A robot is just outside a warehouse and needs to get to the second door.
It can move on a line.
However, it doesn't know where it is.
How does it localize itself?
It has wheel odometry, a camera to detect a door, and a map of the warehouse.
We can model the belief on where the robot is as a probability distribution over the
possible locations.
Just deployed, the probability is uniform as the robot's initial location is unknown.
As the robot gets an image from the camera, it detects a door.
Given that the map shows there are three doors, the probability increases at the locations
in front of each door.
The robot moves right, and the belief is also updated accordingly, with the information
from the wheel odometry.
The robot then senses a second door and the probability corresponding to location at the
second door becomes the highest, localizing the robot.