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{{Short description|Computational navigational technique used by robots and autonomous vehicles}}
[[File:Stanley2.JPG|thumb|[[Stanley (vehicle)|2005 DARPA Grand Challenge winner Stanley]] performed SLAM as part of its autonomous driving system.]]
[[File:RoboCup Rescue arena map generated by robot Hector from Darmstadt at 2010 German open.jpg|thumb|A map generated by a SLAM Robot
'''Simultaneous localization and mapping''' ('''SLAM''') is the computational problem of constructing or updating a [[map]] of an unknown environment while simultaneously keeping track of an [[Intelligent agent|agent]]'s ___location within it. While this initially appears to be a [[chicken or the egg]] problem, there are several [[algorithm]]s known to solve it in, at least approximately, tractable time for certain environments. Popular approximate solution methods include the [[particle filter]], extended [[Kalman filter]], covariance intersection, and GraphSLAM. SLAM algorithms are based on concepts in [[computational geometry]] and [[computer vision]], and are used in [[robot navigation]], [[robotic mapping]] and [[odometry]] for [[virtual reality]] or [[augmented reality]].
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