/iSAM: Fast Incremental Smoothing and Mapping with Efficient Data Association

iSAM: Fast Incremental Smoothing and Mapping with Efficient Data Association

Michael Kaess, Ananth Ranganathan and Frank Dellaert
Conference Paper, IEEE Intl. Conf. on Robotics and Automation, ICRA, pp. 1670-1677, April, 2007

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.


We introduce incremental smoothing and mapping (iSAM), a novel approach to the problem of simultaneous localization and mapping (SLAM) that addresses the data asso- ciation problem and allows real-time application in large-scale environments. We employ smoothing to obtain the complete trajectory and map without the need for any approximations, exploiting the natural sparsity of the smoothing information matrix. A QR-factorization of this information matrix is at the heart of our approach. It provides efficient access to the exact covariances as well as to conservative estimates that are used for online data association. It also allows recovery of the exact trajectory and map at any given time by back- substitution. Instead of refactoring in each step, we update the QR-factorization whenever a new measurement arrives. We analyze the effect of loops, and show how our approach extends to the non-linear case. Finally, we provide experimental validation of the overall non-linear algorithm based on the standard Victoria Park data set with unknown correspondences.

BibTeX Reference
author = {Michael Kaess and Ananth Ranganathan and Frank Dellaert},
title = {iSAM: Fast Incremental Smoothing and Mapping with Efficient Data Association},
booktitle = {IEEE Intl. Conf. on Robotics and Automation, ICRA},
year = {2007},
month = {April},
pages = {1670-1677},