Graphical SLAM using Vision and the Measurement Subspace
John Folkesson, Patric Jensfelt and Henrik I. Christensen
Abstract:
In this paper we combine a graphical approach for simultaneous
localization and mapping, SLAM, with a feature representation that
addresses symmetries and constraints in the feature coordinates, the
measurement subspace, M-space.
The graphical method has the advantages of delayed linearizations and
soft commitment to feature measurement matching. It also allows large
maps to be built up as a network of small local patches, star nodes.
This local map net is then easier to work with. The formation of the
star nodes is explicitly stable and invariant with all the symmetries
of the original measurements. All linearization errors are kept small
by using a local frame. The construction of this invariant star is
made clearer by the M-space feature representation. The M-space
allows the symmetries and constraints of the measurements to be
explicitly represented. We present results using both vision and
laser sensors.
BibTeX Entry:
@InProceedings{Folkesson05b,
author = {John Folkesson and Patric Jensfelt and Henrik Christensen},
title = {Graphical {SLAM} using Vision and the Measurement Subspace},
booktitle = {Proc.~of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'05)},
year = 2005,
month = aug
}
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