airloc mass 2015
TRANSCRIPT
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AirLoc: Mobile Robots Assisted Indoor Localization
IEEE MASS 2015
Chen Qiu and Matt W. Mutka
Dept. of Computer Science and Engineering Michigan State University
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Outdoor Localization Indoor Localization
Ultrasound
WSN
RFID
Smartphone
GPS
Location Based Services
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Smart HomeShopping Mall
Cleaner Hospital
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Simultaneous localization and mapping (SLAM)
TurtleBot
What does the world look like?Where am I? Sensing Mapping Filtering
Constraint: can Not locate people’s positions
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Everyone has a smartphone
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Dead Reckoning
S1S2
SnSn−1a x X
Y
Z
ay
az
Smartphone Based Indoor Localization
Smartphone’s Acceleration Not Equal to User’s body Acceleration
Error Accumulation
0.5 degree error of orientation sensor 308m error within 1 minute
Imperfection
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Conjecture: Could mobile robots assist Indoor localization ?
Smartphone based indoor positioning• Dead Reckoning • Finger Printing / Radio Map • Other Inertial Sensing (Light, Sound, Barometer)
Mobile Robot • Low cost (TurtleBot) • Accurate Position (Error within 0.3m) • Extra Services
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0 5 10 15 20 25 30 35 400
10
20
30
40
50
Time (seconds)
Dev
iatio
n D
ista
nce
(met
ers)
Confidence intervalConfidence intervalDeviations without calibrationDeviations with calibration
Receive AccurateLocation Message
(X,Y)&
(X,Y)& (X,Y)&
(X,Y)&
Building Connection
RSSI>Threshold
Scanning Devices
Sending Location Messages
Preliminary Observation
Sending Accurate Location Information
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Goal of System Design: Mobile Robot send more accurate location to smartphones
Serving Routes Selection: First Serve the places with more people
More Robots, Fast Robots: System Costs, Equipments limitation
Sensing Range: Bluetooth (~10m), WiFi (Energy Concern)
Potential Resolutions:
Sampling Frequency: Cannot Define on Smartphone, Energy Consume
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Crowd Density Estimation
Common Phenomenon: RSSI variation caused by human bodies
3. density levels for generating routes
1. collect features on each sub-grid
High Density Level
Normal Density Level
Low Density Level
2. cluster samples
Steps of Estimation:
Feature 1: Num of Devices Feature 2: Bluetooth RSSI
K-Means EM
First Serve Higher Density
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Problem Formulation
Node
Edge
Room
Hallway
GraphIndoor Map
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Robot’s Traveling Strategy
Traveling Salesman Problem (TSP): • Find the best way to visit all the cities • Minimal travel time • NP-hard Problem
AirLoc focuses on time cost of routes and rooms
Edge Based Algorithm (EBA): • Both edges and nodes are assigned weights • Dynamic Programming to find the route with minimal travel time • Approximate solution (NP-hard Reduction)
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Edge Based Algorithm (EBA)
R1(HD) R5(LD)
R4(HD) R3(LD)
R2(MD)
1.5min
2min 0.5m
in
1min
LD – Low Density
0.5min 0.5min
0.5min
0.5min
0.5min
0.5min
MD – Medium Density HD – High Density
– Delete after round 1 – Delete after round 2
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Parallel Function 1: Computing Serving Routes
Density Levels
Serving Routes
Clustering Dynamic
Programming
RSSI Collection
(X,Y)&
(X,Y)& (X,Y)&
(X,Y)&
Moving Robot
Known Map
Mobile APP
Parallel!Function 2: Sending Accurate Location Information
Building Connection
RSSI>Threshold
1. Bluetooth RSSI 2. Number of Devices
Scanning Devices
Sending Location Messages
Overview of Single Robot Based System
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Multi-robots System Design
Single robot is not enough
Environment with more rooms
Crowd density is dynamic
More robots, better accuracy
Multi-robot design strategy
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Two-robots System Design
Partition the graph to two components
Principle: allocate more time to higher density rooms
Trade off between Distance and Density
Principle: balance between the two aspects
Principle: limit the time costs on the edges
Density First Algorithm (DenFA) Distance First Algorithm (DisFA)
Distance/Density First Algorithm (DDFA)
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1
00
2
1
8
8
9
Density First Algorithm (DenFA)
X axis
Y ax
is
Merge to Low Density Area
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1
00
2
1
8
8
9
Distance First Algorithm (DisFA)
X axis
Y ax
is
Merge to High Density Area
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Distance/Density First Algorithm (DDFA)
Combine DisFA and DenFA
Keep Connectivity in each component
Use thresholds to assign weights
T1 and T2 , Distance First
T1 and T2 , Density First
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Low Density AreaHigh Density Area
nLDAnHDA
≤10%
Preemption Period:
21%
2n%
22%23%
nLDAnHDA
>10%
Exponential growth
Preemption: make robots more efficiency
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Low Density AreaHigh Density Area
Preemption Period:
nLDAnHDA
>10%
Return to Initial State
20%
Preemption: make robots more efficiency
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Extend two robots to multi-robots
- Higher Crowd Density Area
Unbalance Serving Tree
Number of Devices
Aver
age
RSS
I (%
)
Room i
Allocate robots to HDA(P ×θ ) / (θ +1)
Allocate robots to LDAP − (P ×θ ) / (θ +1)
θ = ( ω ii=1
H
∑ ) / ( ω jj=1
L
∑ )
Definition of ω
P - num of robots
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Extend Two Robots to Multi-robots
How robots go back to the root ?
