iSAM: Incremental Smoothing and Mapping

DMCA

iSAM: Incremental Smoothing and Mapping (2008)

Cached

Download Links

by Michael Kaess , Ananth Ranganathan , Frank Dellaert

Citations:

153 - 35 self

BibTeX

@MISC{Kaess08isam:incremental,
    author = {Michael Kaess and Ananth Ranganathan and Frank Dellaert},
    title = {iSAM: Incremental Smoothing and Mapping },
    year = {2008}
}

OpenURL

Abstract

We present incremental smoothing and mapping (iSAM), a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, therefore recalculating only the matrix entries that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering. Also, to enable data association in real-time, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and real-world datasets for both landmark and pose-only settings.

Keyphrases

incremental smoothing real-world datasets exact solution novel approach estimation uncertainty information matrix simultaneous localization efficient algorithm fast incremental matrix factorization many loop robot trajectory factor matrix factored information matrix pose-only setting matrix entry overall algorithm periodic variable reordering mapping problem qr factorization unnecessary fill-in data association different component

DMCA