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. For the past decade, the EKF SLAM has 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

1. 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.
2. Thrun, S.; Burgard, W.; Fox, D. (2005). Probabilistic Robotics. Cambridge: The MIT Press. ISBN 0-262-20162-3.