# Incremental Smoothing and Mapping

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“Incremental Smoothing and Mapping”
by M. Kaess.
Ph.D. dissertation, Georgia Institute of Technology, Dec. 2008.

## Abstract

Incremental smoothing and mapping (iSAM) is presented, a novel approach to
the simultaneous localization and mapping (SLAM) problem. SLAM is the
problem of estimating an observer's position from local measurements only,
while creating a consistent map of the environment. The problem is
difficult because even very small errors in the local measurements
accumulate over time and lead to large global errors. iSAM provides an
exact and efficient solution to the SLAM estimation problem while also
addressing data association. For the estimation problem, iSAM provides an
exact solution by performing smoothing, which keeps all previous poses as
part of the estimation problem, and therefore avoids linearization errors.
iSAM uses methods from sparse linear algebra to provide an efficient
incremental solution. In particular, iSAM deploys a direct equation solver
based on QR matrix factorization of the naturally sparse smoothing
information matrix. Instead of refactoring the matrix whenever new
measurements arrive, only the entries of the factor matrix that actually
change are calculated. iSAM is efficient even for robot trajectories with
many loops as it performs periodic variable reordering to avoid
unnecessary fill-in in the factor matrix. For the data association
problem, I present state of the art data association techniques in the
context of iSAM and present an efficient algorithm to obtain the necessary
estimation uncertainties in real-time based on the factored information
matrix. I systematically evaluate the components of iSAM as well as the
overall algorithm using various simulated and real-world data sets.

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**BibTeX entry:**

@phdthesis{Kaess08thesis,
author = {M. Kaess},
title = {Incremental Smoothing and Mapping},
school = {Georgia Institute of Technology},
type = {{Ph.D.}},
month = dec,
year = {2008}
}

Last updated: March 26, 2021