CN109917404A - A kind of indoor positioning environmental characteristic point extracting method - Google Patents
A kind of indoor positioning environmental characteristic point extracting method Download PDFInfo
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Abstract
The invention discloses a kind of indoor positioning environmental characteristic point extracting method, this method is utilized respectively two-dimentional LiDAR acquisition equipment, intelligent terminal obtains the RSSI of the two-dimentional LiDAR point cloud data of the interior space, the ubiquitous wireless signal of wireless telecommunications system of setting indoors；And indoor environmental characteristic when acquiring RSSI is obtained from two-dimentional LiDAR point cloud data, in conjunction with the strong interior space structure feature of picker's trajectory extraction Up-to-date state, and then ubiquitous wireless signal RSSI sequence peaks are demarcated, constructs indoor positioning environmental characteristic point.It is compared with the traditional method, the present invention avoids the demand to indoor map when extracting indoor positioning environmental characteristic point, the strong indoor particulate matter of Up-to-date state is obtained using two-dimentional LiDAR point cloud and picker's track data, and the automation for realizing characteristic point is extracted, and indoor positioning and navigation are enhanced.The present invention is suitable for indoor positioning technologies field.
Description
Technical field
The present invention relates to indoor positioning technologies fields, more particularly to a kind of indoor positioning environmental characteristic point extraction side
Method.
Background technique
Beidou satellite navigation system is widely used in national economy and social development multiple fields, but due to satellite-signal
Region is blocked and interferes indoors, and the orientation problem in the interior space is not resolved but.Therefore many interior spaces
There is no indoor map data, even if there is indoor map to be also likely to be present the problems such as map datum is old.With smart phone end
End it is universal, industry has appreciated that using smart phone be the ubiquitous wireless signal of intelligent terminal fusion (including cellular signal, WiFi
Signal, Bluetooth signal etc.), multi-source data (indoor map, location fingerprint library, image etc.) to be to solve the problems, such as indoor positioning.In benefit
It before these signals and data positioning, needs to carry out necessary preparation, for example establish signal propagation model, draw indoor
Map constructs location fingerprint library and localizing environment characteristic point library etc..
The present invention is directed to the Construct question of the environmental characteristic point towards indoor positioning and navigation, proposes to utilize two dimension LiDAR
Point cloud and picker's track data identify indoor particulate matter, and then demarcate ubiquitous wireless signal strength sequence peaks, construct room
Interior positioning environmental characteristic point, enhancing indoor positioning and navigation.
Summary of the invention
In order to solve satellite-signal in the prior art, region is blocked and interferes the present invention indoors, can not be accurately positioned
The problem of interior space, the present invention provides a kind of indoor positioning environmental characteristic point extracting methods, by utilizing wireless telecommunications
The energy loss characterization of the wireless signal of equipment transmitting is related with interior of building environment, and the energy loss positions indoors
In usually use received signal strength (Received Signal Strength Indicator, RSSI), received signal strength exists
The distribution of the interior space, which is showed, has stronger correlation with interior space structure.The interior space is made of multiple space structures,
Including room, corridor etc., due to will form several RSSI characteristic points, these features in each space of the barrier of the barriers such as wall
Point is exactly indoor positioning environmental characteristic point.The present invention by building indoor positioning environmental characteristic point, for enhance indoor positioning with
Navigation.
