Intel RealSense is known in the robotics community for its plug-and-play stereo cameras. These cameras make gathering 3D depth data a seamless process, with easy integrations into ROS to simplify the software development for your robots. From the RealSense team, Joel Hagberg talks about how they built this product, which allows roboticists to perform computer vision and machine learning at the edge.
Joel Hagberg
Joel Hagberg leads the Intel® RealSense™ Marketing. Product Management and Customer Support teams. He joined Intel in 2018 after a few years as an Executive Advisor working with startups in the IoT, AI, Flash Array, and SaaS markets. Before his Executive Advisor role, Joel spent two years as Vice President of Product Line Management at Seagate Technology with responsibility for their $13B product portfolio. He joined Seagate from Toshiba, where Joel spent 4 years as Vice President of Marketing and Product Management for Toshiba’s HDD and SSD product lines. Joel joined Toshiba with Fujitsu’s Storage Business acquisition, where Joel spent 12 years as Vice President of Marketing, Product Management, and Business Development. Joel’s Business Development efforts at Fujitsu focused on building emerging market business units in Security, Biometric Sensors, H.264 HD Video Encoders, 10GbE chips, and Digital Signage. Joel earned his bachelor’s degree in Electrical Engineering and Math from the University of Maryland. Joel also graduated from Fujitsu’s Global Knowledge Institute Executive MBA leadership program.
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Transcript
hello and welcome to the robohub podcast
today we will be hearing about intel
realsense from joel hagberg
head of product management and marketing
for their computer vision product lines
at intel realsense they’ve developed a
series of camera products
which allow robots to leverage
off-the-shelf computer vision technology
with intel’s realsense technology robots
can generate
three-dimensional videos extracts for
little movement as people walk
detect gestures and objects and more
joel talks to our interview about a
about the value of leveraging tightly
integrated
hardware and software products for
startups and established companies alike
particularly to enable them to hit the
ground running
he also discusses the integration of
machine learning
into intel realsense products
hello welcome to robohub can you tell me
a little bit about yourself
yeah my name is joel hagberg i lead the
marketing product management and
customer support teams at intel
realsense
i’ve been here about three years uh
prior to that did a lot of consulting in
uh the mobile payment space and some
memory acceleration and also spent
20 plus years in high-tech product
management marketing for
storage devices flash memory
hard disk drives a lot of other things
and many building blocks are used in
robotics uh today and
what brought you to uh the real sense
team at intel
well i was uh looking i actually i was
doing some consulting getting some
uh flash advanced um
apache pass dimms from from intel for an
ai accelerator project
and was talking to their vp of sales and
he said you know we’re really looking
for
somebody come help with product
management marketing for
uh some emerging computer visions uh
products would you be interested in
talking to us
and as a consultant at the time i should
sure i’ll come in and talk and
as i uh met with real sense i realized
you know this technology
uh in real sense has been in this
business you know at the time was like
eight years
uh doing some really fantastic things
with
uh computer vision both uh kind of in a
bunch of different areas
at the time coded lights and stereo
products
and was looking to get into lidar and i
just felt like it’s that
kind of a nascent industry that’s
getting ready to
you know kind of become ubiquitous and
felt it was really a good time to get in
and learn
you know and and if you look at the use
cases robotics is the
largest use case for this and i felt
like well hey robotics is an area that’s
going to
see explosive growth computer vision
this energy explosive growth
auto you know assisted driving adas
you know autonomous vehicles and robots
and
and cars is that an area where we’re i
think it’s going to see tremendous
growth so
that’s really what got me excited to
join and it’s kind of kind of like a
startup within intel it’s part of the
emerging growth
and incubation group so it’s uh we have
an incubator we pull in a lot of other
you know technologies so you’ll see uh
intel does a lot of stuff with
um with uh computer vision whether it’s
lidar
radar and and uh the stereo condition
that we’re we’ll be talking about today
uh but uh we you know there’s a whole
team
and mobile line that’s doing stuff for
for vehicles so we’ve got a
uh a pretty broad you know kind of r d
team doing research our cto is extensive
experience
in in computer vision so it was just
felt like a
very exciting team to join and coming in
it is like a startup within this you
know the big
umbrella of intel but doing a lot of uh
very kind of moving very fast and and
making
uh you know uh changes quickly so that’s
one of the things that really appealed
