cover image

Simultaneous localization and mapping

Computational navigational technique used by robots and autonomous vehicles / From Wikipedia, the free encyclopedia

Dear Wikiwand AI, let's keep it short by simply answering these key questions:

Can you list the top facts and stats about Simultaneous localization and mapping?

Summarize this article for a 10 year old

SHOW ALL QUESTIONS

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 agent's location within it. While this initially appears to be a chicken or the egg problem, there are several algorithms 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.

Stanley2.JPG
2005 DARPA Grand Challenge winner Stanley performed SLAM as part of its autonomous driving system.
RoboCup_Rescue_arena_map_generated_by_robot_Hector_from_Darmstadt_at_2010_German_open.jpg
A map generated by a SLAM Robot

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 cars, unmanned aerial vehicles, autonomous underwater vehicles, planetary rovers, newer domestic robots and even inside the human body.

Oops something went wrong: