Simultaneous localization and mapping: Difference between revisions

Revision as of 16:35, 15 November 2023

Revision as of 16:46, 15 November 2023

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[[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|chicegg]] 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]].

SLAM algorithms are tailored to the available resources and 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 ==