Hello.
So we are finally ready for the good stuff.
That means that we are ready to implement the steps necessary to create the mess.
Solving what?
So if you remember, the very first step was localization.
Now, localization in robotics is essentially extracting the robot boss in the given environment.
For example, if you were in a maze, that would mean extracting the robot position and the robot orientation
at any given moment.
For a robot navigation.
This becomes an important precursor.
As without the robot tools, we would be unable to make decisions about further action.
This also appeals to our second point that we not only to localize the robot, but we also need to do
it at each frame to get the most updated information at all times.
So one question that might come into your mind is, isn't this what I'm supposed to do and why aren't
we talking about it?
Well, yes and no.
Yes.
SLAM Which is simultaneous localization and mapping does in fact localize the robot at each iteration,
but it also creates the map of the environment in the event where there is no global map available.
The reason we have not opted for Islam and instead divided the localization and mapping into two separate
category is because we have the luxury of observing the myth or the complete myth at all times.
So it's like even though being very powerful is right now an overkill and has also been skipped intentionally
for simplicity.
So this begs the question, how do we propose to perform localization for our kids?
Now for that, we need to look at what our case is.
We have chosen the simplest case of an overhead vision system, be it a drone or satellite, observing
the myth at all times.
This means the only moving object will be our mere solving bot.
So this is our assumption.
And of course, a dynamic environment would have been a lot harder to solve.
So the goal for this localization module is to localize the robot at each iteration.
And by localizing, we mean extracting the robot position in the duty man's image.
So let's do that.