EKF SLAM
Encyclopedia
In robotics
, EKF SLAM is a class of algorithms which utilizes the extended Kalman filter
(EKF) for simultaneous localization and mapping
(SLAM). Typically, EKF SLAM algorithms are feature based, and use the maximum likelihood algorithm for data association. For the past decade, the EKF SLAM has been the de facto method for SLAM, until the introduction of FastSLAM.
Associated with the EKF is the gaussian noise assumption, which significantly impairs EKF SLAM's ability to deal with uncertainty. With greater amount of uncertainty in the posterior, the linearization in the EKF fails.
Robotics
Robotics is the branch of technology that deals with the design, construction, operation, structural disposition, manufacture and application of robots...
, EKF SLAM is a class of algorithms which utilizes the extended Kalman filter
Extended Kalman filter
In estimation theory, the extended Kalman filter is the nonlinear version of the Kalman filter which linearizes about the current mean and covariance...
(EKF) for simultaneous localization and mapping
Simultaneous localization and mapping
Simultaneous localization and mapping is a technique used by robots and autonomous vehicles to build up a map within an unknown environment , or to update a map within a known environment , while at the same time keeping track of their current location.- Operational definition :Maps are used...
(SLAM). Typically, EKF SLAM algorithms are feature based, and use the maximum likelihood algorithm for data association. For the past decade, the EKF SLAM has been the de facto method for SLAM, until the introduction of FastSLAM.
Associated with the EKF is the gaussian noise assumption, which significantly impairs EKF SLAM's ability to deal with uncertainty. With greater amount of uncertainty in the posterior, the linearization in the EKF fails.