Monte Carlo localization (MCL), also known as particle filter localization,[1] is an algorithm for robots to localize using a particle filter.[2][3][4][5] Given a map of the environment, the algorithm estimates the position and orientation of a robot as it moves and senses the environment.[4] The algorithm uses a particle filter to represent the distribution of likely states, with each particle representing a possible state, i.e., a hypothesis of where the robot is.[4] The algorithm typically starts with a uniform random distribution of particles over the configuration space, meaning the robot has no information about where it is and assumes it is equally likely to be at any point in space.[4] Whenever the robot moves, it shifts the particles to predict its new state after the movement. Whenever the robot senses something, the particles are resampled based on recursive Bayesian estimation, i.e., how well the actual sensed data correlate with the predicted state. Ultimately, the particles should converge towards the actual position of the robot.[4]
^Ioannis M. Rekleitis. "A Particle Filter Tutorial for Mobile Robot Localization." Centre for Intelligent Machines, McGill University, Tech. Rep. TR-CIM-04-02 (2004).
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Frank Dellaert, Dieter Fox, Wolfram Burgard, Sebastian Thrun. "Monte Carlo Localization for Mobile Robots Archived 2007-09-17 at the Wayback Machine." Proc. of the IEEE International Conference on Robotics and Automation Vol. 2. IEEE, 1999.
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Dieter Fox, Wolfram Burgard, Frank Dellaert, and Sebastian Thrun, "Monte Carlo Localization: Efficient Position Estimation for Mobile Robots." Proc. of the Sixteenth National Conference on Artificial Intelligence John Wiley & Sons Ltd, 1999.
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Sebastian Thrun, Wolfram Burgard, Dieter Fox. Probabilistic Robotics MIT Press, 2005. Ch. 8.3 ISBN 9780262201629.
^Sebastian Thrun, Dieter Fox, Wolfram Burgard, Frank Dellaert. "Robust monte carlo localization for mobile robots." Artificial Intelligence 128.1 (2001): 99–141.
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