Monte Carlo localization

In robotics and sensors, Monte Carlo localization, or MCL, is a Monte Carlo method to determine the position of a robot given a map of its environment based on Markov localization. In this method a large number of hypothetical current configurations are initially randomly scattered in configuration space. With each sensor update, the probability that each hypothetical configuration is correct is updated based on a statistical model of the sensors and Bayes' theorem. Similarly, every motion the robot undergoes is applied in a statistical sense to the hypothetical configurations based on a statistical motion model. When the probability of a hypothetical configuration becomes very low, it is replaced with a new random configuration.