoning information that user is acquired by mobile phone terminal, and it is uploaded to server；
S2, finger print information and dead reckoning information are analyzed by server, obtains fingerprint similarity and user's rail
Mark data construct " vertex-constraint " figure using fingerprint similarity and user trajectory data；
S3, " vertex-constraint " figure is optimized by figure optimization algorithm, obtains fingerprint map.
Based on the above technical solution, the present invention can also be improved as follows.
Further, in the step S2 " vertex-constraint " figure construction method are as follows:
S21, the pose of user is constituted to vertex, the positional relationship between vertex is side；
S22, opposite side carry out closed loop detection, obtain " fingerprint-closed loop " and " turning-closed loop "；
S23, pass through " fingerprint-closed loop " and " turning-closed loop " building " vertex-constraint " figure.
Further, in the step S1 dead reckoning information R (t, τ) calculation formula are as follows:
In formula (1), μ (t, τ) and σ (t, τ) are the average and standard deviations of acceleration between t and t+ τ, and τ is the time
Delay parameter, l are variable, and value 0 arrives τ -1, and a (t+l) is t+l moment acceleration figure, and μ (t+ τ, τ) and σ (t+ τ, τ) are t+ τ and t+
The average and standard deviation of acceleration between 2 τ.
Further, fingerprint similarity s in the step S2ijCalculation method are as follows:
In formula (2), FiFor the finger print information that the i moment acquires, FjFor the finger print information that the j moment acquires, L is to detect
The number of AP, fi,lFor the signal strength of first of AP of i moment, fj,lFor the signal strength of first of AP of j moment.
Further, in the step S22 " fingerprint-closed loop " calculation method are as follows:
A1, the relative distance d for calculating fingerprint i and fingerprint j corresponding positionij, calculation formula are as follows:
In formula (3), xiAnd xjThe respectively abscissa of line i and fingerprint j, yiAnd yjRespectively fingerprint i's and fingerprint j
Abscissa；
A2, the relative angle θ for calculating fingerprint i and fingerprint j corresponding positionij, calculation formula are as follows:
θij=| θi-θj| (4)
In formula (4), θiFor the angle of fingerprint i, θjFor the angle of fingerprint j；
A3, work as relative distance dijLess than distance threshold, relative angle θijLess than angle threshold, and its fingerprint similarity sij
Greater than similarity threshold, then fingerprint i and fingerprint j constitute " fingerprint-closed loop ".
Further, the calculation method of " turning-closed loop " in the step S22 are as follows:
B1, user trajectory data are split by sliding window w, obtain the set Ct of t moment track:
Ct={ xt'} (5)
In formula (5), t is the time, and t' is at the time of meeting sliding window condition, | t'-t |≤w, x are the position of user
Appearance；
B2, by set Ct be less than t moment direction average value be denoted as θ-, greater than t moment direction average value be denoted as θ+,
If θ-and θ+difference be more than 60 °, t moment is in turn condition.
Further, the figure optimization algorithm in the step S3 is to minimize function:
In formula (6), Xt=(xt,yt,θt) it is t moment user dead reckoning data, t=i, j, i and j are vertex,
(xt,yt) be t moment user two-dimensional position information, θtFor the azimuth information of t moment user, C is collection constrained in figure
It closes, zijFor the rigid body translation between vertex i and vertex j.
The beneficial effects of the present invention are: the present invention includes the acquisition of intelligent perception data, closed loop detection and the building of figure, is based on
The figure optimization algorithm of Graph SLAM.Using the perceptually unit of the mobile subscriber in environment, finger print data and user's rail are realized
The automatic collection of mark, exploration workload needed for can reducing fingerprint location are conducive to fingerprint location system in circumstances not known
Rapid deployment.
Detailed description of the invention
Fig. 1 is flow diagram of the invention；
Fig. 2 is the flow diagram of step S2 of the present invention；
Fig. 3 is the constraint schematic diagram in intelligent perception of the present invention inside user between user；
Fig. 4 is the cyclically-varying schematic diagram of IMU acceleration information when user of the present invention walks；
Fig. 5 is the uncertainty models schematic diagram of fingerprint similarity of the present invention and distance；
Fig. 6 is user's initial trace of the present invention and turning schematic diagram；
Fig. 7 is that the present invention carries out turning detection using user trajectory and matches schematic diagram.
Specific embodiment
The principle and features of the present invention will be described below with reference to the accompanying drawings, and the given examples are served only to explain the present invention, and
It is non-to be used to limit the scope of the invention.