Return by the way you came
Dynamic Return Approach
Find node k: the smallest sum of distances between k and other rooms
Arrange k as the “new” root
min( Distj=1
n
∑i=1
n
∑ (i, j))
crowd density change, waste time
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AirLoc System Evaluation
Experiment Setting
•The height of the tablet is 1 meter
•The speed of is 0.3m /s
•0-6 volunteers carry Samsung Galaxy 4 or Google Nexus Tablet
•Employ on Bluetooth Adapter to communicate
•Volunteers in the experimental environment walk freely
MetricsDeviation Distance: Euclidean Distance (meter)
L(x) = − P(xi )log2(P(xi ))i=1
m
∑Location Entropy:
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Data Collection in Indoor Building
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Data collected in a room
Data collected in a hallway
- Robot Calibration - Smartphone Position
Data samples on the map
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1
2
3 1
4
5
7
8
9
10
11
12
13
14 15
16
17
2
3
4 5
8
7
6
10 11 12
15 14
13
16
18
19
21
20
22
24
23
25
17
Ground Truth
Estimated Trace
20 19
18
23
22 21
25 24
R1
R1
R2
R1 R3
- Send Location Message
- Sequence Num of Robot R#
- Calibrate Deviation
1100’s Floor Plan
R3 Cloud Server
9
Single Trace Study in Real Floorpan
• Calibrated by robots, the errors are reduced• Dead reckoning yields obvious deviations
• Mobile robots update the crowd density in a cloud
Conclusion:
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T Slot S Slot R Slot
T Slot
… …
Update Density Levels
Form Final Serving Area Divide Groups
Loop
OPOS (One Period One Sample): T Slot OPMS (One Period Multiple Sample): T Slot & S Slot
Static crowd density: initial state
Crowd Density Updating
Conclusion:• Multi-robots update crowd density continuously• Real Time crowd density improves the localization results
Duty cycle of AirLocCompare different density information
2 4 6 8200
300
400
500
600
Number of rounds
Nu
mb
er
of
d
evi
atio
n g
rid
s
OPMS crowd density
Static crowd density
OPOS crowd density
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0 2 4 6 8 10 120
0.2
0.4
0.6
Number of rounds
Lo
catio
n E
ntr
op
y
BalanceTree−Dynamic Return
BalanceTree−Static Return
UnblanceTree−Static Return
UnblanceTree−Dynamic Return
1 2 3 4 5 60
50
100
Number of smartphones
Ave
rage R
SS
(%
)
Low Density Level
High Density Level
Normal Density Level
Centroids of clusters
1 2 3 4 5 6
100
200
300
400
500
Average degree of nodes
Nu
mb
er
of
d
evi
atio
n g
rid
s
32 Robots 8 Robots EBA
Evaluation Results
Cluster different density levelsfor all the rooms
Unbalanced Tree outperforms Balance Tree
Dynamic Return enhances AirLoc
More robots provide more accuracy
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Summary• Mobile robots interact with smartphones to send
accurate location information
• AirLoc organizes multi-robots for improving the smartphones’ positioning information
• Single robot adopts Edge Based Algorithm (EBA) to generate the optimized serving route
• AirLoc updates the crowd density levels continuously
Distance/Density First Algorithm Dynamic Return
PreemptionUnbalanced Serving Tree
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Thank you !
Questions ?