I just started studying localization for robotics. I started with Markov localization. There was a cool lecture on Udacity. I also came across another localization called hidden Markov localization, but couldn't find many blogs on it. Can anyone explain to me briefly what's the difference between both?
From some basic googling.
Also the wikipedia article on Hidden Markov models is pretty clear.
A hidden markov model is just a case where the state/variables you are interested are not directly observable. This is the more common situation as generally you don't have a nice sensor that directly measures the variable you want. Instead you have to infer the hidden state through measuring other visible variables.
For a localization example, you are interested in the robots global position and heading. These are your hidden variables ,because in many cases you don't have a sensor that can directly measure this. Instead you might have something like range measurements to 2D landmarks, and a motion model which predicts how your robot moves. Now neither of them directly measure the hidden state(position,heading), but you can use a series of them to infer the hidden states.