to me about
the intel realsense team yeah
can you walk us through a couple of
products that are offered by realsense
sure if you look at at real sense uh
as as we you know look at the kind of
the use case of products there was a
series of products that were used uh
encoded light that were
installed in pcs used in very short
range
indoor applications and uh yeah so if
you look at the last 11 years we’ve
shipped over 2 million products into the
market
and coated light was kind of that early
phase probably in 2018 the stereo
products
were launched and i joined in 18 and the
stereo products the d400 family that we
have today
uh was starting to you know kind of see
some uh
growth in the uh both in that we have a
an e-commerce site so we were seeing
direct touch with customers
you know really startups working on new
projects we’re able to source directly
from our website
and then we sell through distribution so
a lot of specialty distributors that are
in the supplying the robotics
manufacturing
uh digital signage markets were
promoting our products so
uh we have a d400 family we have a d415
and d435 at the time which were
our our volume products uh the you know
d415
has a kind of a little bit shorter range
with a rolling shutter
and then the d435 has a
they’re both in the three meter range
but just seems like a little bit you
know
a little bit further range with the d430
f5 and it has a rolling shutter
and that that is the product that’s most
widely adopted
in the robotic space and if you look at
the way intel
you know goes to market we sell a
peripheral which is plug and play
you can start you know testing
immediately or you could take
uh if you want to look at kind of a
tighter integration you can buy a module
you can buy an ac card integrated
actually into your design
and enable you to reduce cost a bit or
you know change the look and feel of the
product
more to your to the specific oem’s uh
you know platform but those are the the
volume products that we launched
in 2018 which have been really widely
adopted into the
into the robotic space and this is a
series of stereo vision cameras that use
a light pattern that they emit onto the
scene and then
that that light pattern it’s an ir light
correct
yeah well if you look at it the stereo
cameras themselves
work in bright sunlight to
to dark so we do just take the you know
natural sunlight
images in through the stereo imagers
and those are able to you know take in
and create depth information
outside of you know uh needing an
illuminator and a projector
to uh to light up a scene so we do have
we do project a stereo a pattern which
the stereo
uh cameras use in low light conditions
or
or no light conditions so we are able to
use ir
so if you look at it we have kind of the
the data comes in through
the uh the the stereo sensors we have
both
rgb ir we have the projector
illuminators which
do light up the scene and project a
pattern so as you indicated with
is the ability to to see texture to see
things
using those uh that that pattern is
projected onto a scene
and i’ve actually used the intel
realsense d435i before
and one of the amazing things about it
you just buy a piece of hardware
connects by a usb very simple to set up
and um that like
that being able to connect something by
usb
and be super simple how has that
affected the design
of the products that you’ve built yeah i
think
that actually what you hit on is one of
the kind of the
the cornerstones of the intel approach
which is let’s make it very easy to get
up and started so you’re
whether you’re you know a researcher at
a university whether you’re a student in
high school
you can plug and play and and start
working immediately and we have
you know ctos of of startups to
to major corporations that like that
they can just buy a camera
plug it in and start testing immediately
and one of the things
in as i talked to the the design team
that they felt was really critical was
to have an open source
community and an open source sdk so we
built the the sdk 2.0 that we
launched that we support for our our
d400 family actually that
sdk applies to all of our cameras so if
you buy the
for example your d435 that you
referenced
a plug and play you can use code
examples from
that intel provides on our github site
or you can use code examples from the
community to get
immediately up and going you know you
might want to do you know
simultaneous slam and or visual slam
with
some algorithms that are available on
the site or you may want to
look at you know collision avoidance
with your robot
you know there’s things there or your
you have an articulated arm you want to
look at you know what code examples are
on there for object detection and so we
built this uh the sdk with the
thought hey let’s make it open to the
community to allow them
to learn from each other to
provide you know kind of support uh as a
broad sense to
to get up and going quicker and that’s
one of the thoughts in the design was
let’s make it
usb plug and play as a peripheral now we
do
see that once uh a company has
adopted it tested it and deployed it uh
depending upon the use case
there are people that like to integrate
it into their device