To realize aforementioned present invention purpose, the technical solution adopted is as follows: a kind of indoor positioning environmental characteristic extracting method,
It is described method includes the following steps:
S1: determining the relationship of initial time intelligent terminal coordinate system and world coordinate system, obtains coordinate conversion matrix；Calibration
The time of intelligent terminal and two dimension LiDAR acquisition equipment obtains the two dimension of the interior space using two-dimentional LiDAR acquisition equipment
LiDAR point cloud data obtain the RSSI of the ubiquitous wireless signal of wireless telecommunications system of setting indoors using intelligent terminal；
S2: the sensing data built in intelligent terminal is resolved using pedestrian's reckoning technology, to obtain picker's rail
Mark；After obtaining wireless signal RSSI and picker track, based on the sample rate of picker track, it will be adopted according to the acquisition moment
Collection person track is associated with one by one with wireless signal RSSI, and rejecting the acquisition moment can not corresponding incomplete data；
S3: extracting linear feature from two-dimentional LiDAR point cloud data, using the scan period as unit division points cloud, according to every
The point cloud of a scan period is handled；The processing includes point cloud segmentation and linear feature parameter Estimation；According to a cloud minute
It cuts processing and obtains point cloud segmentation point, a cloud is divided into several point sets, put the number of centrostigma less than given threshold c0Or composition
Straight length be less than given threshold l0It will be rejected；
S4: trajectory processing is carried out to the picker track of acquisition using breaking point detection and Corner Detection, extracts picker's rail
Straight line portion in mark；After verifying angle point according to Corner Detection, corresponding point set is divided again according to angle point, obtains dividing it
Each section track point set afterwards；In conjunction with the linear feature extracted in two-dimentional LiDAR point cloud data with it is straight in picker track
Line part judges interior space structure type；
S5: after determining interior space structure by above step, by object space structure track and RSSI sequence
Matching and calibration is carried out according to the acquisition moment, location-based RSSI sequence is extracted, RSSI sequence is handled, extracts the peak RSSI
Value constructs indoor positioning environmental characteristic point.
Intelligent terminal of the present invention includes smart phone；The wireless telecommunications system includes WIFI, bluetooth module；It is described
Sensor includes gyroscope, accelerometer.
Preferably, step S1, the relationship of determining the initial time intelligent terminal coordinate system and world coordinate system are obtained and are sat
Transition matrix is marked, specific as follows:
The world coordinate system and intelligent terminal coordinate system is all dimensional Cartesian rectangular coordinate system, and the intelligence is eventually
End coordinate system is obtained by world coordinate system rotation and translation, it is assumed that world coordinate system first rotates angle ψ about the z axis, rotates further around Y-axis
Angle φ finally rotates angle, θ around X-axis, then spin matrix is
In formula, Rz、Ry、RxRespectively around Z, Y, the spin matrix of X-axis；
It rotates and then coordinate origin translation can be obtained into intelligent terminal coordinate system, translation vector are as follows:
T=[Δ x Δ y Δ z]T
In formula, Δ x, Δ y, Δ z are respectively the translational movement along X, Y, Z axis；
Finally, coordinate conversion matrix can be written as
Xl=RXg+T
In formula, XlIndicate the three-dimensional coordinate vector in intelligent terminal coordinate system；XgIndicate the three-dimensional coordinate in world coordinate system
Vector.
Further, step S3 carries out point cloud segmentation using Kalman filtering, comprising the following steps:
S301: quantity of state XkIt is set as laser ranging value rkWith the distance measurement value change rate changed with scanning angleI.e.
In formula: α indicates that scanning angle, k indicate the moment；
S302: state is calculated in different moments using following formula:
S303: according to above step, discrete system model is established
Wherein, ZkIt is the practical distance measurement value of laser for observed quantity；wk-1And vk-1Gaussian noise, variance are respectively Qk-1And Rk-1；
F is state transition matrix and H is observing matrix
State transition matrix F and observing matrix H are acquired by above step, according to kalman filtering theory and statistical check
The specific algorithm of method, point cloud segmentation is as follows:
Initialization filtering: FOR k=1:N
Calculate filter forecasting value:
Calculate new breath and its covariance:
Computational discrimination amountUtilize χ2Test and judge cut-point, DthdFor χ2Examine threshold value:
Cut-point is marked and extracts, resetting filtering:
ELSE
Calculate filter correction value:
END
END
Wherein: the number of iterations needed for N indicates cut-point cloud；X0Indicate initial state vector；P0Indicate original state error
Variance matrix；Indicate k moment State error variance battle array；QkIndicate state-noise variance matrix；SkThe prediction error variance of observation vector
Battle array；RkIndicate observation noise variance matrix；KkIndicate kalman gain matrix；
According to the point cloud segmentation point that above method is extracted, a cloud is divided into several point sets, the number for putting centrostigma, which is less than, to be set
Determine threshold value c0Or the straight length of composition is less than given threshold l0It will be rejected.
