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 feature based, and use the maximum likelihood algorithm for data association. In the 1990s and 2000s, EKF SLAM had been the de facto method for SLAM, until the introduction of FastSLAM.[1]
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.[2]
References
- ^ Montemerlo, M.; Thrun, S.; Koller, D.; Wegbreit, B. (2002). "FastSLAM: A factored solution to the simultaneous localization and mapping problem" (PDF). Proceedings of the AAAI National Conference on Artificial Intelligence. pp. 593–598.
{{cite conference}}: Cite has empty unknown parameter:|conferenceurl=(help); Unknown parameter|booktitle=ignored (|book-title=suggested) (help) - ^ Thrun, S.; Burgard, W.; Fox, D. (2005). Probabilistic Robotics. Cambridge: The MIT Press. ISBN 0-262-20162-3.