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I am starting in robotics, and reading about Markov Localization, I have one doubt, probably very stupid, but, well, I want to solve it.

Let's take the CMU Website example: https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume11/fox99a-html/node3.html

Basically and very summarized:

  1. The robot does not know its location (uniform probability of being at any point), but knows there are 3 doors.
  2. It senses a door, and since that door could be any one out of the 3, the belief distribution spikes at those 3 doors.
  3. It moves to the right, and the belief distribution shifts to the right also, let's say "following the robot movement", and then the convolution is done when finding the 2nd door... This gets described in the next graphic, from the CMU:

enter image description here

But why does the belief distribution get shifted to the right, and not to the left, as the door is left behind?

Shouldn't the robot sense that there's no door between door 1 and 2 (starting from the left)?

Is there something about probability theory I've forgotten (I studied it like 14 years ago)?

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The robot has sensed a door, so the initial belief distribution matches the three possible door positions. i.e. the only three places that it is possible for the robot to be in that scenario.

The robot moves to the right so, since the belief distribution matches the possible positions of the robot, the belief distribution must also move to the right. As the robot moves, so the belief distribution must move with it.

Notice that the three peaks are no longer quite as well defined at this point because of that motion. Movement is inherently noisy, which introduces uncertainty. The more the robot moves, the less certain it becomes about its position, and the belief distribution thus tends to flatten or smooth out.

Finally, the robot senses the second door. Given the (previously known) possible door positions, the belief distribution now also centres on the second door in the diagram.

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I think this is just a way of illustrating the main idea behind the probability distribution and the representation is not complete.

The idea is that there is a moment when the door is detected and the prior distribution not yet considered, this is when the robot assumes this could also be door 1 and therefore the positions of the other doors are as shown. There can be 3 similar images drawn, where the robot assumes standing in front of door 2 or door 3, but when the prior information is mixed in, only door 2 seems plausible.

I am just guessing, I did not read the book.

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