Still further, step S3, the linear feature parameter Estimation the following steps are included:
The data that each point is concentrated are fitted, straight line is extracted；The expression formula of the straight line are as follows:
Ax+By+C=0
Straight line parameter is acquired using linear regression method, is enabled Then
Wherein: xi、yiFor coordinate of i-th of scanning element in intelligent terminal coordinate system.
Still further, step S4, the breaking point detection is the following steps are included: breakpoint is discontinuous in motion track
Point, i.e. mutual distance are more than the front and back tracing point of given threshold；In general, tracing point is all continuously, if adjacent in collection process
The distance between tracing point changes, i.e., the distance between adjacent track point is greater than given threshold d0, then it is assumed that it detects disconnected
Point；Breakpoint is used to Preliminary division tracing point, forms a series of point sets.
Still further, step S4, breaking point detection forms a series of point sets, carries out straight line fitting according to each point set, and
Whether the distance of point to fitting a straight line that test point is concentrated is greater than given threshold dt；If more than given threshold dt, then angle point inspection is carried out
It surveys；
The Corner Detection: if point to be tested and preceding M/2 and rear M/2 scanning element sum of the distance dsumGreater than m-M/2
The distance between point and the m+M/2 point dse, and difference | dsum-dse| it is more than threshold value lc, it is determined that measuring point to be checked is angle point,
Meet
After verifying angle point, corresponding point set is divided again according to angle point；According to each section track after division
Point set further merges the LiDAR point cloud linear feature of its two sides, i.e., if two sides LiDAR point cloud linear distance is in setting area
Between [dmin,dmax] in, and the angle of two sides LiDAR point cloud straight line and track straight line is no more than threshold alpha0, then by two sides LiDAR
Point cloud straight line merges with the point set of corresponding side respectively；To the end of the LiDAR point cloud straight line merging treatment of each section track two sides,
Each section track straight length is further confirmed that, if track straight length is more than threshold value lt, and two sides have LiDAR straight line special
Sign, it is determined that collector passed through is the long and narrow passageway in corridor etc.
Preferably, step S5, specifically, firstly, the sliding window smoothing processing RSSI sequence for being 5 with step-length, eliminates mutation
The influence of value；Then, maximum value in the smooth sequence of RSSI is searched, and determines that RSSI maximum value top n value is monotonic increase, rear N
A value monotone decreasing；Finally, it is smooth to search corresponding position in original series according to the position of maximum value in the smooth sequence of RSSI
Maximum value within step-length, as RSSI peak value；Corresponding coordinate is extracted, together with RSSI peak value and received from other signals source
RSSI tuple constructs indoor positioning environmental characteristic point, i.e.,
In formula: (x, y) indicates characteristic point coordinate；MAC0Indicate that characteristic point corresponds to the media access control address of signal source；Indicate the RSSI sequence peaks that signal source is corresponded at characteristic point, respectively there is the RSSI value of N number of monotone variation in front and back；
{RSSIi,MACiIndicate other signal sources RSSI and media access control address that receive at characteristic point, for reinforcing feature
The discriminability of point.
Beneficial effects of the present invention are as follows: the present invention is utilized respectively two-dimentional LiDAR acquisition equipment, intelligent terminal obtains interior
The RSSI of the two-dimentional LiDAR point cloud data in space, the ubiquitous wireless signal of wireless telecommunications system of setting indoors；And from two dimension
Indoor environmental characteristic when acquisition RSSI is obtained in LiDAR point cloud data, in conjunction with the strong interior of picker's trajectory extraction Up-to-date state
Spatial structure characteristic, and then ubiquitous wireless signal RSSI sequence peaks are demarcated, construct indoor positioning environmental characteristic point；The room
Interior positioning environmental characteristic point building includes wireless signal RSSI acquisition, picker track obtains, two-dimentional LiDAR point cloud processing is with after
Processing, the post-processing include picker's trajectory processing, interior space structure recognition, the calibration of RSSI sequence and RSSI feature
Point extracts.
It is compared with the traditional method, the present invention avoids the need to indoor map when extracting indoor positioning environmental characteristic point
It asks, obtains the strong indoor particulate matter of Up-to-date state using two-dimentional LiDAR point cloud and picker's track data, and realize characteristic point
Automation extract, enhance indoor positioning and navigation.
