Simultaneous localization and mapping: Difference between revisions

Revision as of 17:00, 21 October 2022

Revision as of 17:01, 21 October 2022

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'''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 and egg problem|chicken-and-egg problem]], there are several [[algorithm]]s known for solving 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]].

SLAM algorithms are tailored to the available resources, henceand are not aimed at perfection, but at operational compliance. Published approaches are employed in [[self-driving car]]s, [[unmanned aerial vehicle]]s, [[autonomous underwater vehicle]]s, [[Rover (space exploration)|planetary rovers]], newer [[domestic robot]]s and even inside the human body.

== Mathematical description of the problem ==