All language subtitles for 02_introduction-to-mapping.en

This week,
we'll learn about Robotic Mapping.

Specifically, our goal of this
week is to understand a mapping

algorithm called Occupancy Grid Mapping
based on range measurements.

Before we start talking about the method,

we introduce some basic
concepts about robotic mapping.

Also, later in this week, we introduce
basic algorithms used in 3D mapping.

In this lecture, I will introduce
the problem of robotic mapping.

Everyone already knows what a map is.

But we need to think about
what a map means for robots.

What kinds of maps are useful for them?

And what makes mapping a hard problem?

A map for robots is a special
model of its environment.

We call a process for
building a map, mapping.

To help our robot to build a map, we will
first think about how to represent it.

For example,
our coordinate we are going to use, and

how detailed information
the map should include.

Also, the quality and

the extent of the map will depend
on available sensors for mapping.

Therefore, you should understand

how to interpret your sensor
measurements properly.

Lastly, mapping results can
be different according to

the reason why your robot
wants to have a map.

Is it to build a fine
globally consistent map?

Or does the robot just want to avoid
local collisions while navigating?

You should decide
the level of precision and

accuracy of the map
based on that question.

It is useful to think about various
types of map representations,

which we actually have
seen often in real life.

The first type we are going to see is
the most basic type, a Metric Map.

Here is an example of a metric map.

This is the world map represented in the
longitude and latitude coordinate frame.

To indicate a location, for
example the location of

the University of Pennsylvania, we can
use the coordinate numbers as shown.

Generally, in a metric map,
a location is represented as a coordinate.

This serves as the most basic form of maps

since most mobile robots use some
coordinate frame for self localization.

Here is another type of
map we are familiar with.

This is part of the train map that
runs in and near Philadelphia.

You may notice that the geometric scale
of the map is not correct at all.

But we don't care much about the exact
numeric location of a station on this map.

That is because the purpose of
this map is to show which train

makes stops at which stations, and
which stations connect different lines.

We call this type of
map a topological map.

Where locations are represented as nodes,
and their connections as arcs.

As I mentioned, the exact
coordinate is not important in this

representation, but
the connections among nodes matter.

So the graph on the left side is
equivalent to the graph on the right.

The arcs are used to express costs or
constraints between nodes.

This whole graph representation of the map
is useful for path planning tests.

The last type of map I'm going
to introduce is a Semantic map.

Again, this is not something new to us.

Here is our campus building map
of University of Pennsylvania.

It is an example of semantic map.

What makes it distinct is the labels, and

the relative locations
of the labeled objects.

Instead of using some coordinates,
we may describe Irvine Auditorium,

it is located at the corner of 34th
Street and Spruce Street from this map.

This is useful for high level planning or
human robot interaction.

However, as you can imagine,
building a semantic map requires

advanced object recognition
techniques which go beyond our scope.

In this course, we will focus on
a method to build a metric map.

Before we start talking about
mapping algorithms, let me mention

what makes mapping challenging and
what is our scope of learning.

First, mapping is essentially
a perception problem,

which is about finding what
is where from sensor reading.

The fact that we are relying
on sensors implies two things.

Our measurements are noisy, so
we need robust estimation method.

Also the measurements are usually
measured in the local coordinate frame

which needs to be interpreted in the world
coordinate frame of our interest.

Next.

Mapping actually involves other robotic
problems, such as planning and navigation.

Because mapping usually happens
while a robot is moving around.

Lastly, real world objects
could change over time.

In theory, a map should be constantly
updated to reflect the reality.

Now it sounds almost impossible to learn
mapping in a week, but no worries.

The following lectures,
we will focus on probabilistic treatments

of our noise measurements and
practice the coordinate

transformation from the local to
the world of the measurements.