and for some uh you know
to reduce cost and to to uh to
make it look and feel like you know just
a single oem device with the camera
embedded in it
but in the case of some of the largest
robotics companies we
we have deploying it we’ll see them
still use the peripheral because it’s
calibrated
plugs in and it just works from from the
get-go and they can
they look at it and say hey intel you’re
the computer vision expert
we’re a robotics expert we’re going to
focus on the robotics operating system
the robotics
uh you know uh you know kind of the
the use case for this particular robot
and we’ll let you
worry about giving me the data so i can
make decisions
and so plug and play the peripheral
we’re finding even some
very large robotic manufacturers are
using the peripheral
in their design but we’ve got others
that are saying hey we’re going to a
high volume robot
for food delivery and you know we’re
going to be
you know going outdoors and we want to
embed
you know the module inside of an ip67
rated camera for out in the rain and
and so we have customers that are uh
integrating the product into their own
device in a robot
you know then in the use case of the of
a delivery robot for
you know outdoor food delivery or even
delivery inside of a restaurant or
delivery in a hospital
they’re integrating the module into
their design
in in multiple spots that way they’re
deploying uh they’re they’re able to see
multiple cameras inside of the design
and integrate it into their you know
their base
uh robot platform
yeah and i imagine that designing this
so that it can run off of usb and then
working within the power constraints of
uh what the usb cable can
output and also what the computer and
whatever is powering it
can actually give to over that usb cable
has forced you guys to make the design
very specialized to work off the usb
have there been any trade-offs that you
make and that maybe
as you see for some of these companies
that are trying to integrate this
into a single cohesive
product that doesn’t have these
limitations
yeah i think if you look at it from like
from a design
philosophy intel wanted to to build
a very high performance a computer depth
camera
that would give you very good quality
depth
with a an asic that does all the
calculations at the edge so basically
you’re able to do all that work inside
the camera
and then transfer the data and and we we
have a wide range of frame rates
and resolutions available so you can
really can
you kind of tune what you receive
at the system level uh based on your
requirements so so using the usb
bandwidth obviously
you know going to usb there’s other you
know bandwidth you do some trade-offs
like for ethernet you may have a
a trade-off and resolution so we do have
customers that that buy our
our cameras integrate them into an
ethernet enclosure for some
industrial applications uh we we do see
that but there are some trade-offs on
resolution so
it really is you know you look at the
end
user or the robot manufacturer will look
at it and say
well what do i need from the scene how
much information do i need for object
recognition how much
detail do i need for scanning a room
and building you know kind of a 3d map
of this environment
or to do visual slam what kind of amount
of data do i need what kind of frame
rate what kind of
resolution do i need to to build this 3d
model
so there so we’ve purposely built it
with a with that kind of flexibility to
you know dial up the frame rate or down
or
adjust the resolution up or down and by
keeping it
within that usb power mode we’re able to
to do all that at the edge and still
keep it a very low power you know 150 to
300 milliwatts i mean you’re looking at
a very low
power device and because we’re doing the
processing
within that asic you don’t really need a
a very high end graphics processor at
the system level
to do that work and i think you’ll look
at some other
solutions in the market where you’re
trying to pull data in
you’re you’re having you’re taxing your
system with a very you know a high-end
processor or graphics engine
to do that calculation that we’re
actually doing within the camera
and so realsense has done a really
excellent job at
integrating hardware and software and
then outputting that
over a common format
now how does machine learning and ai fit
into this story at real sense
yeah i think one of the things that
obviously
as we talked to our partners uh there’s
a lot of work
in ai inference engines and machine
learning happening within
the industry happening within an intel
and
uh with with our cameras the the
the ability to do all of this
very fast processing at the edge
really enables the the remote system
to make inference and decisions
on the fly because you’re getting this
data processing at the edge
so it does lend itself to a wide range
of ai applications and machine learning
where
you know the robot can course correct
whether it’s a drone
flying making judgments you know on on
at a high speed or it’s you know trying
to determine where to land
it can you know use its depth uh
information to make a decision on you
know safe places to land or
how to approach or how to avoid an
obstacle and
