Extended Information Filter

Course Note SLAM (by Cyrill Stachniss)

Jihong Ju on October 14, 2018

Dual Representation of Gaussians

Moments parameterization with mean $\mu$ and covariance matrix $\Sigma$

Canonical parameterization with information vector $\xi$ and information matrix $\Omega$

Moments to canonical

Canonical to moments

Marginalization and conditioning

marginalization-conditioning

Marginalization is cheap for moment parameterization whereas conditioning is cheap for canonical parameterization; Contioning is expensive for moment parameterization whereas marginalization is expensive for canonical parameterization.

Extended Inofrmation Filter (EIF) SLAM

eif-slam

EIF vs. EKF

  • Same expressiveness as the EKF
  • Prediction step is more costly, Correction step is cheaper

Literature

Extended Information Filter, Thrun et al.: “Probabilistic Robotics”, Chapter 3.5