University of Massachusetts, Amherst
Ferret: RFID Localization for Pervasive Multimedia
Xiaotao Liu,
Mark Corner, Prashant Shenoy
Scenario: I’ve Lost my Keys
People frequently misplace common items
books, keys, tools, clothing, etc.
difficult due to the sheer scale: we interact with >1000s of items
Need a system to find objects quickly and efficiently
then tell the user where the object is
Problems
Tracking objects can be broken into sub-problems
Locate: find position, perhaps not exact, but a general idea
Store: keep object locations in a convenient place
Update: when objects move, need to change store
Display: Present locations to user in a helpful way
Solution: FerretProvides a real-time augmented reality service
locates, stores, updates, and displays object locations
intended for nomadic objects not mobile ones
Leverage passive RFID, multimedia, and location systems
passive RFID: inexpensive, scalable, maintenance-free
multimedia systems: provide convenient display and storage
location systems: bootstrap process of finding locations
Goal is to pack all functions into a hand held device
including RFID detection, storage, and display
a combination of video camera and RFID reader
OutlineMotivation and Applications
Overview of Use
Design of Ferret
Sensor model
Offline location algorithm
Online location algorithm
Display
In paper: Storage, Update for nomadic objects
Prototype implementation
Experiments
Speed and accuracy
Robustness to different movement patterns
Related Work
Conclusions
Overview of Operation
User selects some object(s) that she is looking for
She wanders around a room, or building, holding Ferret system
During this process, the reader scans for nearby RFID tags
Ferret detects the RFID tag of interest, localizes tag
It then displays an outline of where the object is on the screen
willing to settle for a probable region of where the object is
depend on human skill to find the exact location
refine region as system runs
present improved results in real-time
RFID LocalizationPassive RFID tags are not self-locating
Instead we depend on the handheld to locate tags
Passive RFID tags have significant error rates
false negatives are frequent
false positives due to reflections
Locate using probabilistic model
inspired by [Hähnel et. al]
RFIDreader
1. energy
3. id
2. use RF energyto charge up
Bayesian Probability Model
Goal: p(x|D1:n): Probability of tag at x given readings
Initially, without readings, p(x|D0) is uniformly
distributed
Assume we have p(x|D1:n)
Positive reading
p(Dn+1=True|x)
Bayes’ rule p(x|D1:n+1) = α p(x|D1:n) p(Dn+1|x)
α – normalization factor
Similarly, for negative readings
p(Dn+1=False|x) = 1 - p(Dn+1=True|x)
Object with RFID tag
Ferret with RFID Reader
Coverage region of
RFID reader
P(D=True|x) = 0.3
P(D=True|x) = 0.9
Tag Detection Probability
Manually measure probability of detecting tag (positive reading)p(D =True|x) x – tag’s position
Ferret Localization Algorithm (+ reading)
Multiple readings come from user mobility, previous, or shared readings
Detects objectat time t1
Detects same object at latertime t2 from a different view
Location region is reduced further
Ferret Localization Algorithm (- reading)
Repeated intersection of positive and negative readings
Offline Algorithm Complexity
We refer to the previous algorithm as the “offline” algorithm
Each + or - reading Ferret performs O(n^3) operations
n is the number of sample points
it must rotate, translate the RFID sensor model
multiply each sample point against every other sample point
must do this for each object!
Computational requirements at least 0.7s on a laptop
reader is producing at least 4 readings per second
some readings include multiple objects
Algorithm most useful for back-annotating video
Online AlgorithmTo address real-time concerns use an “online” algorithm
instead of intersecting all interior points, just find convex intersection
only uses positive readings, not negative ones (keeps shape convex!)
