Particle Filter or Montecarlo Localization (MCL) algorithm implementation to localise a two dimensional robot (turtlebot) in a given map.
The MCL localization is an implementation of the Markovian localisation problem where the involved probability density functions are represented through samples (particles) and the Bayes filter is implemented through the Particle Filter. Markov localisation addresses the problem of state estimation from sensor data. Markov localization is a probabilistic algorithm: Instead of maintaining a single hypothesis as to where in the world a robot might be, Markov localization maintains a probability distribution over the space of all such hypotheses.