A Method of Map Matching for Personal Positioning System Center for Spatial Information Science The University of Tokyo Kay Kitazawa, Yusuke Konishi, Ryosuke Shibasaki
A Method of Map Matching for Personal Positioning System
Center for Spatial Information ScienceThe University of Tokyo
Kay Kitazawa, Yusuke Konishi, Ryosuke Shibasaki
Needs for accurate positioning
Information services which depend on one’s location
Platform
Mobile phone (e.g i- mode ) PDA
GPS receiver
P
P
P
the nearest Parking
Full or any vacancy ?
accurate positioning technology is needed
You are here
Problems with current positioning services
Complementary method is necessary
In the valley between high-rise buildings Underground passage Inside of buildings
GPS system is not always available
GPS Point
We have to get trajectory between GPS points
GPS Point
-10.000
-5.000
0.000
5.000
10.000
15.000
20.000
25.000
30.000
35.000
40.000
-20.000
-10.000
0.000 10.000 20.000 30.000 40.000 50.000 60.000
y
Observed tracks
Actual walking path
Complementary method
AccelerometerGyrocompassMagnetic sensor Barometer
Personal Positioning System (PPS) developed by Mr.Yusuke Konishi
Accumulation of errors is inevitable
Map matching
Human can move more freely
Map Matching
・Map matching methods for car navigation
・Map matching methods for PPS (Personal Positioning System )
A new matching algorithm
3-D database
Road network : Nodes & Edges
cars always run
along with network
2-D
Objective
Development of a new method of “Map matching”
track human’s movement
3-D database
Position ( x, y, z )
Combination of algorithm Global matching
Local matching
Matching in 3-D space
room
entrance
corridor
and polygons
Local matching
Obstacle boundaries
Merit : Frequent modification
Intersect boundaries or not ?
Boundaries
Demerit : Hard to correct once mismatching
happened
modified points
??
Possible tracks
Global information is needed
90°turn in between long straight paths
Distinct characteristics
Global matching
Turn at the corner
Road Network
Candidates
Check the fitness
Distance
Orientation
Nodes and edges
Demerit : Long interval between processesMerit : Global consistency
Combine and “switch” two matching algorithms
“Switch” matching algorithm
display
Locally modified
θ> threshold ?
Globally modified
θ
Sharp change in angle
Switch to global matching process
6th floor
Ground floor
5th floor
4th floor
2nd floor
7th floor
8th floor
9th floor
10th floor
Subjective area3rd floor
Place of experiments
Institute of Industrial Science,The University of TokyoView from the East side
staircases
Entrance
Result : walk along corridor (2-D)
Raw data
Corrected data
Result : enter the room (2-D)
Corrected data
Raw data
Entrance
Result : go downstairs (3-D)
Corrected data
Raw data matching nodes of the entrance of staircases
Conclusions
• The combined algorithm is effective
• “Natural and free” trajectory can be reconstructed with least geometric constrains(e.g only obstacle boundaries )
Future works
• Adjustment of the parameter value(e.g threshold of sharp change in angle)
• Extend the matching algorithm for movements in staircases
Discrimination of “action mode”
(e.g walking, pausing,going up/down stairs etc. )
Always apply network in staircases
vector
Flow chartMap Matching
End
Add positional data to Database
Get coordinates of estimatedposition and time (x, y, z, t )
(x, y, z, t )
Calculation of tracks (vector)&
Modification of initial errors
Recent value of local modification
θ・・・ angle error
Ratio of length
GlobalmatchingY
Data of the objects around thecurrent point is obtained
N
Local matching
Y
Return the data of estimated point intact
N
Sharp angle change?
θ
The track crosses the outline of objects?
Flow chart : 2
Global matching
Return
ReturnN
The distance < threshold?
Get Nodes and Edges around the estimated position
Calculate the distance between the current position
and each nodes and edges
d
Choose the nearest node
Y
d
Measure the angle made byeach edge connected to the node
θSelect edges with
least difference in angle
Match the corner and pointwith angle change
Transform the shape of tracks
Start
Return
Y
Return thevalue intact
Measure the angle made by tracks and outlines
Angle < threshold?
N
Intersect with entrance?
Entrance ?
Flow chart : 3
Return
Record coefficients for correctionof cross angle and length
N
N
Initial errors
Return modified points
Get track of 5 more steps
N
Local matchingY
Rotate tracks aroundthe first point counter-
clockwise for thecross angle
Return
Y
Relocate intersectionto the entrance
θ
Distance between entrance< threshold ?
Angle < 90°?Y
*
θ
Return
Start
-45.00000000
-40.00000000
-35.00000000
-30.00000000
-25.00000000
-20.00000000
-15.00000000
-10.00000000
-5.00000000
0.00000000
5.00000000
10.00000000
0.00000000
5.00000000
10.00000000
15.00000000
20.00000000
25.00000000
30.00000000
系列1
-45.00000000
-40.00000000
-35.00000000
-30.00000000
-25.00000000
-20.00000000
-15.00000000
-10.00000000
-5.00000000
0.00000000
5.00000000
-5.00000000
0.00000000
5.00000000
10.00000000
15.00000000
20.00000000
25.00000000
系列1
Data 1
Estimated dataMatched data
y
-45.00000000-40.00000000-35.00000000-30.00000000-25.00000000-20.00000000-15.00000000-10.00000000-5.000000000.000000005.00000000
10.00000000
-10.0000000
0
-5.00000000
0.00000000
5.00000000
10.00000000
15.00000000
20.00000000
25.00000000
30.00000000
y
-45.00000000
-40.00000000
-35.00000000
-30.00000000
-25.00000000
-20.00000000
-15.00000000
-10.00000000
-5.00000000
0.00000000
5.00000000
-10.00000000
0.00000000 10.00000000
20.00000000
30.00000000
系列1
Data 2
Estimated dataMatched data
-50
-40
-30
-20
-10
0
10
20
-30 -20 -10 0 10 20 30 40
系列1
101.49
101.5
101.51
101.52
101.53
101.54
101.55
101.56
0 50 100 150 200 250
系列1
Data 3
Estimated data
Fluctuation in air pressure