Title:
Fast Incremental Square Root Information Smoothing
Fast Incremental Square Root Information Smoothing
Author(s)
Kaess, Michael
Ranganathan, Ananth
Dellaert, Frank
Ranganathan, Ananth
Dellaert, Frank
Advisor(s)
Editor(s)
Collections
Supplementary to
Permanent Link
Abstract
We propose a novel approach to the problem of
simultaneous localization and mapping (SLAM)
based on incremental smoothing, that is suitable for
real-time applications in large-scale environments.
The main advantages over filter-based algorithms
are that we solve the full SLAM problem without
the need for any approximations, and that we do
not suffer from linearization errors. We achieve
efficiency by updating the square-root information
matrix, a factored version of the naturally sparse
smoothing information matrix. We can efficiently
recover the exact trajectory and map at any given
time by back-substitution. Furthermore, our approach
allows access to the exact covariances, as it
does not suffer from under-estimation of uncertainties,
which is another problem inherent to filters.
We present simulation-based results for the linear
case, showing constant time updates for exploration
tasks. We further evaluate the behavior in the presence
of loops, and discuss how our approach extends
to the non-linear case. Finally, we evaluate
the overall non-linear algorithm on the standard
Victoria Park data set.
Sponsor
Date Issued
2007-01
Extent
Resource Type
Text
Resource Subtype
Post-print
Proceedings
Proceedings