> Is this a simulation of a robot using sensor readings to estimate its position in a maze?

Yes.

> How are particles emitted, and how does the sensor measure them?

I think I understand your question, and there might be a misunderstanding here. The "particles" are not emitted in traditional sense. They are merely records in an internal data structure inside the robot's head.

The problem: Your robot has a map of the scene, but doesn't know where it is. You have distance measuring sensors on four sides of the robot, but they can't see enough details to say where you are on the map.

What the "particle filter" aproach suggest is that you keep a list of hypothesises of where the robot might be. Each hypothesis is a full parameterisation (x, y, heading in 2d), and for historical reasons we call them "particles".

At the beginning you initialise a bunch of these "particles" with random x, y, h values. Then in a loop you take measurements with the sensors and calculate how plausible each hypothesis is.

Maybe all your sensors can tell you is that there are walls a meter away at the rear and to the sided but there is 3m free space forward.
This would make the "particles" which are in the middle of a big room very unlikely while any particle which puts you in a cull-the-sack heading out would be more likely.

Next you would like to keep the more likely hypothesises while rejecting the least likely ones. What the "particle filter" approach suggest is that you
re-sample your particles such a way that the probability of keeping a particle equals with their calculated "plausibility".

In the next step you move with your robot using your motors. Usually you don't have perfect actuation, but you have a probabilistic idea of how much you might have traveled. You then update each particle by some movement sampled from that probabilistic motion model. After that repeat from the sensing step.

What usually happens is that after a few iterations all the particles collapse to a few well localised spots on the map. Very frequently to a single spot and that means that the "kidnapped" robot has localised itself. This happens because given enough data all the implausible theories can be eliminated and what remains is where you are.

> What does the "probability" of a particle mean?

It gives a measure of how well the location represented by the particle (a particular theory) fits with the last sensor measurements.

> Does it combine any information about its previous position estimate?

Indirectly. The re-sampling step guarantees that the particle is there because it was at least somewhat plausible in the past, but you don't have to keep an ever growing history explicitly.

> And how would this be applied in the real world without a perfect model of the environment?

You need some model of the environment (an occupancy map for example) but it doesn't need to be perfect. Since everything is already probabilistic, you can have a probabilistic map. Changes of how you calculate the plausibility (probability) of a given hypothesis (particle) but doesn't change the whole algorithm.

About how it is applied in the real world... well I will be frank with you I have seen many robots localise in different ways and neither of them used particle filters. Maybe we were all missing out on something cool. :) Thrun et al. describe in Probabilistic Robotics as something which were useful for them in the past.
So you have that.