EKF SLAM

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 featured based, and uses the maximum likelihood algorithm for data association. For the past decade, the EKF SLAM has been the de facto standard method for SLAM, until the introduction of FastSLAM.cite conference
author = Montemerlo, M.
coauthors = Thrun, S.; Koller, D.; Wegbreit, B.
year = 2002
title = FastSLAM: A factored solution to the simultaneous localization and mapping problem
conference =
booktitle = Proceedings of the AAAI National Conference on Artificial Intelligence
pages = 593-598
publisher =
url = http://www.cs.cmu.edu/~mmde/mmdeaaai2002.pdf
conferenceurl = ]

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.cite book
author = Thrun, S.
coauthors = Burgard, W.; Fox, D.
title = Probabilistic Robotics
publisher = The MIT Press
location = Cambridge
year = 2005
isbn = 0262201623 ]

References