As shown in Figure 1, a kind of indoor fingerprint map constructing method based on mobile gunz perception data, including following step
It is rapid:
S1, the finger print information and dead reckoning information that user is acquired by mobile phone terminal, and it is uploaded to server；
S2, finger print information and dead reckoning information are analyzed by server, obtains fingerprint similarity and user's rail
Mark data construct " vertex-constraint " figure using fingerprint similarity and user trajectory data；
S3, " vertex-constraint " figure is optimized by figure optimization algorithm, obtains fingerprint map.
As shown in Fig. 2, in step S2 " vertex-constraint " figure construction method are as follows:
S21, the pose of user is constituted to vertex, the positional relationship between vertex is side；
S22, opposite side carry out closed loop detection, obtain " fingerprint-closed loop " and " turning-closed loop "；
S23, pass through " fingerprint-closed loop " and " turning-closed loop " building " vertex-constraint " figure.
As shown in figure 3, the constraint schematic diagram in intelligent perception inside user between user, since observation data have centainly
Error, therefore all constraints are all attached with a uncertain parameters, so that the SLAM problem based on figure is converted into optimization position
Appearance is minimum to make constraint bring error.
In embodiments of the present invention, the figure optimization algorithm in step S3 is to minimize function:
In formula (6), Xt=(xt,yt,θt) it is t moment user dead reckoning data, t=i, j, i and j are vertex,
(xt,yt) be t moment user two-dimensional position information, θtFor the azimuth information of t moment user, C is collection constrained in figure
It closes, zijFor the rigid body translation between vertex i and vertex j.
For laser scanner, this transformation can be estimated by scan matching (such as iteration closest approach algorithm, ICP)
It calculates；For visual sensor, this transformation can be obtained by characteristic matching.The signal strength of a certain AP is given, it can be quick
Determine whether a certain user has accessed the region again, because each AP has unique hardware address, but it is strong by signal
The phase alignment of degree two measurement points of estimation is extremely difficult, because RSS data itself is without providing any distance or direction letter
Breath.
Carry out closed loop detection using the method for location fingerprint, the AP and correspondence that fingerprint description is arrived in a certain position detection
Signal strength；As the fingerprint of human body is as DNA, Wi-Fi fingerprint can be used to uniquely describe a certain position, thus gram
Take the influence that environment propagates signal；And the distance of two positions can be measured by the similarity of fingerprint.
If the similarity value of two fingerprints is higher than a certain threshold value, that is, think that the two positions are same places, to detect
This error is compensated to a closed loop, and by increasing a uncertain covariance parameter, is answered actual
It can choose the very big diagonal matrix of numerical value in.For the precision for improving system, phase is established by a training process
Like the uncertainty models of degree and distance.
The changing rule of normal acceleration when being illustrated in figure 4 human locomotion, smart phone is all embedded with low-power consumption at present
Inertial navigation element, simple dead reckoning can be realized by carrying out dual-integration to acceleration, due to data wander and making an uproar
The influence of sound, continuous dual-integration will bring very big cumulative errors.It can be with based on the dynamic (dynamical) step counting algorithm of mankind's walking
Effectively overcome this disadvantage, identified by the behavior to people, and step number is counted, to realize dead reckoning.
The algorithm used is autocorrelation analysis method, and during exercise, whole acceleration value has apparent cyclically-varying, auto-correlation step counting to people
Algorithm carries out step counting using the auto-correlation coefficient size of the acceleration degree series of current meter step period and a upper meter step period, from
And realize dead reckoning, the calculation formula of dead reckoning information R (t, τ) in step S1 are as follows:
In formula (1), μ (t, τ) and σ (t, τ) are the average and standard deviations of acceleration between t and t+ τ, and τ is the time
Delay parameter, l are variable, and value 0 arrives τ -1, a (t)+l) it is t+l moment acceleration figure, μ (t+ τ, τ) and σ (t+ τ, τ) they are t+ τ and t
The average and standard deviation of acceleration between+2 τ.
Under mobile intelligent perception, each user individually acquires data, and motion profile is all based on the starting of each user
Position, without public reference frame, it is therefore desirable to the track between user be merged, so that each user be made to have altogether
Same referential, gives full play to the strength of intelligent perception.According to the direction of motion of user, following two situation is considered: the first
Situation, it is assumed that the direction of user can be obtained by the compass of mobile phone.The direction of motion of mobile phone each in this way is in same ginseng
Examine be under (terrestrial reference system), and only coordinate system exist displacement.Two users a and b are given, seek two using following formula
Maximum two fingerprints of similarity in a trackWith WithBetween deviation be displacement between two coordinate systemsAre as follows:
Second situation, the deflection error of mobile phone compass is often very big, and the mobile phone of some low sides does not support guide
Needle sensor, therefore some dead reckoning systems are not provided relative to the direction of motion under terrestrial reference system.In response to this,
WithWithIndicate displacement,Indicate rotation, these three parameters can be obtained by minimizing following distance function:
In formula (8), R is the spin matrix between two coordinate systems,Most for fingerprint t in the track of user b and a
The position of similar fingerprints, Euclidean distance of the d () between two o'clock.