uh so those things are happening but we
also see a lot of
work with companies that are
doing this running algorithms for
object detection and in the robotic
space
you know if you look at e-commerce
there’s a significant because of the you
know significant growth especially with
coven
of you know e-commerce and ordering at
home and having stuff delivered to your
home office
there’s been a significant push at how
do we improve
the um the performance of
uh robotic arms for picking place and i
think
part of that is enable the machine
to have uh the ability to make a
decision based on what’s in a bin
okay knowing machine learning and
training
and there’s training algorithms you know
have built over
you know repetitive pattern repetitive
uh use
and uh and and and
uh kind of uh applica or data sets that
that may have been purchased or trained
within
one of our our partners that can allow
a pick and place robot to make better
decisions and then
you know we have companies like uh you
know right-hand robotics that are
are looking at how can we use a labor
multiplier and be able to use
you know one person to run multiple
robotic arms and
and as you improve your training
algorithms you then also improve the
efficiency of these robotic arms to make
decisions
which then can discern objects in a bin
recognize locations
and look at how best to pack you know
something for shipment and that would
allow an operator
to you know kind of supervise multiple
screens and
and if there’s a a problem it says okay
i see this robotic arm is having an
issue with this new object that’s coming
into
the test well we need to do some more
training on that particular object so
it’s
it allows the operator to then identify
things that are
troublesome for robotic arm to make a
decision
and then how to approach it how to how
to pick something up how to
you know discern one object from the
other
it’s something that machine learning i
think comes definitely into play and
then
i think the robotic arms and just the
you know kind of the
escalation of uh e-commerce demand has
really driven
how do we improve efficiency in this
machine learning algorithms to to really
train the arm to to be more efficient
and and
and deliver you know improved efficiency
throughout the supply chain and that’s
one of the things that i think when we
we started you know working on some of
these applications
we saw there’s a rippling effect okay if
you can improve efficiency at the
pick and play space well all of a sudden
then you need to improve efficiency on
moving those you know full boxes out
with robots and
now your amr robots become a
a a a much
more focused you know effort on how do
we okay how do we improve the amr
move away from you know liberal tags or
guided lines and guided vehicles to an
autonomous vehicles that can actually
move faster and make decisions uh
and and you know more safely how to stop
quickly
if you know some some person or some
other
object comes into you know an area where
they
felt they could move safely so so i
think machine learning
probably a long-winded answer to your
question i think we see
it kind of bubbling up in certain use
cases
where hey these training algorithms are
really delivering
value to the not only to the robotics
manufacturer
but to the end customer who ultimately
is saying hey here’s the roi i’m getting
by investing in these robots so the real
sense product line
is it’s great for being able to tag
you’ve got your depth data
on top of the video data sure you can
use that to train and tag these ml
algorithms
are you guys also developing these
algorithms
in-house and then making them stay
publicly available
for somebody who’s prototyping and they
want their depth camera but they also
want to
run some standard algorithms on it
object detection facial recognition
without having to rebuild the wheel or
go through a lot of documentation sure
yeah i think one of the things one of
the challenging things in the industry
is that
data sets and and the reality is that we
buy data sets ourselves to help train
algorithms for
certain applications and one of the
challenges is these are not
something we can pass on publicly right
so we we acquire a data set we
use it in an algorithm and for example
we’ve launched uh beginning this year
a facial authentication camera well part
of the last
few years of work has been we we had
uh we saw an increased use of our
standard
actually the d415 which is the
um a little lower cost uh
camera than the d435 and that product
comes in 149 dollar list versus the 179
list of the d435
that camera in its its module form again
for further cost reduction
and with its rolling shutter it has a
very good performance at
that short range we found it to be
starting to see significant uptick in
use
in facial authentication applications
with third-party fa software
well intel realsense at the time had
already been working on
uh facial authentication software for a
customer
who is looking to use our you know
cameras
in uh residential door locks so we
started
partnering with them and and realized
okay for us to really
build this out we really need to to
kind of build uh data sets and you know
buying data sets
and you know testing you know a wide