Detailed description of the invention
Fig. 1 is indoor positioning environmental characteristic point extracting method flow chart of steps.
Specific embodiment
The present invention will be described in detail with reference to the accompanying drawings and detailed description.
Embodiment 1
The present embodiment obtains track and the RSSI information of picker using smart phone as intelligent terminal, is with bluetooth module
The present invention is described in detail using gyroscope, accelerometer as sensor in wireless telecommunications system.
As shown in Figure 1, a kind of indoor positioning environmental characteristic extracting method, it is described method includes the following steps:
Step S1: raw data acquisition and pretreatment
The present embodiment acquires the scene of equipment space acquisition data indoors using smart phone and two dimension LiDAR, is adopted
The data of collection are for extracting indoor positioning environmental characteristic point.Determine initial time smart phone coordinate first before acquiring data
The relationship of system and world coordinate system obtains coordinate conversion matrix, specific as follows:
The world coordinate system and intelligent terminal coordinate system is all dimensional Cartesian rectangular coordinate system, and the intelligence is eventually
End coordinate system is obtained by world coordinate system rotation and translation, it is assumed that world coordinate system first rotates angle ψ about the z axis, rotates further around Y-axis
Angle φ finally rotates angle, θ around X-axis, then spin matrix is
In formula, Rz、Ry、RxRespectively around Z, Y, the spin matrix of X-axis；
It rotates and then coordinate origin translation can be obtained into intelligent terminal coordinate system, translation vector are as follows:
T=[Δ x Δ y Δ z]T
In formula, Δ x, Δ y, Δ z are respectively the translational movement along X, Y, Z axis；
Finally, coordinate conversion matrix can be written as
Xl=RXg+T
In formula, XlIndicate the three-dimensional coordinate vector in intelligent terminal coordinate system；XgIndicate the three-dimensional coordinate in world coordinate system
Vector.
The time for calibrating smart phone and two dimension LiDAR acquisition equipment obtains Interior Space using two-dimentional LiDAR acquisition equipment
Between two-dimentional LiDAR point cloud data, utilize intelligent terminal to obtain the setting ubiquitous wireless signal of wireless telecommunications system indoors
RSSI。
Step S2: the sensing data built in intelligent terminal is resolved using pedestrian's reckoning technology, to obtain acquisition
Person track；After obtaining wireless signal RSSI and picker track, based on the sample rate of picker track, according to the acquisition moment
Picker track is associated with one by one with wireless signal RSSI, rejecting the acquisition moment can not corresponding incomplete data.
Step S3: two-dimentional LiDAR point cloud processing
Points cloud processing mainly extracts linear feature from two-dimentional LiDAR point cloud data, is subsequent interior space structure
It extracts and data is provided.Firstly, with scan period (i.e. laser scanning tour) for unit division points cloud, then for each
Point cloud in scan period is handled, and the processing includes point cloud segmentation and linear feature parameter Estimation.
S301: point cloud segmentation is carried out using Kalman filtering, comprising the following steps:
Quantity of state XkIt is set as laser ranging value rkWith the distance measurement value change rate changed with scanning angleI.e.
In formula: α indicates that scanning angle, k indicate the moment；
State is calculated in different moments using following formula:
According to above step, discrete system model is established
Wherein, ZkIt is the practical distance measurement value of laser for observed quantity；wk-1And vk-1Gaussian noise, variance are respectively Qk-1And Rk-1；
F is state transition matrix and H is observing matrix
H=[1 0].
State transition matrix F and observing matrix H are acquired by above step, according to kalman filtering theory and statistical check
The specific algorithm of method, point cloud segmentation is as follows:
Initialization filtering: FOR k=1:N
Calculate filter forecasting value:
Calculate new breath and its covariance:
Computational discrimination amountUtilize χ2Test and judge cut-point, DthdFor χ2Examine threshold value:
Cut-point is marked and extracts, resetting filtering:
ELSE
Calculate filter correction value:
END
END
Wherein: the number of iterations needed for N indicates cut-point cloud；X0Indicate initial state vector；P0Indicate original state error
Variance matrix；Indicate k moment State error variance battle array；QkIndicate state-noise variance matrix；SkThe prediction error variance of observation vector
Battle array；RkIndicate observation noise variance matrix；KkIndicate kalman gain matrix；
According to the point cloud segmentation point that above method is extracted, a cloud is divided into several point sets, the number for putting centrostigma, which is less than, to be set
Determine threshold value c0Or the straight length of composition is less than given threshold l0It will be rejected.
