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.