Complexity reduced to O(n^2) or 6ms per reading
(x1, y1)
(x2, y1) (x3, y1)
(x4, y1)(x1, y1)
(x3, y1)
DisplayEach RFID location is a 3-D shape
To display we simply project this 3-D shape onto a 2-D screen
Ferret PrototypeThingMagic Mercury4 RFID reader
30dBm (1 Watt), monostatic circular antenna
Alien Technology “M” RFID Tag
EPC Class 1, 915 MHz
Sony Motion Eye web-camera
320x240 at 12fps
Cricket Ultrasound 3-D locationing system
global location not necessary, but need relative locations at least
Sparton SP3003 Digital Compass
Pan, tilt, and roll
Software
translate between coordinate systems, rotate, and display
Ferret Prototype
Cricket locationing sensor
Compass
RFID antenna
ThingMagicRFID reader
Built-in Camera
Evaluation
Evaluation metrics:
Size of location region for many objects
Speed of localization for a particular object
Robustness of localization to mobility patterns
Evaluation setup for many objects:
Place 30+ objects with passive tags around the room
Move Ferret system around the room by human for 20 minutes
CDF of localization over 30 objects
Evaluation setup for single object:
Place single object in room with passive tag
Move Ferret system in and out of view randomly and using a specific pattern
Size of localization after some amount of time
Online Vs Offline (CDF-30 Objects)
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
Volume (m^3)
Pro
babi
lity
Offline
Online
Offline algorithm outperforms online, but most objects localized to 0.2 m^3
Refinement: Relative Volume (1 Object)
0102030405060708090
100
0 50 100
Time (seconds)
Rel
ativ
e V
olu
me
(%)
Volume size drops down 100 times to 0.02m3 in 2 mins
When starting with previous readings, localization is faster
Refinement: Relative Projection Area
00.10.20.30.40.50.60.70.80.9
1
0 50 100
Time (seconds)
Rel
ativ
e A
rea
Final projection area decreases 33 times in 2 mins to a 54 pixel diameter circle
Different Movement Patterns
Straight Head-on z-Line Rotate Circle
online Volume (m^3)
0.020 0.0042 0.023 0.026 0.032
offline Volume (m^3)
0.0015 0.0030 0.0017 0.0011 0.026
Offline/
Online 13.33 1.40 13.52 23.63 1.23
Circular motion pattern performs the worst: no diversity in viewsOffline algorithm’s advantage comes from negative readings
so head-on and circular perform similarly
Related Work
Grown out of our work on Sensor Enhanced Video Annotation
SEVA ACM Multimedia 2005 (Best Paper Award)
Used active sensors for location
RFID Localization inspired by techniques from [Hähnel et. al]
2-D sensor model, application of Bayes rule positive readings
we add 3-D model, negative readings, and online technique
focuses on SLAM/localizing reader, we focus on reverse
LANDMARC and SpotON RFID locationing
active RFID and signal strength
Conclusions
Ferret: a scalable, RFID-based, augmented reality system
localize objects augmented with passive RFID tags
display probable location regions to a user in real-time
Uses two algorithms: online and offline
both are accurate and efficient (localizes objects to 0.2m^3 in minutes)
robust to a variety of user mobility patterns
Ferret lays the ground work for other augmented reality applications
University of Massachusetts, Amherst
Ferret: RFID Localization for Pervasive Multimedia
Xiaotao Liu,
Mark Corner, Prashant Shenoy
Location Storage
Locations (3-Dimensional probability maps)
Storage on reader
simple to implement, but must acquire readings as it goes
Database
any Ferret readers can take advantage of prior knowledge
also permits offline searching, but privacy/authorization concerns
Storage on writable tags
tags self-locating and provide locations to non-Ferret systems
What if objects move?Nomadic objects may have moved since previous readings
when online algorithm detects empty intersection, reset
offline algorithm more complex, uses a probability threshold
?
Bayesian LocationingModule
Device Drivers for Cricket and Compass
RFID Module
(operate RFID
reader)
Ferret Software Architecture
Ferret System
VideoRecordin
g
Visualization Module (modified from FFmpeg)
via TCP, Use SQL-like language
Deal with large amount of
data,Optimized for
real-time usage
Use optics model
Intercept original display function
Fuse video, tag’s location together
Compute projection of location estimates
Display projection boundary
[Hähnel et. al]
“To each of the randomly chosen potential positions we
assign a numerical value storing the posterior probability
p(x | z1:t) that this position corresponds to the true pose of
the tag. Whenever the robot detects a tag, the posterior is
updated according to Equation (1) and using the sensor model
described in the previous section.”
In this paper we analyze whether recent Radio Frequency Identification (RFID) technology can be used to improve the localization of mobile robots and persons in their environment.
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