Step S302: linear feature parameter Estimation
The data that each point is concentrated are fitted, straight line is extracted；The expression formula of the straight line are as follows:
Ax+By+C=0
Straight line parameter is acquired using linear regression method, is enabled Then
Wherein: xi、yiFor coordinate of i-th of scanning element in smart phone coordinate system.
Step S4: trajectory processing and indoor long and narrow space structure recognition
There is a certain error for the picker track obtained using intelligent mobile phone sensor, needs at the person of being acquired track
Reason, picker's trajectory processing is exactly therefrom to extract useful information, assists in identifying interior space structure.Firstly, using disconnected
Point detection and Corner Detection carry out trajectory processing to the picker track of acquisition, extract the straight line portion in picker track.
Step S401: the breaking point detection specifically includes the following steps: breakpoint is discrete point in motion track,
That is front and back tracing point of the mutual distance more than given threshold；In general, tracing point is all continuously, if adjacent track in collection process
The distance between point changes, i.e., the distance between adjacent track point is greater than given threshold d0, then it is assumed that detect breakpoint；It is disconnected
Point is used to Preliminary division tracing point, forms a series of point sets.
Step S402: breaking point detection forms a series of point sets, carries out straight line fitting according to each point set, and test point is concentrated
The distance of point to fitting a straight line whether be greater than given threshold dt；If more than given threshold dt, then Corner Detection is carried out；
The Corner Detection: if point to be tested and preceding M/2 and rear M/2 scanning element sum of the distance dsumGreater than m-M/2
Distance d between point and the m+M/2 pointse, and difference | dsum-dse| it is more than threshold value lc, it is determined that measuring point to be checked is angle point, i.e.,
Meet
After verifying angle point, corresponding point set is divided again according to angle point；According to each section track after division
Point set further merges the LiDAR point cloud linear feature of its two sides, i.e., if two sides LiDAR point cloud linear distance is in setting area
Between [dmin,dmax] in, and the angle of two sides LiDAR point cloud straight line and track straight line is no more than threshold alpha0, then by two sides LiDAR
Point cloud straight line merges with the point set of corresponding side respectively；To the end of the LiDAR point cloud straight line merging treatment of each section track two sides,
Each section track straight length is further confirmed that, if track straight length is more than threshold value lt, and two sides have LiDAR straight line special
Sign, it is determined that collector passed through is the long and narrow passageway in corridor etc.
Step S5: the calibration of wireless signal strength sequence and feature extraction
After determining interior space structure through overmatching, by the track in object space structure with RSSI sequence according to acquisition
Moment carries out matching and calibration, extracts location-based RSSI sequence.RSSI sequence is handled, RSSI peak value is extracted, it is indoor fixed to construct
Position environmental characteristic point.Firstly, eliminating the influence of mutation value with step-length for 5 sliding window smoothing processing RSSI sequences；Then, it searches
Maximum value in the smooth sequence of RSSI, and determine that RSSI maximum value top n value is monotonic increase, rear N number of value monotone decreasing；Finally,
According to the position of maximum value in the smooth sequence of RSSI, the maximum value within the smooth step-length in corresponding position is searched in original series,
As RSSI peak value；Corresponding coordinate is extracted, together with RSSI peak value and the building of the RSSI tuple received from other signals source is indoor
Localizing environment characteristic point, i.e.,
In formula: (x, y) indicates characteristic point coordinate；MAC0Indicate that characteristic point corresponds to the media access control address of signal source；Indicate the RSSI sequence peaks that signal source is corresponded at characteristic point, respectively there is the RSSI value of N number of monotone variation in front and back；
{RSSIi,MACiIndicate other signal sources RSSI and media access control address that receive at characteristic point, in order to distinguish difference
Characteristic point in space structure, for reinforcing the discriminability of characteristic point.
Indoor positioning environmental characteristic point stability with higher, its position is in the case that space layout is constant indoors
Fixed, therefore, characteristic point can be used to calibrate, correct position error.