range
range of ethnicities and you know kind
of
you know and in capturing data in all
low light no light bright lights you
know backlight conditions
it is a pretty daunting task so over
a few years we’ve invested a lot of time
data collection ourselves you know with
tens of thousands of you know
individuals across the world of
different ethnicities
to build an algorithm into our
facial authentication camera so at the
beginning of you know
2021 we launched the interior real sense
id
which is a on-device facial
authentication
camera with an algorithm built into the
product so in that case we’ve invested a
lot of time and effort
to build a product which is a
very very high performance uh very low
cost low power
but also has significant anti-spoofing
and part of the anti-spoofing is
building that
data set and it is really a core ip
to our device that data set so so it’s
something that
you’ll find most companies that are are
doing
the the object recognition or facial
authentication
they’re not putting out those data sets
you know
for the public because there is ip and
and lots of uh time and money invested
to build that data set
so i think it’s it’s something i think
you’ll see there’s a lot of universities
out there that have
data sets available for object
recognition again you end up buying it
or
getting access it but one of the
stipulations is if you’re using it to
train your system
is you’re not going to monetize that set
or you’re not going to you know
you really need to go back to those
universities and and look for
you know what’s possible for you know a
startup or a
uh you know an individual researcher
what data sets are available
you know from the edu community that
might
enable you to do some some preliminary
work but i think
uh for us uh you know today
we have a lot of partners that are doing
object orientation very
uh with our cameras and they’re one of
their core ips is the dataset they built
you know that allows them to improve and
tune that object recognition
so the algorithms um that are the
results of the ml can be
run and given to the consumer but access
to that data set that’s the
gold mine that’s kept separate yeah i
think if you if
we look at our partners that are doing
object recognition and some of the other
uh uh applications that
they are you know training the device
with their data set and and they don’t
you know share that publicly
so i think it’s it’s it’s really one of
those things where i think as we look
forward
we’ve done some work with robots uh in
warehouses for
inventory management um and uh
actually dimensional weight for
measurement for
logistics and billing and shipping and
in that
instance you know robert can a robot can
go along scan a shelf and say
okay this sku number you know one two
three
is this size based on that palette
there’s 23
you know boxes in that so so a robot can
come do inventory
with that but we’ve had to train it with
an algorithm so when we go to a
to a warehouse partner we’re actually
building a data set
unique to them so we’re in in the
warehouse we deployed these for
say incoming inspection any new sku
comes in
it’s scanned we have a you know a
mounted we can have a desk mounted
device which or
or a table mounted device which is
scanning you know
individual packages we can have a cage
scanning
uh and we actually use we have a lidar
camera
which has very good edge fidelity that’s
been used in this
space our l515 lidar product we have
released a
dimensional weight software and now this
dimensionalization software is available
on our website as a trial
so people can pick it up and look at it
and so how do i do you know
uh you know volumetric billing well you
can use this to a very very quick
snapshot put a a uh package on it on the
table
and use our camera to take a quick look
at it and you know give you the volume
for for that legal for trade billing and
so
in that case you know we’re able to
to recognize the size and shape of a
skew
and we can build a database of a
particular
customer for their warehouse of all the
skus they they maintain
then they in turn can use that you know
use take that algorithm
that’s been trained around their unique
skus and have a robot
now do inventory do you know reordering
do
uh a kind of volumetric building as
things are going out to be loaded on a
truck
so there’s a lot of interesting use
cases but in a sense
one of the things that we’re enabling
with this dimensionally software is
the customer to build their own uh kind
of
data set that of their device of their
own unique
you know products that allows them to to
move forward
with a wide range of applications and
and we actually see you know customers
coming and saying okay this is
this is great and now i want to do i’m
moving a lot of things over conveyor
belts can you look at that so we have a
lot of
you know creative um ideas coming
out of the the initial dimensional
weight billing system
now it’s inventory control system but it
is in in a sense
you know building a a data set around a
unique
you know customer uh you know kind of
um environment and so we do see our
cameras being used in that case
and and for us it’s we’re enabling the
customer to build that data set
