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- Monte_Carlo_localization abstract "Monte Carlo localization (MCL), also known as particle filter localization, is an application of the particle filter, a Monte Carlo method, for robot localization. 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 pose of the robot.".
- Monte_Carlo_localization wikiPageID "4093697".
- Monte_Carlo_localization wikiPageRevisionID "606171444".
- Monte_Carlo_localization alt "Robot detects a door.".
- Monte_Carlo_localization alt "Robot detects a wall.".
- Monte_Carlo_localization footer "A robot travels along a one-dimensional corridor, armed with a sensor that can only tell if there is a door or there is no door .".
- Monte_Carlo_localization hasPhotoCollection Monte_Carlo_localization.
- Monte_Carlo_localization image "Corridorbot door.png".
- Monte_Carlo_localization image "Corridorbot wall.png".
- Monte_Carlo_localization width "120".
- Monte_Carlo_localization subject Category:Monte_Carlo_methods.
- Monte_Carlo_localization subject Category:Robot_navigation.
- Monte_Carlo_localization comment "Monte Carlo localization (MCL), also known as particle filter localization, is an application of the particle filter, a Monte Carlo method, for robot localization. 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.".
- Monte_Carlo_localization label "Monte Carlo localization".
- Monte_Carlo_localization sameAs m.0bhn9y.
- Monte_Carlo_localization sameAs Q6904694.
- Monte_Carlo_localization sameAs Q6904694.
- Monte_Carlo_localization wasDerivedFrom Monte_Carlo_localization?oldid=606171444.
- Monte_Carlo_localization isPrimaryTopicOf Monte_Carlo_localization.
