All language subtitles for 04_state-estimation.en

Let's first start with state estimation.

Again, the goal here is to be able to
obtain reliable estimates of the position

and the velocity as the vehicle moves
through a three dimensional environment.

So in the lab,
we have motion capture cameras

that allow the robot to
measure its position.

What essentially happens is through
the reflective markers that are mounted on

the robot, the cameras can estimate
the position of each reflective marker and

these cameras can do it at split second
timing exceeding speeds of 100 times

a second to 200 times a second.

Also, these measurements
are incredibly precise.

The accuracies can be well
below one millimeter.

Of course,
this only happens in a laboratory setting.

What happens when you go outdoors?

Well, in larger vehicles, like this X-47B
made by Northrop Grumman which is capable

of landing on a ship deck autonomously,
the vehicle uses GPS and

other kinds of communication
to determine where it is,

relevant to the ship deck, and
is able to perform autonomously.

If you go outdoors,
in general you might not have GPS, or

your estimates of position from
GPS can be very inaccurate.

This is especially true next
in extra tall buildings.

Certainly when you go indoors,
it’s hard to get GPS.

And we’d like to be able to
operate both indoors and outdoors.

There’s no GPS and
because these vehicles are small and

maneuverable, they can find
themselves in settings

where it’s very difficult to
communicate directly with the vehicle.

We also wanted these vehicles
to move very quickly and

manuever through these
complex environments.

How do we navigate without GPS,

without external motion capture cameras or
any other kinds of external sensors?

Well, imagine that the vehicle is
equipped with sensors such as cameras or

color plus depth cameras as
you see on the bottom, or

laser range finders as
you see on the top right.

These sensors allow the vehicles
to infer information

about the environment, and from this
information allow it to localize itself.

How does that work?

Well, let's look at this cartoon.

Imagine you have a robot and imagine there
are three pillars in the environment.

And let's imagine it has rain sensors that
allow it to detect these obstacles or

the pillars.

And imagine these rain sensors give you
estimates of where these pillars are, d1,

d2 and d3.

Now let's assume that the robot has
something like an inertial measurement

unit that allows it to estimate its
movement as it goes from one position

to another position.

So you have some estimate of delta x, and

when it gets to this new
position this range finder

estimates the positions of the pillars
that it had measured previously.

Except now these range estimates,
d1 prime, d2 prime, and d3 prime,

are different from the original depth
estimates which are d1, d2, and d3.

So, the question we wanna ask ourselves,
is it possible for the robot

to concurrently estimate the locations of
the pillars and the displacement delta x.

So if you think about it, you're trying
to estimate these eight variables.

Three pairs of x y coordinates for
the pillars, and delta x.

This problem is referred to as
Simultaneous Localization And Mapping, or

simply SLAM.

And the idea here is that you're
trying to localize yourself,

in other words you're trying to estimate
delta x, while mapping the pillars,

x1, y1, x2, y2, and x3, y3.

In this video,

you will see a video of a robot entering
a building that it hasn't seen before.

It uses the SLAM methodology to map
the three dimensional building,

while estimating its location relative
to the features in the building.

The map that's building,
you can see in the central screen.

The blue colors are the ground floor and
the red colors are the top floor.

You will see there are intermediate
points where the vehicle

actually plans its trajectory.

If you look at the red snaking curve that
emanates from the vehicle to gold coins.

These gold coins have been designated by
an operator that's viewing this map and

tasking the vehicle.

And the vehicle does everything else.

In other words, you can click and

point your way through this building
without entering the building,

while getting information about
what's inside the building.

In our lab we've built many
different types of vehicles.

Here are four examples.

On the top left you see a vehicle that's
powered by lasers, a set of cameras.

It has a GPS unit as well as
an Inertial Measurement Unit.

On the bottom left you see another vehicle
that's only powered by two cameras and

an Inertial Measurement Unit.

On the top right,
a smartphone drives the robot.

On the bottom right, the vehicle is
actually instrumented with a RGBD camera.

Red, green, blue, and depth camera,

which you can now get as part of
an Xbox video entertainment system.

It also has on it a laser scanner.

You can see that each vehicle has
a different mass and a different size.

And the reason for that is very simple.

As you put more hardware on
the vehicle in terms of sensors and

processors, the vehicle
has to become bigger.

And to support this weight you have to
have bigger motors, and to support those

motor you have to have bigger props,
which in turn requires bigger batteries.

Here's another vehicle we use for
instruction in our classroom, and

this vehicle just has a single camera and
an IMU.

Because it only has a single camera, we
actually instrument the room with beacons.

These are AprilTags.

And as you'll see in this video,

the vehicle is able to localize itself
with respect to these beacons, and hover.

So it's estimating its position and

velocity relative to the beacon and
hovering autonomously.

You can switch between small markers and

large markers which then allow
the vehicle to control its height.

And, of course,
if you have lots of these markers,

then you can actually navigate
over larger distances.

So in our laboratory this is
an inexpensive replacement

to the motion camera system.

Here it's a single camera
which is off the shelf,

it's inexpensive, but we do instrument
the environment with beacons.

And because these beacons, which
are the AprilTags you see on the carpet,

are known to the robot, It's able
to recognize them and estimate its

position and orientation relative to
the tags, and therefore the environment.