over time by capturing information use
of our cameras
yeah so realsense has been immensely
impactful for
the market of 3d reconstruction right
and
it still feels like a field that has a
lot of room to grow and become more
commonplace in product offerings
by various companies what would you how
would you describe the current state of
this market and where it will grow to
yeah i think we we see that there’s
there’s a number of you know partners
revised
like dot products that are building some
uh interesting handheld devices we’ve
got other companies that are
uh you know doing uh kind of 3d
reconstruction
of inspection so they might go into a
an environment after an earthquake and
do inspections
of structures like bridges and
and overpasses and look for structural
cracks in concrete
and they’re doing a very detailed 3d
scan
and today a lot of it is using our
stereo cameras which work
very well indoors and out we do see
others doing the the current lidar
offering the l515 is
like i said it’s very good for that edge
fidelity in the building
it’s also very good at scanning from
from distance
but today that is that l515 is an indoor
only product so if you’re doing
rick and you know reconstruction indoors
of a
of a house or an an indoor environment
lidar works really well and and i guess
again we are looking at the future how
do we expand
you know the the use of that lighter
beyond because if you look at
again there’s a lot of very high
performance outdoor lidar which is
very expensive you know five ten
thousand dollars and we’re talking about
a 349
lidar device which is uh gives you
excellent depth data
you know in that you know you know
forward uh kind of nine meter range
and um so yeah room the cons
reconstruction you’re talking about we
see
new construction um uh kind of uh
home real estate kind of applications we
actually have
some police using it for crime scene
reconstruction
uh so we see see these things coming and
we have a
company using it for ships for
inspection
of uh you know kind of structural
inspection and you know just even just
maintenance as is this area been painted
you know is is there any cracking or
peeling of paint as they scan
as they walk through with a handheld
scanner
uh the structure of a ship or an oil
field
you’re looking for you know potential
maintenance hazards on
pipes and things so so we’ve got
companies looking at
doing uh work with drones for field
inspection
uh scanning with tablets or robots
for sites and i think it’s as you
indicated it’s kind of a
it’s an emerging space um and we see
a a number of customers building these
these handheld or
robot mounted uh devices for the
application but i do think it’s a
it’s a it’s a still an early stage in
that we
if we look at our use cases you know
robotics is
by far the dominant use case i think if
you step down facial authentication
is is another very large use case for us
we see
you know deployed in point of sale atm
systems around the world
people are starting to use face
authentication uh door
uh kind of corporate access control
instead of the
you know the badges that you badge in
you’re now people are scanning faces as
they walk the doors
and we see it deployed in residential
home locks and we’ve already launched
some residential home blocks with
partners in china
we do see them coming to you know the
rest of the world
over time uh for facial authentication
so that’s kind of the second
big bucket of use case and then um
next is scanning so that the scanning
you’re talking about you know we do see
you know body scanning room scanning uh
you know
inspection uh you know and menu
construction
kind of scanning we’ve got customers
using it to
scan a body into avatars for
for gaming uh we see health clubs uh
scanning you know doing a complete you
know 360 scan of a person to look at you
know
how their inches are reducing over the
course of their workout or how their
muscles are expanding
so those are areas where scanning is
starting to be used
and we also see recognition interaction
as another
you know large use case for us and and
that i think that one when you look at
scanning i think it’s it’s it’s a it’s
it’s emerging i think recognition
interaction is is a bigger space for us
today
because we see it in um uh educational
displays
interactive displays where k through 12
you know education where especially in
the you know k
through uh eight uh you know schools
around the world
where they put a a digital sign up put
one of our cameras on top
there’s a number of companies doing
these educational programs where kids
really have interactive play to learn
language or math
with you know using gesture recognition
with that digital sign also
with the recent coping 19 kind of
concerns about
transfer virus uh touch screens have
gotten a lot of
concern over from a number of our
customers so using our cameras to kind
of put a
virtual screen in front of a touch
screen so you can then
get close but not touch it and still
have that that same
user interaction of a touch screen and
you get close maybe a green
dot pops up and it acknowledges you just
selected that
particular fast food item we see we’re
starting to see those
