Monte Carlo localization
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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.
[edit] External links
- D. Fox, W. Burgard, F. Dellaert, and S. Thrun, Monte Carlo Localization: Efficient Position Estimation for Mobile Robots, Proc. of the Sixteenth National Conference on Artificial Intelligence (AAAI'99)
- Thrun, S., Fox, D., Burgard,W., and Dellaert, F., Robust monte carlo localization for mobile robots, Artificial Intelligence, 128(1-2):99–141
