percom 2016 slides
TRANSCRIPT
iFrame: Dynamic Indoor Map Construction through Automatic Mobile Sensing
Chen Qiu and Matt W. Mutka
Dept. of Computer Science and Engineering Michigan State University
Indoor Map Construction
Google Indoor Map (>1000 buildings in US and Japan)
Image Processing Footprints Collection
Draw the map by Hand
References: JigSaw, SLAM References: MapGenie, CrowdInside
Indoor Map Construction
Limitations of traditional indoor map constructions:
Complex Image Processing
Static Indoor Floor Plan
Not Passive Pattern
Dead Reckoning + Radio
Dynamic Updating Layouts
Unattended Mode
WiFi Detection
Bluetooth Detection
Distance - RSSI Relation
space
obstacle
Dead Reckoning
Markov Calibration ��
Merge and Learn
Multi-device Combination
Crowd Noise Filter
Curve Fit Fusion
Parameter Selection
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Space Max <-�-> Space Min
Temporary shadow
Initial Shadow Map Anchor Points Rebuilt Floor Plan ��
b) System Refinementa) Inertial Sensing on Smartphones
c) Indoor Floor Plan Construction
S1S2
SnSn−1a x X
Y
Z
ay
az
Overview of iFrame System
Formulate the Indoor Environment
Original Map
Map Matrix
Shadow Map
Shadow Rate: 1 - occupied by objects0 - empty grid
Samples Value
1
0.1
0.70.9
0
...
g
a x
a!
O X
Y
Z
ay
az
Dead Reckoning Approach
a!= (ax ,ay ,az − g) Sn
!"!− Sn−1! "!!
= 12an−1! "!!
t 2 + vn−1! "!!
tn
Uniformly*Accelerated*Mo2on!
S1S2
SnSn−1
a!
g
Drawback of Dead Reckoning
Smartphone’s Acceleration Not Equal to User’s body Acceleration
Error Accumulation
0.5 degree error of orientation sensor 308m error within 1 minute
S1S2
SnSn−1a x X
Y
Z
ay
az
Enhance Dead Reckoning
Markov Chain State Prediction
User predicts the grids that are his/her next targets
The transition probability of k steps:
By applying C-K equation:
Consider historical and current information of the user’s traces:
In time period t, motion trace does not include the predicted grids
acceleration values for this time period t will be replaced
current Info. historical Info.
Distance)and)RSSI)Traditional formula is not accurate • various obstructions • multipath effect • other factors
Measure the distance and RSSI • Train for different devices • Store in Hashmap on a smartphone
Distance)(meter)) RSSI)(dBm)))1m# $40dBm#2m# $45dBm#
Distance)(meter)) RSSI)(dBm)))
1m# $42dBm#2m# $46dBm#
Device 1 <-> Device 2
… …
d = 10[(P0−Fm−Pr−10×n×log10 ( f )+30×n−32.44)/10×n]
Radio Detection - RSSI
Bluetooth Detection• Build the connection between mobile devices• Describe the interferences between the wireless link• Use the mapping relation between RSSI and Distance • Satisfy the relation —> marked element in matrix as 0
Time (seconds)0 5 10 15
WiF
i RSS
I (dB
m)
-58
-56
-54
-52
-50
-48
Alice
Bob
Alice
RSSI Abruption
P1 P2
WiFi Detection
• Build the connection between mobile devices (WiFi Direct)• Describe the interferences between the wireless link• Use the mapping relation between RSSI and Distance • Not Satisfy the relation —> marked element in matrix as 1
Sensing Data Fusion
Curve Fit Fusion (CFF)
Solution: For each sensing technique, the one contains less errors will be assigned more weights
Matrix Generated by Sensing Approach
Error of each Sensing Approach
Differential Shadow Rate
Extend One Room to Multiple Rooms/Hallways
• Three sensing detection techniques have own features.
• Assign a, b, c values: one for a crowded room, one for a normal room, one for the room with few objects.
• When a user enters a room or a hallway, we set the parameters a, b, c as 1/3.