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.Any modification done within the spirit and principles of the present invention and changes equivalent replacement
Into etc., it should all be included in the scope of protection of the claims of the present invention.
Claims (7)
1. a kind of indoor positioning environmental characteristic extracting method, it is characterised in that: it is described method includes the following steps:
S1: determining the relationship of initial time intelligent terminal coordinate system and world coordinate system, obtains coordinate conversion matrix；Calibration intelligence
The time of terminal and two dimension LiDAR acquisition equipment obtains the two-dimentional LiDAR point of the interior space using two-dimentional LiDAR acquisition equipment
Cloud data obtain the RSSI of the ubiquitous wireless signal of wireless telecommunications system of setting indoors using intelligent terminal；
S2: the sensing data built in intelligent terminal is resolved using pedestrian's reckoning technology, to obtain picker track；?
After obtaining wireless signal RSSI and picker track, based on the sample rate of picker track, according to the acquisition moment by picker
Track is associated with one by one with wireless signal RSSI, and rejecting the acquisition moment can not corresponding incomplete data；
S3: extracting linear feature from two-dimentional LiDAR point cloud data, using the scan period as unit division points cloud, is swept according to each
The point cloud for retouching the period is handled；The processing includes point cloud segmentation and linear feature parameter Estimation；At point cloud segmentation
Reason obtains point cloud segmentation point, and a cloud is divided into several point sets, puts the number of centrostigma less than given threshold c0Or composition is straight
Line length is less than given threshold l0It will be rejected；
S4: trajectory processing is carried out to the picker track of acquisition using breaking point detection and Corner Detection, is extracted in picker track
Straight line portion；After verifying angle point according to Corner Detection, corresponding point set is divided again according to angle point, after being divided
Each section track point set；In conjunction with the straight line portion in the linear feature and picker track extracted in two-dimentional LiDAR point cloud data
Point, judge interior space structure type；
S5: after determining interior space structure by above step, by object space structure track and RSSI sequence according to
Moment progress matching and calibration is acquired, location-based RSSI sequence is extracted, RSSI sequence is handled, extract RSSI peak value,
Construct indoor positioning environmental characteristic point.
2. indoor positioning environmental characteristic extracting method according to claim 1, it is characterised in that: step S1, the determination
The relationship of initial time intelligent terminal coordinate system and world coordinate system obtains coordinate conversion matrix, specific as follows:
The world coordinate system and intelligent terminal coordinate system is all dimensional Cartesian rectangular coordinate system, and the intelligent terminal is sat
Mark system is obtained by world coordinate system rotation and translation, it is assumed that world coordinate system first rotates angle ψ about the z axis, rotates angle further around Y-axisAngle, θ finally is rotated around X-axis, then spin matrix is
In formula, Rz、Ry、RxRespectively around Z, Y, the spin matrix of X-axis；
It rotates and then coordinate origin translation can be obtained into intelligent terminal coordinate system, translation vector are as follows:
T=[Δ x Δ y Δ z]T
In formula, Δ x, Δ y, Δ z are respectively the translational movement along X, Y, Z axis；
Finally, coordinate conversion matrix can be written as
Xl=RXg+T
In formula, XlIndicate the three-dimensional coordinate vector in intelligent terminal coordinate system；XgIndicate world coordinate system in three-dimensional coordinate to
Amount.
3. indoor positioning environmental characteristic extracting method according to claim 2, it is characterised in that: step S3 utilizes karr
Graceful filtering carries out point cloud segmentation, comprising the following steps:
Quantity of state XkIt is set as laser ranging value rkWith the distance measurement value change rate changed with scanning angleI.e.