Radio-frequency fingerprint describes a certain position using radiofrequency signal (such as Wi-Fi, RFID and bluetooth), can be good at gram
Influence of the environment to radio signal propagation is taken, therefore fingerprint is widely used in indoor positioning.As visual signature is the same,
Finger print information can be used for identifying a certain scene, however the extraction of visual signature needs complicated algorithm, and finger print information is not
There are problems that this, because the hardware address of AP is global unique in fingerprint.
In embodiments of the present invention, fingerprint similarity s in step S2ijCalculation method are as follows:
In formula (2), FiFor the finger print information that the i moment acquires, FjFor the finger print information that the j moment acquires, L is to detect
The number of AP, fi,lFor the signal strength of first of AP of i moment, fj,lFor the signal strength of first of AP of j moment.
The number of the computation complexity of fingerprint similarity and two fingerprint detections to AP have very big relationship, on airport or
In the environment of gymnasium, single pass tends to obtain a AP up to a hundred, and computing cost at this time be can not ignore, therefore be drawn
The concept for entering signal strength threshold filters out the measurement data that signal strength is less than.On the one hand, filtering out some AP can be significantly
Saving system computing cost；Still further aspect, due to being influenced by environment multipath effect, too small signal strength is often
More noises are represent, and biggish signal strength can preferably limit position.
It is illustrated in figure 5 the uncertainty models schematic diagram of fingerprint similarity and distance, each of Graph SLAM
While requiring specified uncertainty.For side constructed by odometer, uncertainty can be obtained by motion model, because
This needs the uncertainty of specified fingerprint constraint (fingerprint closed loop), and is counted using mileage to train this uncertain mould
Type.Odometer still maintains very accurate in 30 meters of range under normal circumstances.Therefore all distances are calculated less than 30 meters of fingerprints
Similitude, this results in K training datas:skIndicate the similarity of two fingerprints, dkIndicate two fingerprints
The distance between.Then sample is trained using branch mailbox method, obtains a model for indicating a certain similarity pair
Should distance uncertainty.That is, giving a similarity s, the side that all samples of b are divided between similarity s is calculated
Poor var (s):
In formula (9), c (b) is the number for counting the sample that similarity falls in interval b, and b (s) is located at for similarity
Sample between [s-b/2, s+b/2].
As shown in Figure 6 and Figure 7, a typical indoor environment usually contains many road signs, such as turning, elevator and building
Ladder, the detection of these features often greatly improve the precision of indoor locating system.The present invention melts the turning feature in environment
It closes in SLAM system.After obtaining a fingerprint closed loop, by analyzing user movement track, detect whether it is to turn
It is curved, and turning is matched, to obtain turning closed loop.Due to being limited by environment, the reality of two correct matching turnings
Border position is often very close, more much smaller than fingerprint closed loop bring uncertainty, such as the width in a corridor is often small
In 3 meters, and the positioning accuracy of fingerprint is often much larger than 3 meters.It is trajectory-based turning detection and matching signal as shown in Figure 7
Figure.
In embodiments of the present invention, in step S22 " fingerprint-closed loop " calculation method are as follows:
A1, the relative distance d for calculating fingerprint i and fingerprint j corresponding positionij, calculation formula are as follows:
In formula (3), xiAnd xjThe respectively abscissa of line i and fingerprint j, yiAnd yjThe respectively cross of line i and fingerprint j
Coordinate；
A2, the relative angle θ for calculating fingerprint i and fingerprint j corresponding positionij, calculation formula are as follows:
θij=| θi-θj| (4)
In formula (4), θiFor the angle of fingerprint i, θjFor the angle of fingerprint j；
A3, work as relative distance dijLess than distance threshold, relative angle θijLess than angle threshold, and its fingerprint similarity sij
Greater than similarity threshold, then fingerprint i and fingerprint j constitute " fingerprint-closed loop ".
In embodiments of the present invention, the calculation method of " turning-closed loop " in step S22 are as follows:
B1, user trajectory data are split by sliding window w, obtain the set Ct of t moment track:
Ct={ xt'} (5)
In formula (5), t is the time, and t' is at the time of meeting sliding window condition, | t'-t |≤w, x are the position of user
Appearance；
B2, by set Ct be less than t moment direction average value be denoted as θ-, greater than t moment direction average value be denoted as θ+,
If θ-and θ+difference be more than 60 °, t moment is in turn condition.