Matrix Generated by Sensing Approach
Error of each Sensing Approach
a=b=c=1/3
• Run iFrame and computes the average shadow rate of each room. • If the shadow rate satisfies the low/normal/high shadow rate, for the next period:
Challenge: how to assign weight for building multiple rooms?
a=b=c=1/3 corresponding a,b,c in CFF formula
Multi-device Combination
iFrame is a crowd sourcing mechanism, all the users collect and upload sensing data
iFrame provides three types of organizations
Mdi denotes the matrix computed by device i Maximum Space - combination matrix that has the most spaceMinimum Space - combination matrix that remains the least spaceMean Value - between these two extremes
default valueIndoor Map
Crowd Noise Filter
“Temporary Shadows” : users who are stationary in one room or a hallway, the generated shadow map might include errors
once a user does not change his/her position within 5 minutes, iFrame will not use his/her data until he/she leaves
Filtering “Temporary Shadows”
Phenomenon 1:
Phenomenon 2:Human crowd might cause interferences for the smartphones’ radio signals
Only dead reckoning approach is acceptable for the matrices with high crowd noise level
k-means clustering
Extend Rooms to a Real Building
Anchor Points AnalysisAnchor Points: Initial position of dead reckoning & the joints of rooms/hallways
acceleration ranges
correlation between the acceleration values on different axes
variance of acceleration magnitude Recognize Anchor Points
Hallway Assembling Users cannot cross the wall <—> elements in M are set as 1RSSI-Distance relation is satisfied <—> elements in M are set as 0More Samples, Higher Accuracy
Evaluation of iFrame
Experiment Setting
• The size of each grid as 0.5m x 0.5m
• 1-4 volunteers carry Samsung Galaxy S5 or Google Nexus Tablet
• Employ on Bluetooth and WiFi Adapter to communicate
• Volunteers in the experimental environment walk freely
Metrics
Error of Block Value:
Android 4.4 Kitkat
Original Floor Plan 10 mins
Ground Truth of Shadow Map
7.5 minsConstruction Approaches
Estimated Floor Plan 5 mins
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Dead Reckoning
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Bluetooth
Shadow Map Construction
Original Floor Plan Estimated Floor PlanGround Truth of Shadow Map
Trash Cans
TablesTrash Cans
TablesTables
5 mins 10 mins
Construction Approach
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Compare different sensing techniques
Detect the changes of layouts
Shadow Map Construction
Floor plan case study for the rooms with low shadow rate and high shadow rate
Floor plan case study for the rooms in a longtime
Original Floor Plan
Ground Truth of Shadow Map
Low Shadow Rate
High Shadow Rate
Construction Approaches 5 mins 7.5 mins 10 mins
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Estimated Floor Plan
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Original Floor Plan Ground Truth of Shadow Map
Construction Approaches 7 hours
Estimated Floor Plan 2 hours
!!!!!!!!!!"#$%&'&&&! !!!!!!!!!!"#$%&'&()!!!!!!!!!!!"#$%&')*+!
30 mins
!!!!!!!!!!"#$%&'&,,!
Anchor Points Detection
Entrance
Elevator Stairs
Trace 1
Trace 2
Anchor
Anchor
Anchor
Anchor Types of Anchors: Entrance, Door, Elevator, Stairs, etc.
Sensing Information:1. Acceleration Range2. Air Pressure 3. GPS disappear
Recognize
Evaluation of iFrameSingle Room Case Study
Extended Environment Case Study
Conclusion of Evaluation:1. Indoor shadow map can be generated within 5-10 minutes 2. The updated information is shown on the shadow map 3. Unattended Mode (More users, Higher Accuracy)
Summary• Measure RSSI values with other scanned mobile devices to help
construct the layout of indoor environments
• Sensor fusion approach to combine the indoor maps computed by Dead Reckoning, Bluetooth, and WiFi RSSI detections
• Crowd Noise Filter
interferences caused by human crowd“temporary shadows”
• Generate 2D shadow map of a room within 5-10 minutes, the updating information of map can be represented
• Extend Rooms to a Real Building
Hallway Assembling Anchor Points Analysis