a
fully
immersive
experience
(Figure
2.11).
24
infrared
cameras
communicate
with
the
iPads,
and
a
bank
of
computers
renders
the
real-time
view
with
the
Unity
game
engine.
Controlled
environments
open
up
exciting
possibilities.
A
number
of
museums
have
attempted
to
strengthen
the
connection
between
researchers
and
the
public
by
experimenting
with
open
offices.
But
these
initiatives
have
had
mixed
success;
the
concern
is
that
being
‘put
on
display’
makes
researchers
less
productive.
VR
can
provide
a
suitable
alternative
that
lets
visitors
step
into
researchers’
shoes.
In
the
Peabody’s
case,
a
purpose-built
room
may
simulate
the
site
of
an
excavation,
the
tomb
of
a
mummy
or
the
dangerous
Wild
West
at
the
time
of
O.C
Marsh’s
expeditions.
Modular
rooms
built
with
lightweight
materials
and
an
adjustable
overhead
scaffoldings
of
cameras
and
sensors
may
allow
curators
to
swap
out
scenarios
quickly,
adding
to
a
feeling
of
dynamism
in
the
renovated
museum.
Navigation
There
is
also
some
potential
of
AR/VR
to
assist
with
navigation
through
a
museum
space.
Untethered
headsets,
hypothesized
previously,
may
incorporate
turn-by-turn
navigation
visuals
to
guide
users
to
their
preferred
exhibits.
The
dividends
for
disabled
users
(such
as
those
with
visual
impairments)
may
be
enormous,
since
the
digital
interface
can
fill
up
the
headset’s
entire
field
of
view.
But
until
such
headsets
become
a
reality,
we
may
attempt
to
build
smartphone
and/or
totem-enabled
AR
navigation
tools.
To
give
an
example,
a
hypothesized
Deskfruit-style
totem
may
be
scanned
by
a
smartphone
or
held
under
an
interactive
kiosk
to
transform
into
an
isometric
map
(Figure
2.12).
The
map
can
pinpoint
the
visitor’s
position
(a
digital
You
Are
Here),
inform
about
congestion
via
heatmap,
or
trace
a
path
across
large
spaces
or
multiple
floors.
Few
developers
are
operating
in
this
niche.
By
and
large,
any
museums
experimenting
with
navigation
are
using
smartphone
apps
in
which
they
have
either
built
scale
models
of
the
exhibit
space
(such
as
the
Peabody’s
David
Friend
Hall
app16)
or
interfaced
with
Bluetooth
beacons
to
provide
turn-by-turn
instructions.
A
notable
example
is
Project
Lumin17 from
the
Detroit
Institute
of
Art,
which
uses
Google’s
Tango
framework
for
indoor
tracking
and
incorporates
AR
functionality
by
tracing
a
path
on
the
floor
to
the
desired
exhibit
(Figure
2.13).
Conclusion
AR/VR
technologies
are
developing
rapidly,
and
each
iteration
of
smartphone
tools,
headsets
or
specialized
spaces
provides
novel
opportunities
for
the
Peabody
to
explore,
but
their
use
is
not
without
challenges.
In
addition
to
the
cost
and
effort
required
to
craft
and
curate
AR/VR
experiences,
museums
must
take
care
that
they
do
not
distract
from
the
physical
exhibits—the
stars
of
the
show.
Any
use
of
these
technologies
must
attempt
to
intensify
displays’
salience
and
make
them
come
alive.
Navigation
Technology
Types
of
Localization
Technologies
Traditionally,
there
are
three
types
of
indoor
localization
systems:
1.
Networked-based
systems: localization
systems
based
on
a
network
of
beacons,
such
as
WIFI
routers
or
Bluetooth
beacons.
These
systems
typically
use
the
information
of
the
wireless
signals
to
estimate
the
position
of
a
target
carrying
a
wireless
device.
2.
Inertial-based
system: localization
systems
that
use
built-in
sensors,
such
as
accelerometers
and
cameras
in
a
phone,
to
measure
the
motion
of
a
target
and
estimate
its
position
relative
to
the
starting
point.
3.
Hybrid: localization
systems
that
combine
both
network-based
systems
and
inertial-based
systems
to
measure
the
position
of
the
target.
After
receiving
measurement
signals,
these
navigation
systems
use
a
variety
of
methods
to
calculate
the
position
of
a
target.
There
are
four
main
categories
of
localization
algorithms:
1.
Simultaneous
localization
and
mapping
(SLAM): SLAM
is
a
type
of
algorithm
that
enables
a
measurement
unit
(typically
a
mobile
robot)
to
generate
a
map
of
its
surroundings
and
its
position
in
this
map
at
the
same
time.
This
method
assumes
that,
as
a
measurement
unit
repeatedly
passes
by
a
set
of
features,
it
is
possible
use
these
features
as
referencing
points,
based
on
which
an
algorithm
can
update
the
position
estimate.
In
particular,
the
algorithm
takes
in
sensor
measurements,
predicts
the
expected
position
and
sensor
measurement,
compares
the
predicted
measurements
with
the
actual
measurements,
and
uses
the
error
to
update
the
position
prediction
algorithm.
2.
Range-based
algorithms:
a.
Time
of
flight:
the
algorithm
measures
the
propagation
time
of
a
signal
between
the
transmitter
and
the
receiver,
and
uses
time
divided
by
the
speed
of
signal
to
estimate
the
distance
between
the
transmitter
and
the
receiver.
b.
Angle
based
algorithms:
the
algorithm
measures
the
angle
of
arrival
of
a
signal,
and
uses
triangulation
methods
to
calculate
the
position
of
the
receiver.
3.
Received
signal
strength: the
algorithm
measures
the
received
signal
strength
between
a
transmitter
and
a
beacon,
and
estimates
the
distance
between
the
two
based
on
the
fact
that
signals
attenuate
as
they
travel
over
distances.
4.
Range-free:
a.
Proximity:
the
algorithm
checks
if
the
user’s
device
is
connected
to
any
beacons
in
the
network,
and,
based
on
this
connectivity
data,
infers
the
position
of
the
user.
(See
Fig.
3.1)
b.
Fingerprinting:
the
algorithm
measures
signals
received
by
the
beacon
network
and
compares
the
received
data
with
sample
measurements
in
the
database,
and
selects
the
position
recorded
in
the
database
that
best
matches
the
received
signals.
(See
Fig.
3.2)
Widely-Used
Localization
Technologies
WiFi
WiFi-based
localization
systems
use
the
ranged-based
algorithms,
the
received
signal
strength,
and
the
fingerprinting
algorithm
to
calculate
the
distance
between
a
user’s
device
and
a
WiFi
router.
The
main
advantages
of
WiFi
localization
system
are
wide-reception
range,
low
cost
of
material,
low
energy
consumption,
and
device
availability.
In
particular,
almost
every
mobile
phone
is
equipped
with
WiFi
connection,
and
thus
the
visitors
to
a
museum
do
not
have
to
carry
extra
devices
on
them
to
enjoy
WiFi-based
navigation
technology.
The
main
disadvantages
of
WiFi
localization
system
are
installation
difficulty,
poor
distance
measurement
accuracy,
and
requirement
of
complex
signal
processing
algorithms.
It
typically
takes
an
entire
research
team
to
build
a
reliable
WiFi-based
localization
system,
and
it
is
very
hard
to
guarantee
consistent
measurements
over
time,
because
the
system
is
highly
sensitive
to
environmental
errors.
These
drawbacks
limit
the
application
of
WiFi
localization
system
in
most