About: Monte Carlo localization

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Monte Carlo localization (MCL), also known as particle filter localization, is an algorithm for robots to localize using a particle filter. 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

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rdf:type	software yago:WikicatMonteCarloMethods yago:Ability105616246 yago:Abstraction100002137 yago:Cognition100023271 yago:Know-how105616786 yago:Method105660268 yago:PsychologicalFeature100023100
rdfs:label	Monte Carlo localization (en)
rdfs:comment	Monte Carlo localization (MCL), also known as particle filter localization, is an algorithm for robots to localize using a particle filter. 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 (en)
foaf:depiction
dct:subject	Monte Carlo methods Robot navigation
Wikipage page ID	4093697 (xsd:integer)
Wikipage revision ID	1121242553 (xsd:integer)
Link from a Wikipage to another Wikipage	Probability distributions Multimodal distribution Particle filter Monte Carlo methods Unscented Kalman filter Configuration space (physics) Linear time Periodic boundary conditions Markov property Time-invariant system Time complexity Normal distribution Histogram Kalman filter Kidnapped robot problem Probability density function Robot navigation Pose (computer vision) Kullback–Leibler divergence Non-parametric statistics Extended Kalman filter Robot localization Recursive Bayesian estimation Boolean value
sameAs	Monte Carlo localization Monte Carlo localization Monte Carlo localization Monte Carlo localization Monte Carlo localization
dbp:wikiPageUsesTemplate	dbt:Multiple_image dbt:Reflist
thumbnail	wiki-commons:Special:FilePath/Corridorbot_door.png?width=300
alt	Robot detects a door. (en) Robot detects a wall. (en)
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 . (en)
image	Corridorbot door.png (en) Corridorbot wall.png (en)
width	120 (xsd:integer)
has abstract	Monte Carlo localization (MCL), also known as particle filter localization, is an algorithm for robots to localize using a particle filter. 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. (en)
gold:hypernym	Algorithm
prov:wasDerivedFrom	wikipedia-en:Monte_Carlo_localization?oldid=1121242553&ns=0
page length (characters) of wiki page	18030 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Monte_Carlo_localization
is Link from a Wikipage to another Wikipage of	Robot Operating System Monte Carlo (disambiguation) Particle filter Index of robotics articles List of numerical analysis topics Monte Carlo method Frank Dellaert 2b2t Automated guided vehicle MCL Simultaneous localization and mapping Wolfram Burgard
is Wikipage disambiguates of	Monte Carlo (disambiguation) MCL
is foaf:primaryTopic of	wikipedia-en:Monte_Carlo_localization

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