In formula: α indicates that scanning angle, k indicate the moment；
State is calculated in different moments using following formula:
According to above step, discrete system model is established:
Wherein, ZkIt is the practical distance measurement value of laser for observed quantity；wk-1And vk-1Gaussian noise, variance are respectively Qk-1And Rk-1；F is shape
State transition matrix and H are observing matrix:
H=[1 0]
State transition matrix F and observing matrix H are acquired by above step, according to kalman filtering theory and statistical check side
The specific algorithm of method, point cloud segmentation is as follows:
Initialization filtering: FOR k=1:N
Calculate filter forecasting value:
Calculate new breath and its covariance:
Computational discrimination amountUtilize χ2Test and judge cut-point, DthdFor χ2Examine threshold value:
Cut-point is marked and extracts, resetting filtering:
ELSE
Calculate filter correction value:
END
END
Wherein: the number of iterations needed for N indicates cut-point cloud；X0Indicate initial state vector；P0Indicate original state error variance
Battle array；Indicate k moment State error variance battle array；QkIndicate state-noise variance matrix；SkThe prediction error covariance matrix of observation vector；
RkIndicate observation noise variance matrix；KkIndicate kalman gain matrix；
According to the point cloud segmentation point that above method is extracted, a cloud is divided into several point sets, puts the number of centrostigma less than setting threshold
Value c0Or the straight length of composition is less than given threshold l0It will be rejected.
4. indoor positioning environmental characteristic extracting method according to claim 3, it is characterised in that: step S3, the straight line
Time parameters estimation the following steps are included:
The data that each point is concentrated are fitted, straight line is extracted；The expression formula of the straight line are as follows:
Ax+By+C=0
Straight line parameter is acquired using linear regression method, is enabled Then
Wherein: xi、yiFor coordinate of i-th of scanning element in intelligent terminal coordinate system.
5. indoor positioning environmental characteristic extracting method according to claim 4, it is characterised in that: step S4, described is disconnected
Point detection is the following steps are included: breakpoint is discrete point in motion track, i.e. the mutual distance front and back rail that is more than given threshold
Mark point；In general, in collection process tracing point be all it is continuous, if the distance between adjacent track point changes, i.e., adjacent rail
The distance between mark point is greater than given threshold d0, then it is assumed that detect breakpoint；Breakpoint is used to Preliminary division tracing point, forms one
Serial point set.
6. indoor positioning environmental characteristic extracting method according to claim 5, it is characterised in that: step S4, breaking point detection
Form a series of point sets, according to each point set carry out straight line fitting, and test point concentrate point to fitting a straight line distance whether
Greater than given threshold dt；If more than given threshold dt, then Corner Detection is carried out；
The Corner Detection: if point to be tested and preceding M/2 and rear M/2 scanning element sum of the distance dsumGreater than the m-M/2 point with
Distance d between the m+M/2 pointse, and difference | dsum-dse| it is more than threshold value lc, it is determined that measuring point to be checked is angle point, that is, is met:
After verifying angle point, corresponding point set is divided again according to angle point；According to each section track point set after division,
Further merge the LiDAR point cloud linear feature of its two sides, i.e., if two sides LiDAR point cloud linear distance is in set interval
[dmin,dmax] in, and the angle of two sides LiDAR point cloud straight line and track straight line is no more than threshold alpha0, then by two sides LiDAR point
Cloud straight line merges with the point set of corresponding side respectively；To the end of the LiDAR point cloud straight line merging treatment of each section track two sides, into
One step confirms each section track straight length, if track straight length is more than threshold value lt, and there are LiDAR linear feature in two sides,
Then determine collector's process is the long and narrow passageway in corridor etc.
7. described in any item indoor positioning environmental characteristic extracting methods according to claim 1~6, it is characterised in that: step S5,
Specifically, firstly, the sliding window smoothing processing RSSI sequence for being 5 with step-length, eliminates the influence of mutation value；Then, RSSI is searched
Maximum value in smooth sequence, and determine that RSSI maximum value top n value is monotonic increase, rear N number of value monotone decreasing；Finally, according to
The maximum value within the smooth step-length in corresponding position is searched, as in the position of maximum value in the smooth sequence of RSSI in original series
RSSI peak value；Corresponding coordinate is extracted, together with RSSI peak value and the RSSI tuple received from other signals source constructs indoor positioning
Environmental characteristic point, i.e.,
In formula: (x, y) indicates characteristic point coordinate；MAC0Indicate that characteristic point corresponds to the media access control address of signal source；
Indicate the RSSI sequence peaks that signal source is corresponded at characteristic point, respectively there is the RSSI value of N number of monotone variation in front and back；{RSSIi,
MACiIndicate other signal sources RSSI and media access control address that receive at characteristic point, it can for reinforce characteristic point
Identification.
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