start to be deployed so i think
recognition interaction is an area where
it’s kind of a broad area because it’s
those
interactions with digital signs
interactive with kiosks or
displays but also retail analytics is a
big growth area where people are
mounting
cameras in the ceiling just to track uh
you know customer flow or
customer movement through a department
store to just
to understand you know how they should
lay things out or we also have the
kind of the pay-as-you-go you know the
cashier-less store
where a lot of our cameras are mounted
in the ceiling and customers are able to
you know walk through grab an item and
the camera depth camera can know hey
that was you know shelf number three
and other cameras can triangulate and
say yes that’s the item that was picked
and
charge you as you go out the door so we
see
you know that retail analytics uh the
pointless
you know kind of that the the the uh the
facial authentication
robotics of course is still the biggest
growth you know spot force and the
biggest you know
use case for our products but we see
these other ones starting to to emerge
and i think the
the question you asked about machine
learning i think there’s definitely
a number of customers who are looking at
how can we get more intelligence
at the edge and how can we do more
decision making at the edge and that may
come with you know
surveillance cameras trying to discern
is that a human being or an animal
that’s walking across that
you know yard or is it in a warehouse is
this
you know robot that’s moving along as a
security guard make a decision
is there any you know other movement the
area that’s not
typical is there a person you know a
human being you know enter the
environment where they
we don’t expect one so having a depth
camera
that can make a decision at the edge and
send an alert up rather than
24-hour video feed of everything well
here’s the one
moment that we see something out of the
ordinary make a decision and send that
up so
adding more intelligence to the edge i
think is something that you’ll see
coming
and i i think you know your machine
learning question i think is really
how does the how do we put more
intelligence at the edge of something
we’re still investigating as
as as an industry and i think you’ll see
you know advances coming along along the
way there
awesome and last question for you sure
what’s top of mind for you
at real sense i think when at real sense
obviously
the way we’ve designed uh the cameras is
that
you know we’re gonna ensure that it’s
future proof so if you build
you know something around one of our
cameras and you decide yeah i really
need to
go longer range i really you know need
to move from stereo to lidar for this
application
the development work you’ve done is is
future proof that it’s going to
plug and play with the next camera and
that’s so for us as we look to the next
generation
uh you know this year we launched uh you
know
the um the facial authentication kind of
an extension
of use of depth to to provide
anti-spoofing in that face the kind of
facial authentication space we’ve also
launched a touchless control software so
we’re looking at how do we augment
and enhance the the offerings with
software and other
maybe algorithms that can enable our our
partners to build
you know unique solutions on top of but
we’re also having
have extended the the the cameras range
we introduced a d455 which gives you
kind of instead of a three meter range
you’re now at a six meter range and so
we’re continuing to look at
how do we improve the performance at
distance how can we make
you know let your robot move faster the
further you can see the
faster you can make a decision on when
you need to stop so
maybe you can move a little faster and
if you you’ve got that little bit
longer range so i think we’re continuing
you know to look
and to work with our customers on their
particular
new concerns whether they’re looking at
alternative interfaces
longer range or other
other things that they can think of you
know for example calibration
has been an issue with stereo cameras of
the year so intel worked on
a self calibration so we have health
check
that our cameras can do on their own so
the camera can calibrate itself
you know res so we’ve given
over time as we work with our customers
we see hey what challenges are you
having
and how can intel help you you know uh
you know ease your support requirements
uh on the use of depth cameras since
that’s really a goal for us is
not only to extend the portfolio but how
do we improve
the current you know cameras to ensure
it’s easier for the robotics customers
to be able to
adopt our solution and uh and improve
the performance of their end device
which we want to be the computer vision
experts
to enable that that customer to be the
robotics expert
awesome thank you very much for speaking
with us today yeah about it was
definitely uh appreciate the the chance
to get here
then and uh join the podcast and i look
forward to uh
to watching many more uh episodes of
your podcast in the future
thank you very much see ya okay thank
you take care
we hope you enjoyed listening to joel
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