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Monte Carlo localization (MCL), also known as particle filter localization,〔Ioannis M. Rekleitis. "A Particle Filter Tutorial for Mobile Robot Localization." ''Centre for Intelligent Machines, McGill University, Tech. Rep. TR-CIM-04-02'' (2004).〕 is an algorithm for robots to localize using a particle filter.〔 Frank Dellaert, Dieter Fox, Wolfram Burgard, Sebastian Thrun. "(Monte Carlo Localization for Mobile Robots )." ''Proc. of the IEEE International Conference on Robotics and Automation'' Vol. 2. IEEE, 1999.〕〔 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.〕〔 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. 〕 Given a map of the environment, the algorithm estimates the position and orientation of a robot as it moves and senses the environment.〔 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.〔 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.〔 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.〔 ==Basic description== Consider a robot which has an internal map of its environment. When the robot moves around, it needs to know where it is within this map. Determining its location and rotation (more generally, the pose) by using its sensor observations is known as robot localization. Because the robot may not always behave in a perfectly predictable way, it generates many random guesses of where it is going to be next. These guesses are known as particles. Each particle contains a full description of a possible future state. When the robot observes the environment, it discards particles inconsistent with this observation, and generates more particles close to those which appear consistent. In the end, hopefully most particles will converge to where the robot actually is. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Monte Carlo localization」の詳細全文を読む スポンサード リンク
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