In laser scanning matching, ICP (iteration closest approach algorithm) is frequently utilized for finding the seat between two groups of scanning element clouds
Mark transformation, to make the distance between two groups of point cloud corresponding points and minimum.Therefore judged using ICP two turning whether
Match.Since dead reckoning sample frequency is very low, interpolation is carried out to two groups of motion profiles before executing ICP.If ICP is corresponded to
The average distance value of point is less than a certain threshold θf, it is believed that two turnings are matched, and this closed loop is added in turning closed loop.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all in spirit of the invention and
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (7)
1. a kind of indoor fingerprint map constructing method based on mobile gunz perception data, which comprises the following steps:
S1, the finger print information and dead reckoning information that user is acquired by mobile phone terminal, and it is uploaded to server；
S2, finger print information and dead reckoning information are analyzed by server, obtains fingerprint similarity and user trajectory number
According to utilizing fingerprint similarity and user trajectory data to construct " vertex-constraint " figure；
S3, " vertex-constraint " figure is optimized by figure optimization algorithm, obtains fingerprint map.
2. the indoor fingerprint map constructing method according to claim 1 based on mobile gunz perception data, feature exist
In the calculation formula of dead reckoning information R (t, τ) in the step S1 are as follows:
In formula (1), μ (t, τ) and σ (t, τ) are the average and standard deviations of acceleration between t and t+ τ, and τ is time delay
Parameter, l are variable, and value 0 arrives τ -1, and a (t+l) is t+l moment acceleration figure, μ (t+ τ, τ) and σ (t+ τ, τ) be t+ τ and t+2 τ it
Between acceleration average and standard deviation.
3. the indoor fingerprint map constructing method according to claim 1 based on mobile gunz perception data, feature exist
In the construction method of " vertex-constraint " figure in the step S2 are as follows:
S21, the pose of user is constituted to vertex, the positional relationship between vertex is side；
S22, opposite side carry out closed loop detection, obtain " fingerprint-closed loop " and " turning-closed loop "；
S23, pass through " fingerprint-closed loop " and " turning-closed loop " building " vertex-constraint " figure.
4. the indoor fingerprint map constructing method according to claim 1 based on mobile gunz perception data, feature exist
In fingerprint similarity s in the step S2ijCalculation method are as follows:
In formula (2), FiFor the finger print information that the i moment acquires, FjFor the finger print information that the j moment acquires, L is to detect AP's
Number, fi,lFor the signal strength of first of AP of i moment, fj,lFor the signal strength of first of AP of j moment.
5. the indoor fingerprint map constructing method according to claim 2 based on mobile gunz perception data, feature exist
In the calculation method of " fingerprint-closed loop " in the step S22 are as follows:
A1, the relative distance d for calculating fingerprint i and fingerprint j corresponding positionij, calculation formula are as follows:
In formula (3), xiAnd xjThe respectively abscissa of line i and fingerprint j, yiAnd yjRespectively refer to the abscissa of i Yu fingerprint j；
A2, the relative angle θ for calculating fingerprint i and fingerprint j corresponding positionij, calculation formula are as follows:
θij=| θi-θj| (4)
In formula (4), θiFor the angle of fingerprint i, θjFor the angle of fingerprint j；
A3, work as relative distance dijLess than distance threshold, relative angle θijLess than angle threshold, and its fingerprint similarity sijIt is greater than
Similarity threshold, then fingerprint i and fingerprint j constitute " fingerprint-closed loop ".
6. the indoor fingerprint map constructing method according to claim 2 based on mobile gunz perception data, feature exist
In the calculation method of " turning-closed loop " in the step S22 are as follows:
B1, user trajectory data are split by sliding window w, obtain the set Ct of t moment track:
Ct={ xt'} (5)
In formula (5), t is the time, and t' is at the time of meeting sliding window condition, | t'-t |≤w, x are the pose of user；
B2, by set Ct be less than t moment direction average value be denoted as θ-, greater than t moment direction average value be denoted as θ+, if θ-
With θ+difference be more than 60 °, then t moment is in turn condition.
7. the indoor fingerprint map constructing method according to claim 1 based on mobile gunz perception data, feature exist
In the figure optimization algorithm in the step S3 is to minimize function:
In formula (6), Xt=(xt,yt,θt) it is t moment user dead reckoning data, t=i, j, i and j are vertex, (xt,
yt) be t moment user two-dimensional position information, θtFor the azimuth information of t moment user, C is set constrained in figure, zij
For the rigid body translation between vertex i and vertex j.
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